Amazon DynamoDB Training in Hyderabad India

Amazon DynamoDB

The AWS DynamoDB NoSQL database is one of the most powerful and widely used non-relational databases available today. It is a fault tolerant, highly scalable database with tunable consistency that meets the demanding requirements of the can’t fail, must scale systems driving growth for many of the most successful enterprises of today. However, along with that capability comes a new data and programming model that many organizations lack the expertise to use in an optimal fashion. Databases like Cassandra, Voldemort, Riak and Aerospike have copied some of the internals of DynamoDB based on the original Dynamo papers published by Amazon. Learning DynamoDB will give you a leg up with understanding the basics of many NoSQL solutions.

Duration: 35-40 hrs

Prerequisites

Reasonable Java experience or AWS Lambda Node.js experience

Course Content:

1: Introduction to the DynamoDB Database

Overview:

The motivation for non-relational data stores
Why relational databases don’t support modern applications well
the DynamoDB Database at a high-level
Use cases
Features Strengths (Scalability, robustness, linear performance with scale-out), etc.
High Level Structure

Acquiring and Installing the DynamoDB Database

Local Install
Configuration Structure
Accessing from AWS Lambda

2: Overview of Architecture and Data Model

Basic DynamoDB Database Architecture:

Cluster Structure - Nodes
Consistent Hashing, Tokens, Partitioners
Data Replication
Consistency, the CAP theorem, Eventual Consistency

The DynamoDB Data Model:

Data Model and DynamoDB API Introduction
Using DynamoDB to perform queries
Single primary key tables and how to define them using CQL
Inserting Data (INSERT), Upsert/UpdateItem
Querying for Data (SELECT)
DynamoDB Data Types
Working with Primary Keys

3: The DynamoDB Data Model

Compound Primary Keys

Table definition
The partition key and sort columns

Advanced Capabilities

Batches
Filtering results
Local secondary indexes
Global secondary indexes

Simulating Composite Partition Keys

Motivation and uses
Definition

Indexes and Secondary Indexes

Partition Key Indexes
Non-primary Key (Secondary) Indexes

Atomic Counters

Motivation and Uses
Structure, Characteristics, Usage

Working with time series data

Motivation and Uses
Structure, Characteristics, Usage

Document Types

Document type definition (list, and map)
Inserting, Updating, Deleting with Document types

Session 4: Data Consistency

Data Consistency in DynamoDB
What happens under the covers

Session 5: How Things Work

Write Failures
Key and Row Caches
Multi-Data Center Support
Streaming DynamoDB Activity

Session 6: Programming DynamoDB (Labs are either Java or AWS Lambda/Node.js)

API Introduction
Upserts
Deletes
Working with items
Batch operations
Querying
Scanning
Asynchronous Querying
Working with streams
Working with Scans
Object Mapping
Pagination

Session 7: Best Practices

Understanding Partitions
Avoiding hotspots
When to use caching
Using uniform access

Session 8: Production Support

Using CloudWatch to monitor DynamoDB
Using streams to replicate data in another region
Log aggregation with CloudTrail and CloudWatch
Exporting data to S3 with AWS Data Pipeline
Importing data to DynamoDB from S3 using AWS Data Pipeline
Encryption and Masking
Introduction to IAM (roles, resources)
IAM and DynamoDB

Session 9: Integrating DynamoDB with Elastic Map Reduce and RedShift

Introduction to EMR/Hadoop
Exporting data into Hadoop/EMR
Introduction to Hive
Query data from Hive/EMR
Introduction to RedShift
Uploading DynamoDB Data into AWS RedShift
Perform queries in RedShift against DynamoDB tables loaded