Businesses encounter a variety of challenges in building systems on and around Spark to meet the needs of data engineering. Often engineers need to perform mission-critical data cleansing, transformations, and manipulations, to make business activities real-time dashboards or fraud detection possible. Mastering data engineering is therefore an essential step to automating systems and making data-driven decisions. Spark-based data science platform Databricks is launching a new edition of its cloud platform optimized specifically for data engineering workloads. Databricks for Data Engineering enables more cost-effective data engineering using Spark while empowering data engineers to easily combine SQL, structured streaming, Extract, Transform, Load…
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