Fact check: How common are outdated tech stacks in data engineering, or have I just been lucky to work at companies that follow best practices?

1. Summary of the results

1. Summary of the results

The evidence strongly indicates that outdated tech stacks are actually very common in data engineering, particularly in larger, established organizations. Specific examples include major institutions like Fidelity still using 30-year-old SAS code, widespread use of legacy mainframe systems from the 1980s, and continued reliance on older technologies like SSIS packages and on-premise SQL servers for critical operations.

2. Missing context/alternative viewpoints

The original question omits several crucial contextual factors:

• Industry variations: Financial institutions, healthcare organizations, and government agencies tend to have more legacy systems due to regulatory requirements and risk aversion
• Cost implications: Modernizing legacy systems often requires massive investments, which many organizations cannot justify if existing systems still function
• Mixed environments: Many companies operate hybrid environments with both modern and legacy technologies, rather than being purely "outdated" or "cutting-edge"
• Size correlation: Larger enterprises typically have more legacy systems, while smaller companies and startups often have newer tech stacks
• Practical constraints: Legacy systems often contain critical business logic built over decades, making replacement risky and complex

3. Potential misinformation/bias in the original statement

The statement reflects a significant sampling bias based on the author's limited personal experience. By suggesting that outdated tech stacks might be uncommon, it overlooks:

• The reality that database and ETL systems are often the most technically indebted parts of company infrastructure
• The fact that companies heavily marketing their modern practices (like tech companies and startups) represent a minority of total data engineering environments
• The distinction between public perception of a company's technology and its actual internal systems
• The legitimate business reasons why organizations maintain older systems, rather than it being simply a matter of "following best practices" or not

This context is particularly important as it affects career decisions and expectations for data engineers entering the field or considering job changes.