Databricks Contributes Spark Declarative Pipelines to Apache Spark
Databricks is contributing the technology behind Delta Live Tables (DLT) to the Apache Spark project as Spark Declarative Pipelines, simplifying the development of streaming pipelines.
Both cache() and persist() store an RDD/DataFrame/Dataset in memory (or disk) to avoid recomputation. cache() is shorthand for persist(StorageLevel.MEMORY_ONLY), while persist() offers more control.
Apache Spark: Fix data skew issue using salting technique (practical example)
Data skew in Apache Spark is a performance issue where a few keys dominate the data distribution, leading to uneven partitions and slow queries, especially during operations that require shuffling.
Tokenization is a crucial step in natural language data processing, enabling the breakdown of sentences into individual tokens essential for machine learning applications.
The Word Count program is a key example of distributed computing frameworks, demonstrating how to count word occurrences using methods such as flatMap and reduceByKey.