Load JSON Lines with OPENROWSET BULK, keep UTF-8 text intact, quarantine invalid objects, and insert validated typed rows.
OLTP vs OLAP: Why Transactions and Analytics Need Different Designs
OLTP vs OLAP is the difference between recording what’s happening now and studying what’s already happened. This post compares the two designs, tests the same 500,000 orders as a rowstore and a columnstore table on SQL Server 2025, and shows how data moves from one side to the other.
How Apache Spark Works: Driver, Executors and Partitions
How Apache Spark works comes down to a driver that plans the job, executors that run it, and data cut into partitions. This post follows one sales total through two stages and a shuffle, and compares it with a parallel plan in SQL Server.
Big Data Architecture Today: The Lakehouse and Medallion Layers
Big Data Architecture today is a pipeline with four jobs: ingest, store, process and serve, with governance underneath. This post explains the lakehouse, the bronze, silver and gold layers, batch and streaming, and where SQL Server 2025 fits.
Data Lake vs Data Warehouse: What Each One Is For
Data Lake vs Data Warehouse is a choice between raw files and cleaned, modeled tables. This post compares schema on read with schema on write, who uses each, and why many teams combine them in a lakehouse.
ETL vs ELT: How Big Data Changed the Way We Load Data
ETL vs ELT comes down to when you transform: before the load on a separate engine, or after it inside the platform. This post compares the two and runs a small ELT pipeline in T-SQL on JSON orders.
Change Data Capture or Change Tracking
Change data capture or change tracking serves different sync needs; compare captured detail, retention, cleanup, and operating cost before choosing.