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Category: Spark

Isolated Spark Testing with lakeFS

Published 2022-12-14 by Kevin Feasel

Adi Polak demonstrates lakeFS:

This tutorial demonstrates how to build a development and testing environment for validating your logic on a full-blown production data volume and variety, working with lakeFS and Spark. You will walk through the journey of creating a repository and building a Spark application while using lakeFS capabilities. You will learn how to data changes, revert them in cases of mistakes or other hiccups, and lately merge separate branches to reflect data changes from the isolated environments.

Not too long ago, I had a couple conversations with developers and data engineers about decentralized development and devs having their own environments and data. This seems like it would be a good approach to that common problem, and it works for Azure Synapse Analytics as well.

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InvalidAbfsRestOperationException in Synapse Managed VNet

Published 2022-12-07 by Kevin Feasel

Kamil Nowinski goes down a rabbit hole:

This happens on the customer’s Synapse workspace where we have a public network disabled, so only private endpoint and managed VNET are available. Additionally, you probably spotted, that it took over 3 minutes to actually get this message. Hence, as a next step, in order to minimize the potential causes I simplified the query to make sure I have access to the Storage, by listing the files:

Click through for a story of pain, followed by glorious resolution.

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The Basics of dbt in Spark

Published 2022-12-06 by Kevin Feasel

Ust Oldfield provides an introduction to dbt:

dbt is an abbreviation for data build tools. It is primarily a SQL based transformation workflow, supported by yaml, to allow teams to collaborate on analytics code whilst implementing software engineering best practices like modularity, portability, CI/CD, testing, and documentation.

dbt is available using a CLI in the form of dbt core, or as a paid-for SaaS product in the form of dbt cloud.

Click through to see how the product works, including an example.

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Data Lake Exploration in AWS with Athena for Spark

Published 2022-12-02 by Kevin Feasel

Pathik Shah and Raj Devnath jetski the data lake:

Amazon Athena now enables data analysts and data engineers to enjoy the easy-to-use, interactive, serverless experience of Athena with Apache Spark in addition to SQL. You can now use the expressive power of Python and build interactive Apache Spark applications using a simplified notebook experience on the Athena console or through Athena APIs. For interactive Spark applications, you can spend less time waiting and be more productive because Athena instantly starts running applications in less than a second. And because Athena is serverless and fully managed, analysts can run their workloads without worrying about the underlying infrastructure.

Data lakes are a common mechanism to store and analyze data because they allow companies to manage multiple data types from a wide variety of sources, and store this data, structured and unstructured, in a centralized repository. Apache Spark is a popular open-source, distributed processing system optimized for fast analytics workloads against data of any size. It’s often used to explore data lakes to derive insights. For performing interactive data explorations on the data lake, you can now use the instant-on, interactive, and fully managed Apache Spark engine in Athena. It enables you to be more productive and get started quickly, spending almost no time setting up infrastructure and Spark configurations.

In this post, we show how you can use Athena for Apache Spark to explore and derive insights from your data lake hosted on Amazon Simple Storage Service (Amazon S3).

This feels a lot like the Spark pool in Azure Synapse Analytics, as well as some of what Databricks does

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Unity Catalog in Azure Databricks

Published 2022-11-29 by Kevin Feasel

Meagan Longoria makes a recommendation:

Unity Catalog in Databricks provides a single place to create and manage data access policies that apply across all workspaces and users in an organization. It also provides a simple data catalog for users to explore. So when a client wanted to create a place for statisticians and data scientists to explore the data in their data lake using a web interface, I suggested we use Databricks with Unity Catalog.

Read on to learn more about what the Unity Catalog does.

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Spark RDD Transformations

Published 2022-11-23 by Kevin Feasel

Meenakshi Goyal walks us through the transformation functions available to you when using a Spark RDD:

The role of transformation in Spark is to create a new dataset from an existing one. Lazy transformations are those that are computed only when an action requires a result to be returned to the driver programme.

When we call an action, transformations are executed since they are inherently lazy. Not right away are they carried out. There are two primary types of transformations: map() and filter ().
The outcome RDD is always distinct from the parent RDD after the transformation. It could be smaller (filter, count, distinct, sample, for example), bigger (flatMap(), union(), Cartesian()), or the same size (e.g. map).

Read on to learn more about transformations, including examples of how each works. Even if you’re using the DataFrames API for Spark, it’s still important to understand that transformations are lazy.

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REST APIs for Synapse Spark Pools

Published 2022-11-23 by Kevin Feasel

Abid Nazir Guroo looks at some endpoints:

Azure Synapse Analytics Representational State Transfer (REST) APIs are secure HTTP service endpoints that support creating and managing Azure Synapse resources using Azure Resource Manager and Azure Synapse web endpoints. This article provides instructions on how to setup and use Synapse REST endpoints and describe the Apache Spark Pool operations supported by REST APIs.

Read on to see some of the Spark pool management options are available to you via the REST API.

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Time Travel with Delta Tables in Synapse

Published 2022-11-11 by Kevin Feasel

Liliam Leme reverses the clock:

Scenario

While working with a customer, they had a requirement to restore modified files to a specific point in time. They had built their architecture on top of a Data lake.

Looking for options

While working on this scenario, we explored some storage options available without any side customization, for example, Soft delete for blobs – Azure Storage | Microsoft Docs.

Read on to see what they landed on.

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Azure Synapse Analytics R Language Support

Published 2022-11-01 by Kevin Feasel

Ryan Majidimehr has a short list of updates for Azure Synapse Analytics but it includes a big one:

Azure Synapse Analytics provides built-in R support for Apache Spark. As part of this, data scientists can leverage Azure Synapse Analytics notebooks to write and run their R code. This also includes support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. To learn more read the official how-to Use R for Apache Spark with Azure Synapse Analytics (Preview).

That it took this long for R support was a bit weird, but I’m glad it’s there now.

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