AWS Redshift is a managed data warehouse system from Amazon Web Services. It’s part their popular cloud-based computing platform that is utilized by a variety of well-known companies including Lyft as well as McDonald’s. Data warehouses are storage and analysis solutions to store large amounts of data. They collect data from ETL and ELT services such as AWS Glue and transform into valuable data and data that companies can use to analyze and gain strategic insight. Contrary to Postgres databases Redshift uses columns instead of rows, and is able to handle multiple concurrent parallel queries with speed. Here are eight reasons why businesses opt for AWS Redshift instead of Postgres or other alternatives like Snowflake for business data warehouse.
1. AWS Redshift is Superfast
If you’re searching for the most efficient data warehouse, speed and performance are certainly the main aspects. Amazon declares it is Redshift has three times more efficient in dealing with the data than other comparable products. This is due to the fact that Redshift operates by using “clusters” composed of the data constructed around nodes. Each node is connected to multiple others, and they can operate in parallel, maximizing speed of processing of data. This provides Redshift an enormous performance advantage over traditional database technologies like Postgres however it is, in essence, Redshift has a modified version of PostgreSQL RDBMS, a relational management database (RDBMS) in addition to the technology of ParAccel which is the very first database that offers an operational Massive Parallel Processing (MPP). Redshift makes use of machine learning capabilities to improve the speed and efficiency of its operations, meaning that it is constantly improving and updating. Redshift is also able to search for data with serverless query compilation, which means it’s not restricted by the amount of memory or CPU used.
2. Redshift is cost-effective
Amazon offers Redshift with a sliding cost scale, which makes it affordable for small businesses, but sufficient for large businesses that handle various formats of data. Companies can purchase upfront scheduled instances of running their clusters. They can also select an on-demand arrangement. As your business expands it is possible to alter the plan you’ve purchased and ensure that you have the capacity to cope with sudden increases in your amount of data. If you’re required to run more concurrent queries it is easy to add additional compute nodes, and pay for them according to their capacity.
Amazon’s pricing is simple follow and doesn’t come with any unexpected surprises, which allows enterprises to maximize their budget. The process of running queries within Redshift prioritizes columns over the standard Postgres approach of looking up rows. With this method of storage it’s possible to gain valuable information from smaller amounts of data. Redshift additionally lets users prioritize data columns by using the sort keys feature. Other cluster-based big-data services such as Hadoop are typically more expensive, even for similar volumes of data.
3. Redshift is Scalable
Because pricing is adaptable, Redshift is a completely flexible data warehouse solution that allows data to be integrated. Data companies consume is subject to fluctuation due to many factors including the peak season or general demand, as well as external events which businesses cannot control. The ability to eliminate or add nodes in a matter of minutes allows Redshift an attractive option for all businesses with the ability to scale up completely. Companies that experience an unexpected spike in data, or experiences unprecedented growth can rest assured knowing that their data warehouse can easily expand with them without having to look for another provider. Redshift Workbench is able to handle tasks at the scale of a petabyte. This makes it suitable to handle large data , or massive amounts of unstructured or raw data from a lake, making it suitable to your tools for BI.
4. Redshift is simple to use
The users of SQL commands will be able to find the Redshift system extremely easy to use. In addition there is also AWS Management Console AWS Management Console helps make it easy to make the Redshift data warehouse simple to master and allows users to add and remove, or even scale Amazon Redshift clusters to increase or decrease their size with just only a few clicks. Administrators can also deploy clusters inside the Virtual Private Cloud (VPC). There’s also lots of documentation from Amazon to assist novices understand the node types as well as other features. Beyond the user-friendly layout, Redshift offers automation of numerous administrative tasks that commonly occur to monitor and manage the data that is in use or newly created in a simple manner for various scenarios and also enables administrators to make processing parameter adjustments at a moment’s notice. The tools for BI then employ methods of data visualization to make the data more valuable to businesses.
5. Redshift is highly secure
It’s difficult to quantify the importance of data security. Every business must comply with the regulations governing data, such as the GDPR. Ensuring that storage and management of data is safe and secure. This helps avoid financial loss as well as the loss of confidence from customers and partners. Redshift is a cloud-based storage system that provides end-to end encryption and network isolation, as well as masking of data, and various other options to help businesses keep their data in compliance regardless of the type of data they employ. Redshift also provides SSL connection for SQL queries.
6. The AWS Cloud Computing Platform is part of it. AWS Cloud Computing Platform
Since Redshift is an Amazon product, it comes with built-in connections with the different AWS Cloud Computing products. We’ve already touched on the importance of security for data. Redshift integrates with a third service known as AWS CloudTrail that lets users review the API calls coming from the data warehouse for additional security. The logs can be safely stored to Amazon S3, helping businesses gain the maximum benefit of all AWS services.
7. Redshift is connected to a variety of Data Sources
Redshift clusters are connected to the majority of sources of data using SQL client tools, typically used by the user or through a third-party data management service. The process of setting up data transfer connections requires Python, JDBC, or ODBC drivers which Amazon will make available as downloads. The users can also make use of Postgres drivers, however it is the AWS Redshift team doesn’t offer any assistance in this. Numerous business apps offer their own APIs users can utilize to transfer data for storage as well as analysis in the warehouse. Administrators can also connect pipelines to traditional Postgres databases to facilitate data collection.
8. Cloud-based and managed
Since Redshift is an data warehouse service hosted in the cloud by Amazon and does not occupy any storage space in your server, nor does it require any maintenance other than the instructions you provide and the configuration to determine how you would like the data pipeline to operate. managing an individual data warehouse or internal Postgres databases is constantly trying to search for servers to accommodate your business expands and grows. This isn’t a problem with Redshift and, as we’ve seen before, can be scaled to manage petabytes of information. AWS S3 users AWS S3 also get automated backups of data to give them assurance.
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