Amazon Aurora zero-ETL integration with Amazon
Redshift enables customers to analyze petabytes of transactional
data in near real time, eliminating the need for custom data
pipelines
Amazon Redshift integration for Apache Spark
makes it easier and faster for customers to run Apache Spark
applications on data from Amazon Redshift using AWS analytics and
machine learning services
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced two new
integrations that make it easier for customers to connect and
analyze data across data stores without having to move data between
services. Today’s announcement enables customers to analyze Amazon
Aurora data with Amazon Redshift in near real time, eliminating the
need to extract, transform, and load (ETL) data between services.
Customers can also now run Apache Spark applications easily on
Amazon Redshift data using AWS analytics and machine learning (ML)
services (e.g., Amazon EMR, AWS Glue, and Amazon SageMaker).
Together, these new capabilities help customers move toward a
zero-ETL future on AWS. To learn more about unlocking the value of
data using AWS, visit aws.amazon.com/data.
“The vastness and complexity of data that customers manage today
means they cannot analyze and explore it with a single technology
or even a small set of tools. Many of our customers rely on
multiple AWS database and analytics services to extract value from
their data, and ensuring they have access to the right tool for the
job is important to their success,” said Swami Sivasubramanian,
vice president of Databases, Analytics, and Machine Learning at
AWS. “The new capabilities announced today help us move customers
toward a zero-ETL future on AWS, reducing the need to manually move
or transform data between services. By eliminating ETL and other
data movement tasks for our customers, we are freeing them to focus
on analyzing data and driving new insights for their
business—regardless of the size and complexity of their
organization and data.”
Data is at the center of every application, process, and
business decision and is the cornerstone of almost every
organization’s digital transformation. But, real-world data systems
are often sprawling and complex, with diverse data dispersed across
multiple services and on-premises systems. Many organizations are
sitting on a treasure trove of data and want to maximize the value
they get out of it. AWS provides a range of purpose-built tools
like Amazon Aurora, to store transactional data in MySQL and
PostgreSQL-compatible relational databases, and Amazon Redshift, to
run high-performance data warehousing and analytics workloads on
petabytes of data. But to truly maximize the value of data,
customers need these tools to work together seamlessly. That is why
AWS has invested in zero-ETL capabilities like Amazon Aurora ML and
Amazon Redshift ML, which let customers take advantage of Amazon
SageMaker for ML-powered use cases, without moving data between
services. Additionally, AWS provides seamless data ingestion from
AWS streaming services (e.g., Amazon Kinesis and Amazon MSK) into a
wide range of AWS data stores, such as Amazon Simple Storage
Service (Amazon S3) and Amazon OpenSearch Service, so customers can
analyze data as soon as it is available. Today’s announcement
builds on the strength and deep integrations of AWS’s database and
analytics portfolio to make it faster, easier, and more
cost-effective for customers to access and analyze data across data
stores on AWS.
Amazon Aurora zero-ETL integration with Amazon Redshift makes
it easier to run petabyte-scale analytics on transactional data in
Amazon Aurora in near real time with Amazon Redshift
The requirement for near real-time insights on transactional
data (e.g., purchases, reservations, and financial trades) grows as
organizations seek to better understand core business drivers and
develop strategies to increase sales, reduce costs, and gain a
competitive advantage. Many organizations today rely on a
three-part solution to analyze their transactional data—a
relational database to store data, a data warehouse to perform
analytics, and a data pipeline to ETL data between the relational
database and the data warehouse. Data pipelines can be costly to
build and challenging to manage, requiring developers to write
custom code and constantly manage the infrastructure to ensure it
scales to meet demand. Some companies maintain entire teams just to
facilitate this process. Additionally, it can take days before data
is ready for analysis, and intermittent data transfer errors can
delay access to time-sensitive insights even further, leading to
missed business opportunities.
With Amazon Aurora zero-ETL integration with Amazon Redshift,
transactional data is automatically and continuously replicated
seconds after it is written into Amazon Aurora and seamlessly made
available in Amazon Redshift. Once data is available in Amazon
Redshift, customers can start analyzing it immediately and apply
advanced features like data sharing and Amazon Redshift ML to get
holistic and predictive insights. Customers can replicate data from
multiple Amazon Aurora database clusters into the same Amazon
Redshift instance to derive insights across several applications.
Now, customers can use Amazon Aurora to support their transactional
database needs and Amazon Redshift to power their analysis, without
building or maintaining complex data pipelines.
