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AWS Database Services is a wide range of fully managed, purpose-built and both relational and non-relational database services, from fully managed database services, a data warehouse for analytics, to an in-memory data store for caching. Relational: Amazon Aurora, Amazon RDS, Amazon Redshift. Key-value: Amazon DynamoDB. In-memory: Amazon ElastiCache, Amazon MemoryDB for Redis. Document: Amazon DocumentDB (with MongoDB compatibility) Wide column: Amazon Keyspaces. Graph: Amazon Neptune. Time series: Amazon Timestream. Ledger: Amazon Ledger Database Services (QLDB)
AWS Database Services are Amazon’s fully managed and scalable database solution for your applications. Amazon provides a wide range of database options such as Amazon RDS, Aurora, DynamoDB, ElasticCache, and Redshift. In this blog, I will be covering various AWS database services including Amazon RDS; the first AWS database offering. We will also cover basic features, overview, and use cases for each of these services.
Amazon DynamoDB which provides fast and predictable performance with seamless scalability. Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multi-region, multi-active, durable database with built-in security, backup and restores, and in-memory caching for internet-scale applications. In this blog, we are going to cover one of the important Database Services offered by AWS i.e.
Amazon RDS is a Relational Database Service by Amazon Web Services. A relational database is a type of database that stores data in tables with rows and columns. Amazon RDS provides an industry-standard relational database that is cost-efficient with resizable capacity. It gives users solutions for easier setup, operation, and scale of a relational database in the cloud. In this blog, we will cover everything you need to know about Amazon RDS, Amazon RDS Pricing, and a step-by-step tutorial to create a Relational Database on AWS.
Amazon Redshift has been undergoing several enhancements since 2013. One of the critical aspects of this endeavor was moving data to the “Serverless” Amazon Redshift Spectrum. This data storage solution has a massively-scalable feature, with a serverless query processing engine to allow joining data, that sits in Amazon S3 with data in Amazon Redshift. In this blog, we’ll give you insights on the tactful techniques to gather and process large data columns for better decision-making.
It is common scenario when Lambda needs to write and read data from DynamoDB table, we do not need to worry about connectivity between services as traffic goes via the Internet and is allowed by default. What if our scenario is little bit more complicated our Lambda function is deployed inside VPC?
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