New for Amazon Redshift – Simplify Knowledge Ingestion and Make Your Knowledge Warehouse Extra Safe and Dependable


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Once we speak with prospects, we hear that they need to have the ability to harness insights from knowledge as a way to make well timed, impactful, and actionable enterprise selections. A typical sample with data-driven organizations is that they’ve many different knowledge sources they should ingest into their analytics methods. This requires them to construct handbook knowledge pipelines spanning throughout their operational databases, knowledge lakes, streaming knowledge, and knowledge inside their warehouse. As a consequence of this complicated setup, it could possibly take knowledge engineers weeks and even months to construct knowledge ingestion pipelines. These knowledge pipelines are expensive, and the delays can result in missed enterprise alternatives. Moreover, knowledge warehouses are more and more turning into mission important methods that require excessive availability, reliability, and safety.

Amazon Redshift is a totally managed petabyte-scale knowledge warehouse utilized by tens of 1000’s of consumers to simply, shortly, securely, and cost-effectively analyze all their knowledge at any scale. This yr at re:Invent, Amazon Redshift has introduced a lot of options that will help you simplify knowledge ingestion and get to insights simply and shortly, inside a safe, dependable setting.

On this weblog, I introduce a few of these new options that fit into two fundamental classes:

  • Simplify knowledge ingestion
    • Amazon Redshift now helps auto-copy from Amazon S3 (obtainable in preview). With this new functionality, Amazon Redshift mechanically hundreds the recordsdata that arrive in an Amazon Easy Storage Service (Amazon S3) location that you just specify into your knowledge warehouse. The recordsdata can use any of the codecs supported by the Amazon Redshift copy command, equivalent to CSV, JSON, Parquet, and Avro. On this means, you don’t must manually or repeatedly run copy procedures. Amazon Redshift automates file ingestion and takes care of data-loading steps underneath the hood.
    • With Amazon Aurora zero-ETL integration with Amazon Redshift, you need to use Amazon Redshift for close to real-time analytics and machine studying on petabytes of transactional knowledge saved on Amazon Aurora MySQL databases (obtainable in restricted preview). With this functionality, you possibly can select the Amazon Aurora databases containing the information you need to analyze with Amazon Redshift. Knowledge is then replicated into your knowledge warehouse inside seconds after transactional knowledge is written into Amazon Aurora, eliminating the necessity to construct and preserve complicated knowledge pipelines. You may replicate knowledge from a number of Amazon Aurora databases into the identical Amazon Redshift occasion to run analytics throughout a number of purposes. With close to real-time entry to transactional knowledge, you possibly can leverage Amazon Redshift’s analytics and capabilities, equivalent to built-in machine studying (ML), materialized views, knowledge sharing, and federated entry to a number of knowledge shops and knowledge lakes, to derive insights from transactional and different knowledge.
    • With the overall availability of Amazon Redshift Streaming Ingestion, now you can natively ingest lots of of megabytes of information per second from Amazon Kinesis Knowledge Streams and Amazon MSK into an Amazon Redshift materialized view and question it in seconds. Study extra in this put up.
  • Make your knowledge warehouse safer and dependable
    • Now you can enhance the supply of your knowledge warehouse by selecting a number of Availability Zone (AZ) deployments. Multi-AZ deployments on your Amazon Redshift clusters can be found in preview and scale back restoration instances to seconds via computerized restoration. On this means, you possibly can construct options which can be extra compliant with the suggestions of the Reliability Pillar of the AWS Properly-Architected Framework.
    • With dynamic knowledge masking (obtainable in preview), you possibly can defend delicate data saved in your knowledge warehouse and make sure that solely the related knowledge is accessible by customers primarily based on their roles. You may restrict how a lot identifiable knowledge is seen to customers utilizing a number of ranges of insurance policies so totally different customers and teams can have totally different ranges of information entry with out having to create a number of copies of information. Dynamic knowledge masking enhances different granular entry management capabilities in Amazon Redshift together with row-level and column-level safety and role-based entry controls. On this means, Dynamic Knowledge Masking helps you meet necessities for GDPR, CCPA, and different privateness laws.
    • Amazon Redshift now helps central entry controls for knowledge sharing with AWS Lake Formation (obtainable in public preview). Now you can use Lake Formation to simplify governance of information shared from Amazon Redshift and centrally handle granular entry throughout all data-sharing customers.

There have been different fascinating information for Amazon Redshift at re:Invent you might need already heard about:

  • The overall availability of Amazon Redshift integration for Apache Spark makes it straightforward to construct and run Spark purposes on Amazon Redshift and Redshift Serverless, opening up the information warehouse for a broader set of AWS analytics and machine studying options.
  • AWS Backup now helps Amazon Redshift. AWS Backup lets you outline a central backup coverage to handle knowledge safety of your purposes and can even defend your Amazon Redshift clusters. On this means, you could have a constant expertise when managing knowledge safety throughout all supported providers.

Availability and Pricing
Multi-AZ deployments, central entry management for knowledge sharing with AWS Lake Formation, auto-copy from Amazon S3, and dynamic knowledge masking can be found in preview in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), Europe (Eire), and Europe (Stockholm).

There isn’t a further price for utilizing auto-copy from Amazon S3 and close to real-time analytics on transactional knowledge. There isn’t a additional cost for dynamic knowledge masking and central entry management for knowledge sharing. For extra data, see Amazon Redshift pricing.

These new capabilities take you one step additional in analyzing all of your knowledge throughout knowledge sources with easy knowledge ingestion capabilities, whereas enhancing the safety and reliability of your knowledge warehouse.

Danilo



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