Adeptia’s Innovation Officer Deepak Singh offers commentary on how the holy grail of data integration is AI-driven, seamless, and secure. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The landscape of enterprise data integration has evolved dramatically over the past decade. What was once a purely technical challenge relegated to IT departments has become a strategic business imperative that can determine an organization’s ability to compete in the digital economy.

As companies slice through increasingly vast data domains, the utopia of perfect integration has been achieved: AI-based systems that seamlessly traverse heterogeneous landscapes and are secure by design. This convergence is the holy grail of today’s data integration – a vision being fast realized by technological progress.

McKinsey & Company’s global survey shows a significant increase in the use of AI, from 33 percent to 71 percent between 2023 and 2024. This indicates a growing trend of organizations leveraging AI across different functions, including data integration.

The Evolution of Enterprise Integration

Legacy integration techniques used scripting, fragile point-to-point mappings, and technical know-how. This had some unpleasant effects:

• IT Bottlenecks: Integration requests would accumulate, with business initiatives taking months to come into effect

• Scalability Limitations: Each new connection added made it that much more difficult to uphold

• Flexibility Constraints: Redeploying for changing business requirements involved significant recoding and testing

• Gaps in User Experience: Business users remained reliant on technical teams for access to the data, as well as connectivity

The onset of integration platforms partially bridged these issues using standardized connectors and visual design tools. Nevertheless, integrations that actually revolutionize today’s difficult, multi-cloud worlds with thousands of connections and terabytes of data depend on a wholly different approach.

AI-Driven Integration Revolution

Artificial intelligence is transforming data integration in many important ways:

Smart Mapping

One of the most time-consuming aspects of integration is data mapping between different systems and formats. New AI technologies can now analyze data patterns, field names, and content to suggest accurate mappings automatically—an activity that previously took days but now takes just minutes with AI.

When looking at integration platforms, choose systems that provide more than simple field matching by understanding data context and semantic relationships. Today’s cutting-edge solutions learn from human corrections consistently, continuously refining accuracy.

Automated Data Transformation

In addition to mapping, AI can now understand complicated data shapes and convert them to standard shapes automatically. This is particularly useful for outward-facing unstructured or semi-structured data that previously required a lot of effort.

The ideal solution offers more than just out-of-the-box capabilities of transformation, but more importantly, the opportunity to learn firm-specific patterns as well as rules. This creates benefits in real time and improves much better over time.

Smart Error Handling

Data quality issues and integration flaws have historically required manual intervention, which has resulted in delays and additional operational expense. Integration with AI can now detect patterns in exceptions, automate repetitive issues, and even anticipate problems before they impact business operations.

Enterprises should look for solutions that provide both reactive error correction and proactive anomaly detection, with sufficient visibility into automated resolution.

The Seamless Integration Experience

The second dimension of the integration holy grail is seamlessness—the ability to connect anything, anywhere, frictionlessly.

Business User Enablement

Modern integration solutions must enable business users to make connections without specialized technical expertise. This move from IT-focused to business-enabled integration shortens time-to-value and minimizes bottlenecks.

When assessing platforms, seek out easy-to-use interfaces built for business analysts, not developers, with template-based methodologies that remove technical complexity while offering governance.

Universal Connectivity

Today’s organizations operate on hybrid and multi-cloud infrastructures, and information exists in SaaS applications, on-premises environments, and across multiple cloud infrastructures. Integration must seamlessly span those gaps.

Confirm whether or not it is possible for solutions to support end-to-end connectivity amongst cloud services, old, legacy systems, and modern applications – without requiring heterogeneous tools or techniques for heterogeneous infrastructures.

Real-Time and Batch Processing

Different business use cases require different data movement patterns, from batch for analytics to real-time streaming for operation systems. Best-in-breed integration platforms within a single paradigm service both types.

Imagine if platforms could deliver variable volumes and velocities without having drastically different implementation approaches per use case! That is the dream for data integration.

Secure by Design

The final piece of the integration holy grail includes holistic security – this is critical as information increasingly flows across organizational boundaries.

End-to-End Protection

Modern data integration must safeguard data throughout its life cycle—at at rest, in transit, and in processing. This must be supplemented with encryption, access controls, and complete audit capabilities that do not degrade performance or usability.

Look for platforms that do security by design, not bolted on as an overlay, with features that align with your organization’s security policy.

Governance and Compliance

With regulatory compliance requirements evolving worldwide, integration solutions must include governance features that deliver compliance without compromising business responsiveness.

Evaluate platforms on the ability to enforce policy uniformly across all integration patterns, provide granular audit trails, and adapt to shifting requirements for compliance.

Secure Collaboration

Most integration use scenarios today extend beyond the organizational boundary to engage customers, suppliers, and partners. This requires reliable collaboration facilities that balance data interchange efficiency against safeguarding of confidential data.

Assess whether platforms can offer safe spaces of collaboration with adequate controls for external participants without degrading security or usability.

Selecting the Right Approach to Integration

When choosing next-generation integration platforms, employ these key criteria:

• AI Maturity: Don’t trust marketing buzz to promise real AI capabilities – request demos on your data to test intelligence in the real world

• User Experience: Ensure that the platform is truly usable by business users, not just “less technical” developers

• Architectural Flexibility: Look for support for multiple deployment model scenarios (cloud, on-premises, hybrid) and integration strategies

• Scalability Evidence: Request case studies that describe successful deployments at your estimated scale

• Security Depth: Evaluate security features against your specific requirements and compliance requirements

• Total Cost of Ownership: Consider implementation effort, maintenance needs, and business impact in addition to licensing costs

• Ecosystem Health: Look at the vendor’s partner ecosystem, marketplace integrations, and community involvement

The Future of Integration

In the years to come, what is considered the grail of integration will continue evolving. We’ll have more automation via AI, more integration between emerging technologies like IoT and edge computing, and better security features deflecting new attacks.

But the vision remains the same: integration that’s smart enough to minimize technical complexity, smooth sufficient to integrate anything anywhere, and secure enough to protect your most sensitive information.

Those who adopt such next-generation integration capabilities to support business user enablement and effective governance will gain a strong competitive edge through improved agility, lower operational costs, and a greater ability to monetize their data assets.

Before setting out on your integration path, go beyond merely short-term technical requirements and embrace a broader vision of AI-driven, frictionless, and secure integration—this will transform data from a challenge to a strategic strength.

The post The Holy Grail of Data Integration Is AI-Driven, Seamless & Secure appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Denodo’s VP of Product Marketing Dominic Sartorio offers commentary on outmaneuvering tariffs and navigating disruption with data-driven resilience. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Saying we live in a world of disruption and change has become cliché, but sometimes unpredictable events arise that put this statement into stark reality and challenge even the most resilient organizations. The most recent example of this has been the recent tariff policy of the US and responses from around the world.

The fact that tariffs are coming was expected – President Donald Trump campaigned promising tariffs – but few could have expected their severity (145% on Chinese imports, as of this writing) and their pace of change (prohibitively high “reciprocal” tariffs on 100+ countries, only to be temporarily rescinded days later). Also unpredictable were second-order effects such as stock and bond market reactions, affecting cost of capital, and consumer demand due to changing future expectations of inflation or concerns of future job loss.

In a global economy where most organizations are globally distributed and source components and services from all over the world, this rapidly-changing situation makes it exceedingly hard to answer the following questions:

• What are the margin impacts across our complex supply chain under various tariff scenarios?
• Where are the optimal sources at any given point in time and can we switch on short notice?
• What are the impacts on downstream customer demand, including demand elasticity of a given product as well as propensity to switch to a similar but less-tariffed product?
• What are the tradeoffs between long-term investing in supply capacity in less-tariffed countries or the USA itself, versus short-term switching between supply chain options already in place?

