Amazon Redshift RA3 instances let customers
scale compute and storage separately and deliver 3x better
performance than other cloud data warehouse providers (available
today)
AQUA (Advanced Query Accelerator) for Amazon
Redshift provides a new innovative hardware accelerated cache that
delivers up to 10x better query performance than other cloud data
warehouse providers (available mid-2020)
Amazon Redshift Data Lake Export allows
customers to export data directly from Amazon Redshift to Amazon S3
in an open data format (Apache Parquet) optimized for analytics
(available today)
Amazon Redshift Federated Query lets customers
analyze data across their Amazon Redshift data warehouse, Amazon
Simple Storage Service (S3) data lake, and Amazon RDS and Aurora
(PostgreSQL) databases (available in preview)
UltraWarm offers a new warm storage tier for
Amazon Elasticsearch Service at up to one-tenth the current cost
that makes it easier for customers to retain any amount of current
and historical log data (available in preview)
Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com company (NASDAQ:AMZN), announced significant new
analytics capabilities that help customers embrace data at today
and tomorrow’s scale. AWS introduced several new Redshift
capabilities that bring more than an order of magnitude better
query performance and deliver greater flexibility for customers
when they are working across their data storage, data warehouse,
and operational databases at scale. AWS also announced a new
innovative highly-scalable, cost-saving warm storage tier for
Amazon Elasticsearch Service.
Customers today are regularly trying to operate on petabytes and
even exabytes of data. This new scale of data, along with new
application requirements, mean that analytics tools will have to
change significantly to scale effectively. Customers want to be
able to perform analytics across all of their data, regardless of
the format or where the data lives, and scale their applications to
support millions of users anywhere in the world. AWS provides the
broadest and deepest set of analytics services of any cloud
provider, and is constantly innovating based on customer needs for
this new scale of data.
Amazon Redshift RA3 instances with Managed Storage allow
customers to cost-effectively scale and run 3x faster than any
other cloud data warehouse
As the scale of data continues to get much bigger-- reaching
petabytes per week-- customers are ingesting even more data into
their Amazon Redshift data warehouse. To scale their data
warehouse, customers use Redshift’s Elastic resize capability to
add additional instances to their cluster. Today, Redshift’s
instances include a fixed amount of compute and storage, so it’s
possible for customers to end up over-provisioned on either, and
paying for capacity they don’t use. Customers have asked for the
ability to grow their storage without over-provisioning compute,
and for more flexibility to grow their compute capacity without
increasing their storage costs.
New Amazon Redshift RA3 instances with Managed Storage
(available today) allow customers to optimize their data warehouse
by scaling and paying for compute and storage independently. With
Amazon Redshift RA3 instances, customers choose the number of
instances they need based on their data warehousing workload’s
performance requirements, and only pay for the managed storage that
they use. Redshift Managed Storage uses large, high-performance
SSDs in each Amazon Redshift RA3 instance for fast local storage
and Amazon S3 for longer-term durable storage. If the data in an
instance grows beyond the size of the large local storage, Redshift
Managed Storage automatically offloads that data to Amazon S3.
Customers pay the same low rate for Redshift Managed Storage
regardless of whether the data sits in high-performance local
storage or in Amazon S3, and they only pay for the amount of
storage they use on a local RA3 storage, meaning they don’t end up
wasting spend on unused storage capacity. For workloads that
require a lot of storage, but not as much compute capacity,
customers can automatically scale their data warehouse storage
capacity without adding and paying for additional instances.
Redshift Managed Storage uses a variety of advanced data management
techniques to optimize how efficiently data is offloaded to and
retrieved from Amazon S3. In addition, Amazon Redshift RA3
instances are built on the AWS Nitro System and feature high
bandwidth networking that further reduces the time taken for data
to be offloaded and retrieved from Amazon S3. Together, these
capabilities enable Amazon Redshift RA3 instances with Managed
Storage to deliver 3x the performance of any other cloud data
warehouse service, and existing Amazon Redshift customers using
Dense Storage (DS2) instances will get up to 2x better performance
and 2x more storage capacity at the same cost. RA3 16xlarge
instances are generally available today to support workloads with
petabytes of data (up to 8 PB compressed), with RA3 4xlarge
instances coming early next year. To get started with Redshift RA3
instances, visit https://aws.amazon.com/redshift.
