AWS DynamoDB - The Complete Guide (Build 18+ Hands On Demos)

This optional section covers basics of RDBMS, NoSQL and JSON. This is a completely optional section and if you are already familiar with these topics, feel free to skip over.

Let's go over the basics of relational databases. We'll talk about 1:1, 1:N, M:N relationships in RDBMS.

Let's understand the concept of data normalization and go over differerent normalization forms. This will help you appreciate the need for NoSQL databases.

Let me introduce you to the concept of NoSQL databases and also compare SQL with NoSQL. You'll learn how NoSQL databases solve the challenges we face with using relational databases. By the end of this lecture, you'll greatly appreciate the suitability of NoSQL databases for applications that process big data.

Let us learn about different types of NoSQL databases available today and where Amazon DynamoDB fits in.

Let's learn the basics of JSON in this lecture.

I'll introduce you to Node.js and show you how to code Hello World example using Node.js.

In this lecture, we'll cover the basics of ES6 or ECMAScript 6 JavaScript.

Let's continue our discussion on JavaScript with arrays, conditionals and loops.

Let's understand functions and also understand asynchrony in JavaScript. I'll introduce you to callback functions as the most basic form of asynchronous JavaScript.

We'll take the asynchronous JavaScript further with Promises.

Let's learn how to chain multiple promises together, so we are ready for complex JavaScript programming.

Let's learn how to organize our code in different files using modules in Node.js.

In this lecture, we're going to create our own web server to serve our web app and our RESTful API. We'll use Express.js to set up an HTTP server.

Let's create a lightweight REST API and learn how to test it using Postman.

This lecture will give you a 50,000 ft overview of DynamoDB.

We'll quickly compare NoSQL or DynamoDB terminology with SQL.

Tables are the top level entities in DynamoDB. Let me introduce you to DynamoDB tables and we'll also discuss the naming conventions for these tables.

DynamoDB supports variety of data types, broadly classified into scalar types, set types and document types. Let's explore these data types in detail in this lecture.

In this lecture, you'll learn about DynamoDB consistency model and understand how DynamoDB ensures sub-millisecond latency for the read operations.

DynamoDB allows us to control the performance at table level. We do this by defining the desired level of throughput capacity units - read capacity units and write capacity units. These units also largely control the DynamoDB pricing model.

Concept of DynamoDB Partitions is crucial to understanding how DynamoDB works and then use it more efficiently. In this lecture, I will introduce you to the idea of DynamoDB partitions.

DynamoDB supports two types of primary indexes - simple and composite ones. Let's explore these in this lecture.

DynamoDB supports two types of secondary indexes - local secondary index (LSI) and global secondary index(GSI). Let's explore both LSIs and GSIs in this lecture.

Let me introduce you to the three different ways we can work with DynamoDB - Using AWS Console, using CLI and using the AWS SDK.

The easiest way to get around with DynamoDB is the web-based AWS Console. Let's dive in and see how to use it. We'll create table in DynamoDB and also look at how to rename table and delete table in DynamoDB.

Let us perform different item level operations on DynamoDB tables using the AWS web console.

Let's have a quick look at different options available in the DynamoDB web console.

We are going to discuss how to use the AWS CLI or the command line tool to work with DynamoDB. In this lecture, lets install the AWS CLI on our computer.

We'll learn how to perform different table level operations in DynamoDB using the AWS CLI, e.g. list tables, describe table, create table, delete table etc.

Let's add some data to our DynamoDB table using the AWS CLI.

We'll learn how to perform query and scan operations on our DynamoDB tables using the AWS CLI.

In this section, we’ll learn to play with DynamoDB programmatically. We’re going to use the AWS SDK for JavaScript or Node.JS.

Let's perform table level operations like list table, describe table, create table, delete table in DynamoDB using the low-level api in AWS SDK.

We will now use the high level DocumentClient class in AWS SDK to perform item level operations. Let's perform some write operations in DynamoDB using the AWS SDK. For example - PutItem, UpdateItem, DeleteItem and BatchWriteItem etc.

There could be situations where we may want to perform write operations only if a certain condition was met. Let's learn how to perform conditional writes in DynamoDB.

In this lecture, I'm going to show you how to implement atomic counters in DynamoDB using the UpdateItem API.

