I Reviewed 7 Best ETL Tools for Data Transfer Efficiency

As a marketing professional, I am best friends with data.  If we zoom in to the absolute core of my job nature, you will find visual customer data. As I set foot in the B2B industry, it took me a good number of business days to understand how raw business data is converted and transformed via an ETL tool into a data warehouse or data lake that simplifies data management for teams.

However, managing ETL tools is the domain of genius for backend developers and data engineers. From handling APIs to batch processing or real-time processing to data warehousing, they are in charge of ETL pipelines to transfer data in a compliant and resource-efficient manner.

Although for any experienced customer-oriented professional like me, having access to an ETL tool is mandatory to have a dropdown of customers' profiles and personas.

Because of my growing curiosity to analyze raw data and turn it into a meaningful customer journey, I set out to review the 7 best ETL tools for data transfer and replication for external use.

If you are already contemplating on best ETL tools to handle data securely and offer cost-efficient pricing, this detailed review guide is for you.

Apart from basic research, if you are focusing entirely on developer needs like an ETL tool that handles complex data integrations, offers support for AI/ML workflows, and follows compliance and security guidelines and displays low latency,  this list is a rundown of all top leaders of G2 that are held high in market.

7 best ETL tools that optimized data transfers for me

Even though I operate in the marketing sector, I am a prior developer who probably knows a thing or two about how to crunch data and aggregate variables in a clean and structured way via relational database management system (RDBMS) and data warehousing.

Although my experience as a data specialist is dated, my marketing role made me revisit data workflows and management techniques. I understood that once raw data files enter a company's tech stack, say CRM or ERP, they need to be readily available for standard business processes without any outliers or invalid values.

Evidently, the ETL tools that I reviewed excelled at transferring, managing, and replicating data to optimize performance. 

Whether you wish to regroup and reengineer your raw data into a digestible format, integrate large databases with ML workflows, and optimize performance and scalability, this list of ETL tools will help you with that.

What makes an ETL tool worth it: my opinion

The prime purpose of ETL tools is to help both technical and non-technical users store, organize, and retrieve data without much coding effort. According to my review, these ETL tools not only offer API connectors to transfer raw CRM or ERP data but also eliminate invalid data, cleanse data pipelines, and offer seamless integration with ML tools for data analysis. 

It should also integrate with cloud storage platforms or on-prem platforms to store data in cloud data warehouses or on-prem databases. Capabilities like data mesh, serverless handling, and low latency made it to this list, which are features of a well-equipped ETL tool in 2025.