AQUA (Advanced Query Accelerator) for Amazon Redshift brings
compute to the storage layer for 10x faster performance than any
other cloud data warehouse
Rapid growth in the volume of data that customers need to
process in their data warehouse has led to a difficult balancing
act between performance and cost-effective scaling. The prevailing
approach to data warehousing has been to build out an architecture
in which large amounts of centralized storage is moved to waiting
compute nodes to process the data. The challenge with this approach
is that there is a lot of data movement between the shared data and
compute nodes. As data volumes continue to grow at a rapid clip,
this data movement saturates available networking bandwidth and
slows down performance. Additionally, even if the networking
bottleneck can be overcome, because SSD storage throughput to and
from storage nodes has scaled 6x faster over the last seven years
than the ability for CPUs to process data from memory, absent some
significant change, CPUs aren't able to keep up with the faster
storage capabilities, which will either become a performance
bottleneck itself or create more cost as customers are forced to
provision more compute to get the work done quickly.
AQUA (Advanced Query Accelerator) for Amazon Redshift (available
mid-2020) is a new distributed and hardware-accelerated cache for
Amazon Redshift that provides the next phase of performance
improvement and innovation for analytics at the new scale of data.
AQUA brings compute to the storage layer, so data doesn’t have to
move back and forth between the two, enabling Redshift to run 10x
faster than any other cloud data warehouse. AQUA is a big,
high-speed cache architecture on top of Amazon S3 that can scale
out and process data in parallel across many nodes. Each node
possesses a hardware module comprised of AWS designed analytics
processors that dramatically accelerate data compression,
encryption, and data processing (including filtering and
aggregation). This new architecture makes queries run so much
faster than today’s cloud data warehouses that customers will be
able to query raw data directly, even at scale, giving them more
up-to-date dashboards, less development time, and easier to
maintain systems. AQUA-powered Amazon Redshift will remain 100%
compatible with the current version of Amazon Redshift, so
customers can easily migrate existing data warehouses with no code
changes. AQUA provides the next phase of performance innovation for
analytics at the new scale of data, and will be available in
mid-2020. To learn more about AQUA, visit
https://pages.awscloud.com/AQUA_Preview.html.
Amazon Redshift Data Lake Export makes it easy to save query
results directly to a data lake
Customers require data to be combined across their data
warehouse and data lake, and don’t want data locked in silos and
proprietary formats. For example, an organization may want to
understand what their customer was browsing before they made a
purchase, which requires them to combine the order history sitting
in the data warehouse with the clickstream data sitting in an
Amazon S3 data lake. Amazon Redshift enables customers to directly
query and join data across both their Amazon Redshift data
warehouse and Amazon S3 data lake, giving customers a ‘lake house’
approach to data warehousing. In this lake house world, where data
is stored both in Amazon Redshift and Amazon S3, customers also
need an easy way to get the results from Amazon Redshift queries
back into Amazon S3 in an open format that can be used by other
services.
Amazon Redshift Data Lake Export (available today) allows
customers to export data directly from Amazon Redshift to Amazon S3
in an open data format (Apache Parquet) that is optimized for
analytics. Customers can now save the results of a query they have
done in Amazon Redshift into their data lakes in open formats so
that they can analyze that data with other analytics services like
Amazon SageMaker, Amazon Athena, and Amazon EMR. No other cloud
data warehouse makes it as easy to both query data and write data
back to a data lake in open formats. To get started with Amazon
Redshift Data Lake Export, visit
https://aws.amazon.com/redshift.