In most of the Global 2000’s C-suites and boardrooms, these questions are being asked, and operating leaders are struggling to present data-driven answers that are up-to-date as of that day’s tariff reality.

The root cause problem is the availability of the right data, to make decisions within days, or even same-day, of a new tariff scenario being put in place by the US administration. Most organizations will have siloed views of data, such as having a view of all components coming from a given supplier, or being delivered through a specific transportation provider. They may have a product-centric view, such as all suppliers contributing all the components of a given product.

This data often resides in supplier-management apps, procurement apps, demand forecasting apps, and so forth. Some may be consolidated into a data lake or data warehouse to enable advanced analytics, but the time required by a data engineering team to build the necessary data pipelines is often multiple days or weeks, and will usually be done only for scenarios that the business expects will be stable over time.

Most organizations lack the ability to quickly combine the right combination of data and provide the answers to questions arising from a specific tariff scenario that wasn’t thought of ahead of time. “On the fly” data delivery is a problem. Just telling the data engineering team to build data pipelines faster doesn’t work, especially if the work usually takes multiple days or weeks, and the business wants answers in hours. A fundamentally different approach is needed.

Imagine this hypothetical scenario: An organization has already shifted from Chinese to other southeast Asian suppliers whose countries are tariffed much less, but then the US government’s negotiations with those countries goes sideways, and the organization’s board wakes up the next morning to the news of those countries’ tariffs being unexpectedly raised over 100%, an almost certainly prohibitive level. What are their options now? Can they source from Europe? What are their costs/margins if they do so? Which is optimal? At what point does it make sense to invest in building the equivalent in the USA? How long would that take?

This organization’s operating leader is hearing these questions same-day. “I’ll get back to you in a few days” is an unacceptable answer.

This kind of “black swan” unpredictable scenario has happened in the past. March 15, 2020: Most of the world has suddenly locked down in response to Covid-19. What are the impacts on customer behavior? Have they stopped purchasing or increased in order to “stock up? Are they shifting to ecommerce? Are they changing the mix of products they buy? What is the impact of lockdowns on our suppliers, and the optimal way of sourcing and delivering in response to sudden shifts in demand?

Those organizations who could stand up the right dashboards and make the right decisions quickly, within days, thrived in this environment. Those that couldn’t, struggled to stay in business. Companies that were ready were able to provide solutions like a  Covid-19 contact tracing dashboard for the entire state within days, and this enabled real-time distribution of medical staff and equipment across the entire state on a day-to-day basis.

The company made it operational within days of March 15^th. In addition, a large retailer across North America, was able to optimally allocate product between in-person stores and e-commerce channels on a day-to-day basis. These organizations were asked within days of lockdowns, what are the impacts on our organization?

Become a resilient organization in the face of truly unpredictable and day-to-day changing high-stakes situations. If you have the right technology in place, you will already know the answer, because you have the right data at the right time.

The post Outmaneuvering Tariffs: Navigating Disruption with Data-Driven Resilience appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

DataOps.live’s CTO Guy Adams offers commentary on the great debate on whether AI will help or hinder the data engineering role. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Most people think a software engineer’s job is to develop software and, of course, it is. But what has always made a person an engineer is their drive to solve problems. The tools they use to solve those problems, on the other hand, have changed dramatically, abstracting away the complexity of carrying out the solutions engineers find.

At the dawn of computing, engineering typically involved manipulating raw binary instructions—a painstaking and tedious task. Today, engineers leverage generative AI tools to rapidly produce working code and automate repetitive tasks, freeing humans to solve more complex and impactful challenges.

At every new leap in innovation, every abstraction, skeptics have confidently predicted the newest technology would only impact certain niche scenarios and could never be trusted. Time and again, these skeptics were proven wrong.

Every time we’ve increased abstraction, the skeptics were there with fictitious, but historically accurate, uncertainties such as:

1. Assembly to Early High-Level (FORTRAN/COBOL): “Automation sounds clever, but who would trust a compiler over meticulous human assembly? Assembly code will always be necessary to ensure true performance and reliability.”
2. Structured Programming (C/Pascal): “Sure, structured programming is interesting academically—but professionals won’t put truly valuable software in the hands of these awkward languages.”
3. Object-Oriented Programming (C++/Java): “Object-oriented paradigms seem convoluted. Maybe they’re useful for GUI toys, but they’ll never be suitable for robust enterprise software projects!”
4. Dynamic/Scripting languages (Python, JavaScript): “Sure, scripting languages help in quick prototyping, but they’re too slow, too inefficient, and will never be trusted for large, real-world production systems.”
5. Frameworks and APIs (Django, Ruby on Rails, React): “Frameworks might suit hobbyist websites or small-scale apps, but good engineers wouldn’t trust someone else’s code to power significant apps.”
6. Generative AI-assisted development (GitHub Copilot, ChatGPT): “AI-generated code suggestions might be cute for simple snippets or boilerplate, but we’ll never trust it enough to handle critical programming tasks across the board!”

Yet all these predictions have proven categorically wrong. Once a new abstraction becomes sufficiently trustworthy, engineers swiftly, and almost entirely, stop worrying about what happens underneath.

Consider today’s scenario: no software engineer seriously checks if their compiled C code correctly translates to assembly language. Why bother when compilers reliably handle that flawlessly every time?

The Productivity Paradox and Why GenAI Is Already Winning at Certain Tasks 

At first glance, it seems intuitive to believe vastly greater productivity might reduce the need for engineers. However, history demonstrates precisely the opposite effect. At every major productivity increase, we’ve seen the global number of software engineers significantly increase—not decrease.

That’s for a few reasons. Increasing abstraction lowers the barrier to entry, letting more people learn to program effectively. As more people are working – and tools become more effective – productivity increases, which leads to software products becoming viable for new use cases or previously untapped markets. In other words, more markets mean more demand. And that means more engineering jobs.

So, while today’s generative AI solutions genuinely excel over human engineers in several areas, it’s not necessarily because they’re significantly smarter, but rather because they’re infinitely patient. Consider tedious tasks engineers traditionally dislike and how AI can help:

1. Generating comprehensive unit tests: Human engineers write fewer tests because they find it tedious. AI tools consistently generate reliable, comprehensive tests every single time.
2. Creating documentation: Human-written code documentation is often rushed, incomplete, or neglected. AI, on the other hand, instantly and consistently generates clear, comprehensive descriptions of codebases.
3. Commenting & reviewing merge requests: Humans often overlook details in code reviews or skip certain comments. AI generates thorough, patient reviews and comments every single time.
4. Detailed data flow diagrams: AI-generated documentation is comprehensive and patiently thorough, making diagrams humans frequently find tedious and time-consuming.

As we know, good software engineers aren’t fundamentally programmers tied to one language or toolset. They’re “problem solving engineers” first and foremost. They choose the best tools available, willingly and quickly changing programming languages and paradigms as easily as some change shoes when conditions or problems demand. Good engineers aren’t Python engineers or SQL specialists.” They’re problem solvers who currently prefer Python or SQL.

They’re also eager to adopt innovative technologies precisely because they’re eager to solve bigger, better, more meaningful problems. As a result, today’s Python, JavaScript, or Go engineers will become proficient in totally different tools tomorrow as necessary.

In other words, specific language technologies are temporary choices for good engineers. When something faster, simpler, more efficient, or more powerful arrives, good engineers joyfully adopt it and move forward.

 How to Build a Positive Engineering Future 

Generative AI lifts engineers from the drudgery of documentation, testing, reviewing mundane pull requests, and other repetitive tasks we historically dislike—but which AI handles patiently, accurately, and immediately. Moving forward, Generative AI’s new productivity layer will once again enlarge—not shrink—engineering job opportunities, roles, and impact, continuing a century-long historical pattern. 

