Category
Data & Analytics
Data engineering and analytics tools: ETL and reverse ETL, dbt, BI platforms like Metabase and Superset, product analytics, and Python data libraries.
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Apr 25, 2026
Scaling Your Data Pipeline: 10 dbt Best Practices for Large Teams
Ten dbt practices for large teams: strict layering, dbt Mesh and project splitting, naming conventions, targeted runs, tests in YAML and macros.
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Apr 25, 2026
Streamlining Streamlit Performance Optimization: A Deep Dive into Faster Data Apps
Make Streamlit apps fast: understand the rerun model, use st.cache_data and st.cache_resource, fragments, session state and lighter data loading.
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Apr 25, 2026
How to Build a Modern Data Stack: A Practical Guide for 2026
Build a modern data stack: ingestion, warehouse, dbt transformations, BI and orchestration, layer by layer, with a practical first setup.
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Apr 23, 2026
Scala vs Java for Data Engineering: Which Language Should You Choose in 2026?
Scala vs Java for data engineering: Spark APIs, conciseness against hiring and stability, JVM interop, tooling and how to decide.
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Apr 23, 2026
Python for Data Engineering vs Data Science: Which Path Should You Choose?
Python for data engineering vs data science: building reliable pipelines against analysis and modelling. Skills, libraries and career paths.
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Apr 23, 2026
Helm Chart Best Practices 2026: Scaling Kubernetes Deployments Without the Chaos
Helm chart best practices: library charts, typed and validated values, named templates, resource defaults and careful use of lookup.