Processing Spark Scripts in ADF Using HDInsight

Azure offers Spark processing compute clusters designed with preinstalled Spark libraries. Here, we will explore how to create an HDInsight cluster with Spark and integrate it with Azure Data Factory to design and execute data pipelines.

Spark for big data processing

Spark is a versatile tool for processing and analyzing big data. It has numerous benefits for working with large datasets. Let’s see what makes Spark a great tool for big data analytics.

• In-memory processing: Spark processes data in memory, reducing the need to read and write data to and from disk, which leads to significant performance improvements.

• Resilient Distributed Datasets (RDDs): RDDs are the fundamental data structures in Spark, representing distributed collections of data that can be processed in parallel. RDDs offer fault tolerance through lineage information, allowing lost data to be reconstructed.

• Distributed processing: Spark divides data into smaller chunks and processes them on a cluster of machines in parallel, enabling faster processing of large datasets.

• Lazy evaluation: Spark uses lazy evaluation, optimizing data processing by delaying computation until it’s necessary, reducing unnecessary calculations. It allows data to be cached in memory, improving performance for iterative algorithms or frequently used datasets.

• Performance: Due to its in-memory processing, optimizations, and caching mechanisms, Spark is known for its high-speed data processing capabilities. Its distributed architecture and ability to scale horizontally make it well-suited for handling ever-growing datasets.

Let’s now understand how HDInsight clusters in Azure can be used for running Spark processing scripts using ADF.

HDInsight cluster: Spark

Spark clusters in Azure’s HDInsight service are optimized for Apache Spark workloads, offering high memory and processing capabilities. They are a great way to process and analyze large datasets, emphasizing in-memory computation and providing their own libraries for various data processing tasks. Unlike traditional Hadoop clusters, they rely less on disk I/O.

Step 1: Preparing the Spark processing script

In the first step, we’ll write Spark code that will be run through the ADF pipeline. In this code snippet, we’ll perform a word count operation that counts the occurrences of every word in a text file.