Amazon Redshift Federated Query allows customers to analyze
data across data warehouses, data lakes, and operational
databases
Aggregating, transforming, and uploading large amounts of data
from a relational database to a data warehouse can be
resource-intensive and time-consuming, which is why many customers
choose to do so only once a day. This can create problems when
customers need to query their data warehouse for certain types of
timely information that is initially stored in an operational
database. For example, a customer service representative helping a
customer resolve an issue with a recent order might be served
day-old results when they pull up the customer’s purchase history,
making the information irrelevant. Customers can work around this
problem today by writing custom application code to query the
operational database directly, but building integrated systems that
do this is expensive, time consuming, and difficult to
maintain.
Amazon Redshift Federated Query (available in preview) gives
customers the ability to run queries in Amazon Redshift on live
data across their Amazon Redshift data warehouse, their Amazon S3
data lake, and their Amazon RDS and Amazon Aurora (PostgreSQL)
operational databases. This simplifies application development by
allowing customers to use familiar SQL statements to combine all of
this data across their various data stores. With this capability,
Amazon Redshift queries can now provide timely and up-to-date data
from operational databases to drive better insights and decisions.
To get the best possible performance, the Redshift query optimizer
intelligently distributes as much work as possible to the
underlying databases. To learn more about Amazon Redshift Federated
Query, visit https://aws.amazon.com/redshift.
UltraWarm for Amazon Elasticsearch Service provides fast,
interactive analytics on log data at one-tenth the cost
As more and more applications are built using microservices,
containers, and purpose-built data stores, they produce an
ever-increasing amount of log data. Amazon Elasticsearch Service
makes it simple to collect, analyze, and visualize
machine-generated log data from websites, mobile devices, and
sensors. Amazon Elasticsearch Service is fully managed, so
customers can deploy production-ready clusters in minutes, scale
clusters up and down, and secure data at rest and in transit.
However, given the explosive growth of log data, storing and
analyzing months’ or years’ worth of data is cost-prohibitive at
scale. This has led customers to use multiple analytics tools, or
delete valuable data, missing out on important insights that the
longer-term data could yield.
To solve for this customer challenge, AWS built a new storage
tier for Amazon Elasticsearch Service called UltraWarm, which
finally gives Elasticsearch customers a warm storage tier that both
stores large amounts of data cost-effectively and provides the type
of snappy, interactive experience that Elasticsearch customers
expect. UltraWarm offers a distributed cache for more frequently
accessed data, while using advanced placement techniques to
determine which blocks of data are less frequently accessed and
should be moved outside of the cache to Amazon S3. UltraWarm also
uses high-performance EC2 instances to interact with data stored in
S3, providing 50% faster query execution versus competing warm-tier
solutions, and giving customers the same interactive analytics
experience with all their log data. UltraWarm reduces costs by up
to 90% to store the same amount of data in Elasticsearch today, and
is 80% lower than the cost of warm-tier storage from other managed
Elasticsearch offerings. With UltraWarm, customers can manage up to
3 PB of log data with a single Amazon Elasticsearch Service
cluster; and with the ability to query across multiple clusters,
customers can effectively retain any amount of current and
historical log data for interactive operational analysis and
visualization. UltraWarm is a seamless extension of the Amazon
Elasticsearch Service. Customers can easily query and visualize
across both their recent and longer-term operational data, all from
their Kibana interface, at a fraction of the cost today. This
allows developers, DevOps engineers, and InfoSec experts to use
Amazon Elasticsearch Service for the analysis of recent (weeks) and
longer-term (months or years) operational data without needing to
spend days restoring data from archives (Amazon S3 or Amazon
Glacier) to an active searchable state in an Elasticsearch cluster.
UltraWarm Service is available in preview today. To learn more
about UltraWarm, visit
https://aws.amazon.com/elasticsearch-service/features.
“Our customers tell us they are regularly dealing with
petabytes, and even exabytes of data, and their existing analytics
systems can’t keep up,” said Raju Gulabani, Vice President,
Database Services, AWS. “These customers want to perform fast
analytics on all of their raw data across their data warehouse and
data lake, and cost effectively deal with the explosion in log data
to retain information that might help them run their businesses
better. With today’s announcements we are helping AWS customers do
all of this and fearlessly embrace data at scale.”
