Preview: Azure Synapse Runtime for Apache Spark 3.5
We’re thrilled to announce that we have made Azure Synapse Runtime for Apache Spark 3.5 for our Azure Synapse Spark customers in preview, while they get ready and prepare for migrating to Microsoft Fabric Spark. What Does This Mean for You? You can now create Azure Synapse Runtime for Apache Spark 3.5. The essential changes include features which come from upgrading Apache Spark to version 3.5 and Delta Lake 3.2. Please review the official release notes for Apache Spark 3.5 to check the complete list of fixes and features. In addition, review the migration guidelines between Spark 3.4 and 3.5 to assess potential changes to your applications, jobs and notebooks. For additional details check Azure Synapse Runtime for Apache Spark 3.5 documentation. What is next? We offer Azure Synapse Runtime for Apache Spark 3.5 to our Azure Synapse Spark customers. However, we strongly recommend that customers plan to migrate to Microsoft Fabric Spark to benefit from the latest innovations and optimizations exclusive to Microsoft Fabric Spark. For example, the Native Execution Engine (NEE) significantly enhances query performance at no additional cost. Starter pools allow the creation of a Spark session within seconds, unified security in the lakehouse enables the definition of RLS (Row-Level Security) and CLS (Column-Level Security) for objects in the lakehouse. Additionally, newly announced Materialized Views and many other features are available.Upgrade to Azure Synapse runtimes for Apache Spark 3.4 & previous runtimes deprecation
It is important to stay ahead of the curve and keep services up to date. That's why we encourage all Azure Synapse customers with Apache Spark workloads to migrate to the newest GA version, Azure Synapse Runtime for Apache Spark 3.4. The update brings Apache Spark to version 3.4 and Delta Lake to version 2.4, introduces Mariner as the new operating system, and updates Java from version 8 to 11.
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.Create a Data Solution on Azure Synapse Analytics with Snapshot Serengeti - Part 1
This will be the first article in a four-part series on building an end-to-end data analytics and machine learning solution on azure synapse analytics. By the end of this series, you will have learned how to transform and analyze the data, and use it to train a classification machine learning model capable of classifying camera trap images into one of the 48 species in the dataset.
Using OpenAI GPT in Synapse Analytics
Azure OpenAI hardly needs an introduction, but for those who managed to evade all tech new lately, let me give you a brief overview. Azure OpenAI is a suite of natural language processing (NLP) models developed by OpenAI. The models can be used in a very wide range of applications, including text generation, summarization and translation.
