Field notes on data engineering, applied AI, and the decisions behind them — for engineers who ship pipelines and the leaders who bet budgets on them.
A practical guide to reading SQL execution plans — EXPLAIN vs EXPLAIN ANALYZE, scan types, join algorithms, cost estimates, and the red flags that point to a real performance fix.
A practical comparison of pandas, Polars, and DuckDB for Python-based data analysis — API ergonomics, performance profile, memory model, and where each fits alongside dbt and the warehouse in 2026.
CTEs vs subqueries vs temp tables explained — readability, materialization behavior across Postgres/Snowflake/BigQuery, recursive CTEs, and a practical decision framework.
SQL interview questions and worked answers for data analyst and analytics engineer roles — foundational to advanced, plus how interviews are structured and how to prepare.
The dbt Semantic Layer and MetricFlow explained — measures, dimensions, entities, metric types, how it's queried, and why a shared metrics layer matters for BI and AI-generated queries alike.
Slowly changing dimensions explained end to end — SCD Types 0, 1, 2, 3, 4, 5, 6, and 7, with side-by-side examples, dbt snapshot code, and a decision framework for picking the right type per attribute.
Kimball dimensional, Inmon corporate information factory, Data Vault 2.0, and one big table (OBT) — what each framework optimizes for, when it wins, when it loses, and how to combine them in a modern dbt stack.
A hands-on guide to dimensional data modeling — declaring grain, designing fact and dimension tables, surrogate keys, slowly changing dimensions, and star vs. one big table, with dbt examples throughout.
A practical guide to running dbt on Airflow — the five orchestration patterns, dbt_project.yml and profiles.yml configuration, selectors, concurrency math, retries, slim CI with state:modified, and the mistakes that cause 3am pages.
A practical guide to Apache Iceberg — the metadata layers, hidden partitioning, schema evolution, snapshots and time travel, branching, copy-on-write vs merge-on-read, catalogs, and the maintenance jobs nobody warns you about.
A practical guide to SQL and dbt query optimization — partition pruning, join design, incremental predicates, clustering, and DAG-level fixes that keep incremental runs fast as data volume grows.
A deep dive into dbt tests — generic, singular, custom, and unit tests — plus how they map to Technical Data Quality (TDQ) and Business Data Quality (BDQ), with detailed code examples for every pattern.
Data warehouse, data lake, data mart, and data mesh explained side by side — what each one actually is, the use cases each one wins at, and the real trade-offs behind picking one over another.
A complete, practical guide to SQL joins — INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF joins — with diagrams, worked examples, execution internals, and common mistakes.
A practical guide to every dbt materialization strategy — view, table, incremental, ephemeral, snapshots, and materialized views — with examples for append, merge, delete+insert, insert_overwrite, and microbatch.
AI can write a SQL query that runs perfectly, returns a plausible number, and is completely wrong — with no error message anywhere. A field guide to the five failure modes and how to catch them before they reach a dashboard.
SQL isn't just another line on a data analyst's resume — it's the language the entire data world runs on. Here's why it's mandatory, not optional, for every data analyst and data enthusiast in 2026.
An introduction to dbt (data build tool) — what it actually does, how it turned raw SQL into tested, version-controlled, documented software, and why it became the tool analytics engineers build their entire workflow around.
A field guide to data careers for people switching in from other fields — Data Analyst, Engineer, Scientist, Analytics Engineer, ML/AI Engineer — with 2026 India salary data, a 12-month roadmap, and free resources.
A look at why we built a free, browser-only SQL formatter at tools.techedge.in — what SQL formatting actually does, why it matters for data teams, and what we're building next.