Database Administrator Learning Plan

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Database Administrator Learning Plan (Partner)
Introduction to Building with AWS Databases
Relational databases rely on a series of structures, called tables, to hold data. These tables group data based on a person, place, thing, or event related to that data. These groupings are referred to as entities. Each entity is stored as a table. A column, known as a field, describes one attribute of the entity. A row, known as a record, represents a single instance of an entity. Think of a spreadsheet, where each row has a cell for each column. Each cell can contain a value. Rules within the schema define if the attribute is required or optional. To create a relationship between tables, you first need to ensure that every row in a table is unique. Do this by creating a primary key (for example, a customer ID) in a table to give each record a unique value. A foreign key uses values from the primary key in another table to define a record in the current table. This is what builds a relationship between two tables. Some database engines can enforce this relationship by ensuring that only values from a primary key can be used as a foreign key.
Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks, such as hardware provisioning, database setup, patching, and backups. It frees you to focus on your applications so you can give them the fast performance, high availability, security, and compatibility they need.
Aurora is a MySQL- and PostgreSQL-compatible relational database built for the cloud that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open-source databases.
Semi-structured and unstructured data are often stored in nonrelational database systems, sometimes called NoSQL databases. This term can cause a bit of confusion. It is important to remember that structured query language (SQL) is a way of querying data. It implies precise structure. Nonrelational or NoSQL does not mean the data stored cannot be queried using SQL. One way to think of the term NoSQL is not only SQL.
Using the AWS Schema Conversion Tool
The AWS Schema Conversion Tool (AWS SCT) makes heterogeneous database migrations predictable. It does this by automatically converting the source database schema and a majority of the database code objects—including views, stored procedures, and functions—to a format compatible with the target database. Any objects that cannot be automatically converted are clearly marked so that they can be manually converted to complete the migration.
Introduction to Amazon Aurora with MySQL
Aurora features a distributed, fault-tolerant, self-healing storage system that scales up to 128 tebibytes (TiB) per database instance. It delivers high performance and availability with up to 15 low-latency read replicas, point-in-time recovery, continuous backup to Amazon Simple Storage Service (Amazon S3), and replication across three Availability Zones. With Aurora global databases, you can have one primary AWS Region with up to five secondary, read-only AWS Regions. Write operations are directed to the primary master server while read operations are distributed across secondary Regions for improved read performance by positioning data closer to the user.
Secondary global clusters must be in a different AWS Region than the primary cluster.
Upgrading global databases requires that you upgrade the secondary clusters first before upgrading the primary.
The following features are not supported for Aurora global databases:
 Backtrack
 Parallel Query
 Aurora Serverless v1
If the primary DB cluster of your Aurora global database is based on a replica of an Amazon RDS PostgreSQL instance, you can't create a secondary cluster.
Aurora, by default, uses a shared volume, which replicates your storage six times across multiple Availability Zones.
Note: Aurora MySQL does not currently support MySQL 8.0, so data dictionaries are still stored within metadata and non-transactional tables. However, when using the InnoDB storage engine on an Aurora cluster, Aurora solves the potential crash issue by using a crash-safe, log-structured storage system, which avoids the risk of data loss associated with file-based metadata database engines.
Aurora stores data, schema objects, and metadata in a cluster volume, which is a single, virtual volume that uses solid-state drive (SSD) technology. With Aurora cluster volumes, data is automatically replicated across three different Availability Zones, with each zone containing at least two copies. As long as there is an active instance and at least one copy available in the same AWS Region, your system will continue to operate. This architecture helps ensure that your data is highly durable, lessening the possibility for data loss due to crashes, hardware failure, and outages.
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