Azure Synapse Analytics is designed to analyze large volumes of data and provides a unified platform for both big data and data warehousing.
New features include:
Apache Spark Delta Lake tables in serverless SQL pools
Apache Spark elastic pool storage
Apache Spark R language support
Azure Synapse Data Explorer
Browse ADLS Gen2 folders in the Azure Synapse Analytics workspace
Distributed Deep Neural Network Training
Spark Advisor for Azure Synapse Notebook
Other features include:
Massively Parallel Processing (MPP): Azure Synapse Analytics uses a distributed and parallel processing architecture, allowing it to handle large-scale data processing and analytics tasks efficiently.
Data Warehousing: It provides capabilities for creating and managing data warehouses, allowing organizations to store and analyze structured data.
Big Data Analytics: Azure Synapse Analytics supports the analysis of both structured and unstructured data, making it suitable for big data scenarios. It integrates with Azure Data Lake Storage for handling large-scale data sets.
Data Integration: The service allows users to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. It supports data integration from various sources.
Querying: Users can use T-SQL (Transact-SQL) queries to analyze and retrieve data. The system optimizes these queries for parallel processing, enhancing performance.
Security: It provides robust security features, including encryption, authentication, and role-based access control, to ensure the confidentiality and integrity of the data.
Integration with Power BI: Azure Synapse Analytics seamlessly integrates with Microsoft Power BI for data visualization and business intelligence reporting.
Scalability: The service is designed to scale up or down based on the processing needs of the organization. This elasticity allows users to pay for the resources they consume.
Analyze large volumes of data and provides a unified platform for both big data and data warehousing
Massively Parallel Processing, Data Warehousing, Big Data Analytics