Apache Flink Relational Programming using Table API and SQL

Apache Flink 因其能够以一种可扩展以满足高吞吐量和高性能用例需求的方式执行高级状态计算的能力而广受欢迎。Apache Flink 不仅具有非常高的可扩展性和高性能，它还与各种源和接收器数据系统集成，如平面文件（CSV、TXT、TSV）、数据库和消息队列（Kafka、AWS Kinesis、GCP Pub/Sub、RabbitMQ ）。

在本课程中，学生将学习利用 Apache Flink 的强大功能，Apache Flink 是一种现代分布式计算框架，可为批处理和流数据处理工作负载提供统一的方法。本课程特别关注通过 Apache Flink 的 Table API 和 SQL 接口（带有 Python 示例）公开的关系编程范式，提供直观而强大的抽象来处理有界（批处理）或无界（流）源中的大量数据。

学生通过许多使用、处理和生成 CSV 格式的文件系统结果的示例，学习使用 Flink 进行批处理。

学生还可以通过几个示例来学习使用 Flink 进行流处理，这些示例在本地 Dockerized Kafka 集群中运行的 Apache Kafka 中使用、处理和生成结果。

Apache Flink 支持使用 Java、Scala 和 Python 中的 Table API 和 SQL 接口开发 Flink 应用程序。但是，本课程侧重于为 Apache Flink 使用 Python 绑定。由于 Python 编程语言的流行，特别是在大数据工程生态系统中的流行，以及由于 Python 在现有的 Apache Flink 课程中的代表性不足，这些课程主要涵盖 Flink 的 Java 和 Scala API，因此选择本课程的重点是 Python .

Genre: eLearning | MP4 | Video: h264, 1280×720 | Audio: AAC, 48.0 KHz
Language: English | Size: 1.74 GB | Duration: 4h 12m
Learn Apache Flink Table and SQL Interfaces via Python to process batch and streaming data workloads at scale

What you’ll learn
Apache Flink Table API
Apache Flink SQL Interface
Apache Flink with Python (PyFlink)
Batch Data Processing
Stream Data Processing

Description
Apache Flink is widely growing in popularity for its ability to perform advanced stateful computations in a way that scales to meet the demands of both high throughput and high performance use cases. Not only is Apache Flink very scalable and performant it also integrates with a wide variety of source and sink data systems like flat files (CSV,TXT,TSV), Databases, and Message Queues (Kafka, AWS Kinesis, GCP Pub/Sub, RabbitMQ).

In this course students will learn to harness the power of Apache Flink which is a modern distributed computing framework providing a unified approach to both batch and streaming data processing workloads. This course specifically focuses on the relational programming paradigm exposed through Apache Flink’s Table API and SQL interface (with examples in Python) offering intuitive yet powerful abstractions to process vast amounts of data in either bounded (batch) or unbounded (streaming) sources.

Students learn batch processing with Flink through many examples of consuming, processing, and producing results from/to the filesystem in CSV format.

Students also learn stream processing with Flink through several examples consuming, processing and producing results from/to Apache Kafka running in a local Dockerized Kafka cluster.

Apache Flink offers support for developing Flink applications with the Table API and SQL interface in Java, Scala and Python. However, this course focuses on using the Python bindings for Apache Flink. The focus on Python for this course was chosen due to the popularity of the Python programming language, particularly in the big data engineering ecosystem, but also due to the underrepresentation of Python in existing Apache Flink courses which primarily cover the Java and Scala APIs of Flink.

Who this course is for:
Data centric Python developers