Optimize performance with Spark and Delta Live Tables
|
Learn how to Optimize performance with Spark and Delta Live Tables in Azure Databricks.
Use serverless compute and parallelism with Delta live tables, Perform cost based optimization and query performance, Use Change Data Capture (CDC), Apply enhanced autoscaling capabilities, Implement Observability and enhance data quality metrics
Prerequisites
Before starting this module, you should have a fundamental knowledge of data analytics concepts. Consider completing Azure Data Fundamentals certification before starting this module.
User Reviews
Be the first to review “Optimize performance with Spark and Delta Live Tables”
Related Products
Explore fundamentals of large-scale analytics
Organizations use analytics platforms to build large scale data analytics solutions that generate insights and drive success. Microsoft provides multiple technologies that you can combine to build a large scale data analytics solution.
Perform data engineering with Azure Synapse Apache Spark Pools
Apache Spark is a highly scalable distributed processing solution for big data analytics and transformation. You can leverage its power in Azure Synapse Analytics by using Spark pools.
Secure data and manage users in Azure Synapse serverless SQL pools
Learn how you can set up security when using Azure Synapse serverless SQL pools
Use Delta Lake in Azure Synapse Analytics
Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.
Transfer and transform data with Azure Synapse Analytics pipelines
Azure Synapse Analytics enables data integration through the use of pipelines, which you can use to automate and orchestrate data transfer and transformation activities.
Create a lake database in Azure Synapse Analytics
Why choose between working with files in a data lake or a relational database schema? With lake databases in Azure Synapse Analytics, you can combine the benefits of both.
There are no reviews yet.