- Overview
- Where to Start
- Confluent Cloud
- Stream Processing
- Data Pipelines
- Confluent Platform
- Build Your Own
- Additional Demos
Overview
This is a curated list of demos that showcase Apache Kafka® event stream processing on the Confluent Platform, an event stream processing platform that enables you to process, organize, and manage massive amounts of streaming data across cloud, on-prem, and serverless deployments.
Where to start
The best demo to start with is cp-demo which spins up a Kafka event streaming application using ksqlDB for stream processing, with many security features enabled, in an end-to-end streaming ETL pipeline with a source connector pulling from live data and a sink connector connecting to Elasticsearch and Kibana for visualizations.cp-demo also comes with a tutorial and is a great configuration reference for Confluent Platform.
Confluent Cloud
There are many examples from full end-to-end demos that create connectors, streams, and KSQL queries in Confluent Cloud, to resources that help you build your own demos.You can find the documentation and instructions for all Confluent Cloud demos at https://docs.confluent.io/platform/current/tutorials/examples/ccloud/docs/ccloud-demos-overview.html
| Demo | Local | Docker | Description |
|---|---|---|---|
| Confluent Cloud CLI | Y | N | Fully automated demo interacting with your Confluent Cloud cluster using Confluent Cloud CLI |
| Clients in Various Languages to Cloud | Y | N | Client applications, showcasing producers and consumers, in various programming languages connecting to Confluent Cloud |
| Cloud ETL | Y | N | Fully automated cloud ETL solution using Confluent Cloud connectors (AWS Kinesis, Postgres with AWS RDS, GCP GCS, AWS S3, Azure Blob) and fully-managed ksqlDB |
| ccloud-stack | Y | N | Creates a fully-managed stack in Confluent Cloud, including a new environment, service account, Kafka cluster, KSQL app, Schema Registry, and ACLs. The demo also generates a config file for use with client applications. |
| On-Prem Kafka to Cloud | N | Y | Module 2 of Confluent Platform demo (cp-demo) with a playbook for copying data between the on-prem and Confluent Cloud clusters  |
| GKE to Cloud | N | Y | Uses Google Kubernetes Engine, Confluent Cloud, and Confluent Replicator to explore a multicloud deployment |
| DevOps for Apache Kafka® with Kubernetes and GitOps | N | N | Simulated production environment running a streaming application targeting Apache Kafka on Confluent Cloud using Kubernetes and GitOps |
Stream Processing
| Demo | Local | Docker | Description |
|---|---|---|---|
| Clickstream | N | Y | Automated version of the ksqlDB clickstream demo |
| Kafka Tutorials | Y | Y | Collection of common event streaming use cases, with each tutorial featuring an example scenario and several complete code solutions  |
| Microservices ecosystem | N | Y | Microservices orders Demo Application integrated into the Confluent Platform |
Data Pipelines
| Demo | Local | Docker | Description |
|---|---|---|---|
| Clients in Various Languages | Y | N | Client applications, showcasing producers and consumers, in various programming languages |
| Connect and Kafka Streams | Y | N | Demonstrate various ways, with and without Kafka Connect, to get data into Kafka topics and then loaded for use by the Kafka Streams API |
Confluent Platform
| Demo | Local | Docker | Description |
|---|---|---|---|
| Avro | Y | N | Client applications using Avro and Confluent Schema Registry  |
| CP Demo | N | Y | Confluent Platform demo (cp-demo) with a playbook for Kafka event streaming ETL deployments  |
| Kubernetes | N | Y | Demonstrations of Confluent Platform deployments using the Confluent Operator |
| Multi Datacenter | N | Y | Active-active multi-datacenter design with two instances of Confluent Replicator copying data bidirectionally between the datacenters  |
| Multi-Region Clusters | N | Y | Multi-Region clusters (MRC) with follower fetching, observers, and replica placement |
| Quickstart | Y | Y | Automated version of the Confluent Quickstart: for Confluent Platform on local install or Docker, community version, and Confluent Cloud |
| Role-Based Access Control | Y | Y | Role-based Access Control (RBAC) provides granular privileges for users and service accounts  |
| Replicator Security | N | Y | Demos of various security configurations supported by Confluent Replicator and examples of how to implement them |
Build Your Own
As a next step, you may want to build your own custom demo or test environment.We have several resources that launch just the services in Confluent Cloud or on prem, with no pre-configured connectors, data sources, topics, schemas, etc.Using these as a foundation, you can then add any connectors or applications.You can find the documentation and instructions for these "build-your-own" resources at https://docs.confluent.io/platform/current/tutorials/build-your-own-demos.html.