History clearly shows that abstraction improvements don’t kill engineering jobs, they empower engineers to solve bigger and more complex problems. Generative AI isn’t replacing software engineers; it’s helping them spend time doing what they love most: solving problems. It’s an engineer’s paradise!

The post The Great Debate: Will AI Help or Hinder Data Engineering Roles? appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s Executive Editor Tim King highlights the overarching AI impact on data engineering jobs, to help keep you on-trend during this AI moment.

One of the least surprising things someone can say in 2025 is that artificial intelligence (AI) has impacted data engineering jobs. What is less clear is the specific impact AI has had on those jobs and whether data engineers should be worried. As AI becomes embedded in every stage of the data pipeline—from ingestion to transformation to orchestration—the role and structure of a company’s data engineering team are being fundamentally reimagined.

To keep up with these rapid changes, the Solutions Review editors have outlined some of the primary ways AI has changed data engineering, what engineers can do to stay indispensable, and what the future may look like for the profession and the tools it relies on.

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to extract targeted knowledge and optimize content for relevance and utility.

AI Impact on Data Engineering Jobs: How Has AI Changed the Data Engineering Workforce?

AI is accelerating a tectonic shift in how data engineering is done. What was once a discipline defined by painstaking, manual construction of ETL pipelines, hand-coded transformations, and endless firefighting of data quality issues is increasingly becoming automated, abstracted, and agent-driven. In many ways, this is a win: repetitive, tedious work is disappearing, and engineers are freed up to focus on architecture, design, and innovation. But there are real risks—especially for those who built careers on legacy tools and traditional best practices. Here’s where AI’s impact is hitting hardest:

Pipeline Automation and Orchestration

Classic data engineering revolved around hand-building and maintaining pipelines. Now, AI-powered orchestration platforms can generate, optimize, and self-heal entire pipelines on the fly, adjusting for schema changes, load spikes, or new data sources with little to no human intervention. For example, tools like Datafold, Ascend.io, and Databricks’ AI Functions can auto-detect dependencies, anticipate failures, and recommend (or even execute) remediations before issues hit production. The upside? Fewer late-night emergencies, faster time-to-value, and increased reliability. The downside? The value of manual pipeline wrangling—once the core of a data engineer’s résumé—is dropping fast. Entry- and mid-level jobs focused on classic ETL are at risk of being fully automated.

Data Integration and Transformation

AI-driven integration tools now connect, map, and transform data across disparate systems with minimal human oversight. Generative AI can write SQL, build transformation logic, and auto-document complex flows—tasks that once required both domain and technical expertise. This lets organizations ingest more sources, more quickly, and handle data drift or changes in real time. But there’s a catch: the margin for error grows as complexity rises, and the risk of “garbage in, garbage out” never fully disappears. While AI speeds up and democratizes integration, human oversight becomes less about doing and more about validating, guiding, and correcting.

Monitoring, Observability, and Data Quality

Data quality issues have always been a thorn in the side of data engineers. AI-powered observability tools (like Monte Carlo, Bigeye, and Soda) can now continuously monitor pipeline health, detect anomalies, and even recommend (or auto-apply) fixes. These systems can predict where problems will occur and flag schema mismatches or failed ingestions without constant human vigilance. The good: this greatly reduces the burden of “break/fix” cycles and shifts engineers toward proactive, preventative work. The bad: some of the traditional “craft” of debugging, troubleshooting, and root cause analysis is being codified and commoditized, shrinking the skill gap between seasoned experts and AI-assisted juniors.

Infrastructure Management and Optimization

With the rise of AI, cloud data infrastructure is trending toward “set-and-forget.” Machine learning algorithms can automatically tune query performance, right-size clusters, and optimize storage with little human tuning required. Cloud providers are already pitching “zero-ops” data platforms, promising massive cost savings and efficiency. For engineers, this means fewer hours spent on low-level tuning and firefighting. But it also means a shrinking need for classic infrastructure and DevOps skills—the sort of “invisible” work that once made a good data engineer indispensable.

A 2024 Gartner survey reported that 62% of organizations using AI-driven orchestration saw a 40% or greater reduction in pipeline maintenance time, while 58% expect to reduce their traditional engineering headcount by 2027. However, 64% reported struggling to hire or retain engineers with the advanced AI, automation, and governance skills required for the next phase of their data platform evolution.

The Emergence of AI-Centric Data Engineering Roles

As with data analytics, the AI wave isn’t just destroying old jobs—it’s creating new ones, fast. Data engineers are being asked to master new platforms and workflows, often acting as the bridge between raw data, AI models, and business outcomes. We’re seeing the emergence of roles like “data automation architect,” “AI pipeline engineer,” and “AI governance lead.” Engineers who can build, prompt, or fine-tune AI-powered orchestration agents will be in high demand—at least for the next several years.

But be critical here: there’s a real possibility that even these AI-fluent roles are transitional. As platforms get smarter and more autonomous, the frontier will keep moving. The only durable advantage will be the ability to deeply understand both data and the business context, to design architectures that are resilient and adaptable, and to guide the ethical and safe use of increasingly autonomous systems. Betting on prompt engineering or “AI agent babysitting” as a career-long moat is, at best, a short-term play.

Upskilling for the Future

If data engineering is your craft, treat upskilling as mandatory, not optional. The old advice—learn one orchestration tool, master SQL, automate what you can—isn’t enough anymore. Instead, focus on:

• AI and automation literacy: Understand not just how to use AI tools, but how to build, fine-tune, and debug them. Learn how to “think in systems” and design resilient data architectures in a world where much of the day-to-day work is abstracted away.

• Cloud platform expertise: Stay current with cloud-native, serverless, and zero-ops data stack evolution. Infrastructure-as-code, while still relevant, is morphing fast.

• Data governance, compliance, and ethics: As AI takes over more “decision-making,” engineers with a handle on lineage, observability, and responsible data management will be invaluable.

• Communication and business impact: More of your work will be about translating technical possibility into business value, working across teams, and ensuring the systems you build are both powerful and trustworthy.

For organizations, don’t treat AI as a “bolt-on” for existing workflows. The best engineering teams are being reimagined as “platform teams”—builders of the data/AI fabric that enables everyone in the business to move faster, with more confidence.

AI Will Augment Data Engineering Jobs, Not Replace Them—But the Bar Is Rising

If there’s a single thread connecting every conversation about AI and data engineering, it’s this: AI is turning engineers into architects, advisors, and innovators. The “hands-on keyboard” work is vanishing fast. The real value will be in designing the systems, thinking several steps ahead, and ensuring resilience, security, and ethical guardrails. If you’re still writing hand-coded ETL in 2025, you’re swimming upstream.

The impact of AI on data engineering jobs is only accelerating, and the next three to five years will be more disruptive than anything we’ve seen so far. Yes, there’s real risk: the shrinking of classic engineering roles, the loss of “craft” in favor of automation, and the relentless rise of abstraction. But for those who embrace the change—who learn to orchestrate not just data, but the systems that run on it—the future is brighter than ever.

Bottom line: AI will automate the rote, but it will never automate the visionary. To future-proof your data engineering career, focus on the big picture, develop your AI fluency, and become the architect of tomorrow’s data-driven enterprise. The world will always need builders—but the tools and blueprints are changing fast.

The post What the AI Impact on Data Engineering Jobs Looks Like Right Now appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review Executive Editor Tim King explores the emerging AI application layer with this authoritative list of the best AI agents for data integration.

The proliferation of generative AI has ushered in a new era of intelligent automation — and AI agents are at the forefront of this transformation. From schema-mapping copilots and connector builders to autonomous agents that harmonize formats, resolve metadata conflicts, and maintain referential integrity across systems, AI agents are rapidly reshaping how modern data teams approach integration.

