Want to learn how to light up Big Data Analytics using Apache Spark in Azure?

Businesses struggle with many different aspects of data and technology. It can be difficult to know what technology to choose. Also, it can be hard to know where to turn, when there are so many buzzwords in the mix: analytics, big data and open source. My session at PASS Summit is essentially talking about these things, using Azure and Apache Spark as a backdrop.

Vendors tend to tell their version of events, as you might expect, so it becomes really hard to get advice on how to have a proper blueprint to get you up and running. In this session, I will examine strategies for using open source technologies to improve existing common Business Intelligence issues, using Apache Spark as our backdrop to delivering open source Big Data analytics.

Once we have looked at the strategies, we will look at your choices on how to make the most of the open source technology. For example, how can we make the most of the investment? How can we speed things up? How can we manipulate data?

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These business questions are translated into technical terms. We will explore how we can parallelize your computations across nodes of a Hadoop cluster, once your clusters are set up. We will look combine use of SparkR for data manipulation with ScaleR for model development in Hadoop Spark. At the time of writing, this scenario requires that you maintain separate Spark sessions, only running one session at a time, and exchange data via CSV files. Hopefully, in the near future, we’ll see an R Server release, when SparkR and ScaleR can share a Spark session and so share Spark DataFrames. Hopefully that’s out prior to the session so we can see it, but, nevertheless, we will still look at how ScaleR works with Spark and how we can use Sparkly and SparkR within a ScaleR workflow.

Join my session at PASS Summit 2017 to learn more about open source with Azure for Business Intelligence, with a focus on Azure Spark.

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