From the course: Apache Flink: Exploratory Data Analytics with SQL

Apache Flink for exploratory analysis - SQL Tutorial

From the course: Apache Flink: Exploratory Data Analytics with SQL

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Apache Flink for exploratory analysis

- [Narrator] It's a regular, sunny day in Northern California. A little bit of fog is settling in outside, and all of a sudden you receive an alert on your computer. Looks like your website is getting a ton of hits. Okay, that's great, but what's actually going on? Did a huge selling item just go on sale? Is somebody directing traffic to your website through advertising? Is there something malicious going on? In any case, you need answers. You need to run exploratory analytics in real time, and you need tools for that. That's were Apache Flink comes in. Apache Flink is not only a platform for data processing, it is also a platform for scalable, and fast exploratory data analytics. Apache Flink provides capabilities to analyze large quantities of streaming data, and generate real time insights with low latency. It's growing in popularity because it provides a single platform for big data processing and analytics on both batch and streaming data. Hence, it's a key skill for big data engineers and architects. My name is Kumaran Ponnambalam. In this course, I will show you how to use Flink's relational APIs for both batch and real time exploratory data analytics. I will show you how to consume data from various batch, and streaming sources. Analyze them with Flink's table API and sequels, and push them to various data syncs. I will demonstrate advanced capabilities like stream windowing, and even time processing. I will then help you use these skills in a Use Case project. We will use Java and IntelliJ IDEA for building the course exercises. Please transfer to other related Flink courses for Flink basics, architecture, batch processing, and stream processing. That being said, let's explore how to do exploratory data analytics with Apache Flink.

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