Three New Serverless Analytics Offerings from AWS
CIO Review Europe | Tuesday, August 09, 2022
Small percentage of customers with highly variable or intermittent workloads would prefer to have AWS handle the underlying infrastructure by automatically adding or withdrawing resources in response to application demand
FREMONT, CA: Customers of AWS can NOW select from a wide range of services designed specifically for analytics to get the most out of the data held by their companies. These services include Amazon EMR for processing large amounts of unstructured data using open-source big data frameworks like Apache Spark and Hive, Amazon MSK for ingesting real-time data streams, and Amazon Redshift for data warehousing. While many customers value the fine-grained control these services provide, a small percentage of customers who have workloads that are highly variable or intermittent would prefer to have AWS manage the underlying infrastructure by automatically adding or removing resources in response to application demand.
Serverless Big Data Analytics with Amazon EMR
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Tens of thousands of users use Amazon EMR to perform machine learning applications, interactive SQL queries, and large-scale distributed data processing operations using open-source frameworks like Apache Spark and Hive. Most big data frameworks are supported by Amazon EMR, allowing users to run big data applications and petabyte-scale data analyses faster and for less money than on-premises alternatives. Customers only need to indicate the framework they want to use with Amazon EMR Serverless, and as workload demands vary, Amazon EMR Serverless automatically provisions, manages, and scales the necessary computing and memory resources. The Amazon EMR application programming interface (API), the AWS Command Line Interface (AWS CLI), or an integrated development environment (IDE) with Amazon EMR Studio are the only ways for customers to get started with Amazon EMR Serverless. They next need to choose an open-source framework and submit their jobs. Customers using Amazon EMR in the United States East (North Carolina), United States West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland) can now have general access to Amazon EMR Serverless. Additional AWS Regions will be added shortly.
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Serverless Data Streaming with Amazon MSK
Enterprises today are increasingly using Apache Kafka to capture and analyse real-time data streams from Internet of Things (IoT) devices, website clickstreams, database logs, and many more sources where dynamic data is continuously generated. With the addition of this new serverless option, Amazon MSK Serverless now automatically creates, maintains, and scales clusters, relieving clients of the burden of capacity planning and unpredictable streaming workloads. Customers can utilise Apache Kafka to stream data using new or existing clients by setting up a cluster in the Amazon MSK dashboard, creating a private and secure Apache Kafka endpoint, and utilising Amazon MSK Serverless. Customers using Amazon MSK in the United States East (Ohio), United States East (North Virginia), United States West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm) can now have general access to Amazon MSK Serverless. Additional AWS Regions will be added shortly.
Serverless Data Warehouse with Amazon Redshift
More than two exabytes of data are processed daily by tens of thousands of customers using Amazon Redshift. Amazon Redshift outperforms comparable enterprise cloud data warehouses in terms of pricing performance, allowing users to perform data analytics more quickly and affordably. With Amazon Redshift Serverless, it is now even easier to gain insights from data without having to manage data warehouse infrastructure. Customers that presently manage their own Amazon Redshift clusters can switch to the new serverless option by using the Amazon Redshift UI or API without changing their applications. Amazon Redshift Serverless is now generally available to customers running Amazon Redshift in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Seoul), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), and Europe (Stockholm), with additional AWS Regions to follow.
Data engineering latency is significantly decreased by using Amazon Redshift Serverless, which also has the effect of speeding up development. Able to clear the backlog for data engineering to the implementation of Amazon Redshift Serverless, which now frees up more of our time for gleaning insights from the data.
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