AWS Redshift is a managed data warehouse service offered by Amazon Web Services. It’s part of their renowned cloud-based computing platform that is utilized by a variety of well-known companies like Lyft as well as McDonald’s. Data warehouses are storage and analysis solutions to store large amounts of data. They gather data through ETL and ELT services such as AWS Glue and transform into valuable data and data that companies can use to analyze and gain strategic analysis. In contrast to Postgres databases, Redshift is a column-based database instead of rows, and is able to handle multiple concurrent parallel queries with speed. Here are eight good reasons for businesses to decide to use AWS Redshift instead of Postgres or alternative options such as Snowflake for business data warehouse.
1. AWS Redshift is Fast
If you’re in search of the most efficient data warehouse, speed and performance are the most important elements. Amazon states it is Redshift has three times faster in dealing with data than comparable alternatives. This is due to the fact that Redshift operates by using “clusters” made up of information that are built around nodes. Each node is connected to multiple others, and can function in parallel in order to maximize speedy processing of data. This provides Redshift an enormous performance advantage over traditional database technologies like Postgres but it is, in essence, Redshift has a modified version of PostgreSQL Relational Database Management System (RDBMS) in addition to the technology used by ParAccel which is the very first database to offer the capability of Massive Parallel Processing (MPP). Redshift makes use of machine learning capabilities to improve efficiency and speed, which means it is constantly improving and updating. Redshift also has the ability to access data through 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 at a sliding price scale, which makes it affordable for small businesses, but capable enough for massive businesses that handle various formats of data. Companies can purchase upfront the planned use of their clusters. Or, they may choose an on-demand model. If your company expands and expands, you can alter the plan you’ve bought and ensure that you are able to cope with sudden increases in your amount of data. If you require more concurrent queries then you can simply add more compute nodes, and pay for them according to their capacity.
Amazon’s pricing is simple to understand and does not come with unintentional surprises, allowing businesses to make the most of their budget. The process of running queries within Redshift prioritizes columns rather than the conventional Postgres technique of processing rows. Due to the columnar storage technique it is able to extract useful insights from smaller volumes of data. Redshift Workbench can also allow users to prioritize data columns by using the sort keys feature. Other big data clusters such as Hadoop are typically more expensive, even for similar quantities of data.
3. Redshift is Scalable
Since the price is adaptable, Redshift is a completely flexible data warehouse solution to integrate data. Data companies consume fluctuates due to a variety of reasons such as the peak season as well as general demand and external events which businesses cannot control. The ability to eliminate or add nodes easily allows Redshift an attractive option for companies of all sizes with the ability to scale up completely. Companies that experience suddenly seen a surge in data, or experiences unprecedented growth are able to rest knowing that their data warehouse can easily expand with them without needing to search for a different vendor. Redshift is capable of handling tasks at the scale of a petabyte. This makes it perfect to handle large amounts of unstructured or raw data from a lake, which makes it suitable in tools for BI.
4. Redshift is simple to use
People who use SQL commands will be able to find the Redshift system incredibly simple to navigate. Additionally there is also AWS Management Console AWS Management Console allows Redshift’s 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 couple of clicks. Administrators can also set up clusters within the Virtual Private Cloud (VPC). There’s ample documentation available from Amazon to help 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 easily for a variety of scenarios and also enables administrators to make processing parameter adjustments in real time. The tools for BI then employ methods of data visualization to help make the data more useful to businesses.
5. Redshift is highly secure
It’s difficult to emphasize the importance of data security. Every company must be in compliance with regulations regarding data security, for example the GDPR. Making sure that storage and management of data is secure and secure. This prevents financial loss and losing trust from customers and other partners. Redshift is a cloud-based data storage system that provides end-to end encryption as well as network isolation, data masking, as well as various other options to help businesses keep their data in compliance regardless of the types of data they utilize. Redshift also provides SSL connection for SQL queries.
6. This is part of the 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 significance of data security. 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 and safely in Amazon S3, helping businesses gain the maximum benefit from their AWS services.
7. Redshift connects to the majority of Data Sources
Redshift clusters connect to a variety of data sources using SQL client software, which is typically used by the user or through a third-party service. The process of setting up data transfer connections makes use of Python, JDBC, or ODBC drivers that Amazon will make available as downloads. You can also utilize Postgres drivers, however they won’t be able to use the AWS Redshift team doesn’t offer any assistance in this. Numerous business apps offer their own APIs which allow you the data to store as well as analysis in the warehouse. Administrators can also connect pipelines to traditional Postgres databases to ensure effective data collection.
8. Cloud-based and managed
Since Redshift is an data warehouse service hosted in the cloud by Amazon and does not take up area on servers, nor does it require any maintenance other than your instructions and settings to determine how you would like the data pipeline to function. The management of an individual data warehouse, or your own Postgres databases is constantly trying to locate additional server space as your business expands and grows. This isn’t a problem with Redshift that, has already proven that it can take on petabytes worth of data. Customers of AWS S3 also benefit from automated backups of data for security.
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