• Schema management and data validation: In my experience, schema drift is one of the most common reasons data pipelines break. A good ETL tool needs to handle not just schema changes; it should anticipate them. I specifically looked for tools that offer automated schema detection, validation rules, and alerts when something breaks upstream. This helps maintain data integrity and save countless hours of backtracking and debugging faulty transformations.
• Wide range of prebuilt API connectors: One of the first things I assessed is how many systems the tool can natively connect to. Whether it is Snowflake, Redshift, Salesforce, SAP, or flat files, the support for more API connectors can help me focus on setup and insights for my data on a centralized platform. Tools that offer easy API integrations or webhook support also stood out to me as future-proof investments. 
• Scalability and distributed processing: Good scalability is an important factor that enables you to adapt to your growing needs of data and optimize performance. I've seen teams outgrow tools that couldn't handle growing volumes or velocity of data. I always favor ETL platforms that support parallel processing and distributed workloads. Whether these ETL tools are compatible with Spark, Kubernetes, or serverless frameworks, they have made it to this list so that it doesn't affect the performance as demand scales. 
• Support for both real-time and batch workflows: Whether I am powering a real-time dashboard or doing nightly reconciliations, flexibility matters. I preferred ETL tools that let me toggle between streaming and batch pipelines without switching platforms. The support for real-time and batch workflow helps integrate a new raw data file into the data warehouse as soon as it flows into the system. That adaptability saves licensing costs, time, and complexity across the data stack.  
• End-to-end metadata and data lineage tracking: It is crucial to track how a data point got from the source to the dashboard. I've learned how time-consuming it can be to trace logic without proper data lineage support. That's why I specifically looked for ETL solutions with built-in visual lineage maps and metadata capture. The presence of these services brings transparency, simplifies data debugging, and supports better governance. 
• Enterprise-grade security and role-based access controls: I also think security and encryption in ETL software are non-negotiable. I won't even consider an ETL tool if it lacks granular access control, encryption standards, or compliance certifications like SOC 2 or ISO 270001. Security isn't just a requirement but foundational for building trust in your data and protecting it from external vulnerabilities. 
• Compliance readiness and legal documentation support: Especially when working with sensitive or regulated data, I always verify whether an ETL software provider supports compliance frameworks like GDPR, HIPAA, CCPA, or FINRA. But beyond that, what really adds value is that the ETL tool follows stringent data governance and legal management protocols and policies. I also shortlisted tools that grant access to legal documentation, data processing agreements (DPA), audit logs, and data retention policies.
• AI/ML readiness and native integrations: It is crucial that the ETL tool integrates with AI and ML workflows to help in predictive analytics and ML production. With the rise of predictive analytics and AI-driven decision-making, I prioritized tools that have native AI/ML pipeline support. Whether it is exporting to model training environments, auto-generating feature sets, or embedding ML logic in transformation steps, these features convert raw data to insights. Some platforms also offer anomaly detection or smart AI mapping to accelerate processes. 

After reviewing ETL tools, I got a better hang of how raw data is extracted and transformed for external use and the data pipeline automation processes that secure and protect the data in a safe and cloud environment for enterprise use. 

Out of several tools I scouted and learned about these 7 ETL tools stood out in terms of latency, high security, API support, and AI and ML support. t

This list below contains genuine reviews from the ETL tools category page. To be included in this category, software must:

• Facilitate extract, transform, and load processes
• Transform data for quality and visualization
• Audit or record integration data 
• Archive data for backup, future reference or analysis

*This data was pulled from G2 in 2025. Some reviews may have been edited for clarity.  

1. Google Cloud BigQuery

Google Cloud BigQuery is an AI-powered data analytics platform that allows your teams to run DBMS queries (up to 1 tebibyte of queries per month) in multiple formats across the cloud.

When I first started using Google Cloud BigQuery, what immediately stood out to me was how fast and scalable it was. I am dealing with fairly large datasets, millions of rows, sometimes touching terabytes, and BigQuery consistently processes them in seconds.

I didn't have to set up or manage infrastructure at all. It's fully serverless, so I could jump right in without provisioning clusters or worrying about scaling. That felt like a major win early on.

The SQL interface made it approachable. Since it supports standard SQL, I didn't have to learn anything new. I liked being able to write familiar queries while still getting the performance boost that BigQuery offers. There is a built-in query editor on the web interface, which works fine for the most part. 

What I found genuinely helpful was the way it integrates with other Google services in the ecosystem. I've used it with GA4 and Google Data Studio, and the connections were very seamless and easy. You can also pull data from Google Cloud Storage, run models using BigQuery ML (right from the UI using SQL), and connect to tools like Looker or third-party platforms like Hevo or FiveTran. It feels like BigQuery is built to fit into a modern data stack without much friction.

However, I also encountered some drawbacks. First, if your queries get longer or more complex, the system starts to feel sluggish. Resizing the browser window sometimes messes with the layout and hides parts of the UI, which can be annoying.

I have also encountered issues with pricing. It's a pay-as-you-go model where you're billed based on how much data your query scans. This sounds good in theory, but it makes costs hard to predict, especially during exploration or teaching others how to use the ETL tool.

I've had situations where a single query accidentally scanned gigabytes of data unnecessarily, which added up quickly. There is also a flat rate model (you pay for dedicated slots), but figuring out which plan suits your usage requires some research, especially with newer pricing editions of BigQuery- Standard, Enterprise, and Enterprise Plus- that are not that straightforward.

