HDInsight Data Pipelines in ADF

HDInsight is a managed cloud platform for running Hadoop components like Hive, Spark, HBase, etc. Here, we’ll walk through the process of using Hive with HDInsight in an ADF data pipeline. Hive is a data warehousing and SQL-like querying tool that runs on top of Hadoop. It uses HiveQL, a SQL-like language, to enable batch processing and analysis of large datasets. HDInsight clusters in Azure can be used to run Hive SQL for data processing and analysis.

Hadoop-powered data transformations

Hadoop enables big data processing by distributing data and computation across clusters of commodity hardware, allowing parallel processing. HDFS stores data across nodes, ensuring fault tolerance, while MapReduce processes data in a distributed and scalable manner by breaking tasks into smaller subtasks and aggregating their results. This approach facilitates the efficient handling of massive datasets that exceed the capacity of a single machine, making it suitable for tasks like batch processing, data cleansing, and aggregation in big data scenarios. Below, we’ll see an implementation of this concept using a combination of HDInsight, Hive, and Azure Data Factory.

Data analysis using Hive

Hive is a Hadoop-based data warehousing tool that can be used for big data processing and analytics. It enables the processing of large datasets by using Hadoop’s distributed computing capabilities. Hive provides a SQL-like interface to query the data stored in Hadoop Distributed File System (HDFS) and other compatible file systems and provides operations like filtering, sorting, and aggregation. Additionally, Hive supports the use of user-defined functions, the creation of custom data types, and numerous data formats like CSV, JSON, XML, Avro, and Parquet, making it a powerful tool for data transformations.

Creating and running Hive code in Azure Data Factory

We can leverage HDInsight clusters and Hive activity in Azure Data Factory for distributed computing and powerful querying. This enables seamless data processing and efficient handling of large-scale operations, enhancing parallel processing capabilities. Let’s create an ADF pipeline to establish a hive workflow.

Create and save a Hive SQL script to Azure Blob

1. Create a Hive SQL file named hivescript.hql with the content shown below. Let’s try and break down the code below to understand what this hive script is establishing.