Therefore, you have to leave your SQL Server Big Data Cluster always up and running, which accumulates a lot of money over the time.
My problem with that approach is two-folded: first of all, I have to pay regularly for the deployed SQL Server Big Data Cluster in AKS just to get familiar with Apache Spark.Īnother problem with Big Data Clusters in AKS is the fact that a shutdown of worker nodes can damage your whole deployment, because AKS has still a problem with reattaching Persistent Volumes to different worker nodes during the startup. There are different approaches: you can deploy a whole SQL Server Big Data Cluster within minutes in Microsoft Azure Kubernetes Services (AKS). If you want to get familiar with Apache Spark, you need to have an installation of Apache Spark.
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This is a huge difference to a traditional relational database engine.Īnd you can code against Apache Spark in different programming languages like R, Scale, Python, and – SQL! Apache Spark & Docker Therefore, you have a true parallelism across multiple physical machines. One of the biggest differences is that a query against Apache Spark is distributed across multiple worker nodes in an Apache Hadoop Cluster. To be honest, Apache Spark is not that different from a relational database engine like SQL Server: there are Execution Plans, and there is even a Query Optimizer. Therefore, it is for me mandatory to get an idea how Apache Spark works, and how you can troubleshoot performance problems in this technology stack :-). You might be asking why I – as a relational database expert – concentrates on a Big Data technology? The answer is quite simple: beginning with SQL Server 2019, you have a complete Apache Spark integration in SQL Server – with a feature or technology called SQL Server Big Data Clusters. Over the last few weeks I have done a lot of work with Apache Spark.
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