For beginners or folks without a background in SQL, the learning curve is real. Even for me, given my dedicated SQL experience, concepts like partitioning, clustering and query optimization took a while to get used to. Also I've noticed that the documentation, while extensive, doesn't always go deep enough where it matters, especially around cost management and best practices for performance tuning.

You also need to keep in mind that BigQuery is tightly integrated into the Google Cloud ecosystem. That's great if you are already on GCP, but it does limit flexibility if you are trying to use multi-cloud or avoid vendor lock-in. Something called BigQuery Omni tries to address this, but it's still not as feature-complete as native BQ on GCP.

Overall, Google BigQuery Cloud is a fast and efficient ETL system that helps with data insertions, nested and related fields (like dealing with JSON data), and cloud storage options to manage your data warehousing needs and stay compliant.

What I dislike about Google Cloud BigQuery:

• I feel like if you're not careful, the queries, especially the complex ones on huge datasets, can really add up and end up in you getting a surprise bill. It's also been talked about in G2 reviews.
• I also think that if you are not familiar with SQL, the learning curve requires more time. Getting started can feel overwhelming (a lot of traditional SQL queries do not work on BigQuery). It has also been mentioned in G2 reviews.

What do G2 users dislike about Google Cloud BigQuery:

"Misunderstanding of how queries are billed can lead to unexpected costs and requires careful optimization and awareness of best practices, and while basic querying is simple, features like partitioning, clustering, and BigQuery ML require some learning and users heavily reliant on UI might find some limitations compared to standalone SQL clients of third-party tools."

- Google Cloud BigQuery Review, Mohammad Rasool S. 

2. Databricks Data Intelligence Platform

Databricks Data Intelligence Platform displays powerful ETL capabilities, AI/ML integrations, and querying services to secure your data in the cloud and help your data engineers and developers.

I have been using Databricks for a while now, and honestly, it has been a game changer, especially for handling large-scale data engineering and analytics workflows. What stood out to me right away was how it simplified big data processing. 

I don't need to jump between different tools anymore; Databricks consolidates everything into one cohesive lakehouse architecture. It blends the reliability of a data warehouse and the flexibility of a data lake. That's a huge win in terms of productivity and design simplicity.

I also loved its support for multiple languages, such as Python, SQL, Scala, and even R, all within the same workspace. Personally, I switch between Python and SQL a lot, and the seamless interoperability is amazing. 

Plus, the Spark integration is native and incredibly well-optimized, which makes batch and stream processing smooth. There is also a solid machine-learning workspace that comes with built-in support for feature engineering, model training, and experiment tracking. 

I've used MLflow extensively within the platform, and having integrated means that I waste less time on configuration and more time on training the models.

I also loved the Delta Lake integration with the platform. It brings ACID transactions and schema enforcement to big data, which means I don't have to worry about corrupt datasets when working with real-time ingestion or complex transformation pipelines. It's also super handy when rolling back bad writes or managing schema evaluation without downtime.

But, like all powerful tools, it does have its share of downsides. Let's talk about pricing because that can add up quickly. If you're on a smaller team and don't have the required budget for enterprise-scale tools, the costs of spinning up clusters, especially on premium plans, might be too much to take. 

Some users from my team also mentioned surprise escalations in billing after running compute-heavy jobs. While the basic UI gets the job done, it can feel a bit clunky and less intuitive in some places, like error messages during job failures, which aren't that easy to debug.

As for pricing, Databricks doesn't clearly advertise all tiers upfront, but from experience and feedback, I know that there are distinctions between standard, premium, and enterprise subscriptions. 

The enterprise tier unlocks a full suite, including governance features, Unity Catalog, role-based access control, audit logs, and advanced data lineage tools. These are crucial when scaling out across departments or managing sensitive workloads.

