Module 6 - Database Services Flashcards
What relational databases does AWS offer?
Amazon RDS
Amazon RedShift
Aurora
What NoSQL databases does AWS offer?
DynamoDB
ElastiCache
What do I need to consider when deciding on a database product?
- Read and write needs
- Total storage requirements
- Typical object size and nature of access to these objects
- Durability requirements
- Latency requirements
- Maximum concurrent users to support
- Nature of queries
- Required strength of integrity controls
How do you get high availability for database workloads?
Use Multi-AZ deployments with RDS. It auto replicates in another AZ; in case of failure, it switches over to the backup. Then the backup becomes primary and the newly spun up db becomes the backup.
How should I handle read-heavy workloads?
RDS with a Read Replicas. The primary DB is read/write, replicas are read-only. Replicas can be promoted to primary read/write in case of failure.
How does RDS manage encryption at rest?
AWS KMS.
What is Aurora?
A relational database compatible with MySQL and PostgreSQL, but faster. Built for cloud. Managed by RDS so no maintenance.
Stored across 3 AZs with 2 backup copies for each instance, with up to 15 replicas per cluster.
What database should I use if I need high availability?
Aurora.
What if my database needs are highly variable?
Use Aurora Serverless. Auto scales on-demand from a pool of prewarmed instances.
I need a NoSQL database but I don’t want to deal with managing the cluster.
DynamoDB. Handles all the architecture decisions, optionally auto-scales.
What are 2 ways to auto-scale?
Provisioned - set a min and max, looks like steps
On-Demand - for spiky workloads
What are the consistency options for DynamoDB?
Strongly consistent Eventually consistent (default)
What is an AWS option for a data warehouse?
Amazon RedShift. Fully managed, cloud-native.
What are some use cases for RedShift?
- Business intelligence: run high-performance queries on petabytes of semi-structured and structured data
- Operational analytics on events: Bring together structured data from your data warehouse and semi-structured data such as application logs from your S3 data lake to get real-time operational insights on your applications and systems.
- Data as a service: Share data inside and outside your organization for secure and governed collaboration on live data with Amazon Redshift data sharing.
- Predictive analytics: Use SQL to automatically create, train, and deploy Amazon SageMaker models on the data in your data warehouse with Amazon Redshift Machine Learning (preview)
How does RedShift organize data?
By column; ideal for warehousing and analytics, large data sets.