Title: Theory / Unimportant / CS / Cloud / AWS / Database / Generale

Question: Common database categories
Answer: Relational
Page: https://youtu.be/9kclkAqchC0
Context: AWS
Answer2: Key-value
Answer3: Document
Answer4: In-memory
Answer5: graph
Answer6: time-series
Answer7: Ledger

Name: Transactional workload
Definition: Application use cases that are characterized by a high number of concurrent operations and where each operation is reading or writing a small number of rows
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS
Example: most user-facing applications, including ecommerce, mobile gaming, and social networking.
Name2: online transactional processing
Abbreviation2: OLTP

Name: Analytical workload
Definition: use cases that aggregate and summarize large volumes of data.
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS
Definition2: There are usually far fewer concurrent queries in analytical data stores, but they are operating on many more rows per query
Example: Reporting
Name2: online analytical processing
Abbreviation2: OLAP

Name: Caching workload
Definition: you compute and store frequently accessed data in a separate database for faster response times
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS
Intuition: can reduce the load on your transactional database and improve response times to your end users

Name: Application workload
Definition: transactional
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Definition2: analytical
Conjdef: mostly
Definition3: caching
DefType: describes the type of data being stored by your application and the data access patterns

Name: Relational database
Definition: you normalize your data into separate tables and assemble related entities together at query time
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Definition2: strict schema validation and normalized data model help to ensure data integrity across your application.
Conjdef: mostly

Name: Key-value data store
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Name2: Wide-column data store
Intuition: designed for scale. Your data is split across multiple storage nodes, and additional storage nodes can be added as your data grows. This partitioning scheme allows for nearly infinite scalability with no performance degradation.

Name: Document data model
Definition: uses large records called documents to assemble heterogeneous bits of data that are frequently accessed together
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Intuition: Rather than spreading this data across multiple tables, you can keep the data together in a document for faster access.

Name: Graph database
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Example: social networking
Example2: fraud-detection services.
Intuition: emphasizes relationships between data. With a graph database, you traverse relationships between objects to find hidden connections between data

Name: Operations burden
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Example: Being prepared for instance failures, configured backups, and created a plan for upgrades.

Question: What need to be considered to choose a database
Answer: Application workload
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/why-purpose-built-databases/
Context: AWS database
Answer2: Data shape
Answer3: Performance requirements
Answer4: Operations burden

Name: DynamoDB
Definition: a fully managed NoSQL database that provides fast, consistent performance at any scale
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS database

Question: May use of DynamoDB
Answer: High-scale application
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS database
Answer2: Serverless applications

Question: How to model NoSQL database?
Answer: Create an entity-relationship diagram (ERD). An ERD lists the different objects (or entities) in your application and shows how they relate to each other via relationships. This diagram helps you to understand the core concepts in your application.
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS database
Answer2: Understand your access patterns. After you have created your ERD, list the various ways you access your objects in your application. This includes all read-based and write-based access patterns.
Answer3: Design your data model to handle your access patterns. After you have listed your access patterns, craft your data model to handle those specific access patterns.

Name: Simple primary key
Definition: A partition key
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS database

Name: A composite primary key
Definition: A partition key and a sort key
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS database

Name: A primary key
Definition: Either a simple or a composite primary key
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS DynamoDB

Name: A sort key
Definition: ?
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS DynamoDB
Abbreviation: SK

Name: An item collection
Definition: A set of items with the same partition key value
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS DynamoDB

Name: Partition key
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS DynamoDB
Abbreviation: PK

Name: universally unique identifiers
Definition: An ID that is guaranteed to be universally unique
Page: https://aws.amazon.com/getting-started/hands-on/purpose-built-databases/dynamodb/
Context: AWS DynamoDB
Abbreviation: UUID