Azure Synapse Analytics
Azure Synapse Analytics is an unlimited analytics service that brings together data warehousing and Big Data analytics. It gives you the freedom to collect data from all of your sources, process, manage, and serve data from relational and non-relational databases with a lightning fast, security, and privacy-centric platform.
Overview
Synapse Analytics combines the capabilities of Azure SQL Data Warehouse, Azure Data Lake Analytics, and Apache Spark for Hadoop into a single, unified environment. This integration streamlines big data, data warehousing, and data governance workloads.
- Unified Experience: A single pane of glass for all your analytics needs with Synapse Studio.
- Scalability: Scales compute and storage independently to meet demands.
- Integration: Seamless integration with Azure services like Azure Data Factory, Azure Machine Learning, and Power BI.
- Hybrid Approach: Supports both SQL and Spark engines for diverse analytical tasks.
Key Components
SQL Pools
Dedicated SQL pools (formerly SQL DW) provide enterprise-grade data warehousing capabilities. They offer massively parallel processing (MPP) architecture for high-performance querying on large datasets.
- Provisioning: Create and manage dedicated SQL pools with configurable compute resources (DWUs).
- Performance: Optimize queries using statistics, indexing, and distribution strategies.
- T-SQL: Use familiar T-SQL syntax for data manipulation and querying.
Spark Pools
Apache Spark pools provide an open-source big data analytics platform for large-scale data processing. They are ideal for ETL, machine learning, and interactive data analysis.
- Languages: Support for Scala, Python, Spark SQL, and .NET.
- Integration: Seamlessly read and write data from Azure Data Lake Storage Gen2.
- Auto-scaling: Dynamically adjust cluster size based on workload demands.
Data Integration
Synapse Analytics includes built-in capabilities for data ingestion, transformation, and orchestration, similar to Azure Data Factory.
- Pipelines: Create complex ETL/ELT workflows with a visual interface.
- Activities: Execute data movement, transformations, and control flow operations.
- Triggers: Schedule pipeline executions or trigger them based on events.
Azure Data Explorer Pools
Azure Data Explorer pools enable real-time analytics on streaming data. They are optimized for log and telemetry data analysis.
- Ingestion: Support for various data ingestion methods, including Kafka and Event Hubs.
- Kusto Query Language (KQL): Powerful language for analyzing time-series data.
- Low Latency: Designed for near real-time data insights.
Synapse Studio
Synapse Studio is a web-based application that provides a unified interface for managing and developing analytics solutions in Azure Synapse Analytics.
- Code-Free & Code-Based: Supports both visual development and direct coding for pipelines, notebooks, and SQL scripts.
- Monitoring: Monitor pipeline runs, Spark applications, and SQL query performance.
- Collaboration: Facilitates collaboration among data engineers, data scientists, and analysts.
API and SDK Reference
REST API
The Azure Synapse Analytics REST API allows you to programmatically manage your Synapse workspace and its resources. This includes creating, deleting, and managing SQL pools, Spark pools, and other Synapse objects.
You can find detailed information on the REST API endpoints, request methods, and response formats in the official Microsoft REST API documentation.
SDKs
Azure Synapse Analytics is supported by various SDKs, enabling you to integrate Synapse capabilities into your applications.
- .NET SDK: For .NET developers.
- Python SDK: For Python developers, particularly useful for data science and machine learning workflows.
- Java SDK: For Java developers.
- JavaScript SDK: For web and Node.js applications.
Refer to the Developer Guide for links to specific SDKs and usage examples.
Common Use Cases
- Enterprise Data Warehousing: Centralize and analyze large volumes of structured data.
- Big Data Analytics: Process and analyze unstructured and semi-structured data using Spark.
- Real-time Analytics: Gain insights from streaming data with Azure Data Explorer pools.
- Machine Learning: Build, train, and deploy machine learning models using Spark MLlib or integration with Azure Machine Learning.
- Data Exploration: Interactively explore data using Spark notebooks or SQL queries.
Pricing Information
Azure Synapse Analytics pricing is based on the services you use, such as Dedicated SQL pool compute (DWUs), Spark pool compute, data ingress/egress, and storage. For detailed pricing, please visit the Azure Synapse Analytics pricing page.
Tutorials and Quickstarts
Get started quickly with the following resources: