What is Amazon Redshift? What Is Unique About Redshift? Amazon Redshift processes petabytes of data, making it one of the most popular data warehousing solutions on the market.
It uses Massively Parallel Processing (MPP) technology to process massive volumes of data at lightning speeds. Plus, Redshift costs a fraction of the cost of other data platforms.
This guide will provide a deeper understanding of Redshift to help you determine whether it’s the best data warehouse solution for your organization.
What is Amazon Redshift?
AWS Redshift is a data warehousing solution from Amazon Web Services. Redshift shines in its ability to handle huge volumes of data — capable of processing structured and unstructured data in the range of exabytes (1018 bytes). However, the service can also be used for large-scale data migrations.
Similar to many other AWS services, it can be deployed with just a few clicks and provides a plethora of options to import data. Additionally, the data in Redshift is always encrypted for added security.
Redshift helps to gather valuable insights from a large amount of data. With the easy-to-use interface of AWS, you can start a new cluster in a couple of minutes, and you don’t have to worry about managing infrastructure.
What Is Unique About Redshift?
Redshift is an OLAP-style (Online Analytical Processing) column-oriented database. It is based on PostgreSQL version 8.0.2. This means regular SQL queries can be used with Redshift. But this is not what separates it from other services. The fast delivery to queries made on a large database with exabytes of data is what helps Redshift stand out.
Fast querying is made possible by Massively Parallel Processing design or MPP. The technology was developed by ParAccel. With MPP, a large number of computer processors work in parallel to deliver the required computations. Sometimes processors situated across multiple servers can be used to deliver a process.
Unlike most MPP vendors, ParAccel does not sell MPP devices. Their software can be used on any hardware to harness the power of multiple processors. AWS Redshift uses the MPP technology of ParAccel. In fact, Redshift was started following capital investment by AWS in ParAccel and using MPP technology from ParAccel. Now the company is part of Actian.
When Would You Want To Use Amazon Redshift?
Amazon Redshift is used when the data to be analyzed is humongous. The data has to be at least of a petabyte-scale (1015 bytes) for Redshift to be a viable solution. The MPP technology used by Redshift can be leveraged only at that scale. Beyond the size of data, there are some specific use cases that warrant its use.
Many companies need to make decisions based on real-time data and often need to implement solutions quickly too. Take Uber for example.
Based on historical and current data, Uber has to make decisions quickly. It has to decide surge pricing, where to send drivers, what route to take, expected traffic, and a whole host of data.
Thousands of such decisions have to be made every minute for a company like Uber with operations across the globe. The current stream of data and historical data has to be processed in order to make those decisions and ensure smooth operations. Such instances can use Redshift as the MPP technology to make accessing and processing data faster.
Combining multiple data sources
There are occasions where structured data, semi-structured data, and/or unstructured data have to be processed to gain insights. Traditional business intelligence tools lack the capability to handle the varied structures of data from different sources. Amazon Redshift is a potent tool in such use cases.
The data of an organization needs to be handled by a lot of different people. All of them are not necessarily data scientists and will not be familiar with the programming tools used by engineers.
They can rely on detailed reports and information dashboards that have an easy-to-use interface. Highly functional dashboards and automatic report creation can be built using Redshift. It can be used with tools like Amazon Quicksight and also third-party tools created by AWS partners.
Behavior analytics is a powerful source for useful insights. Behavior analytics provide information on how a user uses an application, how they interact with it, the duration of use, their clicks, sensor data, and a plethora of other data.
The data can be collected from multiple sources — including a web application used on a desktop, mobile phone, or tablet — and can be aggregated and analyzed to gain insight into user behavior. This coalescing of complex datasets and computing data can be done using Redshift.
Redshift can also be used for traditional data warehousing. But solutions like the S3 data lake would likely be better suited for that. Redshift can be used to perform operations on data in S3, and save the output in S3 or Redshift.
What Are the Limitations of AWS Redshift?
Redshift has some drawbacks that need to be considered before choosing it as your data warehousing solution.
- Parallel Uploads. Redshift does not support all databases for parallel upload. Amazon S3, EMR, and DynamoDB are supported by Redshift for parallel uploads using ultra-fast MPP. For other sources, separate scripts have to be used to upload data. This can be a very slow process.
- Uniqueness. One of the basic tenets of a database is to have unique data and avoid redundancies. AWS Redshift does not provide any tool or means to ensure the uniqueness of data. If you are migrating overlapping data from different sources to Redshift, there will be redundant data points.
- Indexing. This becomes a problem when Redshift is used for data warehousing needs. Redshift uses distribution and sorts keys to index and store data. You will need to know the concepts behind the keys to work on the database. AWS does not provide any system to change the keys or manage them with minimal knowledge.
- OLAP Limitations. OLAP databases (which Redshift is) are optimized for analytical queries on a large volume of data. Compared to traditional OLTP (Online Transaction Processing) databases, OLAP lacks in performing basic database tasks. Insert/update/delete operations have performance limitations in OLAP databases. It is often easier to recreate a table with changes than to insert/update tables in Redshift. While OLAP works well with static data, OLTP databases perform better for data modification operations.
- Migration cost. Redshift is used in cases where the data to be stored or worked with is humongous. It will at least be in the range of petabytes. At this level, bandwidth becomes a problem. You will need to transfer this data to AWS locations before you can begin the project. This could be a potential problem for businesses that have network caps for bandwidth. The additional cost will have to be borne by the user. AWS does provide the option to send the data using physical storage devices.
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