Both AWS services, Amazon Redshift and Amazon Relational Database Services (RDS) can be used together very effectively, in our latest blog, we are looking to find out the functions and features of both database services will allow the customer to identify the differences and which best meets their requirements.
What is Amazon RDS?
Amazon Relational Database Service (Amazon RDS) makes it easy to setup, operate and scale a relational database in the cloud. Running databases pose a lot of repetitive management work. The need to stay ahead of growth and not allow your database to become a bottleneck presents a real problem. With Amazon RDS managed database services these problems can be eliminated. Amazon RDS provide six database engines Amazon Aurora, Oracle, Microsoft SQL server, PostgreSQL, MySQL and MariaDB. These allows customers to keep using existing tools, no need to buy, rack and stack hardware or install software. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined period. This is a cost efficient, resizable and time efficient manner in which to handle your database administrative tasks.
So, how does it work?
Typically when you purchases a server, CPU, memory, storage and IOPs are all included in your package. With AWS RDS these are individual, to allow for scaling independently. The basic building block for Amazon RDS is the DB instance. This is an isolated database environment in the cloud. It can contain multi user-created databases, accessed by client tools and applications as you might use 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 host access. Amazon RDS creates a master user account for your DB instance in the creation process. This master user has permissions to create databases and to perform create, delete, select, update and insert operations on tables the master user creates. You must set the master user password when you create a DB instance, but you can change it at any time using the Amazon AWS command line tools, Amazon RDS APIs, or the AWS Management Console. You can also change the master user password and manage users using standard SQL commands.
What is Amazon Redshift?
With the name Redshift taken from Hubble’s law, in astronomy, which explains the expansion of the universe or today commonly known as the “big bang theory “ Amazon by association are saying their Redshift can handle any amount of data that your service requires. And this may be so, as it was the fastest growing service when launched. This was due to Amazon Web Services making it easy for customers to adopt and implement Redshift, swapping out your Enterprise Data Warehouse (EDW) in hours saving valuable time and money for enterprise customers. Redshift Amazon’s analytics database is designed and suited to crunch data, i.e. running “big” or “heavy” queries against large datasets and is a fully managed, petabyte-scale data warehouse service in the cloud. An Amazon Redshift data warehouse is a collection of computing resources called nodes, which are 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. DW2 are very fast solid state memory drives (SSD) with large disk I/O benefits. DW1 nodes can scale up to petabyte of storage run on traditional storage disks, not as fast as SSD, but customers gain a greater advantage because it’s much cheaper.
Amazon RDS is a relational database for primary data, running software like SQL, MySQL, Aurora, MariaDB, Oracle and PostgreSQL. Can be used as your main, supporting or transactional database. Redshift is Amazon’s analytic database with ParAccel technology this is designed for heavy lifting, crunching big data queries against large datasets. By using the AWS Data Pipeline, data collecting on RDS databases, users interact with that side of your infrastructure with Amazon EC2, Amazon S3 jobs could then move the data in bulk to your Redshift cluster to run those “heavy” queries.
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