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Category: Spark

Secure Cluster Connectivity in Azure Databricks

Published 2021-02-08 by Kevin Feasel

Abhinav Garg and Premal Shah have an announcement:

We’re excited to announce the general availability of Secure Cluster Connectivity (also commonly known as No Public IP) on Azure Databricks. This release applies to Microsoft Azure Public Cloud and Azure Government regions, in both Standard and Premium pricing tiers. Hundreds of our global customers including large financial services, healthcare and retail organizations have already adopted the capability to enable secure and reliable deployments of the Azure Databricks unified data platform. It allows them to securely process company and customer data in private Azure Virtual Networks, thus satisfying a major requirement of their enterprise governance policies.

Read on fore more detail about how this works.

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Connecting Confluent and Databricks on Azure

Published 2021-02-08 by Kevin Feasel

Angela Chu, et al, take us through a streaming data ingestion process:

How do you process IoT data, change data capture (CDC) data, or streaming data from sensors, applications, and sources in real time? Apache Kafka® and Azure Databricks are widely adopted technologies in the industry, but they require specific skills and expertise to run. Leveraging Confluent Cloud and Azure Databricks as fully managed services in Microsoft Azure, you can implement new real-time data pipelines with less effort and without the need to upgrade your datacenter (or set up a new one).

This blog post demonstrates how to configure Azure Databricks to interact with Confluent Cloud so that you can ingest, process, store, make real-time predictions and gain business insights from your data.

Click through for a detailed demonstration.

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Using Spark Pools in Azure Synapse Analytics

Published 2021-01-29 by Kevin Feasel

Rahul Mehta shows how to create and use an Apache Spark pool in Azure Synapse Analytics:

In the last part of the Azure Synapse Analytics article series, we learned how to create a dedicated SQL pool. Azure Synapse support three different types of pools – on-demand SQL pool, dedicated SQL pool and Spark pool. Spark provides an in-memory distributed processing framework for big data analytics, which suits many big data analytics use-cases. Azure Synapse Analytics provides mechanisms to use SQL on-demand pool to query data as a service, SQL dedicated pool for data warehousing using distributed data processing engine, and Spark pool for analytics using in-memory big data processing engine. This article shows how to create a Spark pool in Azure Synapse Analytics and further how to process the data using it.

Click through for a demo on setup and a sample notebook to get started.

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Grouping Data with Spark

Published 2021-01-19 by Kevin Feasel

Ed Elliott has two quick examples of grouping data in Spark:

I have been playing around with the new Azure Synapse Analytics, and I realised that this is an excellent opportunity for people to move to Apache Spark. Synapse Analytics ships with .NET for Apache Spark C# support many people will surely try to convert T-SQL code or SSIS code into Apache Spark code. I thought it would be awesome if there were a set of examples of how to do something in T-SQL, then translated into how to do that same thing in Spark SQL and the Spark DataFrame API in C#.

Click through for the first example, GROUP BY.

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Loading a Spark DataFrame in .NET

Published 2021-01-18 by Kevin Feasel

Ed Elliott shows how to get data and convert it into a Spark DataFrame using .NET:

When I first started working with Apache Spark, one of the things I struggled with was that I would have some variable or data in my code that I wanted to work on with Apache Spark. To get the data in a state that Apache Spark can process it involves putting the data into a DataFrame. How do you take some data and get it into a DataFrame?

This post will cover all the ways to get data into a DataFrame in .NET for Apache Spark.

Click through for several methods.

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DataFrame Cleaning in Spark

Published 2021-01-11 by Kevin Feasel

Craig Covey has an update to the Spark Starter Guide:

Real-world datasets are hardly ever clean and pristine. They commonly include blanks, nulls, duplicates, errors, malformed text, mismatched data types, and a host of other problems that degrade data quality. No matter how much data one might have, a small amount of high quality data is more beneficial than a large amount of garbage data. All decisions derived from data will be better with higher quality data. 

In this section we will introduce some of the methods and techniques that Spark offers for dealing with “dirty data”. The term dirty data means data that needs to be improved so the decisions made from the data will be more accurate. The topic of dirty data and how to deal with it is a very broad topic with a lot of things to consider. This chapter intends to introduce the problem, show Spark techniques, and educate the user on the effects of “fixing” dirty data. 

It’s interesting to see what’s available in Spark and how you can take advantage of it.

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