Data is the new oil, but it’s not enough to just collect it. You need to analyze it, extract insights from it, and use it to power your business decisions and applications.
But how do you do that efficiently and effectively across different types of data sources, formats, and scenarios?
That’s where Microsoft Synapse Analytics comes in.
It’s an enterprise analytics service that brings together the best of SQL technologies for data warehousing, Apache Spark technologies for big data, Data Explorer for log and time series analytics, and Pipelines for data integration and ETL/ELT.
It also integrates with other Azure services, such as Power BI, CosmosDB, and AzureML, to enable end-to-end analytics solutions.
In this article, I’ll show you some of the key features and benefits of Microsoft Synapse Analytics and how it can help you accelerate time to insight across your data landscape.
One of the core components of Microsoft Synapse Analytics is Synapse SQL, a distributed query system that supports T-SQL and extends it to address streaming and machine-learning scenarios. Synapse SQL offers both serverless and dedicated resource models, so you can choose the best option for your workload and budget.
With serverless SQL, you can query data on demand from various sources using native connectors without having to provision or manage any infrastructure. You only pay for the resources you consume per query. This is ideal for unplanned or bursty workloads, such as ad-hoc analysis or exploratory queries.
With dedicated SQL pools, you can create a scalable data warehouse that stores data in SQL tables and reserves processing power for predictable performance and cost. You can also use built-in streaming capabilities to ingest data from cloud data sources into SQL tables in near-real-time. And you can integrate AI with SQL by using machine learning models to score data using the T-SQL PREDICT function.
Another core component of Microsoft Synapse Analytics is Apache Spark for Azure Synapse, which deeply and seamlessly integrates Apache Spark–the most popular open-source big data engine used for data preparation, data engineering, ETL, and machine learning. Apache Spark for Azure Synapse offers several advantages over other Spark offerings:
- Simplified resource model that frees you from having to worry about managing clusters. You can easily create Spark pools with predefined sizes or customize them according to your needs. The pools start up fast and scale up or down automatically based on demand.
- Built-in support for Linux Foundation Delta Lake, a storage layer that provides ACID transactions, schema enforcement, and time travel on top of your data lake. Delta Lake enables you to create reliable and performant data pipelines that handle complex data types and schema evolution.
- Built-in support for .NET for Spark, allowing you to reuse your C# expertise and existing .NET code within a Spark application. You can also use other languages, such as Python, Scala, or R, to write your Spark code.
- ML models with SparkML algorithms and AzureML integration for Apache Spark 3.1, enabling you to train, deploy, and monitor machine learning models at scale using the best of both worlds.
One of the key benefits of Microsoft Synapse Analytics is that it removes the traditional technology barriers between using SQL and Spark together. You can seamlessly mix and match based on your needs and expertise. And you can do all that on top of your existing data lake without any data movement or duplication.
Synapse Analytics supports Azure Data Lake Storage Gen2 as the primary storage layer for both SQL and Spark workloads. You can query both relational and non-relational data using the language of your choice. For example, you can use SQL to query Parquet files stored in the data lake or use Spark to query SQL tables defined on the data lake.
You can also use Synapse Link to automatically move data from both operational databases and business applications into your data lake without time-consuming ETL processes. This way, you can go from after-the-fact analysis to near-real-time insights by eliminating barriers between your transactional and analytical systems.
Microsoft Synapse Analytics also contains the same Data Integration engine and experiences as Azure Data Factory, allowing you to create rich at-scale ETL pipelines without leaving Azure Synapse Analytics Studio. The Studio is a web-based tool that provides a unified experience for developing end-to-end analytics solutions.
With Data Integration, you can:
- Ingest data from 90+ data sources, including on-premises, cloud-based, structured, unstructured, or semi-structured sources.
- Transform data using code-free ETL with Data Flow activities or code-based ETL with Spark, SQL, or Python activities.
- Orchestrate data pipelines with triggers, dependencies, parameters, variables, and loops.
- Monitor and troubleshoot pipeline runs with rich dashboards and alerts.
Microsoft Synapse Analytics also provides a new component called Data Explorer, which is optimized for efficient log analytics using powerful indexing technology. Data Explorer allows you to unlock insights from log and telemetry data, such as application logs, weblogs, IoT data, or network data.
With Data Explorer, you can:
- Ingest data from various sources using native connectors or REST APIs.
- Query data using a simple and expressive query language that supports aggregation joins filters and more.
- Visualize data using charts, tables, maps, and more.
- Analyze data using built-in functions for time series analysis, anomaly detection, geospatial analysis, and more.
Microsoft Synapse Analytics is a game-changer for data analytics. It enables you to access, analyze, and act on all your data with a single service that offers industry-leading SQL, industry-standard Apache Spark, seamless integration with your data lake, built-in data integration, and a new component for log analytics. Whether you are a data engineer, a data scientist, a business analyst, or a database administrator, you can use Microsoft Synapse Analytics to accelerate time to insight across your data warehouses and big data systems. And you can do that with a unified experience that simplifies development and management.