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Data Engineering Guides & Tutorials
Build the data backbone — ETL/ELT, warehouses, lakes, Spark, Kafka, Airflow, Databricks and the medallion architecture.
Data Engineering articles
Data Modeling: Star Schema, Snowflake and Data Vault
The three main warehouse modelling approaches compared by building each from the real Chinook database: a star with 3 tables, a snowflake with 6 and a data vault with 11, the joins the same question needs in each, why all three give identical answers, what each optimises for, and how teams combine them.
Designing Fact and Dimension Tables
Practical rules for designing fact and dimension tables: declaring the grain, choosing transaction, snapshot or factless fact tables, why averages and distinct counts must not be summed, shown on three million taxi trips where an average of averages nearly doubled the true fare, junk dimensions, and keys.
Lambda vs Kappa Architecture Explained
Lambda and Kappa architectures compared: Lambda's batch, speed and serving layers, Kappa's single replayable stream, a simulation on a week of real taxi trips in which Lambda's two code paths disagree by 982 trips until the nightly batch runs, how Kappa recomputes by replaying the log, and where lakehouses fit.
Data Engineering vs Data Science: Roles Compared
Data engineering and data science compared through the work each role does: what they are responsible for according to published role definitions, a typical week in each, the skills and tools that differ and overlap, how the two depend on each other on one project, hybrid roles in between, and how to choose.
Data Engineer Salary in Hyderabad 2026: How the Role Pays Compared With Others
Is data engineering a well-paid role, or just a popular one? This guide answers with real 2025 survey data rather than unsourced ranges: data engineers compared with analysts, data scientists, AI/ML engineers and developers in the same country and with the same experience, and what that means for a Hyderabad offer.
Spark vs Hadoop: What Changed and Why
What changed when Spark overtook Hadoop MapReduce: the three parts of Hadoop, the MapReduce model emulated step by step on three million taxi trips with its intermediate data written to disk, why in-memory processing and DAGs mattered, what Spark still borrows from Hadoop, and what that means today.