Azure Synapse Analytics - Key Points


 Azure Synapse Analytics - Services provided by Microsoft Cloud Platform to do data warehousing stuff i.e. brings or extract data, work or compute on those data, execute analytical part like predictive analysis, or prepare the C-Level analytical report.

Following the standard execution/implementation approach about Synapse.


Below are some key points about Synapse Analytic Services and Platform.


#Why and when we need to use Azure Synapse Analytics
  • Large-scale data warehousing
  • Advanced analytics
  • Data exploration and discovery
  • Real time analytics
  • Data integration
  • Integrated analytics

#Azure Synapse Data Explorer is a data processing engine in Azure Synapse Analytics that is based on the Azure Data Explorer service. Data Explorer uses Kusto Query Language (KQL) to enable high performance, low-latency analysis of batch and streaming data.


#You can create a Synapse Analytics workspace in an Azure subscription interactively by using the Azure portal, or you can automate deployment by using Azure PowerShell, the Azure command-line interface (CLI), or with an Azure Resource Manager or Bicep template.


#Synapse Studio; a web-based portal for Azure Synapse Analytics.


#A workspace typically has a default data lake, which is implemented as a linked service to an Azure Data Lake Storage Gen2 container.


#Pipelines in Azure Synapse Analytics are based on the same underlying technology as Azure Data Factory.


#Azure Synapse Analytics supports SQL-based data querying and manipulation through two kinds of SQL pool that are based on the SQL Server relational database engine:
  • A built-in serverless pool that is optimized for using relational SQL semantics to query file-based data in a data lake.
  • Custom dedicated SQL pools that host relational data warehouses.

#The Azure Synapse SQL system uses a distributed query processing model to parallelize SQL operations, resulting in a highly scalable solution for relational data processing.

#Apache Spark is an open source platform for big data analytics which supported languages include Python, Scala, Java, SQL, and C#.

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