Distributed Databases with Apache Ignite

Course details

Apache Ignite is an open source, distributed database and computing platform that delivers high performance, scalability, and durability for both data-intensive applications and real-time processing. In this course, certified Google cloud architect and data engineer Janani Ravi highlights advanced content on using distributed databases with Apache Ignite. Learn about memory-centric architecture and how it applies to Apache Ignite. Go over running a single node Ignite cluster, and explore the Ignite node running in Docker. Find out how to create an in-memory Ignite cluster and connect to Ignite using DBeaver. Cover ways to store and monitor data, as well as data partitioning and replication. Learn how to run SQL queries and bulk load data using an SQL script, COPYINTO, and streaming mode. Plus, step through how to programmatically access Ignite with Python.

Concepts

0. Introduction

01 - Overview of Apache Ignite

1. Getting Started with Apache Ignite

02 - Memory-centric architecture
03 - The Ignite data model
04 - Running a single node Ignite cluster
05 - Exploring the Ignite node running in Docker
06 - Running an in-memory Ignite cluster
07 - Connect to Apache Ignite using DBeaver

2. Storing and Monitoring Data

08 - Working with in-memory data
09 - Running a cluster with native persistence enabled
10 - Persistent storage in an Ignite cluster
11 - Running a multi-node Ignite cluster
12 - Configuring monitoring using GridGain
13 - Monitoring clusters with GridGain

3. Data Partitioning and Replication

14 - Data partitioning and replication
15 - Creating partitioned tables with affinity keys
16 - Handling partition losses
17 - Creating tables with backups
18 - Handling partition losses with backups
19 - Creating tables with replication
20 - Handling partition losses with replication

4. Running SQL Queries and Bulk Loading Data

21 - Resetting back to Ignite version 2.14.0
22 - Setting up for bulk loading of data
23 - Bulk loading data using a SQL script
24 - Bulk loading data using COPYINTO
25 - Bulk loading data using streaming mode
26 - Indexing

5. Programmatically Accessing Ignite with Python

27 - Using the cache API with pyignite
28 - Populating complex data types in the cache
29 - Retrieving complex data from the cache
30 - Configuring cache expiry
31 - Configuring cache consistency modes
32 - Client failover

Conclusion

33 - Summary and next steps