Harness analytical and predictive power with Azure Synapse Analytics

Publié le 7 décembre, 2020

Corporate Vice President, Azure Data

Since its preview announcement, we’ve witnessed incredible excitement and adoption of Azure Synapse from our customers and partners. We want to sincerely thank everyone that provided feedback and are now helping us bring the power of limitless analytics to all.

As announced last week, Azure Synapse is now generally available.

Unified experience

Azure Synapse brings together data integration, enterprise data warehousing, and big data analytics—at cloud scale. The unification of these workloads enables organizations to massively reduce their end-to-end development time and accelerate time to insight. It now also provides both no-code and code-first experiences for critical tasks such as data ingestion, preparation, and transformation.

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With this release, the management and monitoring of your analytics system becomes significantly easier. With one click, teams can secure their entire analytics system and prevent data exfiltration by simply selecting the managed virtual network feature when creating a Synapse workspace. This gives valuable time back to teams hired to discover insights—rather than investing considerable time securing connections between services, building firewalls, or managing subnets.

Unified analytics

Over the past years, we’ve set out to rearchitect and create the next generation of query processing engine and data management to meet the needs of the modern, high-scale data workloads. The result is the new, cloud-native, distributed SQL engine that powers Azure Synapse. It can scale from queries on a handful of cores to thousands of nodes—all depending on your workload needs.

Azure Synapse enables a level of productivity and collaboration among data professionals that previously wasn’t possible by deeply integrating Apache Spark and its new SQL engine. And it supports popular languages that developers prefer including T-SQL, Python, Scala, and Java.

The new flexible service model for query processing allows data teams to use both serverless and dedicated options. Organizations can now choose the most cost-effective option for each use case—enjoying the advantages of a data lake for quick data exploration with pay-per-query pricing and/or a dedicated data warehouse for more predictable and mission-critical workloads.

"Azure Synapse was a game changer as it allowed us to deliver a big data solution at the scale required and the integration with Power BI empowered our analysts to do their work efficiently."

Adhi Utama, Data Solution Architect, Grab

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Unified data teams

Azure Synapse is also deeply integrated with Microsoft Power BI and Azure Machine Learning.

With Power BI directly integrated in the Synapse Studio, BI professionals can work in the same service that houses data pipelines, data lakes, and data warehouses—reducing the time it takes to access clean and secure data for dashboards. And for lightning fast query performance, the new Power BI performance accelerator for Azure Synapse automates the creation and optimization of materialized views with just a few clicks.

For predictive analytics, teams can deploy machine learning models from the Azure Machine Learning model registry directly to Azure Synapse using a simple, guided user experience—no data movement required. The in-engine ML scoring can generate millions of predictions in seconds all while maintaining full data security as data doesn’t leave the platform. And with AutoML, all data teams—even organizations without highly trained data scientists—can automatically apply machine learning models to their data and generate predictive insights.

The code-free and programmatic integration—including CI/CD support with Git integration—enables seamless version control, collaboration, and code management between data engineers, data scientists, and BI professionals, allowing them to be highly productive across a variety of use cases.

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Learn more about Azure Synapse Analytics latest innovations and features.

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