Azure Synapse Analytics: Taking Cloud Data Warehousing to the next level

In the last year, we witnessed the initial stage of analytics revolution, where companies have started using their growing data for business intelligence. Data is spread across data pipelines, big data lakes, data warehousing and it becomes difficult to transform that data into actionable insights.

Recently, Microsoft has announced Azure Synapse Analytics, a rebrand of its Azure SQL Data Warehouse. This Azure service brings Big Data analytics and data warehousing together to create large scale end-to-end analytics. It’s fascinating to note that Analytics in Azure costs 94% less and 14 times faster than other cloud providers.

Here’s why Azure Synapse Analytics is taking the cloud data warehousing to the next level.

Brings Top-notch Analytics experience in real-time

With synapse Analytics, you will enjoy unique experience for big data, data warehousing, management, artificial intelligence tasks. Azure Analytics consulting experts can use a code-free visual environment to manage data pipelines quickly. Synapse can also be used to create PoCs or build Power BI dashboards in lesser time.

Delivers Limitless Scalability

Don’t worry to derive insights from your data across multiple data warehouses and big data analytics system. Azure Synapse analytics speeds-up the approach. And, Data Scientists can bring both relational and non-relational data together in data lake using simple SQL query.

Offers Advanced Privacy and Security

Microsoft Azure offers most advanced privacy and security features in the market. With built-in features like data encryption and automated threat detection, businesses can ensure safe & protection of sensitive data; by utilizing Azure synapse analytics for their critical implementations.

Provides powerful insights quickly with integration to ML & Power BI

Apply intelligence capabilities to your applications smoothly and quickly using Synapse. It allows to significantly expand the discovery of data insights to applying ML models. This helps reduce development time to embed analytics to your BI & ML projects effortlessly.