What is Apache Spark?

Apache Spark, originally developed to enhance machine learning workloads within the Hadoop ecosystem, offers significant performance improvements by caching datasets in memory, making it a preferred clustering technology for large-scale data processing. Spark provides a set of APIs in various programming languages like Python and Scala, primarily using the DataFrame API, and competes with Trino, which mainly employs SQL for interaction. Though Spark and Trino differ in their interfaces, they can be effectively used together in data architectures, such as leveraging Spark for initial data streaming and processing, while Trino, particularly through Starburst products, handles data aggregation and querying. This combination supports modern data architectures like the data lakehouse medallion model, with Spark excelling in data ingestion and transformation, and Starburst offering robust data product capabilities. Recent developments include the integration of Spark into Dell's Data Lakehouse appliance alongside Starburst, facilitating AI initiatives by handling unstructured data. Ultimately, organizations can benefit from using Spark and Starburst together, as Starburst continues to enhance its Spark integration, providing flexible options for data engineers based on their familiarity with SQL or programming languages.