![]() We then talk about autonomics that make Amazon Redshift easy to use. We present storage and compute scalability discussing innovations such as Managed Storage, Concurrency Scaling, Data Sharing and AQUA. We take a peek under the hood of Amazon Redshift and give a detailed view of its architecture, explaining how Amazon Redshift maintains its industry-leading performance. In the last few years, the use cases for Amazon Redshift have evolved and in response, Amazon Redshift has delivered and continues to deliver a series of innovations that delight customers. Today, tens of thousands of customers use Redshift in AWS’s global infrastructure to process exabytes of data daily. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. This launch was a significant leap from the traditional on-premise data warehousing solutions which were expensive, not elastic, and required significant expertise to tune and operate. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. Glue Elastic Views is a good start.In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully managed, petabyte-scale enterprise-grade cloud data warehouse. But instead, we'll leave with this: AWS's challenge is to build on the synergies that could bind its diverse services together. We're tempted to use the metaphor that by running enough database services, AWS is hoping some of them would stick (together). With the original connector, this would have been a task that would have required significant manual (and potentially error-prone) coding.įor AWS, variety has been the spice of life, stretching from its hundreds of services and permutations of EC2 compute and storage infrastructure to the variety of analytic, machine learning, container development, and database services, among others. ![]() While you could previously perform this task with the dedicated DynamoDB-Elasticsearch connector, the advantage of Elastic Views is that the processes are far simpler and, as part of the service, changes are automatically replicated. ![]() With Glue Elastic Views, you could stream real-time changes to product catalogs maintained in DynamoDB into Elasticsearch, which presents a more intuitive environment for customers to find product listings. Consider Athena as the exploratory query capability so, when you decide to operationalize a query, you'll likely migrate and transform the data to run in Redshift. The serverless Athena was intended for ad hoc query because, no matter what performance optimizations are applied, querying cloud storage will never be as efficient compared to having a database that is either indexed or laid out as a column store that uses compression and filters to optimize performance. It uses Presto to run massively parallel queries in S3. In turn, you can also stream updates from DynamoDB into Amazon Elasticsearch Service through a plugin for Logstash.Īnd, if you just want to query data in your data lake without setting up a database, there's Amazon Athena. You can use a customized version of Hive to run operations from EMR on data inside DynamoDB or load data from DynamoDB into EMR. There have been interactions between some of the other data services, including a bidirectional open source connector between Amazon EMR and DynamoDB.
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