On the pro or mid-tier plans, you still get core Delta Lake functionality and robust data engineering capabilities but might miss out on some of the governance and security add-ons unless you pay extra.

Also, integrations are strong, whether you are syncing with Snowflake, AWS, S3, Azure Blobs, or building custom connectors using APIs. I've piped in data from Salesforce, performed real-time transformations, and dumped analytics into Tableau dashboards without breaking a sweat. That's a rare kind of visibility.

However, the platform has a couple of downsides. The pricing can get a little expensive, especially if workloads are not optimized properly. And while the notebooks are great, they can use a better version control facility for collaborative work.

Also, users who aren't well-versed in ETL workflows might find the learning curve to be a bit steep. But once you get the hang of it, you'll be able to handle your data pipelines effectively.

Overall, Databricks is a reliable ETL platform that optimizes data transfers, builds source logic, and easily stores your data while offering integrations. 

What I dislike about G2 Users dislike about Databricks Data Intelligence Platforms:

• While it was nice to have granular billing information, predicting costs for large projects or shared environments can still feel opaque. This also resurfaces in G2 reviews.
• Understanding its interface and features can be difficult at first for beginners. Otherwise, it is an extremely powerful tool, and it has also been highlighted in G2 reviews.

What do G2 users dislike about Databricks Data Intelligence Platform:

"Databricks has one downside, and that is the learning curve, especially for people who want to get started with a more complex configuration. We spent some time troubleshooting the setup, and it’s not the easiest one to begin with. The pricing model is also a little unclear, so it isn’t as easy to predict cost as your usage gets bigger. At times, that has led to some unforeseen expenses that we might have cut if we had better cost visibility."

- Databricks Data Intelligence Platform Review, Marta F. 

3. Domo

Domo is an easy-to-use and intuitive ETL tool designed to create friendly data visualizations, handle large-scale data pipelines, and transfer data with low latency and high compatibility.

At its core, Domo is an incredibly robust and scalable data experience platform that brings together ETL, data visualization, and BI tools under one roof. Even if you are not super technical, you can still build powerful dashboards, automate reports, and connect data sources without feeling overwhelmed.

The magic ETL feature is my go-to. It's a drag-and-drop interface that makes transforming data intuitive. You don't have to write SQL unless you want to get into deeper customizations. 

And while we're on SQL, it is built on MySQL 5.0, which means advanced users can dive into "Beast Mode," which is Domo's custom calculated fields engine. Beast mode can be a powerful ally, but it has some drawbacks. The learning curve is a bit steep, and the documentation might not offer the right alternative. 

However, Domo also shines on integration capabilities. It supports hundreds of data connectors, like Salesforce, Google, Analytics, or Snowflake. The sync with these platforms is seamless. Plus, everything updates in real-time, which can be a lifesaver if you are dealing with live dashboards or key performance indicator (KPI) monitoring.

Having all your tools and data sets consolidated in one platform just makes collaboration much easier, especially across business units.

However, the platform has some limitations. The new consumption-based pricing model complicated what used to be a straightforward licensing setup. What used to be unlimited access to features is now gated behind "credits." I found that out the hard way. It's a little annoying when your team unknowingly adds up to costs because you weren't given enough insight into how changes would impact usage.

Another issue is performance. Domo can get sluggish, especially if you are working with large datasets or trying to load multiple cards on the dashboard. It is not a dealbreaker, but can disrupt your workflow. Also, the mobile experience doesn't hold up to the desktop. You lose a lot of functionality, and don't get the same amount of responsiveness.

There were some issues with customer service as well. Okay, they were not terrible. But when I had complex queries with Beast Mode or had pricing questions during the migration to a new model, I felt like I was being ignored. For a premium product, the support should be more proactive and transparent.

If you are looking at premium plans, the differences boil down to scalability and advanced features. The enterprise-level plans unlock more granular permissions, embedded analytics, and higher connector limits. AI and app building are part of newer expansions, but these features still feel a little half-baked. The AI sounds exciting on paper, but in practice, it hasn't aided my workflow.

