Now that we understand different layers or components of the architecture let’s understand the core pillars of Synapse.Īzure Synapse Studio – This tool is a web-based SaaS tool that provides developers to work with every aspect of Synapse Analytics from a single console. Data is typically populated in Synapse from Data Lake Storage for various analytical purposes This storage layer acts as the data source layer for Synapse. Synapse also provides integrated management, security, and monitoring related services to support monitoring and operations on the data and services supported by Synapseĭata Lake Storage is suited for big data scale of data volumes that are modeled in a data lake model. Synapse is integrated with numerous Azure data services as well, for example, Azure Data Catalog, Azure Lake Storage, Azure Databricks, Azure HDInsight, Azure Machine Learning, and Power BI Synapse supports two types of analytics runtimes – SQL and Spark (in preview as of Sept 2020) based that can process data in a batch, streaming, and interactive manner NET, Java, Scala, and R that are typically used by analytic workloads Synapse supports a number of languages like SQL, Python. Synapse Analytics Studio is a web-based IDE to enable code-free or low-code developer experience to work with Synapse Analytics Synapse Workspaces (in preview as of Sept 2020) provides an integrated console to administer and operate different components and services of Azure Synapse Analytics Synapse Analytics is basically an analytics service that has a virtually unlimited scale to support analytics workloads Let’s understand all these components one by one. There are multiple components of Synapse Analytics architecture on Azure. Azure Data Lake Storage forms the bedrock of big data storage, and Power BI forms the visualization layer, as shown below. The latter is the use-case where Synapse Analytics fits in the overall data landscape, as shown below. Online Analytical Processing (OLAP) applications typically store and process large volumes of data collected from various sources, which may be transformed and/or modeled in the OLAP repository, and then large datasets are aggregated for ad-hoc reporting and analytical use-cases. And data ingestion generally happens through user transactions in small batches of rows. The data access pattern usually involves a lot of scalar and tabular datasets. Online Transaction Processing Workloads (OLTP) typically involve transactional data that is voluminous in terms of high reads and writes. In this article, we would learn the details of this service. This service is the de-facto service for combining data warehousing and big data analytics, with many new features of the service in preview as well.
Since then, this service has gone through several iterations, and towards the end of 2019, Microsoft announced that the Azure SQL Data Warehouse service would be rebranded as Azure Synapse Analytics.
In the mid of 2016, Azure made Azure SQL Data Warehouse service generally available for data warehousing on the cloud.
This article will discuss one of the powerful analytics services in Azure – Azure Synapse Analytics, along with its components, features, security, and more.
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