Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all your data using your existing business intelligence tools. Start small for $0.25 per hour with no commitments and scale to petabytes for $1,000 per terabyte per year, less than a tenth the cost of traditional solutions. Customers typically see 3x compression, reducing their costs to $333 per uncompressed terabyte per year.
Amazon Redshift delivers fast query performance by using columnar storage technology to improve I/O efficiency and parallelizing queries across multiple nodes. Data load speed scales linearly with cluster size, with integrations to Amazon S3, Amazon DynamoDB, Amazon Elastic MapReduce, Amazon Kinesis or any SSH-enabled host.
You only pay for what you use. You can have unlimited number of users doing unlimited analytics on all your data for just $1000 per terabyte per year, which is 1/10th the cost of traditional data warehouse solutions.
Amazon Redshift allows you to easily automate most of the common administrative tasks to manage, monitor, and scale your data warehouse. By handling all these time-consuming, labor-intensive tasks, Amazon Redshift frees you up to focus on your data and business.
You can easily resize your cluster up and down as your performance and capacity needs change with just a few clicks in the console or a simple API call.
Amazon Redshift supports standard SQL and provides custom JDBC and ODBC drivers that you can download from the console, allowing you to use a wide range of familiar SQL clients. You can also use standard PostgreSQL JDBC and ODBC drivers.
Optimized for Data Warehousing
Amazon Redshift uses a variety of innovations to obtain very high query performance on datasets ranging in size from a hundred gigabytes to a petabyte or more. It uses columnar storage, data compression, and zone maps to reduce the amount of I/O needed to perform queries. Amazon Redshift has a massively parallel processing (MPP) data warehouse architecture, parallelizing and distributing SQL operations to take advantage of all available resources. The underlying hardware is designed for high performance data processing, using local attached storage to maximize throughput between the CPUs and drives, and a 10GigE mesh network to maximize throughput between nodes.
With a few clicks in console or a simple API call, you can easily change the number or type of nodes in your data warehouse and scale up all the way to a petabyte or more of compressed user data. Dense Storage (DS) nodes allow you to create very large data warehouses using hard disk drives (HDDs) for a very low price point. Dense Compute (DC) nodes allow you to create very high performance data warehouses using fast CPUs, large amounts of RAM and solid-state disks (SSDs). While resizing, Amazon Redshift allows you to continue to query your data warehouse in read-only mode until the new cluster is fully provisioned and ready for use.
No Up-Front Costs
You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing. On-Demand pricing starts at just $0.25/hour per 160GB DC1.Large node or $0.85/hour per 2TB DS2.XLarge node. With Partial Upfront Reserved Instances, you can lower your effective price to $0.10/hour per DC1.Large node ($5,500/TB/year) or $0.228/hour per DS2.XLarge node ($999/TB/year). For more information, see the Amazon Redshift Pricing page.
Amazon Redshift has multiple features that enhance the reliability of your data warehouse cluster. All data written to a node in your cluster is automatically replicated to other nodes within the cluster and all data is continuously backed up to Amazon S3. Amazon Redshift continuously monitors the health of the cluster and automatically re-replicates data from failed drives and replaces nodes as necessary.
Amazon Redshift automatically and continuously backs up new data to Amazon S3. It stores your snapshots for a user-defined period from 1 up to 35 days. You can take your own snapshots at any time, and they are retained until you explicitly delete them. Amazon Redshift can also asynchronously replicate your snapshots to S3 in another region for disaster recovery. Once you delete a cluster, your system snapshots are removed, but your user snapshots are available until you explicitly delete them.
You can use any system or user snapshot to restore your cluster using the AWS Management Console or the Amazon Redshift APIs. Your cluster is available as soon as the system metadata has been restored and you can start running queries while user data is spooled down in the background.
With just a couple of parameter settings, you can set up Amazon Redshift to use SSL to secure data in transit and hardware-accelerated AES-256 encryption for data at rest. If you choose to enable encryption of data at rest, all data written to disk will be encrypted as well as any backups. By default, Amazon Redshift takes care of key management but you can choose to manage your keys using your own hardware security modules (HSMs), AWS CloudHSM, or AWS Key Management Service.
Amazon Redshift enables you to configure firewall rules to control network access to your data warehouse cluster. You can run Amazon Redshift inside Amazon VPC to isolate your data warehouse cluster in your own virtual network and connect it to your existing IT infrastructure using industry-standard encrypted IPsec VPN.
Audit and Compliance
Amazon Redshift integrates with AWS CloudTrail to enable you to audit all Redshift API calls. Amazon Redshift also logs all SQL operations, including connection attempts, queries and changes to your database. You can access these logs using SQL queries against system tables or choose to have them downloaded to a secure location on Amazon S3. Amazon Redshift is compliant with SOC1, SOC2, SOC3 and PCI DSS Level 1 requirements. For more details, please visit AWS Cloud Compliance.
For more Amazon Redshift customer stories across industries and company sizes, see the customer success page »
While music video streaming service Vevo may have launched in 2009, this past year or so the company has been acting a lot like a fresh-faced startup.
Starting earlier this year, the company began an ambitious re-boot with the aim of no longer being solely dependent on platforms like YouTube. That meant overhauling its apps and desktop experiences over the course of the year to get more people watching music videos on its own products, and adding new features, like personalization and profiles.
Publishing misleading performance benchmarks is a classic old guard marketing tactic. It’s not surprising to see old guard companies (like Oracle) doing this, but we were kind of surprised to see Google take this approach, too. So, when Google presented their BigQuery vs. Amazon Redshift benchmark results at a private event in San Francisco on September 29, 2016, it piqued our interest and we decided to dig deeper.
In this post, Periscope presents results of their comparative bechmarking of Amazon Redshift, Snowflake and Google BigQuery.
For a full list of blog posts related to Amazon Redshift, see the blog posts page »
|Dec 09||Amazon Redshift now supports Python UDF logging module|
|Dec 08||Announcing the AWS Canada (Central) Region|
|Dec 07||Record and govern Amazon Redshift configurations with AWS Config|
For information about all the new features in Amazon Redshift, see the what's new page »