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Category: Synapse Analytics

Azure Synapse Data Explorer Pools

Manoj Raheja tries announces another pool type:

At Ignite, we announced the public preview of Azure Synapse data explorer that makes it possible to query huge amounts of structured, semi-structured, and free-text telemetry and time-series data. The following are some of the key capabilities that make this possible:

– Powerful distributed query engine that indexes all data including free text and semi-structured data. The data is automatically compressed, indexed, auto-optimized, and cached on local SSDs and persisted on storage. Compute and storage are decoupled that gives you full elasticity to auto scale in/out without a downtime.

– Intuitive Kusto Query Language (KQL) that is highly optimized for exploring raw telemetry and time series data using Synapse data explore’s best-in-class text indexing for efficient free-text search, regex, and parsing on traces\text data.

– Comprehensive JSON parsing capabilities for querying semi-structured data including arrays and nested structure.

– Native, advanced time series support for creation, manipulation, and analysis of multiple time series with in-engine Python and R execution support for model scoring.

Click through for a demonstration, showing that this is for more than just logs.

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Starting a Synapse Proof of Concept

Hope Foley shares a secret with us:

I love my job!  One of the things I do for a living is to help customers get started with new services in Azure to finagle their data.  Many times we’ll start with a small POC to just start to understand the parts and pieces, and I teach them along the way.  I work with a lot of customers so being quick and nimble helps.  Lately I’ve been using PowerShell to setup the pieces needed for a full Synapse Analytics environment, including an example set of 4 pipelines (2 to extract to ADLS, 2 to upload to dedicated SQL pool).  Pulling data out of large relational databases into the data lake became a request I heard over and over so I automated it.  I’ve added and tweaked this over the years into a project I called “Synapse Load” and put a version out in my github. 

Click through to see what this includes and how you can use it.

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Azure Synapse Analytics Announcements

Kaiser Larsen has some Azure Synapse Analytics announcements for us:

As businesses worldwide navigate a new normal, data teams find themselves pressured to deliver transformative insights quicker than ever. Customer interactions are increasingly digital and multi-channel, supply chains are constantly adapting to changing demand, and operations are being reconfigured to accommodate remote and hybrid work. Business agility has never been more critical. And data teams are being asked to create new solutions, accelerate project deployments, and deliver real-time insights to power that agility.

For Ignite 2021, we’ve focused on delivering new features that enable data teams to deliver insights to the business faster than ever. Here is the summary of the latest innovations on Azure Synapse.

Read on to see some of what they’ve just dropped in.

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Serverless SQL Pool CI/CD

Kevin Chant doesn’t have time for manual deployments:

I want to cover one way you can do CI/CD for Azure Synapse Analytics serverless SQL pools using Azure DevOps in this post. Because I know it is a popular topic.

It’s related to my post about how you can create a dacpac for an Azure Synapse Analytics dedicated SQL pool using Azure DevOps. Since they are both based in the same service.

Plus, a while ago I wrote about the increase in demand for Data Platform automation. So, I really wanted to do a post about how you can do CI/CD for Azure Synapse Analytics serverless SQL pools.

Read on to learn how.

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Azure Synapse Analytics October 2021 Update

Saveen Reddy summarizes the newest updates in Azure Synapse Analytics:

Use Stringify in data flows to easily transform complex data types to strings

Mapping data flows helps you perform code-free data transformation your Synapse pipelines. When you work with complex data types such as structures, arrays, map, you need to transform them into strings. You can do this by using the new Stringify data transformation simplifying this common task.

Read on for the full set of updates.

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Optimizing Blob Storage Query Performance

Dennes Torres compares several strategies for querying data stored in Azure Blob Storage:

In the third part of the series Querying Blob Storage with SQL, I will focus on the performance behaviour of queries: What makes them faster, slower, and some syntax beyond the basics.

The performance tests in this article are repeated, and the best time of the queries is recorded. This doesn’t mean you will always achieve the same timing. Many architectural details will affect the timing, such as cache, first execution, and so on. The timing exposed on each query is only a reference pointing to the differences of the query methods that can affect the time and the usual result for better or worse performance.

Click through to see which patterns perform well and which don’t.

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Using Query Labels in Azure Synapse Analytics

Gauri Mahajan shows one of the pieces of functionality in Azure Synapse Analytics dedicated SQL pools that I’d like to see on-premises:

Azure Synapse supports a concept known as “query labels” that allows tagging any DDL or DML queries that are executed on the dedicated SQL pool. These labels can be queried using the dynamic management views (DMVs). One can use these labels to describe the purpose of the query or add any metadata to the query being executed and the same can be used later for instrumenting the queries, specifically to identify the queries that meet the desired search criteria. Let’s walk through a step-by-step exercise to understand this concept practically.

Click through for the process.

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Synapse vs Snowflake

Travis Manning has a throw-down:

Data warehousing has become a hot topic for most organizations as data volume grows exponentially, and yet the capacity to manually manage it all but diminishes. The ecosystem is replete with options, each with a host of features and integrations. In this article, we will discuss two of the most common (and commonly discussed!) data warehousing services, Azure Synapse and Snowflake Data Warehouse (DW). For this article, we will try to focus on use cases, and which option is appropriate in that context.

Click through for the product comparison. One big difference not covered is pricing uncertainty. If you have a good understanding of the number of executions and computational complexity of your queries, as well as data quantities, Snowflake can be very competitively priced. But what can happen is that the competitive price turns into a much-less-competitive price by the time you’re fully up to speed.

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Deploying Synapse Artifacts to a Managed vNet Workspace

Rui Cunha takes us through an Azure Synapse Analytics deployment scenario:

In my previous article, I demonstrated how we could easily use the Synapse Workspace Deployment extension to accomplish this second stage of the process. I’m now coming back to this topic as I realized that many of our customers were reporting difficulties in completing this second stage of their Synapse CICD process because they were failing to deploy Synapse artifacts to a Managed VNET Synapse Workspace.

In this particular scenario, the deployment was failing because their target workspace was not allowing access from public networks.

Fortunately, the answer isn’t “Allow access from public networks.” Click through to see what you can do instead.

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