Overall, Domo is an efficient ETL tool that stores your data securely, builds easy querying processes, and empowers you to monitor data or integrate data with third-party applications. 

What I dislike about Domo:

• Sometimes, the updates/changes and their impact on existing dataflows aren't well communicated, making the platform prone to glitches. G2 reviews also discuss this.
• Sometimes, it was really hard to actually get someone from Domo on a call to help answer questions. This has also been highlighted in G2 reviews. 

What do G2 users dislike about Domo:

"Some BI tools have things that Domo doesn’t. For example, Tableau and Power BI can do more advanced analysis and allow you to customize reports more. Some work better with certain apps or let you use them offline. Others can handle different types of data, like text and images, better. Plus, some might be cheaper. Each tool has its own strengths, so the best one depends on what you need."

- Domo Review, Leonardo d. 

4. Workato

Workato is a flexible and automated ETL tool that offers data scalability, data transfer, data extraction, and cloud storage, all on a centralized platform. It also offers compatible integrations for teams to optimize performance and automate the cloud.

What impressed me about Workato was how easy and intuitive system integrations were. I didn't need to spend hours writing scripts or dealing with cryptic documentation. The drag-and-drop interface and its use of "recipes," also known as automation workflows, made it ridiculously simple to integrate apps and automate tasks. Whether I was linking Salesforce to Slack, syncing data between HubSpot and NetSuite, or pulling info via APIs, it felt seamless and easy.

I also loved the flexibility in integration. Workato supports over 1000 connectors right out of the box, and if you need something custom, it offers the custom connector software development kit (SDK) to build custom workflows.

I've used the API capabilities extensively, especially when building workflows that hinge on real-time data transfers and custom triggers.

Recipes can be set off using scheduled triggers, app-based events, or even manual inputs, and the platform supports sophisticated logic like conditional branching, loops, and error handling routines. This means I can manage everything from a simple lead-to-CRM sync to a full-blown procurement automation with layered approvals and logging.

Another major win for me is how quickly I can spin up new workflows. I am talking hours, not days. This is partly due to how intuitive the UI is but also because Workato's recipe templates (there are thousands) give you a running start. 

Even non-tech folks on my team started building automations- yes, it is that accessible. The governance controls are pretty robust, too. You can define user roles, manage versioning of recipes, and track changes, all useful for a team setting. And if you need help with on-premises systems, Workato's got an agent, too.

However, there are some areas for improvement in the platform. One of the biggest pain points is scalability with large datasets. While Workato is great for mid-sized payloads and business logic, it creates issues when you use it for massive data volumes, especially with batch processing or complex data transformations.

I am not saying that it breaks, but performance takes a hit, and sometimes, workflows are rate-limited or timed out.

Another sore spot is pricing. The "Pro" plan, which most teams seem to choose, is powerful but pricey. Once you start needing enterprise features, like advanced governance, on-prem agent use, or higher API throughput, the costs scale up fast. 

If you are a startup or SMB, the pricing model can feel a bit prohibitive. There is no "lite" version to ease into; you're pretty much completely inside the platform from the very start.

A few team members even mentioned that customer support sometimes takes longer than expected, though I personally have never had any major issues with that.

In short, Workato offers simple API integrations to handle complex data pipelines, support lead-to-CRM workflows, and build custom data pipelines with robust compliance and data governance.

What I dislike about Workato:

• While Workato offers custom integrations, it can be pricey, especially if you are not using the correct licensing model. It has also been reflected in G2 reviews.
• I also noticed occasional delays in syncing data during peak times, and the pricing model may be challenging for smaller businesses. G2 reviews mention this too.

What do G2 users dislike about Workato:

"If I had to complain about anything, I'd love to get all the dev-ops functionality included in the standard offering. Frankly, I'm not sure if that's still a separate offering that requires additional spending."

- Workato Review, Jeff M.

