Amazon DynamoDB: Advanced Developer's Guide

Compare NoSQL and relational databases, highlighting data models, schemas, scalability, consistency, and query languages, and discuss suitable use cases and polyglot persistence.

DynamoDB and MongoDB contrast data models, query capabilities, scalability, and consistency. DynamoDB supports key-value and document models with automatic scaling, single-item transactions, and managed deployment for real-time and IoT use.

Master data modeling in DynamoDB by designing tables, items, and attributes with partition keys and sort keys. Use composite keys to organize data and optimize NoSQL access patterns.

Explore how composite keys in DynamoDB use a partition key and a sort key to uniquely identify items and enable efficient, date-based queries by customer.

Compare provisioned throughput and on-demand pricing in DynamoDB, and learn how reads, writes, storage, global tables, streams, and data transfer influence costs.

Explore how to model DynamoDB costs using the AWS pricing calculator, comparing provisioned and on-demand capacity, table classes, storage, writes, reads, backups, and API usage.

Explore how items and attributes populate DynamoDB, detailing primary keys, scalar types, sets, maps, and lists through product item examples and the JSON structure.

Explore how to manage a DynamoDB table with the AWS Management Console, including updating settings, CloudWatch alarms, capacity modes, backups, secondary indexes, export to S3, streams, TTL, and access control.

Install and configure the AWS CLI, create an IAM user with DynamoDB access, and use AWS CLI commands to describe, create, put items, and scan DynamoDB tables.

Explore DynamoDB SDK options across languages (dotnet, JavaScript, Java, etc.), learn to create tables, put and update items, query, and list tables with code examples in AWS SDK documentation.

Set up a local DynamoDB using Docker, run the DynamoDB local image on port 8000, and use AWS CLI with the endpoint URL for development.

Discover NoSQL workbench for DynamoDB, a graphical tool that combines DynamoDB local with remote connections to visualize data modeling, run operations, and generate code.

Explore DynamoDB scan operations that read all items and are less efficient than queries. Learn to limit, paginate, and filter results while managing RCUs and costs.

Explore DynamoDB query operations that retrieve items by primary key with an optional sort key, using key conditions, filter expressions, and projection expressions to improve efficiency and reduce costs.

Learn put item, update item, and delete item in Amazon DynamoDB; put item adds or replaces by primary key, update item changes attributes, and delete item removes the item.

Explore batch operations in dynamodb, using batch get item to fetch multiple records and batch write item to insert, update, or delete items across tables with a performance boost.

Explore DynamoDB console metrics and CloudWatch monitoring, including RCUs, WCUs, throttling, latency, conditional checks, item count, capacity utilization, and global secondary index metrics.

Explore CloudWatch alarms for DynamoDB, covering metric thresholds, periods, evaluation, and actions; monitor okay, alarm, and insufficient data states while sending notifications via SNS.

Learn DynamoDB error handling by distinguishing service and user errors, HTTP status codes, and key codes like provisioned throughput exceeded, not found, and conditional check failed, with troubleshooting tips.

Compare provisioned and on-demand capacity in DynamoDB, noting minimum capacity, budgets, throttling, auto scaling, region-based pricing, and first 25GB free.

Learn how DynamoDB auto scaling adjusts read and write capacity to meet traffic, using a target utilization and configurable min and max units, and apply to tables and GSIs.

Learn how local and global secondary indexes in DynamoDB boost query performance, with LSIs using the table's partition key and a sort key, and GSIs offering separate keys and projections.

Import data from S3 into DynamoDB tables via the console, configure CSV settings and keys, then create a global secondary index with defined keys and projections, monitoring imports with CloudWatch.

Learn to create and validate DynamoDB global secondary indexes with NoSQL Workbench, import CSV data from S3, and ensure correct data types for numbers vs strings.

Explore how sparse indexes and global secondary indexes optimize DynamoDB queries for a subset of data based on availability, such as available books.

Migrate data from MySQL or SQL Server to DynamoDB using AWS data migration service, creating replication instances, endpoints, and a migration task with options like migrate existing data.

Enable on-demand backups and point-in-time recovery to continuously protect DynamoDB data, create backups in the same region, and restore tables with configurable settings and post-restore steps.

Offload large attribute values to S3 to stay under 400 KB per item, enabling direct browser delivery via CloudFront and lighter DynamoDB scans.

Discover how DynamoDB shards data across partitions and how adaptive capacity scales throughput. Identify hot and cold partitions and mitigate with auto scaling, on-demand capacity, and global secondary indexes.