Duolingo is the most popular language-learning platform and the
most downloaded education app in the world, with more than 300
million users. The company's mission is to make education free,
fun, and accessible to all. “We use Amazon Redshift to analyze the
events from our app to gain insight into how users learn with
Duolingo. We load billions of events each day into Amazon Redshift,
have hundreds of terabytes of data, and that is expected to double
every year. While we store and process all of our data, most of the
analysis only uses a subset of that data,” said Jonathan Burket,
Senior Software Engineer, Duolingo. “The new Redshift RA3 instances
with Managed Storage deliver 2x performance for most of our queries
compared to our previous DS2 instances-based Redshift cluster.
Redshift Managed Storage automatically adapts to our usage
patterns. This means we don't need to manually maintain hot and
cold data tiers, and we can keep our costs flat when we process
more data.”
Yelp’s mission is to connect people with great local businesses;
to do so, data mining and efficient data analysis is important in
order to build the best user experience. “We continue to adopt new
Redshift features and are thrilled with the new RA3 instance type,”
said Stephen Moy, Software Engineer, Yelp. “We have observed a 1.9x
performance improvement over DS2 and 1.5x performance improvement
over DC2 in our workload, while keeping the same costs and
providing scalable managed storage. This allows us to keep pace
with explosive data growth and have the necessary fuel to train our
machine learning systems.”
Western Digital (WD) is a leading global data storage brand that
empowers users to create, experience, and preserve digital content
across a range of devices. WD enables users to be in control and
smartly save what matters to them most in one secure place. “At WD
we use Amazon Redshift to enable the enterprise to gain value and
insights from large, complex, and dispersed datasets,” said Fayaz
Syed, Sr. Manager, Big Data Platform, Western Digital. “Our data is
nearly doubling every year and we run six Redshift clusters with a
total of 78 nodes and 631+ TB of compressed data stored to get
insights that our business analysts and leadership depend on. The
new Redshift RA3 instances offer us the ability to process our
growing data more cost-effectively while we double our storage
capacity compared to our previous Redshift cluster. We also like
that our ETL, BI, and data ingestion process did not have to change
to take advantage of the RA3 instances with Managed Storage.”
NTT DOCOMO is the largest mobile service provider in Japan,
serving more than 79 million customers. “Migrating to Amazon
Redshift in 2014 allowed us to scale to over ten petabytes of
uncompressed data with a 10x performance improvement over our prior
on-premises system. Today, it is the center of our analytics
environment,” said Takaaki Sato, General Manager of Service
Innovation Department, NTT DOCOMO. “Since we started using Amazon
Redshift, both our data and number of users have increased
dramatically. We are impressed with the flexibility and ease of
use, even as we scale users and data. The new Amazon Redshift Data
Lake Export feature allows us to simplify our workflows to make use
of more data across our data lake. We are excited about the new
Amazon Redshift RA3 instances with Managed Storage, enabling us to
scale compute and storage separately. We also look forward to
realizing the benefits of AQUA (Advanced Query Accelerator) for
Amazon Redshift as we continue to increase the performance and
scale of our Amazon Redshift data warehouse. We appreciate AWS’s
continual innovation on behalf of its customers.”
Intuit, makers of TurboTax, QuickBooks and Mint, is a global
financial platform company designed to empower consumers,
self-employed, and small businesses to improve their financial
lives. “We are looking forward to exploring how AQUA can empower
our team to spend more time innovating on behalf of customers,”
said Alex Balazs, Chief Architect, Intuit. “These new capabilities
complement our strategy to create more data-driven insights at
scale with speed and efficiency across our platform.”