5. SnapLogic Intelligent Integration Platform (IIP)

SnapLogic Intelligent Integration Platform (IIP) is a powerful AI-led integration and plug-and-play platform that monitors your data ingestion, routes data to cloud servers, and automates business processes to simplify your technology stack and take your enterprise to growth.

After spending some serious time with the SnapLogic Intelligent Integration Platform, I have to say that this tool hasn't received the recognition it should. What instantly won me over was how easy it was to set up a data pipeline. You drag, you drop, and snap, and it is done. 

The platforms low-code/no-code environment, powered with pre-built connectors (called Snaps) helps me build powerful workflows in minutes. Whether I am integrating cloud apps or syncing up with on-prem systems, the process just feels seamless.

SnapLogic really shines when it comes to handling hybrid integration use cases. I loved that I could work with both cloud-native and legacy on-prem data sources in one place without switching tools. 

The Designer interface is where all the magic happens in a clean, user-friendly, and intuitive way. Once you dive deeper, features like customizable dashboards, pipeline managers, and error-handling utilities give you control over your environment that many other platforms miss.

One thing that surprised me (in the best way) is how smart the platform feels. The AI-powered assistant, Iris, nudges you in the right direction while building workflows. This saved me loads of time by recommending the next steps based on the data flow that I was constructing. It is also a lifesaver when you're new to the platform and not sure where to go next.

But there are some areas of improvement to look forward to. The biggest gripe I had, and many others have, is the pricing. It's steep. SnapLogic isn't exactly budget-friendly, especially for smaller companies or teams that just need basic ETL functions.

If you are a startup, this might be hard to digest unless you are ready to invest heavily in integration automation. The free trial is a bit short at 30 days, which doesn't give much time to explore all the advanced features.

Another pain point I encountered was the documentation issue. While the platform is intuitive once you get going, it doesn't offer in-depth guidance too much. Especially for advanced use cases or debugging complex pipelines, I often find myself wishing for clearer, more comprehensive help docs.

Also, not all Snaps (those pre-built connectors) work perfectly. Some were buggy and lacked clarity in naming conventions, which slowed down development when I had to review and guess how things worked.

Also, working with large datasets a few times can lead to noticeable performance lag and some latency issues, which you should consider if your workloads are massive or time-sensitive. While SnapLogic claims to be low-code, the truth is that you will still require a good understanding of data structures, scripting, and sometimes even custom solutions if you are integrating your ETL with legacy systems.

The SnapLogic subscription plans aren't very transparent, either. Based on user input, core features like real-time data processing, AI guidance, and cloud or on-prem integrations are all part of higher-tier plans, but there is no clear breakdown unless you talk to sales.

Overall, SnapLogic is a reliable and agile data management tool that offers seamless integrations, allows custom prebuilt connectors for managing data pipelines, and improves performance efficiency for data-sensitive workflows.

What I dislike about SnapLogic:

• Although SnapLogic is designed for scalability, I felt that sometimes users face performance bottlenecks when dealing with high data volume or complex pipelines. It has also been mentioned in G2 reviews.
• I also feel that sometimes pipeline behavior is unexpected, and hanging pipelines are difficult to deal with. This has also been reflected in G2 reviews.

What do G2 users dislike about SnapLogic:

"SnapLogic is solid, but the dashboard could be more insightful, especially for running pipelines. Searching pipelines via task could be smoother. CI/CD implementation is good, but migration takes time – a speed boost would be nice. Also, aiming for a lag-free experience. Sometimes, cluster nodes don't respond promptly. Overall, great potential, but a few tweaks could make it even better."

- SnapLogic Intelligent Integration Platform Review, Ravi K.

6. Azure Data Factory

Azure Data Factory is a cloud-based ETL that allows users to integrate disparate data sources, transform and retrieve on-prem data from SQL servers, and manage cloud data storage efficiently. 

