alpinegizmo commented on a change in pull request #11826: URL: https://github.com/apache/flink/pull/11826#discussion_r411665254
########## File path: docs/tutorials/index.md ########## @@ -0,0 +1,186 @@ +--- +title: Hands-on Tutorials +nav-id: tutorials +nav-pos: 2 +nav-title: '<i class="fa fa-hand-paper-o title appetizer" aria-hidden="true"></i> Hands-on Tutorials' +nav-parent_id: root +nav-show_overview: true +always-expand: true +--- +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +* This will be replaced by the TOC +{:toc} + +## Goals and Scope of these Tutorials + +These tutorials present an introduction to Apache Flink that includes just enough to get you started +writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of +(ultimately important) details. The focus is on providing straightforward introductions to Flink's +APIs for managing state and time, with the expectation that having mastered these fundamentals, +you'll be much better equipped to pick up the rest of what you need to know from the more detailed +reference documentation. The links at the end of each page will lead you to where you can learn +more. + +Specifically, you will learn: + +- how to implement streaming data processing pipelines +- how and why Flink manages state +- how to use event time to consistently compute accurate analytics +- how to build event-driven applications on continuous streams +- how Flink is able to provide fault-tolerant, stateful stream processing with exactly-once semantics + +These tutorials focus on four critical concepts: continuous processing of streaming data, event +time, stateful stream processing, and state snapshots. This page introduces these concepts. + +{% info Note %} Accompanying these tutorials are a set of hands-on exercises that will guide you +through learning how to work with the concepts being presented. Review comment: Done. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
