Why Azure Synapse Analytics?
Enhance your organization's data management with Azure Synapse Analytics (formerly known as Azure SQL Data Warehouse) to combine the power of Data warehousing & Big Data Analytics under one single platform. With Azure Synapse, companies can leverage the power of Massively Parallel Processing to manage enormous loads.
If you are looking to build a new cloud datawarehouse or migrate an existing warehouse or bring Big Data Analytics to your warehouse by building a single source of truth, then we have your back. As Microsoft Gold Partners, we at Polestar Solutions have helped many organizations manage their data & analytics with microsoft azure.
Share your data effortlessly & expand the discovery of insights & ML application
Bring relational and non-relational data from multiple warehouses with simple SQL query
Reduce development time for end-to-end analytics projects with a unified data base
With quick DWH deployement eliminate data barriers with advanced security and privacy
Ingest data from native connectors & build ETL processes or use your preferred language
Integrate with Power BI, Azure ML, Azure congitive services, and more for deep analytics integration
Our Azure Synapse services include Azure Synapse Migration, DWH integrations, and consulting services to manage the data collected and turn it into valuable insights for your business. Some of the applications of Synapse includes:
By managing both Data Lakes and Data Warehouse, Azure Synapse Analytics lets the users process large workloads seamlessly. You can also leverage Data Integration solutions of Qlik, Alteryx, SaS, and more to create your data warehouse solution
You can use powerful tools for data orchestration & data science projects with Azure Synapse & Azure Data Factory with Apache Spark, Serverless SQL Pools, or train Auto M data with Spark MLlib, Azure Machine Learning, Python, Synapse Spark Pools, and more
Azure Synapse provides a secure cloud based analytics ecosystem with AI, ML, IoT, and BI in a single space. You can analyze your business performance by analyzing bottlenecks, cause-effect, forecasting and prediction, what-if analysis, and so much more
Azure Synapse Analytics brings together datawarehousing, big data analytics, and data integration under one roof allowing users to have limitless analytics. It is presented as the next generation of Azure SQL Data Warehouse. It provides the freedom to handle huge amounts of information and combine with business intelligence and data prediction needs.
While Azure Synapse Analytics is a limitless analytics service, combining Analytics and Data warehouse, Azure Databricks is a Apache-Spark based analytics platform designed to streamline workflows & create an interactive workspace. Though both of have some overlap, at the end of the day: Databricks is an Managed Apache Spark services and Azure Synapse is a SQL Datawarehouse. One other difference is that Databricks has Git Integration whereas Synapse doesn't.
Azure Synapse Analytics is the rebranded version of Azure SQL Datawarehouse, to further simplify the use and set-up of modern data platforms. Some of the additions done are: on-demand queries, SQL Data Analytics on Data Lake, manage enterprise grade warehousing, and more.
Azure Data Factory is a hybrid data integration service that simplifies ETL by managing data from both on-premises and cloud like a SSIS. Some of the key differences between Azure Synapse Analytics and Azure Data Factory are that Synapse has Spark notebooks, Spark job definitions and SQL pool stored procedure activities which are not available in ADF. Also, Synapse does not have the SSIS package execution activity and Git Hub integration
Azure Synapse Analytics can be used for SQL Analyses and Data warehousing, Ad-hoc Data Lake Discovery, Descriptive and Diagnostic Queries using T-SQL, for Reporting and Analytics, etc.
Azure Synapse Analytics provides choice of language between python, scala, sql, etc., provides synaptic pathway for azure, provides support for Data Lake, helps with managing and monitoring resources, and exploration of data lakes
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