AWS Touts Five New Database and Analytics Capabilities
December 2, 2022
Web Services introduced five new capabilities across its database and analytics
portfolios that make it faster and easier for customers to manage and analyze
data at petabyte scale. These new capabilities for Amazon DocumentDB (with
MongoDB compatibility), Amazon OpenSearch Service, and Amazon Athena make it
easier for customers to run high-performance database and analytics workloads at
scale. Additionally, AWS announced a new capability for AWS Glue to
automatically manage data quality across data lakes and data pipelines. Finally,
Amazon Redshift now offers support for a high availability configuration across
multiple AWS Availability Zones (AZs). Today’s announcement helps customers get
the most out of their data on AWS by empowering them to access the right tools
for their data workloads, operate at scale, and increase availability.
Amazon DocumentDB Elastic Clusters power petabyte-scale applications with millions of writes per second: Tens of thousands of customers use Amazon DocumentDB to run their document workloads because it is fast, scalable, highly available, and fully managed. While each Amazon DocumentDB node can scale up to 64 tebibytes of data and support millions of read requests per second, a subset of customers with extremely demanding workloads needs the ability to scale beyond these limits to support millions of writes per second and store petabytes of data. Previously, these customers had to manually distribute data and manage capacity across multiple Amazon DocumentDB nodes. Amazon DocumentDB Elastic Clusters allow customers to scale beyond the limits of a single database node within minutes, supporting millions of reads and writes per second and storing up to 2 petabytes of data. As workload demands increase, Amazon DocumentDB Elastic Clusters take advantage of a distributed storage system to automatically divide large datasets across multiple nodes. This removes the need for customers to write custom code to distribute datasets and manually manage capacity across nodes. The underlying infrastructure is managed automatically, so customers can easily scale capacity based on their needs without needing to provision, scale, or manage database clusters.
Amazon OpenSearch Serverless automatically scales search and analytics workloads: To power use cases like website search and real-time application monitoring, tens of thousands of customers use Amazon OpenSearch Service. Many of these workloads are prone to sudden, intermittent spikes in usage, making capacity planning difficult. Amazon OpenSearch Serverless automatically provisions, configures, and scales OpenSearch infrastructure to deliver fast data ingestion and millisecond query responses, even for unpredictable and intermittent workloads. With Amazon OpenSearch Serverless, data ingestion and search resources scale independently, allowing these operations to run concurrently without any performance impact. Customers using Amazon OpenSearch Serverless get access to serverless benefits (e.g., automatic provisioning, on-demand scaling, and pay-for-use pricing), along with Amazon OpenSearch Service features, such as built-in data visualizations, that help them understand log data, identify anomalies, and see search relevance rankings.
Amazon Athena for Apache Spark accelerates startup of interactive analytics to less than one second: Customers use Amazon Athena, a serverless interactive query service, because it is one of the easiest and fastest ways to query petabytes of data in Amazon Simple Storage Service (Amazon S3) using a standard SQL interface. Many customers are looking for that same ease of use when it comes to using Apache Spark, an open-source processing framework for big data workloads that supports popular language frameworks (i.e., Java, Scala, Python, and R). While developers enjoy the fast query speed and ease of use of Apache Spark, they do not want to invest time setting up, managing, and scaling their own Apache Spark infrastructure each time they want to run a query. Now, with Amazon Athena for Apache Spark, customers do not have to provision, configure, and scale resources themselves. Interactive Apache Spark applications start in less than one second and execute faster than open source using AWS’s optimized Spark runtime. Because Amazon Athena is integrated with other AWS services, customers can query data from multiple sources, chain calculations together for complex analyses, and visualize the results. Amazon Athena for Apache Spark automatically determines the resources required based on application demand and scales as needed, so customers only pay for the queries they run.
AWS Glue Data Quality automatically monitors and manages data freshness, accuracy, and integrity: Hundreds of thousands of customers use AWS Glue to build and manage modern data pipelines quickly, easily, and cost-effectively. Organizations need to monitor the data quality—a measure of the freshness, accuracy, and integrity of data—of the information in their data lakes and data pipelines to ensure it is high quality before using it to power their analysis or machine learning applications. But effective data-quality management is a time-consuming and complex process, requiring data engineers to spend days gathering detailed statistics on their data, manually identifying data-quality rules based on those statistics and applying them across thousands of datasets and data pipelines. Once these rules are implemented, data engineers must continuously monitor for errors or changes in the data to adjust rules accordingly. AWS Glue Data Quality automatically measures, monitors, and manages the data quality of Amazon S3 data lakes and AWS Glue data pipelines, reducing the time for data analysis and rule identification from days to hours. AWS Glue Data Quality computes statistics for customer datasets (e.g., minimums, maximums, histograms, and correlations) and uses them to automatically recommend rules to ensure data freshness, accuracy, and integrity. Customers can schedule AWS Glue Data Quality to run periodically as data changes, automatically analyzing the data and proposing changes to quality rules to ensure relevance. Data engineers can configure actions to alert users or stop data pipelines when quality issues occur, without having to write code.
Amazon Redshift now supports multi-AZ deployments: Tens of thousands of AWS customers collectively process exabytes of data with Amazon Redshift every day. To support these customers’ mission-critical workloads, Amazon Redshift offers capabilities that increase availability and reliability, such as automatic backups and the ability to relocate a cluster to another AZ in minutes. Many databases today use a primary-standby replication mode to support high availability where a single database serves live traffic, and standby copies replicate data from the live version in case they need to replace it. Building on these capabilities, Amazon Redshift now offers a high-availability configuration to enable fast recovery while minimizing the risk of data loss. With Amazon Redshift Multi-AZ, clusters are deployed across multiple AZs and use all the resources to process read and write queries, eliminating the need for under-utilized standby copies and maximizing price performance for customers. Since a multi-AZ data warehouse is still managed as a single Amazon Redshift data warehouse with one endpoint, no application changes are required to maintain business continuity.
Rippling brings together payroll, benefits, HR, IT, and more so their customers
can manage employee operations in one place. “As our business continues to grow,
we need the ability to scale beyond the limits of a single document database
node,” said Nitin Aggarwal, data engineering lead at Rippling. “Amazon
DocumentDB Elastic Clusters will help us solve this challenge by enabling us to
quickly and easily scale to support millions of reads and writes per second and
store petabytes of data. We are excited to explore Amazon DocumentDB Elastic
Clusters as our business and customer demands grow.”