New Training: Choose a Datastore at AWS
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New Training: Choose a Datastore at AWS

In this 8-video skill, CBT Nuggets trainer Bart Castle teaches you how to choose an appropriate AWS datastore based on data use and access pattern criteria. Cover AWS services, such as RDS, TimeStream, ElasticSearch, MemcacheD, Redis, and QLDB. Watch this new AWS training.

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This training includes:

  • 8 videos
  • 56 minutes of training

You’ll learn these topics in this skill:

  • Choosing a Datastore at AWS
  • Designing for Data and Workload Characteristics
  • Planning for Relational Normalized Data
  • Designing for Key-Value & NoSQL Datastores
  • Searching and Documenting Databases
  • Low Latency with In-Memory Data
  • Relationships and the Graphing Datastore
  • Time-Series and Ledger Datastores

What are In-Memory Data Stores and What Options Are Available in AWS?

With in-memory datastores, the data is stored not on disks as found in traditional datastores but in fast, random-access memory (RAM). This significantly reduces latency, which makes these types of datastores especially useful for applications that require fast response times where large spikes in traffic can occur, such as real-time analytics, caching, session stores and gaming leaderboards.

AWS offers two in-memory datastore options: Amazon Elasticache for Redis and Amazon ElastiCache for Memcached.

Amazon Elasticache for Redis provides an in-memory nonrelational database with sub-millisecond latency. These datastores can support Redis workloads of more than 6 TB of in-memory data within one cluster. The clusters themselves can support up to 15 shards and you can dynamically scale them to adapt to changes in demand.

Amazon ElastiCache for Memcached provides a Memcached-compatible key-value store that you can use as either a cache or a datastore. This makes it useful for web and e-commerce sites as well as for mobile apps and games.

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