Data engineering

Streaming Upserts Done Right: Deduping and Idempotency at Scale  💻 Did you know? In many high-velocity streaming environments, the “same” event can be sent or processed multiple times due to network retries or distributed system failures.  The Art of the Upsert  At its core, a streaming upsert (a portmanteau of “update” and “insert”) is the process of synchronising incoming data with an existing

Streaming Without the Sting: Data Quality Rules That Never Break the Flow  💻 Did you know? A single minute of downtime in a high-velocity streaming environment can result in the loss of millions of data points, potentially costing a business thousands of pounds in missed opportunities or regulatory fines. —  Defining Resilient Streaming Quality  Data quality in

The Silent Pipeline Killer: Surviving Schema Drift in the Wild  📊 Did you know? In the early days of big data, a single column change in a source database could trigger a “data graveyard” effect, where downstream analytics remained broken for weeks.  The silent pipeline killer  Schema drift occurs when the structure of source data changes

The Invisible Erosion: Detecting and Managing Data Drift in Modern Architectures  📊 Did you know? According to recent industry surveys, over 70% of organisations experience significant data drift within the first six months of deploying a production system.  The Concept of Data Drift  Data drift occurs when the statistical properties or the underlying structure of incoming data change

Democratising Spark: How IOblend enables Data Analysts to build production-grade Spark pipelines without writing Scala or Java   Did You Know? The average enterprise now manages over 350 different data sources, yet nearly 70% of data leaders report feeling “trapped” by their own infrastructure.    The Concept: Democratising the Spark Engine  At its core, Apache Spark is a lightning-fast, distributed computing