Amazon Redshift integration for Apache Spark makes it easier
to use AWS analytics and ML services to build and run Apache Spark
applications on data from Amazon Redshift
Many developers use Apache Spark, an open-source processing
framework used for big data workloads, to support a broad range of
analytics and ML applications. Today, AWS supports Apache Spark on
Amazon EMR, AWS Glue, and Amazon SageMaker with a fully compatible,
AWS-optimized runtime that is 3x faster than open source. Customers
often want to analyze Amazon Redshift data directly from these
services. This requires them to go through the complex,
time-consuming process of finding, testing, and certifying a
third-party connector to help read and write the data between their
environment and Amazon Redshift. Even after they have found a
connector, customers must manage intermediate data-staging
locations, such as Amazon S3, to read and write data from and to
Amazon Redshift. All of these challenges increase operational
complexity and make it difficult for customers to use Apache Spark
to its full extent.
Amazon Redshift integration for Apache Spark makes it easier for
developers to build and run Apache Spark applications on data in
Amazon Redshift using AWS-supported analytics and ML services.
Amazon Redshift integration for Apache Spark is certified,
packaged, and supported by AWS, eliminating the cumbersome and
error-prone process associated with third-party connectors.
Developers can begin running queries on Amazon Redshift data from
Apache Spark-based applications within seconds using popular
language frameworks (e.g., Java, Python, R, and Scala).
Intermediate data-staging locations are managed automatically,
eliminating the need for customers to configure and manage these in
application code. To get started with Amazon Redshift integration
for Apache Spark, visit
aws.amazon.com/redshift/features/integration-for-apache-spark.
Adobe empowers everyone, from individuals and small businesses
to government agencies and global brands, to create and deliver
exceptional digital experiences. “Adobe’s mission is to change the
world through digital experiences, and in today’s world, that means
having analytics that can deliver both deep and real-time
insights,” said Jack Lull, principal scientist for Adobe Acrobat
Sign. “As an Amazon Aurora customer, we are excited for Amazon
Aurora support for zero-ETL integration with Amazon Redshift, which
will provide our growing Acrobat Sign customer base with new
insights and faster analytics performance as their usage
increases—all without the need for ongoing maintenance for our own
teams.”
Infor is a global leader in business cloud software and
industry-specific enterprise resource planning solutions. “At
Infor, we use AWS to build and deploy modern tools to help our
customers transform their business and accelerate innovation. This
includes a new managed data warehouse service for our customers'
industry cloud data, which will help our customers make faster
decisions with advanced analytics and ML,” said Jim Plourde, senior
vice president for Cloud Services at Infor. “We are excited for
Amazon Aurora to support zero-ETL integration with Amazon Redshift,
which will reduce our operational burden by making transactional
data from Amazon Aurora available in Amazon Redshift in near real
time. Now, we can benefit from the performance of Amazon Aurora as
our relational database management system, while easily leveraging
the analytics and ML capabilities in Amazon Redshift for our new
managed data warehouse service.”
GE Aerospace is a global provider of jet engines, components,
and systems for commercial and military aircraft. The company has
been designing, developing, and manufacturing jet engines since
World War I. “Amazon Redshift is a focal point of our strategy to
make data extremely accessible and usable across our organization,”
said Alcuin Weidus, senior principal data architect at GE
Aerospace. “Data scientists, engineers, and developers leverage
Apache Spark to build data products and run analytics workloads on
Amazon EMR, AWS Glue, and third-party ML platforms hosted on AWS.
We are excited for the Amazon Redshift integration for Apache
Spark, which will streamline our developers’ building process and
help make applications more performant and secure.”
The Goldman Sachs Group, Inc. is a leading global financial
institution that delivers a broad range of financial services
across investment banking, securities, investment management, and
consumer banking to a large and diversified client base that
includes corporations, financial institutions, governments, and
individuals. “Our focus is on providing self-service access to data
for all of our users at Goldman Sachs. Through Legend, our open
source data management and governance platform, we enable users to
develop data-centric applications and derive data-driven insights
as we collaborate across the financial services industry,” said
Neema Raphael, chief data officer at Goldman Sachs. “With Amazon
Redshift integration for Apache Spark, our data platform team will
be able to access Amazon Redshift data with minimal manual
steps—allowing for zero-code ETL that will increase our ability to
make it easier for engineers to focus on perfecting their workflow
as they collect complete and timely information. We expect to see a
performance improvement of applications and improved security as
our users can now easily access the latest data in Amazon
Redshift.”
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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