AWS Database Services for Cloud/DevOps Engineers

📝Introduction

In this post, we will cover the main Technologies from AWS Database Services.

📝Databases

• Databases -> It allows us to collect, store, retrieve, sort, graph, and manipulate data.

  • A database is an organized collection of various forms of data.

  • Databases are used by many applications: web, mobile, service, and more.

• AWS Relational Database Service (RDS) -> It is a service that makes it easy to launch and manage relational databases.

  • Support popular database engines

  • Offer HA and FT using the Multi-AZ deployment option

  • AWS manages the DBs with automatic software patching, automated backups, operating system maintenance, and more.

  • Launch read replicas across Regions in order to provide enhanced performance and durability

    

• AWS Aurora -> It is a relational database compatible with MySQL and PostgreSQL that was created by AWS.

  • Supports MySQL and PostgreSQL database engines

  • 5x faster than normal MySQL and 3x faster than normal PostgreSQL

  • Scales automatically while providing durability and HA

  • Managed by RDS

    

• AWS DynamoDB -> It is a fully managed NoSQL key-value and document database.

  • NOSQL key-value DB

  • Fully managed and serverless

  • Non-relational

  • Scales automatically to massive workloads with fast performance

    

• AWS DocumentDB -> It is a fully managed document DB that supports MongoDB.

  • Document DB

  • MongoDB compatible

  • Fully managed and serverless

  • Non-relational

• AWS ElastiCache -> It is a fully managed in-memory datastore compatible with Redis or Memcached.

  • In-memory datastore

  • Compatible with Redis or Memcached engines

  • Data can be lost

  • Offers high performance and low latency

    

• AWS Neptune -> It is a fully managed graph DB that supports highly connected datasets.

  • Graph DB service

  • Supports highly connected datasets(i.e. social media networks)

  • Fully managed and serverless

  • Fast and reliable

• AWS RedShift -> It uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning.

  • Optimizing high-concurrency, low-latency workloads

  • Data-driven performance optimization

  • 5x better price performance than any other cloud data warehouse at any scale

  • Gain real-time and predictive insights with no data movement or data transformation

  • Insights in second without infrastructure management

  • Most secure and reliable data warehouse service

• Amazon Keyspaces (for Apache Cassandra) -> It is a scalable, highly available, and managed Apache Cassandra–compatible database service.

  • Compatible with Apache Cassandra

  • Fully managed and serverless

  • Data is encrypted

  • Performance at scale

  • High available and secure

• AWS Timestream -> It is a fast, scalable, and serverless time-series database service that makes it easier to store and analyze trillions of events per day up to 1,000 times faster.

  • Automatically scales up or down to adjust capacity and performance

  • Fully managed and serverless

• Amazon Quantum Ledger Database (Amazon QLDB) -> It maintains an immutable, cryptographically verifiable log of data changes.

  • Fully managed and serverless

  • Provides a transparent, immutable, and cryptographically verifiable

  • Trust the integrity of your data

  • Track and maintain a sequenced history of every application data change

  • Supports real-time streaming to Amazon Kinesis

• AWS Database Migration Service (AWS DMS) -> It is a managed migration and replication service that helps move your database and analytics workloads to AWS quickly, securely, and with minimal downtime and zero data loss.

  • Supports homogeneous and heterogeneous database and analytics engines migration (i.e. Oracle to Amazon Aurora MySQL-Compatible Edition, MySQL to Amazon RDS for MySQL, etc)

  • Multi-ZA data replication and monitoring

  • Low cost to migrate Terabyte-sized databases

  • Automated migration

• Databases in the Real World Scenarios:

  • Migrate an on-premises Oracle DB to the Cloud (i.e. RDS)

  • Migrate an on-premises PostgreSQL DB to the Cloud (i.e. RDS, Aurora)

  • Alleviate DB load for data that is accessed often (i.e. ElastiCache)

  • Process large sets of user profiles and social interactions (i.e. Neptune)

  • NoSQL DB fast enough to handle millions of requests per second (i.e. DynamoDB)

  • Operate MongoDB workloads at scale (i.e. DocumentDB)

  • Improve financial and demand forecasts (i.e RedShift)

  • Move Cassandra workloads to the Cloud (i.e Amazon Keyspaces (for Apache Cassandra))

  • Quickly analyze time-series data generated by IoT (i.e Timestream)

  • Store financial transactions (i.e. Amazon Quantum Ledger Database)

  • Build data lakes and perform real-time processing on change data from data stores (i.e. AWS DMS)

Thank you for reading. I hope you were able to understand and learn something helpful from my blog.

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