In this up-to-date and authoritative guide, we break down the top AI agents and agent platforms available today for data integration, grouped into clear categories to help you find the right tool for your specific needs — whether you’re unifying siloed data sources, enabling real-time sync between platforms, or embedding AI into your integration fabric.

This resource is designed to help you:

• Understand what makes AI agents different from traditional data integration tools and middleware
• Explore the capabilities and limitations of each available agent or agent-enabled platform
• Choose the best solution for your team based on integration complexity, scale, and architecture

Whether you’re managing multi-source ETL, transforming data on the fly, syncing across APIs, or enabling AI-ready pipelines — there’s an AI agent for that.

Note: This list of the best AI agents for data integration was compiled through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to employ targeted prompts to extract critical knowledge to optimize the content for relevance and utility. Our editors also utilized Solutions Review’s weekly news distribution services to ensure that the information is as close to real-time as possible.

The Best AI Agents for Data Integration

The Best AI Agents for Data Integration: Data Integration and Management Platforms

These tools assist in integrating, managing, and analyzing data from various sources.

Databricks

Use For: Unified analytics, ETL, and machine learning in a scalable lakehouse environment

Databricks is a cloud-based data and AI platform built around Apache Spark and the lakehouse architecture, which combines the scalability of data lakes with the performance of data warehouses. Designed for collaborative data engineering, analytics, and machine learning, Databricks enables teams to ingest, process, transform, analyze, and model data in a single unified environment.

With native support for Delta Lake, Databricks ensures ACID-compliant data reliability and version control, making it a top choice for enterprise-grade workflows that require data quality, reproducibility, and scalability.

Key Features:

• Collaborative notebooks with support for Python, SQL, R, and Scala

• Auto-scaling clusters for cost-efficient batch and stream processing

• Native support for Delta Lake, MLflow, Spark Streaming, and structured streaming

• Integration with Snowflake, dbt, BI tools, and cloud data lakes

• Built-in tools for model tracking, deployment, and governance

Get Started: Use Databricks when your data engineering workflow spans ETL, real-time pipelines, and AI, and you need a centralized platform to manage everything from raw data ingestion to model deployment — with collaboration, scalability, and governance built in.

Snowflake

Use For: Scalable, low-maintenance cloud data warehousing with AI-ready integrations

Snowflake is a fully managed cloud data warehouse-as-a-service (DWaaS) that separates compute and storage for on-demand scalability, making it ideal for modern data engineering teams. It supports structured and semi-structured formats (like JSON and Avro), and runs on AWS, Azure, or Google Cloud — offering fast, elastic performance with zero infrastructure management.

Snowflake simplifies the process of data ingestion, transformation (via SQL or Snowpark), and sharing, while providing native support for AI and ML integrations.

Key Features:

• Virtual warehouses that scale compute resources independently

• SQL-native development with Snowpark for Python, Java, and Scala

• Support for JSON, Parquet, Avro, XML, and other semi-structured formats

• Native integrations with dbt, Fivetran, Apache Kafka, and AI/ML tools

• Secure data sharing across organizations and cloud regions

Get Started: Use Snowflake when your data engineering workload revolves around scalable ingestion, storage, and transformation of structured/semi-structured data, and when you need fast query performance, data sharing, and AI/BI integrations without infrastructure management headaches.

dbt (Data Build Tool)

Use For: In-warehouse SQL transformations with version control, testing, and modular logic

dbt (short for data build tool) is a command-line tool and development framework that enables data teams to transform data inside modern cloud data warehouses like Snowflake, BigQuery, Redshift, and Databricks. Rather than performing ETL outside the warehouse, dbt promotes the ELT (Extract, Load, Transform) paradigm — with transformations written in modular, testable SQL files that live in version control.

dbt brings software engineering best practices like modularity, code reuse, testing, documentation, and CI/CD into the SQL transformation layer — helping data teams build clean, reliable data pipelines that scale.

Key Features:

• Modular SQL models that are compiled into optimized queries

• Built-in testing for schema, nulls, and relationships

• Auto-generated documentation with column-level lineage

• Compatible with git-based workflows and CI/CD pipelines

• Rich ecosystem with dbt Cloud, dbt Core, and dbt packages

Get Started: Use dbt when you want to build, document, and test your SQL transformation logic like software code — especially effective for teams centralizing transformation work within cloud data warehouses using ELT.

Fivetran

Use For: Fully managed data ingestion with zero-maintenance ELT pipelines

Fivetran is a fully managed ELT (Extract, Load, Transform) platform that automates the process of syncing data from hundreds of sources into cloud data warehouses like Snowflake, BigQuery, Redshift, and Databricks. Known for its plug-and-play experience, Fivetran offers prebuilt connectors for popular services like Salesforce, Stripe, HubSpot, PostgreSQL, and many others — enabling engineers to stop writing and maintaining custom ingestion code.

Fivetran handles schema drift, API changes, and sync failures, allowing teams to focus on modeling and analysis instead of low-level extraction and integration.

Key Features:

• 700+ source connectors for SaaS, databases, cloud storage, and files

• Automatic schema detection, column mapping, and change data capture (CDC)

• Incremental loading for efficiency and freshness

• Built-in monitoring, logging, and alerting

• Works seamlessly with dbt for downstream transformations

Get Started: Use Fivetran when your goal is to quickly and reliably centralize third-party or operational data into your warehouse — especially useful in analytics-driven environments where pipeline reliability and simplicity are more valuable than deep customization.

Talend

Use For: Enterprise-grade data integration, governance, and hybrid data pipeline management

Talend is a comprehensive data integration and transformation platform designed to support ETL, ELT, data quality, governance, and API services across both cloud and on-premises environments. With a visual drag-and-drop interface and a vast library of prebuilt connectors, Talend enables teams to connect disparate systems — from legacy databases to modern cloud platforms — in a single, centralized workflow.

Talend excels in highly regulated industries or large enterprises where data quality, lineage, and compliance are critical.

Key Features:

• Visual workflow designer with 1,000+ prebuilt connectors

• Support for batch, real-time, and hybrid integration

• Built-in tools for data quality, masking, lineage, and stewardship

• Integration with Snowflake, AWS, Azure, SAP, Salesforce, and more

• Enterprise deployment options: on-premises, hybrid, and cloud-native

Get Started: Use Talend when you need a scalable, secure platform to build, manage, and govern data pipelines across hybrid or legacy environments, especially in sectors where compliance, lineage, and quality enforcement are top priorities.

Stitch (Qlik)

Use For: Lightweight, developer-friendly cloud ETL for fast and simple data replication

Stitch is a simple, cloud-native ETL service designed to help teams quickly extract and load data from dozens of sources into modern cloud data warehouses like Snowflake, BigQuery, and Redshift. Acquired by Talend, Stitch provides a developer-friendly interface and open-source foundation (Singer), making it a great choice for fast-moving teams who want to stand up data pipelines with minimal overhead.

It focuses on simplicity and speed, offering basic transformation features while encouraging teams to handle modeling downstream with tools like dbt.

Key Features:

• 100+ built-in connectors for SaaS apps, databases, and APIs

• Automatic data extraction and loading with incremental sync support

• Scheduling, logging, and usage tracking

• Built on Singer — open-source standard for connectors and replication

• REST API and CLI for integration into DevOps workflows

Get Started: Use Stitch when your team needs a lightweight, no-fuss way to centralize data from multiple systems into your warehouse — especially when combined with dbt for transformation and modeling downstream.

Want the full list? Register for Insight Jam [free], Solutions Review‘s enterprise tech community enabling the human conversation on AI, to gain access here.

The post The 17 Best AI Agents for Data Integration to Consider in 2025 appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review Executive Editor Tim King explores the emerging AI application layer with this authoritative list of the best AI agents for data engineering.

