Tag:best practices
All the articles with the tag "best practices".
- 5 MIN READ•Feb 18, 2026
Data Engineering Best Practices: The Complete Checklist
Best practices documents are easy to write and hard to use. They list principles without context, advice without prioritization, and rules without explaining...
data engineeringbest practiceschecklist - 5 MIN READ•Feb 18, 2026
Data Modeling Best Practices: 7 Mistakes to Avoid
A bad data model doesn't announce itself. It hides behind slow dashboards, conflicting numbers, confused analysts, and AI agents that generate wrong SQL. By ...
data modelingdata engineeringbest practices - 7 MIN READ•Feb 18, 2026
Semantic Layer Best Practices: 7 Mistakes to Avoid
Semantic layers don't fail because the technology is wrong. They fail because of design decisions made in the first two weeks : choices that seem reasonable ...
semantic layerdata engineeringbest practices - 5 MIN READ•Feb 18, 2026
Pipeline Observability: Know When Things Break
An analyst messages you on Slack – "The revenue numbers look wrong. Is the pipeline broken?" You check the orchestrator – all green. You check the target tabl...
data engineeringbest practicesobservability - 5 MIN READ•Feb 18, 2026
Testing Data Pipelines: What to Validate and When
Ask an application developer how they test their code and they'll describe unit tests, integration tests, CI/CD pipelines, and coverage metrics. Ask a data e...
data engineeringbest practicestesting - 5 MIN READ•Feb 18, 2026
Partition and Organize Data for Performance
A table with 500 million rows takes 45 seconds to query. After partitioning it by date, the same query : filtering on a single day , returns in 2 seconds. Th...
data engineeringbest practicespartitioning - 5 MIN READ•Feb 18, 2026
Batch vs. Streaming: Choose the Right Processing Model
"We need real-time data." This is one of the most expensive sentences in data engineering – because it's rarely true, and implementing it when it's not neede...
data engineeringbest practicesbatch processing - 4 MIN READ•Feb 18, 2026
Schema Evolution Without Breaking Consumers
A source team renames a column from `user_id` to `customer_id`. Twelve hours later, five dashboards show blank values, two ML pipelines fail, and the data en...
data engineeringbest practicesschema evolution - 4 MIN READ•Feb 18, 2026
Idempotent Pipelines: Build Once, Run Safely Forever
A pipeline runs, processes 100,000 records, and loads them into the target table. Then it fails on a downstream step. The orchestrator retries the entire job...
data engineeringbest practicesidempotency - 5 MIN READ•Feb 18, 2026
Data Quality Is a Pipeline Problem, Not a Dashboard Problem
When an analyst finds null values in a revenue column, the typical response is to add a calculated field in the BI tool – `IF revenue IS NULL THEN 0`. That "f...
data engineeringbest practicesdata quality - 5 MIN READ•Feb 18, 2026
How to Design Reliable Data Pipelines
Most pipeline failures aren't caused by bad code. They're caused by no architecture. A script that reads from an API, transforms JSON, and writes to a databa...
data engineeringbest practicespipelines - 5 MIN READ•Feb 18, 2026
How to Think Like a Data Engineer
The median lifespan of a popular data tool is about three years. The tool you master today may be deprecated or replaced by the time your next project ships....
data engineeringbest practicespipelines