Amazon DocumentDB Elastic Clusters scales
customers’ document workloads to support millions of writes per
second and store petabytes of data
Amazon OpenSearch Serverless helps customers
run search and analytics workloads without having to configure,
scale, or manage underlying infrastructure
Amazon Athena for Apache Spark enables
customers to get started with interactive analytics using Apache
Spark in less than a second, instead of minutes
AWS Glue Data Quality cuts time for data
analysis and rule identification from days to hours by
automatically measuring, monitoring, and managing data quality in
data lakes and across data pipelines
Amazon Redshift now supports a high
availability configuration across multiple AWS Availability
Zones
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced 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. To learn more about unlocking the
value of data using AWS, visit aws.amazon.com/data.
“Data is inherently dynamic, and harnessing it to its full
potential requires an end-to-end data strategy that can scale with
a customer’s needs and accommodate all types of use cases—both now
and in the future,” said Swami Sivasubramanian, vice president of
Databases, Analytics, and Machine Learning at AWS. “To help
customers make the most of their growing volume and variety of
data, we are committed to offering the broadest and deepest set of
database and analytics services. The new capabilities announced
today build on this by making it even easier for customers to
query, manage, and scale their data to make faster, data-driven
decisions.”
Organizations today create and store petabytes—or even
exabytes—of data from a growing number of sources (e.g., digital
media, online transactions, and connected devices). To maximize the
value of this data, customers need an end-to-end data strategy that
provides access to the right tools for all data workloads and
applications, along with the ability to perform reliably at scale
as the volume and velocity of data increase. To support customers
designing their own end-to-end data strategies, AWS offers the
industry’s most comprehensive set of data services and solutions.
This includes fully managed databases optimized for customers’ most
important use cases, such as Amazon Aurora for relational databases
and Amazon DocumentDB for document databases. It also includes a
broad range of analytics services to help customers gain valuable
insights from their data, including Amazon OpenSearch Service for
search and analytics workloads (e.g., real-time application
monitoring, log analytics, and website search), Amazon Athena for
interactive analytics, AWS Glue for data integration, and Amazon
Redshift for data warehousing. Today's announcement builds on these
services with advanced capabilities.
- 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. To learn more about Amazon DocumentDB Elastic Clusters,
visit aws.amazon.com/documentdb/features/#elastic_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. To learn more about
Amazon OpenSearch Serverless, visit
aws.amazon.com/opensearch-service/features/serverless.
- 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. To get started with
Amazon Athena for Apache Spark, visit
aws.amazon.com/athena/spark.
- 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.
To learn more about AWS Glue Data Quality, visit
aws.amazon.com/glue/features/data-quality.
- 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. To learn more about
Amazon Redshift Multi-AZ, visit
aws.amazon.com/redshift/reliability.
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.”
riskCanvas, a software as a service (SaaS) product offering from
Genpact, is a financial crime compliance solution that leverages
cutting-edge big data, automation, and machine learning
technologies to deliver compliance, efficiency, and automation to
its clients. “riskCanvas’ Entity-Centric Monitoring incorporates
transaction monitoring, external enrichment, watchlist screening,
and negative news to automatically assess risk and alert high-risk
customers only as the true risk of a customer exceeds predefined
thresholds, substantially reducing the effort to meet regulatory
compliance requirements. This requires significant and varied
analytic processing that often experiences spiky and unpredictable
data load,” said Ryan Skousen, chief technology officer at
riskCanvas and vice president of technology at Genpact Financial
Crimes. “We are excited about Amazon OpenSearch Serverless, which
will scale automatically to meet the data ingestion and analytic
processing requirements of our workloads, and then scale back down
as demand decreases to reduce costs drastically—all with no
reengineering or maintenance impact.”
FINRA, a regulator for securities firms doing business with the
public in the US, regulates trading in equities, bonds, and
options. “At FINRA, we develop applications on Amazon Athena to
enable analysts and business partners to securely query financial
trading data with multiple terabytes in daily updates,” said
Ratnakar Korem, senior director at FINRA. “We are excited about
Amazon Athena for Apache Spark, which will bring the speed and ease
of use we enjoy with Amazon Athena to our on-demand and batch
analytics. This serverless feature will enable FINRA to conduct
analytics against Big Data without the overhead of explicitly
defining compute resources and tuning Apache Spark performance.
This ultimately helps regulatory users and data analysts quickly
respond to changing market dynamics and share results with others
in a cost-effective and timely manner.”
United Airlines operates a large domestic and international
route network, spanning cities large and small across the US and
all six inhabited continents. “United Airlines is building hundreds
of data- and analytics-driven tools for our customers and
employees, which makes managing and maintaining data quality
critical to our operations,” said Sarang Bapat, director of Data
Engineering at United Airlines. “We are excited about AWS Glue Data
Quality, which will enable us to automatically identify, analyze,
and act on data-quality issues in a matter of minutes. This will
help us make informed, timely, and accurate decisions and save
countless hours in manually identifying and fixing all data
issues.”
Janssen Pharmaceuticals, a subsidiary of Johnson & Johnson,
researches and manufactures medicines with a focus on the changing
needs of patients and the healthcare industry. “Janssen
Pharmaceutical uses Amazon Redshift to enable critical insights
that drive important business decisions for our data scientists,
data stewards, business users, and external stakeholders,” said
Shyam Mohapatra, director of Information Technology at Janssen
Pharmaceutical Companies of Johnson & Johnson. “With Amazon
Redshift Multi-AZ, we can be confident that our data warehouse will
be available without any disruptions that might delay or impact our
ability to make important business decisions.”
About Amazon Web Services
For over 15 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud offering. AWS has been
continually expanding its services to support virtually any cloud
workload, and it now has more than 200 fully featured services for
compute, storage, databases, networking, analytics, machine
learning and artificial intelligence (AI), Internet of Things
(IoT), mobile, security, hybrid, virtual and augmented reality (VR
and AR), media, and application development, deployment, and
management from 96 Availability Zones within 30 geographic regions,
with announced plans for 15 more Availability Zones and five more
AWS Regions in Australia, Canada, Israel, New Zealand, and
Thailand. Millions of customers—including the fastest-growing
startups, largest enterprises, and leading government
agencies—trust AWS to power their infrastructure, become more
agile, and lower costs. To learn more about AWS, visit
aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather
than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
be Earth’s Most Customer-Centric Company, Earth’s Best Employer,
and Earth’s Safest Place to Work. Customer reviews, 1-Click
shopping, personalized recommendations, Prime, Fulfillment by
Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire
tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology,
Amazon Studios, and The Climate Pledge are some of the things
pioneered by Amazon. For more information, visit amazon.com/about
and follow @AmazonNews.
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