The proliferation of generative AI has ushered in a new era of intelligent automation — and AI agents are at the forefront of this transformation. From code-writing copilots and pipeline orchestration assistants to autonomous agents that validate data, monitor pipeline health, and streamline MLOps, AI agents are rapidly reshaping how modern data teams design, maintain, and scale their infrastructure.

In this up-to-date and authoritative guide, we break down the top AI agents and agent platforms available today for data engineering, grouped into clear categories to help you find the right tool for your specific needs — whether you’re building real-time ETL pipelines, managing complex data ecosystems, or embedding AI into your operational workflows.

This resource is designed to help you:

• Understand what makes AI agents different from traditional data engineering and pipeline tools

• Explore the capabilities and limitations of each available agent or agent-enabled platform

• Choose the best solution for your team based on use case, architecture, and team size

Whether you’re automating data ingestion, monitoring pipeline health, orchestrating cross-cloud workflows, or embedding machine learning into infrastructure — there’s an AI agent for that.

Note: This list of the best AI agents for data engineering was compiled through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to employ targeted prompts to extract critical knowledge to optimize the content for relevance and utility. Our editors also utilized Solutions Review’s weekly news distribution services to ensure that the information is as close to real-time as possible.

The Best AI Agents for Data Engineering

The Best AI Agents for Data Engineering: Data Pipeline Automation and Orchestration

Tools focused on automating data workflows, scheduling, and transformation.

Apache Airflow

Use For: Authoring and scheduling complex, dependency-aware data workflows

Apache Airflow is one of the most widely adopted open-source tools for workflow orchestration in modern data engineering. Originally developed at Airbnb and now part of the Apache Software Foundation, Airflow allows engineers to define workflows as Python-based DAGs (Directed Acyclic Graphs) — giving full control over task execution order, retries, failure alerts, and dependencies.

Airflow has become a cornerstone of production-grade data pipelines, powering everything from nightly ETL jobs to multi-step ML retraining pipelines. Its flexible, plugin-friendly architecture enables seamless integration with virtually any system or service in the modern data stack.

Key Features:

• Define workflows in Python for full programmatic control

• Built-in scheduler and executor for running tasks in order or parallel

• Extensible with hundreds of community-contributed operators (e.g., BigQuery, Snowflake, Spark, Kubernetes)

• Centralized UI for tracking DAG runs, task logs, and job status

Get Started: Use Apache Airflow when you need fine-grained control over complex pipelines, especially in batch processing, data warehouse jobs, or ML model orchestration — and when your workflows involve multiple interdependent systems or tools.

Prefect

Use For: Modern, Pythonic orchestration of data workflows with better observability and lower setup overhead than Airflow

Prefect is a next-generation workflow orchestration platform designed as a modern alternative to Apache Airflow. With a code-first, Python-native interface, Prefect lets developers define workflows using intuitive constructs called Flows and Tasks, rather than complex DAGs. It emphasizes observability, flexibility, and ease of use, making it especially appealing to agile data teams.

Prefect is built to support both local development and enterprise-scale production deployments, offering hybrid execution (run locally, monitor in the cloud) and automatic retries, caching, and parameterization out of the box.

Key Features:

• Python-native workflow definitions — no custom DSL or configuration files

• Cloud or on-prem monitoring of job runs, logs, failures, and retries

• First-class integrations with tools like dbt, Snowflake, GCS, S3, and Kubernetes

• Dynamic workflows, parameterization, and input/output passing

Get Started: Use Prefect when your data engineering team wants a modern, developer-friendly orchestration tool that offers both local flexibility and production-ready monitoring — perfect for fast-moving teams that value observability and clean code.

Luigi

Use For: Lightweight orchestration of batch data workflows and pipeline dependencies

Luigi is an open-source Python package developed by Spotify for building batch data pipelines with complex task dependencies. It allows users to create workflows by defining Python classes for each task, specifying input/output requirements, and linking them via dependency chains. Luigi is especially useful for internal automation, batch processing, and building one-off jobs that need to run in a specific order.

While not as feature-rich or scalable as Airflow or Prefect, Luigi remains a trusted option for simpler, dependency-aware workflows — especially when low infrastructure complexity and high customizability are priorities.

Key Features:

• Define tasks as Python classes with dependency logic baked in

• Automatically resolves task order and ensures upstream completion

• Visualizes workflow execution and status in a simple web UI

• Works well for file-based, database, or shell-script-based pipelines

Get Started: Use Luigi when you need a simple, Python-native orchestration framework for running ETL jobs or automation scripts with clear dependencies — ideal for smaller workflows or development environments.

Mage AI

Use For: Notebook-style pipeline building with AI-powered suggestions and smart debugging

Mage AI is a modern open-source data pipeline tool that blends the flexibility of notebooks with the robustness of a workflow orchestration engine. Built for the modern data stack, Mage lets users build, visualize, and debug data pipelines in a low-code interface using Python, SQL, and R — all while offering AI-driven insights to help optimize logic, catch errors, and accelerate development.

Mage is particularly appealing to smaller data teams or analytics engineers who want a smooth UX, fast iteration cycles, and helpful guidance without having to manage complex infrastructure.

Key Features:

• Notebook-style UI for building batch and streaming pipelines

• Support for Python, SQL, and R tasks

• Real-time pipeline execution with step-by-step visual monitoring

• AI-powered suggestions for error resolution and performance optimization

• Native integration with Snowflake, BigQuery, Redshift, Databricks, and more

Get Started: Use Mage AI when your team wants an intuitive, visual environment to build and debug pipelines, especially in fast-moving analytics environments where speed, clarity, and low overhead matter more than raw orchestration power.

Dagster

Use For: Asset-centric orchestration with strong data lineage, testing, and governance support

Dagster is a modern workflow orchestration platform that reimagines pipelines as a system of data assets rather than just a chain of tasks. Instead of focusing solely on execution order, Dagster emphasizes data lineage, types, documentation, and validation, giving engineers greater control over the lifecycle and quality of the data being processed.

Built with software engineering principles and data quality in mind, Dagster helps teams structure ELT pipelines, ML workflows, and analytics systems in a way that is testable, debuggable, and transparent.

Key Features:

• Declarative, asset-driven pipeline definitions in Python

• Automatic lineage tracking and metadata for every pipeline run

• First-class support for testing, logging, and monitoring

• Integrations with dbt, Spark, Snowflake, Redshift, S3, and more

• Rich UI with visual DAGs, asset graphs, and event logs

Get Started: Use Dagster when you want to treat data pipelines as a well-governed system of reproducible assets, particularly in environments where lineage, quality, and modularity are core concerns.

CrewAI

Use For: Coordinating multiple specialized AI agents to work collaboratively on complex data workflows

CrewAI is an emerging open-source framework that allows developers to create and orchestrate teams of AI agents — each with a defined role, objective, and responsibility. Built to simulate real-world collaboration, CrewAI enables agents to communicate, plan, delegate, and execute tasks in sequence or parallel, making it a unique tool for advanced data engineering automation.

For data engineers, CrewAI is a powerful experimental playground for automating data validation, transformation, documentation, and monitoring, assigning agents to handle distinct pipeline components (e.g., one for QA, one for ingestion), simulating how human teams coordinate on engineering workflows, and prototyping intelligent systems that plan, execute, and self-improve.

Key Features:

• Multi-agent collaboration with memory, role assignment, and task delegation

• Integration with LLMs like GPT-4, Claude, or custom APIs

• Command-line or Python-based configuration with modular architecture

• Ability to define reusable roles (e.g., Data Cleaner, SQL Generator, Pipeline Auditor)

Get Started: Use CrewAI when you’re exploring next-gen AI automation by assigning multiple agents to collaborate on distinct stages of a data pipeline — a great fit for innovation labs, internal R&D, or agent-based system exploration.

Want the full list? Register for Insight Jam [free], Solutions Review‘s enterprise tech community enabling the human conversation on AI, to gain access here.

