Unleashing the capabilities of Azure Synapse Analytics for a healthcare customer
This blog post aims to provide you with insights into the best practices for optimizing performance in data ingestion and transformation. You will learn how to efficiently use available resources, compute capacity, and workload management in Azure Synapse Analytics workspace. While the solution discussed in this blog pertains to a healthcare industry customer, the optimization techniques presented here are applicable to other industries as well.Synapse pipelines storage event trigger security deep dive
ADF and Synapse Pipelines offer a feature that allows pipeline execution to be triggered based on various events, such as storage blob creation or deletion. This can be used by customers to implement event-driven pipeline orchestration. In this blog post, we will address some common security questions and challenges that may arise during storage event trigger creation.Create a data solution on Azure Synapse Analytics with Snapshot Serengeti - Part 2 (Analytics)
This is the second blog in a four-part series on building an end-to-end data analytics and machine learning solution on Azure Synapse Analytics. If you haven't already, be sure to check out the first blog at https://aka.ms/synapseserengeti before proceeding.Improve Spark pool utilization with Synapse Genie
Synapse Genie Framework improves Spark pool utilization by executing multiple Synapse notebooks on the same Spark pool instance. It considers the sequence and dependencies between notebook activities in an ETL pipeline, which results in higher usage of a full cluster for resources available in a Spark pool.
How Data Exfiltration Protection (DEP) impacts Azure Synapse Analytics Pipelines
Do you want to know how enabling Data Exfiltration Protection (DEP) against a Synapse Workspace will impact Synapse Pipelines? This article discusses the impacts of DEP on Synapse Pipelines and potential workarounds. If you are considering enabling DEP in your Synapse deployment - this is a must read.
Data mesh: A perspective on using Azure Synapse Analytics to build data products
This is a multi-part blog series, and it discusses various aspects of implementing data mesh architecture on Azure. This part focuses on data as a product principle and presents a perspective on using Azure Synapse Analytics as a data product. We discuss (at a high-level) data product functions & capabilities and apply that lens to Synapse Analytics. We discuss how workspaces can be partitioned to give domains scale and agility to build data products.
Introduce a Notebook gallery image to process Geospatial data from Planetary Computer with STAC API
We are introducing a spaceborne data processing Notebook, which has been published to Azure Synapse Analytics Gallery. The Notebook uses STAC API (SpatioTemporal Asset Catalog) to search and download geospatial data from Microsoft Planetary Computer to an Azure Storage account and perform basic geospatial transformation. The sample code and documentation of the notebook will be published as part of Azure Orbital Analytics reference architecture later this year.How to – Advanced Properties of Linked Services
Azure Data Factory and Synapse Pipelines have a wealth of linked service connection types that allow them to connect and interact with many services and data stores. The Workspace UI provides the most important properties that are needed for the connection. However, at times we need more control that the UI doesn’t offer. In this tutorial, we will focus on customer scenario to see how you can find all the available properties for the Linked Service you are working with, then how to incorporate that property into your Linked Service directly from within the UI.
