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Category: Data Lake

Use Cases for Multiple Data Lakes

Published 2023-01-24 by Kevin Feasel

James Serra explains why you might want multiple data lakes in an organization:

A question I get asked frequently from customers when discussing Data lake architecture is “Should I use one data lake for all my data, or multiple lakes?”. Ideally, you would use just one data lake, but I have seen many valid use cases where customers are using multiple data lakes. Here are some of those reasons:

I’d quibble with a couple of these (and given James’s intro, I’m not sure he’s fully on board with all of the reasons) but this is a good list of reasons why you might see several data lakes in an organization.

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Reading the Data Lake with the Serverless Pool via OPENROWSET

Published 2023-01-23 by Kevin Feasel

Ryan Adams begins a series on reading data from the data lake:

There are two ways to read data inside Data Lake using the Synapse Serverless engine.  In this article, we’ll look at the first method which uses OPENROWSET to query a path within the lake. 

Synapse is a collection of tools with four different analytical engines (Dedicated Pool, Spark Pool, Serverless Pool, Data Explorer Pool).  This gives you a lot of options for ingesting, transforming, storing, and querying your data.  The article will focus on how you can use the Synapse Serverless Pool to query the data in your ADLS account.   

Click through for a primer on the topic, as well as a demo video.

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External Objects in Databricks Unity Catalog

Published 2022-12-29 by Kevin Feasel

Meagan Longoria adds external tables and views to an Azure Databricks Unity Catalog:

I’ve been busy defining objects in my Unity Catalog metastore to create a secure exploratory environment for analysts and data scientists. I’ve found a lack of examples for doing this in Azure with file types other than delta (maybe you’re reading this in the future and this is no longer a problem, but it was when I wrote this). So I wanted to get some more examples out there in case it helps others.

I’m not storing any data in Databricks – I’m leaving my data in the data lake and using Unity Catalog to put a tabular schema on top of it (hence the use of external tables vs managed tables. In order to reference an ADLS account, you need to define a storage credential and an external location.

Read on for examples of what you can do with this.

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Capturing Event Hubs Data in Delta Lake Format with Stream Analytics

Published 2022-12-21 by Kevin Feasel

Xu Jiang announces a public preview:

The Stream Analytics no-code editor is a drag and drop design tool that helps customers to develop the Stream Analytics jobs without writing a single line of code. The experience provides a canvas that allows you to connect to input sources to quickly see your streaming data. Then you can transform and preview it before writing to your destination of choice in Azure. To learn more, see No-code stream processing through Azure Stream Analytics | Microsoft Learn.

Read on to see how you can capture and process data into Delta Lake format via their designer.

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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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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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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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Reading Delta Tables from Power BI via Synapse Serverless

Published 2022-10-10 by Kevin Feasel

Dan English is up for a data lake change:

In this post I just wanted to show the ability to use the Delta Lake format that is very common now with Power BI. I will go over a quick example of creating the files to reference, building a view to use with Power BI, and then querying the data in Power BI.

In my Synapse Workspace I created a Linked service connection to an Azure SQL Database that has the AdventureWorksLT database loaded which is the sample database you can create when you first create a SQL instance in Azure and here is a walkthrough link and see the Additional settings section.

Dan shows how to create the lake files in delta format via Synapse pipeline and then how to query the data from there.

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Architecting a Data Lake

Published 2022-10-05 by Kevin Feasel

James Serra provides some guidance:

I have had a lot of conversations with customers to help them understand how to design a data lake. I touched on this in my blog Data lake details, but that was written a long time ago so I wanted to update it. I often find customers do not spend enough time in designing a data lake and many times have to go back and redo their design and data lake build-out because they did not think through all their use cases for data. So make sure you think through all the sources of data you will use now and in the future, understanding the size, type, and speed of the data. Then absorb all the information you can find on data lake architecture and choose the appropriate design for your situation.

The concepts are simple but there are some interesting implications to what James includes as well as additional resources, so check it out.

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