Let's go over the syntax for read operations using AWS SDK. For example DynamoDB GetItem, DynamoDB Query, DynamoDB Scan and DynamoDB BatchGetItem operations. We'll also learn how to apply filter criteria to our read requests.

DynamoDB returns maximum of 1MB of data for DynamoDB queries and scans. So what do we do if we need more data beyond this limit of 1MB? Let's find that out in this lecture.

Let's look at the DynamoDB architecture in this lecture and understand how DynamoDB partitions are organized.

Before we dive in to DynamoDB, it is crucial to understand the partition behavior in  DynamoDB. Let's dive deeper in to DynamoDB partitions.

You might have realized by now that choosing the table keys, or in other words, key design in DynamoDB is one of the crucial aspects of ensuring efficient and cost-effective performance from DynamoDB.

In this lecture, let's learn how to choose the table keys wisely and ensure efficient performance from DynamoDB.

DynamoDB expects uniform distribution of reads and writes across partition key values. So what happens when DynamoDB sees non-uniform access patterns? Let's discuss two typical use cases here - time-series data and popular datasets and understand how to prevent the issue of hot keys or hot partitions.

In this lecture, let's discuss different design patterns and how to model them in DynamoDB. Specifically, we'll look at how to model one-to-one (1:1), one-to-many (1:N) and many-to-many (M:N) relationships in DynamoDB. We'll also look at modelling hierarchical data structures in DynamoDB.

DynamoDB allows only one attribute as the sort key. What if we wanted to sort the items based on multiple attributes? Let's understand how to model multi-value sorts and filters.

There are certain limits that define the extent to which we can stretch DynamoDB. Some are soft limits, while some are hard limits. In this lecture, let's look at what some of these limits are.

Let's look at some common errors or exceptions in DynamoDB.

Throughout this course, we've learned how to leverage DynamoDB in the most efficient and cost-effective manner. In this lecture, we'll consolidate or summarize these best practices and also introduce some more such approaches.

Let's continue our discussion on DynamoDB Best Practices in this lecture.

Throughout this course, we've discussed different approaches of modelling as well as interacting with DynamoDB tables so as to use it in as cost-efficient way as possible. In this lecture, let's look at DynamoDB Pricing and explore some more ways to cut down DynamoDB cost.

Many a times, read/write loads on our DynamoDB tables vary over the course of the day, week or even month. So, some tables could see high throughput in the morning while low throughput towards the close of the day. Some tables could see high throughput during non-business hours or over weekends.

Let's look at how we can use Auto Scaling featur in DynamoDB to adjust throughput during these uneven workloads.

Many a times, read/write loads on our DynamoDB tables vary over the course of the day, week or even month. So, some tables could see high throughput in the morning while low throughput towards the close of the day. Some tables could see high throughput during non-business hours or over weekends.

DynamoDB Accelerator or DAX provides in-memory caching service for DynamoDB which brings down DynamoDB response times from single digit milliseconds to microseconds.

In this lecture, why don’t we explore DAX in more detail?

In this lecture, let me introduce you to a very powerful feature of DynamoDB, called as DynamoDB Streams. 

So, what are the DynamoDB Streams? Put simply, DynamoDB Streams maintains a time-ordered log of all changes in the given DynamoDB table. This log stores all write activity that took place in the last 24 hours period.

Time to Live, or TTL is a very simple and convenient feature in DynamoDB. It allows us to tell DynamoDB, when to delete an item from the table. Let's have a look.

In this lecture, let us explore global tables. Global tables are DynamoDB’s built-in solution for cross-region replication.

We have covered a lot of ground and by now, you are well-acquainted with Amazon DynamoDB. And, I can as well say, that you’ve acquired a great deal of knowledge, enough to tackle real-world DynamoDB projects.

Now, it’s the time to fine-tune and sharpen your DynamoDB skills further. Rest of the course is designed to boost your confidence levels and take them to the next level. As you follow along, you’ll become highly comfortable to work on real-world DynamoDB projects as a DynamoDB expert.

So, throughout rest of the course, we are going to build various solutions using DynamoDB. These solutions and demos will help you in getting practical exposure in using different advanced features of DynamoDB as well as integrate DynamoDB with other services. We will cover as many examples as possible.