What attracted me about Azure was how easy it was to get started. The drag-and-drop interface is a lifesaver, especially if you are dealing with complex ETL pipelines. 

I am not a fan of writing endless lines of code for every little transformation, so the visual workflows are very refreshing and productive.

Connecting to a wide variety of data sources, such as SQL, Blob storage, and even on-prem systems, was way smoother than I had expected.

One of the things I absolutely love about ADF is how well it plays into the rest of the Azure ecosystem. Whether it is Azure Synapse, Data Lake, or Power BI, everything feels like it's just a few clicks away. The linked services and datasets are highly configurable, and parameterization makes reusing pipelines super easy.

I use triggers frequently to automate workflows, and the built-in monitoring dashboard has been helpful when debugging or checking run history.

The platform also has a few drawbacks. Logging is a bit underwhelming. When pipelines fail, the error messages aren't always the most helpful. Sometimes, you're stuck digging through logs, trying to figure out what's wrong.

While ADF supports data flows for more complex transformations, it struggles when things get more technical and difficult. For example, if I try to implement multiple joins and conditionals in a single step, the performance can tank, or worse, it doesn't work as expected.

Another issue is the documentation. It's okay, but definitely not beginner-friendly. I found myself hopping back and forth between GitHub issues, Stack Overflow, and Microsoft forums to fill in the gaps.

Now, on to the pricing tiers. Azure Data Factory offers a pay-as-you-go model, which means you will be charged based on activity runs, pipeline orchestration, and data movement volumes. 

There is also a premium tier that includes SSIS integration runtime, useful if you are migrating legacy SSIS packages to the cloud. It is a great touch for enterprises that don't want to rewrite their entire data stack. However, the pricing can cause worries if you are not careful about optimizing data movements or turning off unused pipelines.

One feature I wish they'd improve is the real-time purview or simulation before actually running a pipeline. Right now, testing something small appeared to involve waiting too long for provisioning or execution. Also, VM issues occasionally cause annoying downtime when setting up integration runtimes, which isn't ideal if you are on the right schedule.

Overall, Azure Data Factory helps automate data integration, monitor ETL workflows, and offer low-code/no-code support to save yourself from scripting hassles and retrieve data securely and easily.

What I dislike about Azure Data Factory:

• I felt that it failed to perform complex transformations in cases where the data volume grew or processes became too intricate. This has also been highlighted in G2 reviews.
• Another issue is that there is no easier way to integrate with Power BI. I wish they could have provided more features or an easier way to refresh and load Power BI semantic models. It has also been mentioned in G2 reviews.

What do G2 users dislike about Azure Data Factory:

"I am happy to use ADF. ADF just needs to add more connectors with other third-party data providers. Also, logging can be improved further."

- Azure Data Factory Review, Rajesh Y.

7. 5X

5X is a data analytics and visualization solution that manages your cloud operations, optimizes data production, and gives you control over data pipelines while maintaining role-based access control and scalability.

I have been using 5X for a few months now, and honestly, it has been a refreshing experience in the world of ETL tools. What stood out to me right away is how fast and seamless the setup was. 

I had the platform up and running in 24 hours, and that wasn't some shallow integration but a full-on and ready-to-use service across our stack. The platform is designed with speed and simplicity at its core, and that comes through in every click.

One of my favorite things is how well 5X integrates with other tools in the modern data ecosystem. It offers seamless connections with common data warehouses, ingestion tools, and analytics platforms. So whether you are pulling data from Snowflake or FiveTran or pushing it to Looker or Tableau, everything just fits.

Its use of pre-vetted tools behind the scenes to build your data infrastructure is a massive win. It's like having a data ops team baked into the product.

Performance-wise, 5X really hits the mark. Transformations are lightning fast, and scaling up doesn't require much thought, as the platform handles them well.

I also appreciate how it lets us manage the full data lifecycle, from ingestion to transformation to visualization, all while keeping the learning curve manageable.

When I did hit a bump, like a slightly confusing implementation step, the customer support team assisted me actively, without any back-and-forth.

