#data-engineering

#data-engineering

The combined company aims to deliver an open data infrastructure - unifying data movement, transformation, metadata and activation - while preserving freedom of choice for analytic compute and AI, according to the announcement. The company's vision for an open data infrastructure will reduce engineering complexity by automating data management end-to-end, and work across any compute engine, catalog, BI tool or AI model.

In any enterprise application, user-provided data is often messy and incomplete. A user might sign up with a "company name," but turning that raw string into a verified domain, enriched with key technical or business contacts, is a common and challenging data engineering problem. For many development teams, this challenge often begins as a seemingly simple request from sales or marketing. It quickly evolves from a one-off task into a recurring source of technical debt.

Muse helps creative and launch teams at Netflix understand which artwork and video assets resonate with audiences, and its growth required supporting advanced filtering and audience-affinity analysis at massive scale. To meet these demands, Netflix reports that it redesigned the data serving layer, cutting query latencies by about 50% while maintaining accuracy and responsiveness. Muse started as a Spark-powered dashboard backed by a modest Apache Druid cluster.

The arrival of truly intelligent, always-on, AI-native revenue engines is dismantling the way we've structured go-to-market motions for 20 years.

Lakeflow and OpenFlow reflect two philosophies: Databricks integrates data engineering into a Spark-native, open orchestration fabric, while Snowflake's OpenFlow offers declarative workflow control.

Fivetran's Connector SDK empowers developers to create custom connectors easily, addressing data gaps and enabling centralized data management without the need for extensive DevOps support.

To create a daily aggregated revenue dashboard, we need to sum the total revenue for each product category by region, helping managers make informed business decisions.

Traditional monitoring fails to meet the needs of complex data organizations; instead, engineers must develop interactive observability frameworks to quickly identify anomalies.

The article provides a step-by-step guide to setting up a Change Data Capture (CDC) pipeline using PostgreSQL, Debezium, Apache Kafka, and Python.

We are thrilled to welcome Syntio to the Datatonic family. This acquisition is a key step in our strategy to expand our global reach and enhance our service capabilities.