Video description
Building near real-time analytical applications that combine real-time data inserts, updates, and fast analytics is almost impossible with any single Hadoop storage technology. The introduction of Apache Kudu and the "KIKS" stack breaks through this barrier, making it possible to build near real-time analytical applications that are simple, fast, and reliable. In this course, designed for developers, architects, and engineers with some experience working with common Hadoop components (Kafka, Hive, Spark, Impala, etc.), you'll use "KIKS" to create an app that demonstrates the real-time ingestion, persistence, and visualization of time-series events.
Kudu is at the center of this architecture. It combines real-time inserts, random lookups, and fast analytics into a single storage layer without the need for the complexities of the lambda architecture, making time-series and IOT use-cases much easier to conquer than with previous generation big data technologies. The app you'll build uses real-time financial data, but it also applies to use cases in IOT, retail, manufacturing, and other industries with real-time analytical needs.
- Gain hands-on experience building a powerful near real-time analytical application
- Discover how Kudu combines random lookups and fast analytics into a single storage layer
- See how Kudu eliminates the need for the complexities of lambda architecture
- Understand how the "KIKS" stack works to make apps that are fast, simple, and reliable
Publisher resources
Table of contents
- Welcome To The Course 00:00:33
- About The Author 00:01:27
- Time Series Introduction And Data Generation With Kafka 00:11:34
- Kudu Time Series Table Design And Creation 00:11:37
- Near Real-Time Data Ingestion In Kudu With Spark Streaming 00:15:46
- Fast Data Consumption And Analytics In Kudu With Impala 00:12:07
Product information
- Title: Building a Near Real-Time Analytical Application with Kudu
- Author(s):
- Release date: March 2017
- Publisher(s): Infinite Skills
- ISBN: 9781491985731
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