That said, no tool is perfect. While I found most features to be intuitive, documentation could have been better. It covers the basics well, but for more advanced use cases, I found myself reaching out for support more often than I'd like.

Also, there is a slight learning curve initially, especially when diving into more complex pipeline setups. There is limited flexibility in customization, too, though it's not a dealbreaker.

While the alerts for failed jobs are helpful, I did notice the timestamps sometimes don't sync perfectly with our timezone settings. It's a minor bug, but it's worth noting.

What's unique about 5X is that it doesn't follow a traditional freemium model. Instead, it offers subscription tiers tailored to your company's data maturity. From what I gathered, earlier-stage teams get access to essential ETL functionality, intuitive interfaces, and helpful templates.

As you scale up, you can unlock more premium features like real-time job monitoring, more granular access controls, support for advanced connectors, and priority engineering support. It's modular and feels enterprise-ready, without being an overfitted tool.

Overall, 5X is monumental in offering scalable ETL functionalities, optimizing your data lifecycle, and transforming your pipeline into visually organized and structured data. 

What I dislike about 5X:

• While 5X offers end-to-end data support, I feel that the tool is still in its baby phase and needs more sophistication. It has also been mentioned in G2 reviews.
• While the platform offers great features, I feel there are still some areas under development (such as integrating data build tool docs). As highlighted in G2 reviews, this might be a minor inconvenience for now.

What do G2 users dislike about 5X:

"With a newer platform, there are always a few hiccups and features that are still in the works"

- 5X Review, Cameron K.

Best ETL tools: Frequently asked questions (FAQs)

1. What are the best ETL tools for SQL servers?

Top ETL tools for SQL servers include Microsoft SSIS, Fivetran, Talend, and Hevo Data. These tools offer strong native connectors and transformation capabilities and support syncs, real-time ingestion, and seamless integration with the SQL server ecosystem.

2. What are the best open-source ETL tools?

The best open-source ETL tools include Apache NiFi, Airbyte, Apache Hop, and Singer. Each offers modular, extensible pipelines.

3. Is SQL an ETL tool?

No, SQL is not an ETL tool. It is a query language used to manipulate and manage data in databases. However, SQL is often used with ETL processes for data extraction, transformation, and loading when combined with ETL tools.   

4. How does the ETL tool handle schema changes and maintain compatibility in real-time pipelines?

An ETL tool is equipped with built-in schema markup to evaluate and automate file data fields during ingestion. Built-in filtering and data segmentation allow it to maintain compatibility with real-time pipelines.

5. Does ETL software support advanced workflow orchestration and error handling?

Yes, ETL software supports built-in orchestration with DAG support, conditional logic or multiple joins, retry policies, and alerting, which is ideal for managing complex databases at scale.

6. What’s the ETL platform’s performance for high-velocity ingestion to cloud data lakes?

Enterprise ETL platforms are optimized for low-latency ingestion, offering high throughput, distributed processing, and native connectors for streaming data sources.

7. Can it integrate CI/CD pipelines using API, SDK, or laC tools like Terraform?

Yes, you can integrate CI/CD pipelines with prebuilt connectors and SDK functionality to retrieve structured data pipelines into production. Modern ETL tools support complete DevOps integration, enabling pipeline versioning, deployment automation, or infrastructure provisioning through APIs or laC frameworks.

Exchanging and transforming processes, one gigabyte at a time

My analysis allowed me to list intricate and crucial factors like performance optimization, low latency, cloud storage, and integration with CI/CD that are primary features of an ETL tool for businesses. Before considering different ETL platforms, note your data's scale, developer bandwidth, data engineering workflows, and data maturity to ensure you pick the best tool and optimize your return on investment (ROI). If you eventually struggle or get confused, refer back to this list for inspiration.

Optimize your data ingestion and cleansing processes in 2025, and check out my colleague's analysis of the 10 best data extraction software to invest in the right plan.