Warner Bros. Interactive Entertainment is a premier worldwide
publisher, developer, licensor, and distributor of entertainment
content for the interactive space across all platforms, including
console, handheld, mobile, and PC-based gaming for both internal
and third-party game titles. “We utilize many AWS and third party
analytics tools, and we are pleased to see Amazon Redshift continue
to embrace the same varied data transform patterns that we already
do with our own solution,” said Kurt Larson, Technical Director of
Analytics Marketing Operations, Warner Bros. Analytics. “We’ve
harnessed Amazon Redshift’s ability to query open data formats
across our data lake with Redshift Spectrum since 2017, and now
with the new Redshift Data Lake Export feature, we can conveniently
write data back to our data lake. This all happens with
consistently fast performance, even at our highest query loads. We
look forward to leveraging the synergy of an integrated big data
stack to drive more data sharing across Amazon Redshift clusters,
and derive more value at a lower cost for all our games.”
FOX Corporation produces and distributes content through some of
the world’s leading and most valued brands, including: FOX News,
FOX Sports, the FOX Network, and the FOX Television Stations. FOX
empowers a diverse range of story creators to imagine and develop
culturally significant content, while building an organization that
thrives on creative ideas, operational expertise, and strategic
thinking. “Amazon Redshift allows us to ingest, optimize,
transform, and aggregate billions of transactional events per day
at scale, coming to us from a variety of first and third party
sources,” said Alex Tverdohleb, Vice President Data Services,
Consumer Products & Engineering, FOX Corporation. “We query
live data across our data warehouse and data lake, and now with the
new Amazon Redshift Federated Query feature we can easily query and
analyze live data across our relational databases as well. Our
petabyte scale data is rapidly growing, and with the innovation in
Amazon Redshift RA3 instances and AQUA (Advanced Query Accelerator)
for Amazon Redshift we’re thrilled about the prospect of getting
10x faster performance for our most demanding workloads, while
keeping our costs flat. AQUA for Amazon Redshift is a great example
of how AWS innovates across every layer of the stack to deliver the
best solution for their customers.”
Sophos is a worldwide leader in next-generation cybersecurity.
“Amazon Web Services, including Amazon Redshift, give us the power
to make live data generated by our range of next-gen security
solutions available to more than 409,000 organizations for
analysis,” said John Peterson, Vice President, Central Content
Group, Sophos. “The new Federated Query feature in Amazon Redshift
could help us take this to the next level, allowing us to query
data directly across our Aurora and RDS PostgreSQL databases
without having to setup workflows for data movement. We’re excited
to see how this could speed up our time to insight and help to make
it easier to incorporate the most up to date data from a number of
transactional databases with the data in our data warehouse and our
data lake.”
Ancestry is the global leader in family history and consumer
genomics, empowering journeys of personal discovery to enrich
lives. “With Amazon Elasticsearch Service we collect and analyze
our company’s operational logs in real time,” says Clint Smith,
Senior Manager, Engineering Development, Ancestry. “Now UltraWarm
for Amazon Elasticsearch Service will help us identify correlations
between logging events and quickly root cause application problems.
Before UltraWarm for Amazon Elasticsearch Service, our cost
constraints meant we could only store five days of data. With
UltraWarm for Amazon Elasticsearch Service we will be able to
extend that window to 90 days, and analyze the data via Kibana at a
significantly lower cost. This extra data will help us identify
application problems that we just couldn’t see with the five days
of data we were storing before.”
About Amazon Web Services
For 13 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud platform. AWS offers over
165 fully featured services for compute, storage, databases,
networking, analytics, robotics, 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 69
Availability Zones (AZs) within 22 geographic regions, with
announced plans for 13 more Availability Zones and four more AWS
Regions in Indonesia, Italy, South Africa, and Spain. 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. Customer reviews,
1-Click shopping, personalized recommendations, Prime, Fulfillment
by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets,
Fire TV, Amazon Echo, and Alexa are some of the products and
services pioneered by Amazon. For more information, visit
amazon.com/about and follow @AmazonNews.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20191203005923/en/
Amazon.com, Inc.
Media Hotline
Amazon-pr@amazon.com
www.amazon.com/pr
Amazon.com (NASDAQ:AMZN)
Gráfico Histórico do Ativo
De Mar 2024 até Abr 2024
Amazon.com (NASDAQ:AMZN)
Gráfico Histórico do Ativo
De Abr 2023 até Abr 2024