Docs Release 2024-02-001

.github/workflows/build-commit-subfolder.yaml

@@ -166,7 +166,7 @@ jobs:
     # sed -i 's/old-text/new-text/g' input.txt
     - name: Update intro.md
       shell: bash
-      run: "sed -i 's,https://github.com/quixio/.github/blob/main/profile,./assets,g' docs/quix-streams/quix-streams-intro.md"
+      run: "sed -i 's,https://github.com/quixio/quix-streams/blob/main/images,./assets,g' docs/quix-streams/quix-streams-intro.md"
 
     # Change image paths from ./images to ./assets/client-library
     - name: Update intro.md

.github/workflows/sync-build-deploy.yaml

@@ -161,7 +161,7 @@ jobs:
       # sed -i 's/old-text/new-text/g' input.txt
       - name: Update intro.md
         shell: bash
-        run: "sed -i 's,https://github.com/quixio/.github/blob/main/profile,./assets,g' docs/quix-streams/quix-streams-intro.md"
+        run: "sed -i 's,https://github.com/quixio/quix-streams/blob/main/images,./assets,g' docs/quix-streams/quix-streams-intro.md"
 
       # Change image paths from ./images to ./assets/client-library
       - name: Update intro.md

README.md

@@ -1,5 +1,5 @@
 # Quix Developer Documentation
-
+ 
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Build](https://github.com/quixio/quix-docs/actions/workflows/sync-build-deploy.yaml/badge.svg)](https://github.com/quixio/quix-docs)
 

changelogs/2022-archive.md

@@ -1,4 +1,4 @@
-# 2022 changelog archive
+# Quix product 2022 changelog archive
 
 ## 1.43.0 | 3 NOVEMBER 2022
 

docs/get-started/welcome.md

@@ -1,6 +1,6 @@
-# Welcome to Quix developer documentation
+# Quix developer docs
 
-<p style="font-size: 1rem;">Quix is a complete solution for building, deploying, and monitoring event streaming applications.</p>
+<p style="font-size: 1rem;">Quix is a complete platform for building, deploying, and monitoring stream processing pipelines.</p>
 
 <div>
 <a class="md-button md-button--primary" href="./quickstart.html" style="margin-right:.5rem;">Quickstart</a>
@@ -27,25 +27,25 @@ If you're new to Quix, here are some resources to help get you started quickly.
 
     ---
 
-    Build a complete event streaming application in under 30 minutes.
+    Build a complete Python stream processing application in under 30 minutes.
 
     [Quix Tour :octicons-arrow-right-24:](./quixtour/overview.md)
 
-- __Create a project from scratch__
+- __Get started with a project template__
 
     ---
 
-    Learn how to create your own Quix project from scratch.
+    Dig deep into Quix using one of our project templates.
 
-    [Create a project :octicons-arrow-right-24:](../create/overview.md)
+    [Project templates :octicons-arrow-right-24:](../get-started/project-templates.md)
 
-- __Get started with a project template__
+- __Learn about Quix Streams__
 
     ---
 
-    Dig deep into Quix using one of our project templates.
+    Learn about Quix Streams, the open source Python stream processing library for Quix.
 
-    [Project templates :octicons-arrow-right-24:](../get-started/project-templates.md)
+    [Quix Streams :octicons-arrow-right-24:](../quix-streams/quix-streams-intro.md)
 
 </div>
 

docs/get-started/what-is-quix.md

@@ -1,6 +1,11 @@
+---
+title: What is Quix?
+description: Quix is a complete solution for building, deploying, and monitoring stream processing pipelines with Python, Docker, Kafka, and Git.
+---
+
 # What is Quix?
 
-Quix is a complete solution for building, deploying, and monitoring event streaming applications. 
+Quix is a complete platform for building, deploying, and monitoring streaming data pipelines. It enables software developers, data scientists, and data engineers who use Python to focus on app development and extracting value from real-time data pipelines, rather than dealing with the underlying infrastructure. 
 
 Streaming data applications, where you need to process time series or event data in order to make decisions in real time, is what Quix is designed for. 
 