Additional Demos
Here are additional GitHub repos that offer an incredible set of nearly a hundred other Apache Kafka demos.They are not maintained on a per-release basis like the demos in this repo, but they are an invaluable resource.
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Oracle的官方安装包，除了软件外，还会自带一个示例数据库，准确地说，是几个示例schema，像HR用户，很多教程中用到的测试数据，其实就来自这。 如果是11g的examples，需要从官网，下载这个包， p13390677_112040_Linux-x86-64_6of7.zip 上传服务器，解压，生成examples文件夹，内容如下， [oracle@app examples]$ ls in
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基于矩阵分解的推荐算法（评分数据集中，并不是每个用户都对每个产品进行过评分，所以这个矩阵往往是很稀疏的【所以更应该多注意过拟合的问题】，也就是说用户i对产品j的评分很多地方是空的，ALS所做的事情就是将这个稀疏矩阵通过一定的规律填满，这样就可以从矩阵中得到任意一个user对任意一个product的评分，然后以此为据做推荐。所以说，ALS算法的核心就是：打分矩阵是近似低秩的。换句话说，打分矩阵A(m
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An implementation of the @handsontable/react wrapper.import React from 'react'; import ReactDOM from 'react-dom'; import { HotTable } from '@handsontable/react'; import Handsontable from 'handsontable
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通用范例/范例七: Face completion with a multi-output estimators http://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html 这个范例用来展示scikit-learn如何用 extremely randomized trees, k neares
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http://scikit-learn.org/stable/auto_examples/missing_values.htm 在这范例说明有时补充缺少的数据(missing values)，可以得到更好的结果。但仍然需要进行交叉验证。来验证填充是否合适 。而missing values可以用均值、中位值，或者频繁出现的值代替。中位值对大数据之机器学习来说是比较稳定的估计值。 (一)引入函式库及内
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http://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html 迴归函数採用递增函数。 y[] are inputs (real numbers) y_[] are fitted 这个范例的主要目的： 比较 Isotonic Fit Linear Fit (一) Regression「迴归」 「迴归」就是找一个函
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http://scikit-learn.org/stable/auto_examples/feature_stacker.html 在许多实际应用中，会有很多方法可以从一个数据集中提取特征。也常常会组合多个方法来获得良好的特征。这个例子说明如何使用FeatureUnion 来结合由PCA 和univariate selection 时的特征。 这个范例的主要目的： 资料集：iris 鸢尾花资料集
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通用范例/范例一: Plotting Cross-Validated Predictions http://scikit-learn.org/stable/auto_examples/plot_cv_predict.html 资料集：波士顿房产 特征：房地产客观数据，如年份、平面大小 预测目标：房地产价格 机器学习方法：线性迴归 探讨重点：10 等分的交叉验証(10-fold Cross-Vali
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Ex 1: Plotting Cross-Validated Predictions Ex 2: Concatenating multiple feature extraction methods Ex 3: Isotonic Regression Ex 4: Imputing missing values before building an estimator Ex 7: Face compl
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我正在尝试在Ubuntu14.04 LTS上使用Python2.7构建tensorflow，没有GPU。当我在终端上运行本教程中的以下命令时： 它说无法使用日志进行构建： 这是我的构建环境： OS:Ubuntu14.04 LTS 64位内存12Gib gcc版本：4.8.4 python版本：2.7.6 bazel版本：0.3.2 git版本的tensorflow源代码：v0。11.0rc0-15