The post The 27 Best AI Agents for Data Engineering to Consider in 2025 appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

Solutions Review’s listing of the best data engineering tools is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best data engineering tools and software based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.

The editors at Solutions Review have developed this resource to assist buyers in search of the data engineering tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data engineering tools and software providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best data engineering tools are listed in alphabetical order.

The Best Data Engineering Tools

Amazon Web Services

Platform: Amazon Redshift

Description: Amazon Redshift is a fully-managed cloud data warehouse that lets customers scale up from a few hundred gigabytes to a petabyte or more. The solution enables users to upload any data set and perform data analysis queries. Regardless of the size of the data set, Redshift offers fast query performance using familiar SQL-based tools and business intelligence applications. AWS also has multiple ways to do cluster management depending on user skill level.

Cloudera

Description: Cloudera provides a data storage and processing platform based on the Apache Hadoop ecosystem, as well as a proprietary system and data management tools for design, deployment, operations, and production management. Cloudera acquired Hortonworks in October 2018. It followed that up with a buy of San Mateo-based big data analytics provider Arcadia Data last September. Cloudera’s new integrated data management product (Cloudera Data Platform) enables analytics across hybrid and multi-cloud.

Fivetran

Platform: Fivetran

Description: Fivetran is an automated data integration platform that delivers ready-to-use connectors, transformations and analytics templates that adapt as schemas and APIs change. The product can sync data from cloud applications, databases, and event logs. Integrations are built for analysts who need data centralized but don’t want to spend time maintaining their own pipelines or ETL systems. Fivetran is easy to deploy, scalable, and offers some of the best security features of any provider in the space.

Google Cloud

Platform: Google BigQuery

Description: Google offers a fully-managed enterprise data warehouse for analytics via its BigQuery product. The solution is serverless and enables organizations to analyze any data by creating a logical data warehouse over managed, columnar storage, and data from object storage and spreadsheets. BigQuery captures data in real-time using a streaming ingestion feature, and it’s built atop the Google Cloud Platform. The product also provides users the ability to share insights via datasets, queries, spreadsheets and reports.

Looker

Platform: Looker

Related products: Powered by Looker

Description: Looker offers a BI and data analytics platform that is built on LookML, the company’s proprietary modeling language. The product’s application for web analytics touts filtering and drilling capabilities, enabling users to dig into row-level details at will. Embedded analytics in Powered by Looker utilizes modern databases and an agile modeling layer that allows users to define data and control access. Organizations can use Looker’s full RESTful API or the schedule feature to deliver reports by email or webhook.

Microsoft

Platform: Power BI

Related products: Power BI Desktop, Power BI Report Server

Description: Microsoft is a major player in enterprise BI and analytics. The company’s flagship platform, Power BI, is cloud-based and delivered on the Azure Cloud. On-prem capabilities also exist for individual users or when power users are authoring complex data mashups using in-house data sources. Power BI is unique because it enables users to do data preparation, data discovery, and dashboards with the same design tool. The platform integrates with Excel and Office 365, and has a very active user community that extends the tool’s capabilities.

Mongo DB

Platform: Mongo DB Atlas

Description: MongoDB is a cross-platform document-oriented database. It is classified as a NoSQL database program and uses JSON-like documents with schema. The software is developed by MongoDB and licensed under the Server Side Public License. Key features include ad hoc queries, indexing, and real-time aggregation, as well as a document model that maps to the objects in your application code. MongoDB provides drivers for more than 10 languages, and the community has built dozens more.

Segment

Platform: Segment

Description: Segment offers a customer data platform (CDP) that collects user events from we band mobile apps and provides a complete data toolkit to the organization. The product is available in three iterations, depending on the user persona (Segment for Marketing Teams, Product Teams or Engineering Teams). Segment works by letting you standardize data collection, unify user records, and route customer data into any system where it’s needed. The solution also touts more than 300 integrations.

Snowflake

Platform: Snowflake Cloud Data Platform

Description: Snowflake offers a cloud data warehouse built atop Amazon Web Services. The solution loads and optimizes data from virtually any source, both structured and unstructured, including JSON, Avro, and XML. Snowflake features broad support for standard SQL, and users can do updates, deletes, analytical functions, transactions, and complex joins as a result. The tool requires zero management and no infrastructure. The columnar database engine uses advanced optimizations to crunch data, process reports, and run analytics.

Tableau Software

Platform: Tableau Desktop

Related products: Tableau Prep, Tableau Server, Tableau Online, Tableau Data Management

Description: Tableau offers an expansive visual BI and analytics platform, and is widely regarded as the major player in the marketplace. The company’s analytic software portfolio is available through three main channels: Tableau Desktop, Tableau Server, and Tableau Online. Tableau connects to hundreds of data sources and is available on-prem or in the cloud. The vendor also offers embedded analytics capabilities, and users can visualize and share data with Tableau Public.

The post The 10 Best Data Engineering Tools (Commercial) for 2025 appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

The editors at Solutions Review have compiled this list of the best Informatica training, online courses and classes to consider.

Informatica is one of the most widely used data integration platforms in the world. It combines advanced hybrid integration capabilities and centralized governance with self-service business access for a variety of analytic functions. Informatica PowerCenter is a metadata-driven integration tool that accelerates projects in order to deliver data to the business more quickly than manual hand-coding. It also allows developers and analysts to collaborate, prototype, analyze, and deploy projects. Informatica is used by more than 7,000 organizations and features strong interoperability between its growing list of data products.

With this in mind, we’ve compiled this list of the best Informatica online training and certifications to consider if you’re looking to grow your data warehouse and integration skills for work or career advancement. This is not an exhaustive list, but one that features the best Informatica online training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well.

The Best Informatica Online Training

TITLE: Informatica Training & Certification

OUR TAKE: This six-week Edureka Informatica training provides hands-on training to install the product on Windows using Oracle as a database, as well as creating the services and connecting clients to a server.

Platform: Edureka

Description: Edureka’s Informatica Training will help you master data integration concepts such as ETL and data mining using Informatica PowerCenter. It will also make you proficient in advanced transformations, Informatica Architecture, data migration, performance tuning, installation and configuration of Informatica PowerCenter. Throughout the Informatica training course, you will be working on real-life industry-based use cases.

TITLE: Informatica IT Certification

OUR TAKE: Informatica Exams deliver a consistent measurement and validation of the skills needed to ensure a successful implementation and maximum return on your Informatica technology investment.

Platform: Informatica

Description: Informatica Certifications focus on not only your Informatica skills and capabilities but also on your performance and the outcomes of your Informatica product implementations. They have been developed by recognized subject matter experts to measure competencies in tasks by role and to provide clear expectations of requirements and key factors for success.

TITLE: Informatica Certification Training

OUR TAKE: Intellipaat’s Informatica course features 42 hours of instructor-led training, 42 hours of self-paced video, 60 hours of project work and exercises, and can be completed via a flexible schedule.

Platform: Intellipaat

Description: Intellipaat Informatica training is an industry-designed course for mastering the Informatica tool for ETL. You will learn how to configure, install and administer the PowerCenter. As part of the training, you will also do testing and monitoring of data processing using automated, scalable and auditable approach. You will get trained in Workflow Informatica, data warehousing, Repository Management and other processes.

More “Top-Rated” Intellipaat paths: Informatica Big Data Edition Training, Informatica MDM Training

TITLE: Informatica Tutorial: Beginner to Expert Level

OUR TAKE: With more than 5,000 ratings, this is one of the web’s most popular Informatica training modules. The course touts 73 sections with 288 lectures, as well as 35 hours of on-demand video and 17 downloadable resources.

