Azure Synapse Analytics is the next incarnation of Azure SQL Data Warehouse from Microsoft. Like SQL Data Warehouse, Azure Synapse Analytics is a cloud-based, relational data warehouse system with MPP (massively parallel processing), virtually unlimited scaling capacity, and the power to process and store petabytes of data. The difference is that Azure Synapse Analytics has added business intelligence, machine learning, and other advanced features to its data warehousing profile. Microsoft has also boosted the platform's capacity to ingest, transform, manage, and process larger volumes of relational and non-relational data faster and more efficiently.
Amazon Kinesis is a powerful analytics solution that overcomes the batch-processing challenges of Hadoop — and similar solutions — which don't allow real-time precision in decision-making because they can't rapidly process high volumes of streaming data. With its ability to process hundreds of terabytes of streaming data per hour, Kinesis allows you to develop apps that rely on real-time data to fuel AI analytics, machine learning insights, and other applications. Kineses enables instant responses by eliminating the delay associated with batch processing.
Bring all your Amazon Kinesis data to Amazon Redshift
Load your Amazon Kinesis data to Google BigQuery
ETL all your Amazon Kinesis data to Snowflake
Move your Amazon Kinesis data to MySQL
In addition to serving as a powerful, scalable, cloud-based data warehouse, Azure Synapse adds advanced business intelligence and machine learning data analytics to its list of services.
Whether you need a non-relational data lake, relational data warehouse, or a combination of both, Azure Synapse integrates the two and lets you query the data in SQL while serving as a unified, end-to-end analytics solution. Within a single workspace, Azure Synapse allows you to achieve your data warehousing, data preparation, data management, AI, machine learning, and business intelligence goals. Access all of your data and create stunning dashboards with Power BI via a single interface.
With Azure Synapse Link, cloud-native HTAP implementation allows you to integrate Azure Synapse with Azure databases to extract near real-time insights from operational databases. This allows Azure Synapse to extract machine learning and business intelligence analyses from live data without disrupting the transactional performance of operational systems.
Azure Synapse allows your team to work with their preferred language. Whether it's T-SQL, Scala, Spark SQL, Python, or .Net, Azure Synapse is compatible with your language of choice while using either provisioned or serverless processing resources.
Azure Synapse lets you query data with provisioned or serverless on-demand computational resources.
Azure Synapse natively connects with a wide range of Azure and Microsoft services. The platform includes native connectors for Azure Machine Learning, Azure Data Lake, Azure Blob Storage, Azure Active Directory for authentication, and Microsoft Power BI for visualizing data. Azure Synapse also integrates its machine learning and business intelligence tools with Open Data Initiative tools and services. Led by Microsoft, Adobe, and SAP Software solutions, the Open Data Initiative seeks to boost the connectivity and interoperability of cloud-based SaaS services. Open Data Initiative compatible services include the Microsoft Office 365 suite, the Microsoft Dynamics 365 suite, and more.
Microsoft Azure Synapse makes it easy to optimize your query performance through limitless concurrency, workload isolation, workload management.
Azure Synapse offers cutting-edge security and privacy that includes dynamic, real-time data masking, always-on data encryption, automated threat detection, authentication through single-sign-on and Azure Active Directory. The platform also includes access control features like column-level security and native row-level security for additional security and privacy within your team.
In terms of compliance, Azure offers more certifications than any cloud provider to ensure that your data collection and data use practices comply with industry-specific, regional, state, and national compliance standards.
Amazon Kineses Video Streams allow you to safely ingest streaming video data from millions of linked devices into AWS for machine learning, analytical, and other processing purposes. The platform then encrypts, stores, and indexes the video data so you can access video with simple APIs, play live video streams, and offer on-demand playback. When incorporating this technology with Amazon Rekognition Video, TensorFlow, ApacheMxNet, and OpenCV, Amazon Kineses Video Streams makes it possible to build video analytics and computer vision processes into your applications.
By capturing, processing, and storing data streams, Amazon Kinesis offers a real-time data streaming solution to ingest large amounts of information from hundreds of thousands — even millions — of sources at the gigabytes-per-second scale. The massive scalability of this solution lets you capture and produce immediate analytics on data pertaining to financial transactions, database event streams, location tracking data, clickstreams, social media activity, and more. Since the availability of streaming data happens in milliseconds, the platform enables real-time analytics of this information for instant detection of anomalies, dynamic price adjustments, precise dashboard metrics, and more.
Amazon Kinesis Data Firehose provides a simple and durable way to pull your streaming data into data warehouses, data lakes, and analytics solutions. Due to its compatibility with Splunk, Amazon Redshift, Amazon S3, and Amazon Elasticsearch Service, Kinesis Data Firehose empowers real-time data analytics for the dashboarding and BI tools you've come to trust. Fully managed and automatically scaling, you can use Firehose to encrypt, batch, transform, and compress your information before ingestion to boost security and save on disk space.
Amazon Kinesis Data Analytics helps users without programming knowledge to analyze data streams with SQL or Java. For team members who know SQL, an SQL editor and templates are available for creating streaming applications or querying streaming data. Meanwhile, those with Java knowledge can develop more nuanced streaming applications that perform real-time data transformations and analytical processes.