@@ -14,28 +19,39 @@ Quix has excellent synergy with Machine Learning (ML) systems too. You can quick
 
     [Sign up for free](https://portal.platform.quix.io/self-sign-up){target=_blank}.
 
+## Quix in brief
+
+Briefly, here's how you would build a stream processing pipeline with Quix:
+
+1. Configure data sources and destinations (see [connectors](#connectors)). 
+2. Write your stream processing application in Python using [Quix Streams](../quix-streams/quix-streams-intro.md), an open source stream processing library. 
+3. Deploy your application to production. The application takes input data from a source and transforms it. When stream processing is finished, output data is sent to a destination system.
+4. Use Quix’s built-in observability tools to continuously monitor your pipeline and quickly identify any issues. 
+
 ## Reducing complexity
 
-Quix is also designed to remove as much complexity as possible from the process of creating, deploying, and monitoring your event streaming applications.
+Quix is designed to remove as much complexity as possible from the process of creating, deploying, and monitoring your streaming data pipelines.
 
-Quix leverages industry-standard technologies, such as Kafka to provide the core functionality for streaming data, Kubernetes for scaling your deployments, InfluxDB and MongoDB for data persistence, Git for revision control, and Python as the main language for programming your solutions.
+Quix leverages industry-standard technologies, such as Kafka to provide the core functionality for data streaming, Kubernetes for scaling your deployments, InfluxDB and MongoDB for data persistence, Git for revision control, and Python as the main language for programming your solutions.
 
 The following sections take a look at the key components of creating your streaming data solutions:
 
-* Connecting your data to Quix
-* Developing your application
+* Quix Cloud online development platform
+* Quix architecture
+* Integrating your data with Quix
+* Building your Python stream processing pipelines with Quix
 * Deploying (and scaling) pipelines
 * Monitoring and managing your data
 
 While this short introduction to Quix is intentionally brief, there are abundant links for more detailed information you can follow to increase your knowledge of Quix. Alternatively, simply drop into the [Quix Community](https://quix.io/slack-invite){target=_blank} and ask any question you may have.
 
 ## Quix Cloud
 
-Quix Cloud provides everything a developer needs to build industrial-strength event streaming applications. 
+Quix Cloud provides everything a developer needs to build industrial-strength stream processing applications. 
 
 The components that make up the Quix Cloud enable developers to:
 
-* Use a full web-based IDE with version control and logging, to build, deploy, and monitor their event streaming applications. 
+* Use a full web-based IDE with version control and logging, to build, deploy, and monitor their Python stream processing applications. 
 * Have abstracted access to underlying broker infrastructure, including fully-managed Kafka topics.
 * Single-click deployment to the Quix serverless compute environment for hosting your web-based real-time streaming applications.
 * Connect existing web applications and IoT clients.
@@ -48,7 +64,7 @@ Similarly, Quix provides Quix-hosted Kafka, but you can also use Confluent Cloud
 
 This section describes the main technical components and architecture of Quix.
 
-![Quix Technical Architecture](../images/quix-technical-architecture.png)
+![Quix Technical Architecture](../images/quix-technical-architecture.jpg)
 
 ### Quix UI
 
@@ -64,16 +80,22 @@ To achieve these goals, the Quix UI includes the following features:
 
 * **Code Samples**: Choose from the [prebuilt Code Samples](../connectors/index.md) ready to run and deploy from the IDE.
 
+* **Project templates**: Open source application templates that demonstrate what’s possible with Quix. You can fork them and use them as a starting point to build your own Python stream processing pipelines. 
+
 * **One-click deployments**: Configure, deploy, and manage your streaming applications with a simple user interface.
 
 * **Monitoring tools**: Monitor the status and data flow of your streaming applications in real time.
 
 * **Broker management**: Create, delete, explore and configure your self-hosted message broker infrastructure from the UI.
 
+* **Built-in secrets management**: Store the passwords and APIs your stream processing pipelines need securely.
+
 * **Pipeline view**: Visualize your pipeline architecture with the information provided from the deployment variables. You can also monitor your pipeline in real time.
 
