This is the code repository for Building a Search Server with Elasticsearch [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
As technology evolves, the constant upsurge of data creation leads to a continuous need for a more flexible, secure, and reliable manner of storage. Luckily, Cassandra is able to tackle this growing challenge. Cassandra is a flagship NoSQL database with decentralized, fault-tolerant, scalable, and low-cost features making it a core component of cloud computing systems. The more recent versions have greatly improved the security features, making it suitable for use in enterprise systems. When combined with Java and Spring frameworks, Cassandra can formulate a complete application stack thereby enabling efficient data management.
Mastering Cassandra is a power house of various advanced techniques that will help you become an expert in professional-level data management. This video course starts off from the essence of the web application architecture to setting up the environment with a single-node Cassandra cluster, and takes you to a level where you can build, implement, and secure web applications with ease.
As the amount of available data continues to grow worldwide, successful search experiences are increasingly becoming a major competitive advantage. The best content in the world is useless if it isn’t easy to find. Elasticsearch is a powerful and well-designed search engine used to build custom search applications that allow users to quickly find the relevant information from their application or website.
This course will give you the tools you need to create your own custom search application. Throughout this course, you’ll see how rewarding Elasticsearch can be to work with and how to use it to build a real search application complete with results, highlighting, filters, and autocomplete.
Starting with an introduction to Elasticsearch and client-side applications, we’ll then move on to learn how Elasticsearch automatically classifies field types, and what to do if they need to be overridden. We will also cover many of the query types that Elasticsearch provides to return results for our AngularJS application. Once we have some basic results, we will add filters (called aggregations in Elasticsearch) to make it easy for users to narrow down the results to a specific topic. Then we will cover how to implement autocomplete and highlighting, and ultimately wrap up with an overview of deployment and security.
This course will equip you with the tools that you need to craft rich search interfaces that deliver great results to your users.
- Ingest real data into your index and create a working Elasticsearch cluster
- Categorize different types of data automatically with Elasticsearch and manage them effectively
- Work with a variety of queries and learn how and when to use them correctly
- Implement exciting features that help you search and highlight data
- Build custom search filters that help you with advanced data search
- Get to grips with the best practices for separating out the structure of an AngularJS application into its various components
- Add a bit more to your search server by applying relevancy tuning and addressing security concerns
To fully benefit from the coverage included in this course, you will need:
This course presents the concepts of data search and shows you how to put them into action with bite-size videos that balance in-depth content with practical implementations.
This course has the following software requirements:
. RAM :
Minimum: 16 GB
Maximum: 64 GB
b. CPU Cores for each Cluster:
Minimum: 2 Cores
Up to: 8 Cores
c. Disk :
Recommended: SSD
SSD-backed nodes see boosts in both query and indexing performance.
d. Disk Size: Should depend on Number of cluster and data on each cluster.