Here's my comparison between the tools: I haven't used Informatica, but if you haven't already bought it then now is not the time with the recent Salesforce acquisition, it will be a stale product at best for the next few years and was already getting long in the tooth. Fivetran is the easiest tool in the market to implement/run etc, but would need to pair it with something like DBT core (free), DBT cloud (paid), or coalesce (native integrations with DBT for orchestration etc, coalesce a bit newer so I don't think that exists yet) ADF - Microsoft's do-everything ETL/ELT workbench. Great for deep Azure integration, but painful to build, expensive to run, and fragile under change. AWS Glue (incl. DMS/Zero-ETL) - A grab-bag of migration jobs, Spark notebooks and "zero-ETL" marketing. Log-based CDC only on a few engines; everything else is manual. Iceberg/Delta available only with heavy code (Glue ETL) and only inside AWS. Airbyte - Open-source connector zoo. Fast to install; cheap; but QA, SLAs and automatic schema drift handling are hit-or-miss. Cloud vs self-hosted are two separate worlds.
We use the Azure Data Factory service to work with the integration of data from the booking system, CRM and fleet telemetry. The advantages of this service are stability, scalability and convenient automation of ETL processes. However, its disadvantages are high cost and difficulties with initial setup. A greater effect from implementation: the time spent on preparing reports for management has been reduced from several hours to minutes, which has allowed decisions to be made much faster.
We used Fivetran to integrate CRM data from HubSpot and Stripe into our analytics system. This allowed us to create a complete picture of customer LTV and speed up marketing decision-making. The advantage is "set it and forget it", because after configuring the connectors, the data is updated automatically, without the involvement of the technical team. As a result, this saves us a lot of time. But Fivetran is a rather expensive tool, especially when it comes to scaling data. The costs can be unpredictable. We also used AWS Glue to transform data from our service logs before loading it into Redshift. This took the load off the backend and made it possible to do complex data aggregation. Glue scales well and integrates with other AWS services, which is critical for us. Automatic jobs on Spark saved a lot of time for our DevOps team. However, the entry threshold is quite high. Without a strong DevOps/engineer, Glue is difficult to configure. It is not suitable for non-technical teams or companies without AWS expertise.
When you work with e-commerce clients, you're pooling a lot of ad spend data in the same place, from Google, Meta, and a couple of smaller affiliate networks. So you'd have to have someone going in and downloading reports, fixing mismatched date formats, and manually piecing together LTV by channel. That can take hours, and you'll still end up with missing rows if an export fails. Airbyte can connect all those platforms to BigQuery. We integrated this into our system and then built a Looker Studio dashboard so we could see spend, revenue, and ROAS in one place. The first month we had it running, we caught a Facebook campaign overspending by about 25% halfway through the month, before that would have gone unnoticed until the next report. Airbyte's biggest strength is how quickly you can get weird, niche platforms talking to your warehouse. But some of the community-built connectors can be fragile. We had one break after the ad network updated its API, so you do need someone keeping an eye on sync logs.
Our team uses Airbyte to integrate data from CRM, advertising platforms and internal databases. Pros: the flexibility of the system allows you to connect almost any source, including non-standard ones, without using custom scripts. This is also a minus: for the initial setup, you need basic technical knowledge and an understanding of the information transfer processes. As a result of the implementation, we no longer waste time collecting and processing data, we have more accurate analytics, and we make decisions on the distribution of the marketing budget based on comprehensive and up-to-date information.
Our team uses the AWS Glue platform to integrate data from multiple internal and marketing sources. Two key advantages of the AWS Glue engine include its ability to process large volumes of data and automatically perform ETL processes. Initial setup and optimization of streams requires technical skills, which is a disadvantage of the service. Summarizing the experience of using AWS Glue: acceleration of analytics and specialists' work, as well as higher accuracy of reports. This allowed us to make marketing decisions based on the latest and most relevant data.
I have spent the last 10 years, and as the CEO and founder of an AI-powered education platform, knee deep in data integration tools at various companies. Azure Data Factory is what I loved to use as a workhorse in my team when it came to enterprise projects. Two years in that visual pipeline designer actually saved us 40% development time. My business partners would no longer have to bother the engineering team over what our data flows were. The pipelines used to just... stop working, the headaches began. Notice no error messages, nothing. Also the price of watching grew with bigger datasets was painful. The AWS Glue caught me by surprise when we had a huge migration of 50TB of customers. Also, no servers to baby-sit, and that was a mercy to our small group. The schema discovery did indeed pick up data issues that we did not pick up at all. But these cold starts? Brutal. Any hopes of live dashboards were killed by waiting 2-3 minutes every time jobs needed to spin up. Informatica is a data tool of Mercedes. Three companies down the line, I do not find anything that can compete with its data profiling. The thing is it is like purchasing a Mercedes - costly and scary to a newbie. Fivetran was magic magic between Salesforce and BigQuery in 10 minutes. That is typically weeks of bespoke coding and debugging over coffee. Works awesome until you need to do something that their connectors are unable to.