Platform: Udemy

Description: The course covers all topics starting from data warehouse concepts, roles and responsibilities of an ETL developer, installation and configuration of Informatica Power Center 10x/9.X, in detailed explanation of transformations with practical examples, performance tuning tips for each transformation (clearly shown and explained), usually asked interview questions, quizzes for each section and assignments for your hands on and in-depth explanation of the Repository Service, Integration Service and other basic administration activities.

More “Top-Rated” Udemy paths: LEARNING PATH: Complete Roadway to Informatica Powercenter 9, Informatica Cloud – Data Integration

Solutions Review participates in affiliate programs. We may make a small commission from products purchased through this resource.

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Solutions Review’s listing of the best ELT tools (Extract, Load, Transform) is an annual sneak peek of the top tools included in our Buyer’s Guide for Data Integration Tools and companion Vendor Comparison Map. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials.

The editors at Solutions Review have developed this resource to assist buyers in search of the best ELT tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best ELT tools providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best ELT tlisted in alphabetical order.

The Best ELT Tools

Adeptia

Platform: Adeptia Connect

Description: Adeptia offers enterprise data integration tools that can be used by non-technical business users. Adeptia Connect features a simple user interface to manage all external connections and data interfaces. It also includes self-service partner onboarding and a no-code approach that lets users and partners view, setup and manage data connections. The platform touts a suite of pre-built connections and Cloud Services Integration, as well as B2B standards and protocol support.

Alooma

Platform: Alooma Platform

Description: Alooma offers a data pipeline service that integrates with popular data sources. The Alooma platform features end-to-end security, which ensures that every event is securely transferred to a data warehouse (SOC2, HIPAA, and EU-US Privacy Shield certified). The solution responds to data changes in real-time to make sure no events are lost. Users can choose to manage changes automatically or get notified and make changes on-demand. The tool also infers data automatically to provide customizable control.

CData Software

Platform: CData Driver Technologies

Description: CData Software offers data integration solutions for real-time access to online or on-prem applications, databases, and Web APIs. The vendor specializes in providing access to data through established data standards and application platforms such as ODBC, JDBC, ADO.NET, SSIS, BizTalk, and Microsoft Excel. CData Software products are broken down into six categories: driver technologies, enterprise connectors, data visualization, ETL and ELT solutions, OEM and custom drivers, and cloud and API connectivity.

Fivetran

Platform: Fivetran

Description: Fivetran is an automated data integration platform that delivers ready-to-use connectors, transformations and analytics templates that adapt as schemas and APIs change. The product can sync data from cloud applications, databases, and event logs. Integrations are built for analysts who need data centralized but don’t want to spend time maintaining their own pipelines or ETL systems. Fivetran is easy to deploy, scalable, and offers some of the best security features of any provider in the space.

Hevo Data

Description: Hevo Data offers a no-code data pipeline for loading data into data warehouses. Data can be loaded from a wide variety of sources like relational databases, NoSQL databases, SaaS applications, files or S3 buckets into any warehouse (Amazon Redshift, Google BigQuery, Snowflake) in real-time. Hevo supports more than 100 pre-built integrations, and all of them are native and tout specific source APIs. The solution features a streaming architecture as well. Hevo detects schema changes on incoming data and automatically replicates the same in your destinations.

Hitachi Vantara

Platform: Pentaho Platform

Related products: Lumada Data Services

Description: Hitachi Vantara’s Pentaho platform for data integration and analytics offers traditional capabilities and big data connectivity. The solution supports the latest Hadoop distributions from Cloudera, Hortonworks, MapR, and Amazon Web Services. However, one of the tool’s shortcomings is that its big data focus takes attention away from other use cases. Pentaho can be deployed on-prem, in the cloud, or via a hybrid model. The tool’s most recent update to version 8 features Spark and Kafka stream processing improvements and security add-ons.

IBM

Platform: IBM InfoSphere Information Server

Related products: IBM InfoSphere Classic Federation Server, IBM InfoSphere Data Replication, IBM InfoSphere DataStage, IBM App Connect, IBM Streams, IBM Data Refinery, IBM BigIntegrate, IBM Cloud Integration

Description: IBM offers several distinct data integration tools in both on-prem and cloud deployments, and for virtually every enterprise use case. Its on-prem data integration suite features tools for traditional (replication and batch processing) and modern integration synchronization and data virtualization) requirements. IBM also offers a variety of prebuilt functions and connectors. The mega-vendor’s cloud integration product is widely considered one of the best in the marketplace, and additional functionality is coming in the months ahead.

https://www.youtube.com/watch?v=6koDBI1fINE

Informatica

Platform: Informatica Intelligent Data Platform

Related products: Informatica PowerCenter, Informatica PowerExchange, Informatica Data Replication, Informatica B2B Data Transformation, Informatica B2B Data Exchange, Informatica Big Data Integration Hub, Informatica Data Services, Informatica Big Data Management, Informatica Big Data Integration Hub, Informatica Big Data Streaming, Informatica Enterprise Data Catalog, Informatica Enterprise Data Preparation, Informatica Edge Data Streaming, Informatica Intelligent Cloud Services

Description: Informatica’s data integration tools portfolio includes both on-prem and cloud deployments for a number of enterprise use cases. The vendor combines advanced hybrid integration and governance functionality with self-service business access for various analytic functions. Augmented integration is possible via Informatica’s CLAIRE Engine, a metadata-driven AI engine that applies machine learning. Informatica touts strong interoperability between its growing list of data management software products.

Keboola

Platform: Keboola

Description: Keboola is a cloud-based data integration platform that connects data sources to analytics platforms. It supports the entire data workflow process, from the point of data extraction, preparation, cleansing, warehousing, and all the way to its integration, enrichment, and loading. Keboola offers more than 200 integrations and features an environment that allows users to build their own data applications or integrations using GitHub and Docker. The product can also automate low-value activities while account for audit trail, version control and access management.

Matillion

Platform: Matillion ETL

Related products: Matillion Data Loader

Description: Matillion offers a cloud-native data integration and transformation platform that is optimized for modern data teams. It also features built on native integrations to popular cloud data platforms like Snowflake, Delta Lake on Databricks, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse. Matillion uses an extract-load-transform approach that handles the extract and load in one move, straight to an organization’s target data platform, then using the power of a cloud data platform’s processes to perform transformations once loaded.

Microsoft

Platform: SQL Server Integration Services (SSIS)

Related products: Azure Data Factory cloud integration service

Description: Microsoft offers its data integration functionality on-prem and in the cloud (via Integration Platform as a Service). The company’s traditional integration tool, SQL Server Integration Services (SSIS), is included inside the SQL Server DBMS platform. Microsoft also touts two cloud SaaS products: Azure Logic Apps and Microsoft Flow. Flow is ad hoc integrator-centric and included in the overarching Azure Logic Apps solution.

Oracle

Platform: Oracle Data Integration Cloud Service

Related products: Oracle GoldenGate, Oracle Data Integrator, Oracle Big Data SQL, Oracle Service Bus, Oracle Integration Cloud Service (iPaaS)

Description: Oracle offers a full spectrum of data integration tools for traditional use cases as well as modern ones, in both on-prem and cloud deployments. The company’s product portfolio features technologies and services that allow organizations to full lifecycle data movement and enrichment. Oracle data integration provides pervasive and continuous access to data across heterogeneous systems via bulk data movement, transformation, bidirectional replication, metadata management, data services, and data quality for customer and product domains.

Panoply

Description: Panoply automates data management tasks associated with running big data in the cloud. Smart Data Warehouse require no schema, modeling, or configuration. Panoply features an ETL-less integration pipeline that can connect to structured and semi-structured data sources. It also offers columnar storage and automatic data backup to a redundant S3 storage framework.

