Amazon Redshift is among the best solutions to consider for cost-effectively creating a cloud-based data warehouse. Redshift is a fully-managed big data warehousing product from Amazon Web Services (AWS), built specifically to cost-effectively collect and store up to one petabyte of data in the cloud. According to AWS, Redshift is used by tens of thousands of customers in many industries, including enterprise brands like McDonald’s, Pfizer, Philips, and Lyft,
A cloud data warehouse is an infrastructure solution to perform analytics on one or more big data sets, especially when the size or velocity of data makes a premises-based warehouse too costly. Hosted data warehouse solutions fully automate administration requirements such as data backups, patching, and performance monitoring. Alternatives to Redshift include Google BigQ, Hadoop, Amazon Athena, and Amazon DynamoDB.
4 Common Use Cases for Amazon Redshift
Redshift is a flexible, highly-scalable way to create a cloud-based cluster solution for big data. “Big” is relative, but Redshift has the capability to accommodate projects which range in size from several gigabytes to a petabyte of data. Some of the most common reasons companies adopt a cloud data warehouse include a need for flexible warehousing or real-time analytics.
Flexible Cloud Warehousing
A cloud-based data warehouse allows businesses to combine multiple data sets for storage and analysis, including compiling data from various sources such as a single warehouse for customer transactions, app events, and third-party data insights. Redshift provides flexibility to quickly and cheaply create new warehouses in the crowd, especially when compared to the time-consuming, costly process of creating a premises-based warehouse.
Redshift is an affordable option to store and collect raw event data for analyzing logs in real-time. It’s possible to retain the quality of fast-moving data streams from IoT sensor logs and other sources without paying high storage costs.
A cloud data warehouse offers the infrastructure to create a link between Redshift and a business intelligence application for affordable real-time analytics. Redshift is a tool to cost-effectively explore new analytical capabilities such as real-time fraud detection or personalized product recommendations.
Cloud data warehouses offer the advantage of speed and reliability to support mission-critical reporting on multiple data sources or continuously feed predictive analytics models.
6 Reasons Why Amazon Redshift Shines
Traditional approaches to data warehousing have been a barrier to speed, ease-of-use, or flexibility for many organizations, especially when budget for warehousing was limited. Jeff Barr of AWS announced the first generation Redshift in a 2012 blog post as a groundbreaking solution for speed and cost savings. Other benefits of Redshift include superior ease-of-use, flexibility, and consistent performance.
- Cost Efficiency
Switching to Redshift can yield significant cost savings compared to cloud alternatives or premises-based data warehouse solution. Redshift’s pricing is transparent and based on data storage volume. The annual cost of Redshift with 1 terabyte of data is $1,000, which includes unlimited users and analytics.
The ParAccel-based query engine in Redshift is nearly-identical to the PostgreSQL interface. Database analysts who have previously used PostgreSQL or SQL server can easily begin creating and optimizing queries in Redshift for business intelligence.
Redshift also simplifies data warehouse administration by providing automation for many of the most time-consuming cluster configuration responsibilities. It offers built-in management solutions to automate provisioning, patching, replication, and data backups.
Redshift is three times faster than alternative cloud data warehouse products, according to AWS. Customer success stories indicate larger performance gains are possible, including ten-fold improvements in speed. A columnar storage database structure reduces Redshift’s I/O operations and provides built-in support for consistent query performance on big datasets. Redshift’s performance edge is also aided by a parallel performance structure when collating data from AWS S3. The cloud warehouse offers built-in capabilities for data compression and optimizes queries with simultaneous execution across multiple nodes.
Scaling Redshift to increase storage or speed is fast and simple. Push-button scaling to add nodes doesn’t sacrifice continuous data availability for read-only query operations. Redshift moves data horizontally between old cluster nodes and new clusters while making clusters available for read-only. The ability to scale Redshift resources down on demand can also help companies achieve flexible cost savings when performance demands decrease.
Out-of-the-box, Redshift is built to accommodate a petabyte of data storage without sacrificing performance. Redshift can also cost-effectively scale to accommodate over a petabyte with dense storage notes.
Redshift offers many flexible ways to load data from multiple sources, especially for customers who house data on AWS ecosystem products like EC2, S3, or RDS. A single COPY command can create a link to quickly load data into Redshift from S3 infrastructure.
How to Get Started with Amazon Redshift
Adopting Amazon Redshift can provide a cost-effective, reliable path to adopt cloud data warehousing capabilities quickly and achieve real-time analytics on big data sources. While it’s easy to get started with Redshift, data integration from non-AWS sources is among the most common challenges companies face during implementation. Creating a solid implementation plan which addresses the unique risks and requirements of your data sources can ensure a streamlined cloud migration experience.
Amazon Redshift is a powerful tool which can be a fantastic business use case. If you’re looking to explore Amazon Redshift, Datavail has assisted numerous companies realize its benefits. We can provide a demo and assist your organization in a proof of concept. Contact us to learn more.
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