Hey :), here's my two cents on Airbyte: First off, I'm nowhere near a data engineering expert, but Airbyte was so easy to use that I decided to stick with it. They have tons of solutions, but I mainly use it to pull my data from Google Ads / Analytics and send it where it needs to be. I used to waste hours copy-pasting numbers, but now Airbyte just runs in the background and does all the work for me. My reports are always up to date, and I don't even have to think about it anymore. Hope that helps, Raph
The tool that I found interesting is Fivetran, especially because of its capability to manage schema automatically and minimize the manual effort that one should normally spend to look after data pipelines. Although this is not the most glamorous tool, it is the type of solution that drives scalability without any fanfare. The move towards automation of the data sources synchronization and flexibility to changes without constant technological control can be a turning point when businesses are developing rapidly. There are no flashy features to focus on, it is about reliability, and ease, something that will really add value in the long-run.
SEO and SMO Specialist, Web Development, Founder & CEO at SEO Echelon
Answered 7 months ago
Good Day, Azure Data Factory: Complex for complex workflows and integration in the Microsoft stack, though has a big learning curve. Informatica: Enterprise grade governance and scaling so would have a heavy setup and licensing. Airbyte: Flexible and open source and growing very fast in connectors, but requires hands-on maintenance. AWS Glue: Server less and cost effective for scalable jobs, but may be complex to beginners. If you decide to use this quote, I'd love to stay connected! Feel free to reach me at spencergarret_fernandez@seoechelon.com
After working with AWS Glue for over a year, I can definitely attest to the ability of the serverless architecture to remove setup time - we were able to take days instead of weeks to get a data pipeline up and running, allowing our engineers to focus on analytics work rather than infrastructure building. Of course, tradeoffs came with this: the UI was clunky and wordy, and Glue did not have so many processes contained in built-in transforms that even the littlest tasks typically came with custom scripts. I'm Dario Ferrai, co-founder at all-in-one-ai.co, and my advice would be: leverage Glue as a powerful accelerator, but don't forget to do your due diligence and ensure that your engineering time to wrap the edge cases isn't underestimated with any of the complexity. From a combined standpoint, our projects that built Glue with good team internal coding guidelines avoided "script sprawl", while sooner achieving stable, scalable pipelines. Glad to provide more information on what we do if that's helpful. Website: https://all-in-one-ai.co/ LinkedIn: https://www.linkedin.com/in/dario-ferrai/ Headshot: https://drive.google.com/file/d/1i3z0ZO9TCzMzXynyc37XF4ABoAuWLgnA/view?usp=sharing Bio: I'm the co-founder of all-in-one-AI.co. I build AI tooling and infrastructure with security-first development workflows and scaling LLM workload deployments. Best, Dario Ferrai Co-Founder, all-in-one-AI.co
One such tool that caught my attention is Azure Data Factory. It has seamless integration with other Microsoft products and offers a wide range of connectors to different data sources such as SQL databases, cloud storage solutions, and even social media platforms. This makes it incredibly convenient for businesses like ours that deal with large amounts of diverse data. One of the main reasons why we chose to use Azure Data Factory is its scalability. As our business grows and our data needs evolve, we can easily scale up or down our data processing pipelines without any disruptions. This helps us save on costs and ensures that our operations run smoothly. I would point out that it has robust security features, including encryption at rest and in transit, role-based access control, and regular security updates. This gives us peace of mind knowing that our sensitive data is well-protected from potential cyber threats. According to a study by IBM, the average cost of a data breach for U.S. companies in 2025 rose 9% to a record $10.22 million, while the global average fell 9% to $4.44 million.
Being in the healthcare space, we prefer to refrain from implementing complicated technical solutions. Fivetran connects our various systems including some of our CRM and healthcare systems all without any complicated configuration. This allows us to start quickly & focus on our core business. Fivetran automatically updates healthcare data which is important for accuracy. In a healthcare setting, obsolete data could create significant problems and with Fivetran's automatic data sync capabilities, we can trust that our data is up to date and reliable without having to constantly track the changes. This allows all of us to focus on providing the best products and services to our customers. Another positive that we found to be significant is the scalability factor. With growth comes increasing data needs so it is important to know that Fivetran can grow with us. Once our demands increase, we need a system that allows us to continue to use more data without a major system change. This will allow us to add different data sources like inventory systems or marketing solutions, without disruption or any additional complexity. Also, Fivetran makes sure that our data is not just safe but also compliant with various healthcare regulations such as GDPR. Being in the healthcare space, we appreciate knowing that patient privacy & data protection are two significant components in our industry. Lastly, it has flexible pricing. As we grow, we can manage costs effectively while scaling our data systems.