English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 07m | 203 MB
Exploratory data analytics is a key phase in data science that deals with investigating data to extract insights. In a world of big data, exploring massive datasets is a challenge, since it requires technologies that are scalable, fast, and feature rich. Apache Flink—the popular stream-processing platform—is well suited for this effort. This course focuses on exploring datasets with SQL on Apache Flink. Instructor Kumaran Ponnambalam starts off by reviewing the relational APIs that Flink provides for big data analytics. Kumaran then takes a deeper look at the Table API and SQL functions. He explores various SQL capabilities available for exploring data, including filtering, aggregations and joins. To wrap up, he provides a use case project that allows you to practice your new skills.
Topics include:
- Connectors and integrations available in Flink APIs
- Creating tables from a CSV
- Selecting and filtering table data
- Using aggregation functions in SQL
- Joining tables
- Windowing on streams
- Event time with Flink tables
Table of Contents
1 Apache Flink for exploratory analysis
2 What is Apache Flink
3 Flink relational APIs
4 Integrations and connectors
5 Course prerequisites
6 Setting up the exercise files
7 Creating a table environment
8 Creating tables from a CSV
9 Selecting table data
10 Filtering data in tables
11 Writing tables to files
12 Aggregations on tables
13 Ordering and limiting data
14 Adding new columns
15 Joining tables
16 Working with datasets
17 Challenges with streaming SQL
18 Dynamic tables
19 Appending and retracting data
20 Consuming Kafka sources
21 Running continuous queries
22 Windowing on streams
23 Using tumbling and sliding windows
24 Writing tables to Kafka
25 Working with data streams
26 Using event time
27 Use case problem definition
28 Read source data into a Flink table
29 Compute total scores
30 Compute aggregations
31 Next steps
Resolve the captcha to access the links!