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 When you work with PostgreSQL in real environments, one thing becomes very clear very quickly: Backups are not optional. It doesn’t matter how stable your database is — failures happen. Disks crash Files get corrupted Someone runs the wrong delete query A patch goes wrong A standby needs rebuilding. So having a proper backup strategy is not just good practice, it’s survival. That’s exactly why tools like pgBackRest are so widely used in PostgreSQL production systems. In this post, I’ll walk through a simple installation of pgBackRest on a Linux server (RHEL 9 / Rocky Linux / AlmaLinux). Why pgBackRest? PostgreSQL already provides tools like: pg_dump pg_basebackup And yes, they work. But once your database grows, or you start managing HA setups, these tools start feeling limited. pgBackRest is built for serious PostgreSQL backup requirements. Some of the reasons it stands out: Supports full, differential, and incremental backups Built-in compre...

  Introduction Have you ever started a bulk data load that ran lightning-fast initially, only to watch it grind to a crawl hours later? You're not alone. This is one of the most common—and most misunderstood—performance issues in Oracle databases. In this post, I'll share a real-world case study where a data load that started at 550,000 rows/hour degraded to just 40,000 rows/hour—a 93% performance drop. More importantly, I'll explain exactly why this happens and how to fix it. The Scenario Our team was loading data into a staging table as part of a nightly ETL process. The setup looked like this: Table Configuration: CREATE TABLE STG_CUSTOMER_EXTRACT ( CUSTOMER_ID NUMBER, ACCOUNT_NUMBER VARCHAR2(50), BILLING_ID VARCHAR2(30), REGION_CODE VARCHAR2(10), LOAD_DATE DATE ); -- Three indexes to support downstream queries CREATE INDEX IDX_CUSTOMER_ID ON STG_CUSTOMER_EXTRACT(CUSTOMER_ID); CREATE INDEX IDX_ACCOUNT_NUM ON STG_CUSTOMER...