Abdus Sattar One of the most common operations in a data pipeline is the upsert — insert new records, update existing ones, and optionally delete removed ones, all in a single atomic operation. The SQL MERGE statement handles all three in one query. Understanding it is essential for anyone building incremental load pipelines in Snowflake, BigQuery, Databricks … Read more
After every pipeline run, you need to verify that what landed in your warehouse actually matches what was in the source. Row counts alone are not enough — a pipeline can load the correct number of rows but with wrong values, missing columns, or shifted amounts. Reconciliation SQL catches these problems before your data consumers … Read more
The most dangerous bugs in a data pipeline are not the ones that throw errors — those are easy to find and fix. The dangerous bugs are the ones that silently produce wrong results: queries that run successfully but return incorrect data that flows into your warehouse, corrupts reports, and goes undetected for weeks. This … Read more
Time-series calculations — running totals, moving averages, period-over-period comparisons — are among the most common requirements in data engineering. Analytics teams need them in dashboard tables, finance teams need them for revenue tracking, and ML pipelines use them as features. This tutorial covers the full range of time-series SQL patterns with real data, including how … Read more
Slowly Changing Dimensions (SCD) solve one of the most fundamental questions in data warehousing: when a customer changes their address, do you overwrite the old address or keep it? The answer depends on whether your business needs to report on current state only, or historical state at any point in time. This tutorial walks through … Read more
In today’s digital economy, data is no longer just a byproduct of business operations—it is the fuel that powers innovation, decision-making, and competitive advantage. From streaming platforms and e-commerce giants to healthcare systems and financial institutions, organizations rely on robust data infrastructures to process massive volumes of information in real time. At the center of … Read more
In the world of big data, few technologies have had as much impact as Apache Spark. Today, Spark powers some of the largest data platforms on Earth, enabling companies to process petabytes of information at lightning speed. From machine learning and real-time analytics to large-scale ETL pipelines, Spark has become a cornerstone of modern data … Read more
Apache Spark has become one of the most important technologies in modern data engineering. It enables organizations to process massive datasets quickly using distributed computing. Whether you are working with batch processing, streaming data, machine learning, or large-scale analytics, Spark provides a unified platform for handling big data efficiently. This guide walks through the core … Read more
Common Table Expressions (CTEs) and subqueries often produce identical results, but they are not interchangeable in a data engineering context. The choice between them affects readability, debuggability, and in some databases, query performance. Getting this right matters when you are writing transformations that run millions of rows in production. This tutorial explains the practical differences, … Read more
Microsoft Fabric is transforming the modern data platform landscape by bringing together: …all inside a single unified SaaS platform. One of the biggest strengths of Microsoft Fabric is flexibility. Fabric gives organizations multiple ways to store, process, and analyze data depending on: At the center of everything is OneLake — Microsoft Fabric’s unified data lake. … Read more