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Both AWS, Amazon Redshift, and Amazon Relational Database Services (RDS) can be used together very efficiently. In our last blog, we look for the features and functionality of the two database services that will allow the customer to identify differences. Best meets their needs.
The Amazon Relational database services is to stay ahead of growth and not let your database become a bottleneck poses a real problem. With Amazon RDS Managed Database Services, these issues can be eliminated. Amazon RDS provides six database engines: Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB.
These allow customers to continue using existing tools without buying, racking, stacking hardware, or installing software. Amazon RDS automatically corrects the database software and backs up your database, storing the backups for a user-defined period of time. It’s a cost-effective, resizable, and fast way to manage your administrative database tasks.
So how does it work?
Generally, when you buy a server, your computer contains your CPU, memory, storage, and IOPs. With AWS RDS, they are individual, to allow independent scaling. The basic building block for Amazon RDS is the database instance. This is an isolated database environment in the cloud. It can contain databases created by multiple users, which client tools and applications have access to, as you might for a stand-alone database. These are simple to create and modify.
Amazon RDS supports access to databases using any standard SQL client application and does not allow direct access to the host. Amazon RDS creates a primary user account for your database instance during the creation process. This primary user has permissions to create databases and perform create, delete, select, update, and insert operations on tables created by the primary user. You must set the primary user’s password when you create a database instance, but you can change it at any time by using Amazon AWS Command Line Tools, Amazon RDS APIs, or AWS Management Console. You can also change the master user’s password and manage users using standard SQL commands.
What is Amazon Redshift?
Under the name of Redshift, taken from Hubble’s law, in astronomy, which explains the extension of the universe or better known as the “big bang theory,” Amazon asserts by association that their Redshift can handle n any amount of data required by your service. And that may be true, because it was the fastest growing service when it was launched. This is because Amazon Web Services made it easy for customers to adopt and implement Redshift, swapping your Enterprise Data Warehouse (EDW) in hours, saving business time and money.
The Amazon RedShift analytics database is designed and adapted to process data, that is, execute “large” or “heavy” queries on large datasets. It is a fully cloud-managed data warehouse service. An Amazon Redshift data warehouse is a collection of computer resources called nodes, organized into a group called a cluster.
Each cluster runs an Amazon Redshift engine and contains one or more databases. With two types of dense storage nodes DW1 and DW2. DW2s are very fast SSDs (Solid State Memory) that offer advantages in terms of large disk I / O. DW1 nodes can scale up to the petabyte of storage running on traditional storage disks, but not as fast as SSDs, but customers have a bigger advantage because they are much cheaper.
Conclusion:
Amazon RDS is a relational database for primary data, running software such as SQL, MySQL, Aurora, MariaDB, Oracle and PostgreSQL. Can be used as a main, supporting or transactional database. Redshift is Amazon’s analytics database with ParAccel technology. It is designed to handle large voluminous data requests on large data sets. By using AWS Data Pipeline, collecting data on RDS databases, users interact with that part of your infrastructure with Amazon EC2. Amazon S3 jobs can then transfer bulk data to your Redshift cluster to perform these “heavy” queries.
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