Summary
Junior data engineers struggle with legacy ETL tools like SSIS and Informatica
A noticeable pattern in data engineering teams: junior engineers are uncomfortable with legacy tools like SSIS and Informatica while being immediately productive with modern alternatives.
What is happening
Junior data engineers approach legacy ETL tools with caution and hesitation. They briefly touch the tools and pull back. With modern tools like Python, dbt, and cloud-native solutions, they feel at home immediately. The gap between tool generations becomes visible in team dynamics.
Why this matters
Many organizations still run on SSIS, Informatica, or similar platforms. When new engineers cannot manage these tools, knowledge risk emerges. At the same time, investing in legacy skills may not be wise when modern alternatives exist.
Takeaway
Make a deliberate choice: invest in migrating to modern ETL tools or organize knowledge transfer for legacy systems. Both strategies require action now, not later.
Deepen your knowledge
Knowledge Base
ETL Explained — Extract, Transform, Load in plain language
What is ETL? Learn how Extract, Transform, and Load works, the difference with ELT, and which tools to use. Clearly expl...
Knowledge BaseData Lakehouse Explained — The best of both worlds
What is a data lakehouse and why does it combine the best of data warehouses and data lakes? Architecture, comparison, a...
Knowledge BaseChatGPT and BI — How AI is transforming data analysis
Discover how ChatGPT and generative AI are changing business intelligence. From generating SQL and DAX to automating dat...