In this article, we’ll learn about Apache Spark and specifically focus on multitudes of offerings of Apache Spark within Azure. Apache Spark is the go-to name for any big-data applications and it plays a vital role for projects which need to realize big data and analytics.
Apache Spark
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Source: Microsoft
Developed at the AMPLab of University of California, Berkeley, Apache Spark is an analytics engine dedicated to the processing of large-scale data. Fault Tolerance and Data Parallelism is provided with programming cluster interface. The big-data analytics application performance can be boosted with Apache Spark with its parallel processing framework which supports in-memory processing.
In-Memory cluster computing is supported by Apache with loading and caching of data into memory performed with spark job which can then be queried thereafter. We know that when compared to the disk-based applications like Hadoop that used Hadoop Distributed File System (HDFS), in-memory computing outshines with its faster processing. Moreover, distributed data sets can be manipulated as local collections with the integration of Spark into Scala.
In Azure, there are numerous offerings of Apache Sparks. From Azure HDInsight, Azure Synapse Analytics, Azure Data Bricks, the Apache Spark has been implemented for various use cases in Microsoft. Let us learn about each of them in brief.
Apache Spark in Azure HDInsight
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Source: Microsoft
Azure HDInsight enables creating and configuring the Spark Clusters extremely easily in Apache Spark. The entire Spark environment is provided thus making it convenient to customize in Azure itself. Data can be stored and processed all within Azure with Apache Spark in Azure HDInsight. Azure Data Lake Storage Gen 1 and Gen 2, Azure Blob Storage, all support Spark Clusters. Hence, we can process our Spark onto the pre-existing data stores.
Apache Spark in Azure Synapse Analytics
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Source: Microsoft
The Azure Synapse Analytics allows to create Spark Pools which enables to load, model, process, and distribute data to produce analytic insights in Azure. Out of many, Apache Spark in Azure Synapse Analytics is one of the implementation of Apache Spark offerings provided by Microsoft in cloud. Creating and Configuring of Apache Spark Pool is extremely easy. Furthermore, the Spark Pools can be used with Azure Storage and Azure Data Lake Generation 2 Storage. Similar to Spark Clusters in Azure HDInsight, the Spark Pool in Apache Spark with Azure Synapse Analytics uses in-memory cluster computing which is faster than traditional disk-based application. Thus, there is also no need to structure like in the traditional way of map and reduce operations.
Benefits of Spark Clusters in Azure HDInsight and Spark Pools in Azure Synapse Analytics
There are tons of benefits to using Apache Spark Clusters in Azure HDInsight. The Spark Clusters in HDInsight can be setup in minutes through the Azure Portal, HDInsight, and Powershell. Moreover, using the Apache Zeppelin Notebooks and Jupyter Notebooks, the Spark Clusters are extremely easier to use too. With Apache Livy, jobs can be remotely submitted and monitored through the job server based on REST API. Furthermore, the Azure Data Lake Storage Gen 1 and Gen 2 are supported as both primary and additional storage. Integration with Azure Services and third-party IDEs can also be done with the Azure Event Hubs, IntelliJ Idea, VSCode, and Eclipse respectively. Power BI can also be integrated with Apache Spark in Azure HDInsight. Access to over 200 libraries with Anaconda for data analysis, visualization, and machine learning is also provided. Finally, Spark Clusters has 24/7 support and an impressive 99.9% SLA up-time.
Use Cases
Machine Learning
MLlib which is built on top of Spark can be used in Spark Pool within the Azure Synapse Analytics. Furthermore, Anaconda which is basically a Python Distribution with dozens of packages for data science and machine learning are also included. Combined with the built-in supports for notebook such as Jupyter Notebook and Zeppelin Notebooks, machine learning environment has never been this easy to create before. The same MLlib can also be used from Spark Cluster within the Azure HDInsight.
Data Engineering
Data Engineering and Data Preparation are possible with Apache Spark in Azure Synapse Analytics. Numerous languages are supported in order to prepare and process huge volume of data. With Azure Synapse Analytics, these data can be made more valuable. For this, Spark SQL, PySpark, C#, and Scala are supported in Spark pools with other libraries for connectivity and processing.
Business Intelligence and Interactive Data Analysis
Using Apache Spark in HDInsight we can store our data within Azure Data Lake Storage Gen1 and Gen2 as well as Azure Blob Storage. We can analyze these data and build reports from it. Extending to this, we can also integrate Microsoft Power BI to create interactive reports from these data. Other third-party tools such as Tableau can also be used with Spark Clusters in Azure HDInsight for Business Intelligence tools usage.
Apache Spark using Azure Databricks
The Apache Spark available from Azure Databricks includes a fully interactive workspace which makes collaboration between numerous data sources and user easy as possible to produce breakthrough insights. From creating Spark jobs, loading and working with data, Azure Databricks enables all of it. Furthermore, we can focus on our data work with swift Spark queries enabled by Databricks.
Difference between Azure HDInsight VS Azure Synapse Analytics VS Azure Databricks
By now, we've come to realize that both Azure HDInsight, as well as Azure Synapse Analytics, allows to run Apache Spark. But both of these offerings of Azure are very different products. The Azure HDInsight is majorly a cloud distribution of Hadoop components and brings both Hadoop and Apache Spark together using the same tools of Ambari and Apache Ranger to manage them. The configuration of HDInsight is a bit complex as compared to Azure Synapse Analytics and is basically always on. This is well suited for cases where one needs heavy compute and specific requirements. Moreover, the learning curve with Azure HDInsight is quite steep too. In fact, a lot of features of HDInsight are based on Apache Spark. The Azure HDInsight has been around for quite some time now and provides a range of cluster type to choose from.
The Azure Synapse Analytics is moreover a consumption-based service and contrary to always-on Azure HDInsight, the Azure Synapse Analytics can be paused. The Synapse is moreover focused toward bringing Big Data Analytics and Enterprise Data Warehousing together. From Serverless resources to integration with Business Intelligence tools and Machine Learning demands, Azure Synapse Analytics fulfills it. Besides, it is also easier to learn Azure Synapse Analytics compared to Azure HDInsight. The major difference compared with Azure HDInsight is that Azure Synapse Analytics incorporates numerous Azure services and is on the verge to become a one-stop solution hub for Data Orchestration and Analytics. You can learn more about Azure Synapse Analytics from this Azure Synapse Analytics Article Series.
Azure Databricks on other hand is majorly an analytics platform based on Apache Spark that is optimized for the cloud platform of Microsoft - Azure. The premium Spark offering with Azure Databricks provides an industry leading performance for data scientists working on Spark workloads. If all we need is Spark Cluster, it is recommended to use Data Bricks over HDInsight as it provides better performance. But, if in case you need heavy power for logs of batch job, Azure HDInsight is the best way to go. While we compare Azure Databricks to Azure Synapse Analytics, we can say that Databricks is a managed Apache Spark while Azure Synapse is more so a managed SQL Data Warehouse.
Conclusion
Thus, in this article, we learned about Apache Spark and the various services in Azure which offers Apache Spark. We then dived into each of these services and thus learned about the Apache Spark offering in Azure HDInsight, Azure Synapse Analytics, and Azure Databricks. Moreover, we also learned about the benefits and uses cases of Apache Spark in Azure. Lastly, we discussed the differences between Azure HDInsight, Azure Synapse Analytics, and Azure Databricks in peripheral with Apache Spark.