Category Archives: Azure Synapse

What is HTAP in Azure

Hybrid Transactional and Analytical Processing, or HTAP, is an advanced database capability that allows for both types of workloads to be performed without one impacting the performance of the other.

In this Video Blog, I cover some of the history of HTAP, some of the challenges and benefits of these systems, and where you can find them in Azure.

Overview of Azure Synapse Link featuring CosmosDB

Azure Synapse Link allows you to connect to your transactional system directly to run analytical and machine learning workloads while eliminating the need for ETL/ELT, batch processing and reload wait times.

In this vLog, I explain how to turn the capability to use Link on in CosmosDB, and what’s happening under the covers to give access to that analytical workload without impacting the performance of your transactional processing system.

Check it out here and let me know what you think!

Getting started with Spark Pools in Azure Synapse

In my latest video blog I discuss getting started on the newly Generally Available Spark Pools as a part of Azure Synapse, another great option for Data Engineering/Preparation, Data Exploration, and Machine learning workloads

Without going too deep into the history of Apache Spark, I’ll start with the basics. Essentially, in the early days of Big Data workloads, a basis for machine learning and deep learning for advanced analytics and AI, we would use a Hadoop cluster and move all these datasets across disks, but the disks were always the bottleneck in the process. So, the creators of Spark said hey, why don’t we do this in memory and remove that bottleneck. So they developed Apache Spark as an in memory data processing engine as a faster way to process these massive datasets.

When the Azure Synapse team wanted to make sure that they were offering the best possible data solution for all different kinds of workloads, Spark gave the ability to have an option for their customers that were already familiar with the Spark environment, and included this feature as part of the complete Azure Synapse Analytics offering.

Behind the scenes, the Synapse team is managing many of the components you’d find in Open-Sourced Spark such as:

  • Apache Hadoop Yarn – for the management of the clusters where the data is being processed
  • Apache Livy – for the job orchestration
  • Anaconda – a package manager, environment manager, Python/R data science distribution and a collection of over 7500 open source packages for increasing the capabilities of the Spark clusters

I hope you enjoy the post. Let me know your thoughts or questions!

Connecting to External Data with Azure Synapse

In my latest video blog I discuss and demonstrate some of the ways to connect to external data in Azure Synapse if there isn’t a need to import the data to the database or you want to do some ad-hoc analysis. I also talk about using COPY and CTAS statements if the requirement is to import the data after all. Check it out here