Precisely

Platform: Precisely Data Integrity Suite, Precisely Connect

Related products: Precisely Data Integrity Suite Data Integration Module, Precisely Ironstream

Description: The data integration module of the Precisely Data Integrity Suite is one of seven SaaS modules that ensure data is accurate, consistent, and contextual. It is complemented by Precisely Connect, an on-prem data integration solution that supports a broad range of source and target systems. Both solutions leverage Precisely’s deep expertise in mainframe and IBM i systems to integrate complex data formats into modern cloud platforms like Snowflake and Databricks. Precisely Ironstream also integrates mainframe and IBM i machine and log data into IT platforms like Splunk and ServiceNow for IT operations management, analytics, and security.

Qlik

Platform: Qlik Replicate

Related products: Qlik Compose, Qlik Catalog, Qlik Blendr.io

Description: Qlik offers a range of integration capabilities that span four product lines. The flagship product is Qlik Replicate, a tool that replicates, synchronizes, distributes, consolidates, and ingests data across major databases, data warehouses, and Hadoop. The portfolio is buoyed by Qlik Compose for data lake and data warehouse automation and Qlik Catalog for enterprise self-service cataloging. Qlik also offers Integration Platform as a Service functionality through its Blendr.io product, which touts API connectivity, no-code integration and application automation.

SAP

Platform: SAP Data Services

Related products: SAP Replication Server, SAP Landscape Transformation Replication Server, SAP Data Hub, SAP HANA, SAP Cloud Integration Platform Suite, SAP Cloud Platform

Description: SAP provides on-prem and cloud integration functionality through two main channels. Traditional capabilities are offered through SAP Data Services, a data management platform that provides capabilities for data integration, quality, and cleansing. Integration Platform as a Service features are available through the SAP Cloud Platform. SAP’s Cloud Platform integrates processes and data between cloud apps, 3^rd party applications, and on-prem solutions.

SAS

Platform: SAS Data Management

Related products: SAS Data Integration Studio, SAS Federation Server, SAS/ACCESS, SAS Data Loader for Hadoop, SAS Data Preparation, SAS Event Stream Processing

Description: SAS is the largest independent vendor in the data integration tools market. The provider offers its core capabilities via SAS Data Management, where data integration and quality tools are interwoven. It includes flexible query language support, metadata integration, push-down database processing, and various optimization and performance capabilities. The company’s data virtualization tool, Federation Server, enables advanced data masking and encryption that allows users to determine who’s authorized to view data.

Skyvia

Description: Skyvia’s Data Integration tool contains a wide range of data-related scenarios which can be created directly from the user interface. Users can migrate data from one source to another, set up bi-directional data synchronization with flexible scheduling, import or export data to different sources, including CSV, as well as replicate cloud data to relational databases. According to the company, all Data Integration functionalities remain free at this stage in time.

StreamSets

Platform: StreamSets Data Collector

Related products: StreamSets DataOps Platform

Description: StreamSets offers a DataOps platform that features smart data pipelines with built-in data drift detection and handling, as well as a hybrid architecture. The product also includes automation and collaboration capabilities across the design-deploy-operate lifecycle. StreamSets monitors data in-flight to detect changes and predicts downstream issues to ensure continuous delivery without errors or data loss. The tool’s live data map, data performance SLAs and data protection functionality are major value-adds.

Striim

Platform: Striim Platform

Related products: Striim for Azure, Striim for Amazon Web Services, Striim for Google Cloud Platform, Striim for Snowflake

Description: Striim offers a real-time data integration solution that enables continuous query processing and streaming analytics. Striim integrates data from a wide variety of sources, including transaction/change data, events, log files, application and IoT sensor, and real-time correlation across multiple streams. The platform features pre-built data pipelines, out-of-the-box wizards for configuration and coding, and a drag-and-drop dashboard builder.

Talend

Platform: Talend Open Studio

Related products: Talend Data Fabric, Talend Data Management Platform, Talend Big Data Platform, Talend Data Services Platform, Talend Integration Cloud, Talend Stitch Data Loader

Description: Talend offers an expansive portfolio of data integration and data management tools. The company’s flagship tool, Open Studio for Data Integration, is available via a free open-source license. Talend Integration Cloud is offered in three separate editions (SaaS, hybrid, elastic), and provides broad connectivity, built-in data quality, and native code generation to support big data technologies. Big data components and connectors include Hadoop, NoSQL, MapReduce, Spark, machine leaning and IoT.

Xplenty

Description: Xplenty allows organizations to integrate, process, and prepare data for analytics in the cloud. The tool’s package designer can be used to implement a variety of data integration use cases from replication to data preparation and transformation, all within a point-and-click environment. Xplenty includes out-of-the-box data transformation, and users can execute packages either from the UI or the API. The tool allows users to integrate data from more than 100 different data stores and SaaS applications.

The post The 22 Best ELT Tools (Extract, Load, Transform) for 2025 appeared first on Best Data Integration Vendors, News & Reviews for Big Data, Applications, ETL and Hadoop.

A directory of the best DataCamp training courses for data engineering, compiled by the editors at Solutions Review.

Data engineering is the process of designing and building pipelines that transport and transform data into a usable state for data workers to utilize. Data pipelines commonly take data from many disparate sources and collect them into data warehouses that represent the data as a single source. To do so, data engineers must manipulate and analyze data from each system as a pre-processing step.

With this in mind, the editors at Solutions Review have compiled this list of the best DataCamp courses for data engineering and big data. DataCamp’s mission is to “democratize data skill for everyone” by offering more than 350 different data science and analytics courses and 12 distinct career tracks. More than 2,000 companies, 3,000 organizations, and 8 million users from 180 countries have used DataCamp since its founding. DataCamp’s entire course catalog is interactive which makes it perfect for learning at your own pace.

The Best DataCamp Courses for Data Engineering and Big Data

TITLE: Streaming Data with AWS Kinesis and Lambda

OUR TAKE: By the end of this training you’ll know how to create live ElasticSearch dashboards with AWS QuickSight and CloudWatch. The module features four different chapters, 22 videos, and 56 exercises.

Description: In this course, you’ll learn how to leverage powerful technologies by helping a fictional data engineer named Cody. Using Amazon Kinesis and Firehose, you’ll learn how to ingest data from millions of sources before using Kinesis Analytics to analyze data as it moves through the stream. You’ll also spin up serverless functions in AWS Lambda that will conditionally trigger actions based on the data received.

TITLE: Data Engineering for Everyone

OUR TAKE: DataCamp’s data engineering training takes 2 hours to complete and consists of 11 videos and 32 unique exercises. By the end of the module, you will uncover how data engineers lay the groundwork for data science.

Description: In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data.

More “Top-Rated” DataCamp paths: Building Data Engineering Pipelines in Python, Introduction to Data Engineering

TITLE: Feature Engineering with PySpark

OUR TAKE: This course details data wrangling and feature engineering through 4 hours of interactive video including 16 videos and 60 unique exercises. Nearly 9,000 DataCamp users have taken this training.

Description: The real world is messy and your job is to make sense of it. Toy datasets like MTCars and Iris are the result of careful curation and cleaning, even so, the data needs to be transformed for it to be useful for powerful machine learning algorithms to extract meaning, forecast, classify, or cluster. This course will cover the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

TITLE: Big Data Fundamentals with PySpark

OUR TAKE: The DataCamp Big Data Fundamentals training will teach you the basics of working with big data and PySpark. It features 4 hours of training, 16 videos, and 55 separate exercises.

Description: This course covers the fundamentals of Big Data via PySpark. Spark is a “lightning-fast cluster computing” framework for Big Data. It provides a general data processing platform engine and lets you run programs up to 100x faster in memory, or 10x faster on disk than Hadoop. You’ll use PySpark, a Python package for spark programming and its powerful, higher-level libraries such as SparkSQL, MLlib (for machine learning), etc., to interact with works of William Shakespeare, analyze Fifa football 2018 data, and perform clustering of genomic datasets.

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