 * **Data Explorer**: Explore live and historical data of your applications to test that your code is working as expected.
 
+* **Command-line Interface (CLI)**: Powerful command-line tool for managing all aspects of your Python stream processing pipelines, including permissions.
+
 ### Git integration
 
 Quix has the ability to create projects where all code and configuration is contained in a Git repository. This Git repository can be hosted by Quix (using Gitea), or on any third-party Git provider, such as GitHub, or Bitbucket, where you can configure the Quix SSH public key provided to you for Git provider authentication. This helps integrate Quix with your existing workflows.
@@ -92,7 +114,7 @@ This enables you to select a Kafka hosting option according to requirements. For
 
 !!! tip
 
-    To get your event streaming application to the testing stage as quickly as possible, the Quix-hosted Kafka option is recommended, as it requires zero configuration to get Kafka running. You can focus on your application code, without needing to do the up front work of creating a powerful scalable Kafka cluster, as that work has already been done for you by Quix.
+    To get your Python stream processing applications to the testing stage as quickly as possible, the Quix-hosted Kafka option is recommended, as it requires zero configuration to get Kafka running. You can focus on your application code, without needing to do the up front work of creating a powerful scalable Kafka cluster, as that work has already been done for you by Quix.
 
 Read more about [selecting your broker option](../create/create-environment.md#broker-settings).
 
@@ -102,15 +124,17 @@ When you create an application from a template in Quix, a Dockerfile is provided
 
 ### Infrastructure as code
 
-When you develop your event streaming solution, you will build a pipeline of services that can be viewed in Quix. Each service in the pipeline is individually coded, using a standard component, a modified template, or even completely from scratch. Depending on the use case, pipelines can be quite complex, and in the past, this has made them time consuming to recreate. Now Quix supports infrastructure as code. Your entire pipeline can be defined by a single `quix.yaml` file, and this file can be used to completely and easily reconstruct a pipeline from its corresponding repository.
+When you develop your Python stream processing applications, you build a pipeline of services that can be viewed in Quix. Each service in the pipeline is individually coded, using a standard component, a modified template, or even completely from scratch. Depending on the use case, pipelines can be quite complex, and in the past, this has made them time consuming to recreate. Now Quix supports infrastructure as code. Your entire pipeline can be defined by a single `quix.yaml` file, and this file can be used to completely and easily reconstruct a pipeline from its corresponding repository.
 
-## Interfacing with Quix
+## Integrating your data with Quix
 
 There are [various ways](../develop/integrate-data/overview.md) to connect your data to Quix. Quix provides a number of [connectors](../connectors/index.md) that you can use with only some simple configuration. In addition, there are a range of [APIs](#apis), both REST and WebSockets that are available. There is also the [Quix Streams](#quix-streams) client library, that can be used to get data quickly and easily into Quix.
 
 For a simple example of getting data from your laptop into Quix, see the [Quickstart](quickstart.md).
 
-Quix provides numerous standard [connectors](../connectors/index.md) for both source, and destination functions. In addition a number of transforms are also available. 
+### Connectors
+
+Quix provides numerous standard [connectors](../connectors/index.md) for both source, and destination functions. These enable you to easily stream data in and out of Quix. In addition, a number of prebuilt data transforms to perform processing on your streaming data are also available. 
 
 !!! tip
 
@@ -127,13 +151,11 @@ Quix provides several APIs to help you work with streaming data. These include:
 
 ### Quix Streams
 
-As you will notice as you explore the various open source code samples and connectors that come with Quix, Quix also provides a complete client library, Quix Streams, to reduce development times, and provide advanced features such as automatic scaling through Streams.
+As you will notice as you explore the various open source code samples and connectors that come with Quix, Quix also provides a complete client library, Quix Streams, to reduce development times, and provide advanced features such as automatic scaling through Streams. Quix Streams is somewhat similar to Kafka Streams, in the sense that both libraries process data from a Kafka topic. However, unlike Kafka Streams, Quix Streams enables you to process data using pure Python.
 
 Python is the dominant language for data science, data engineering, and machine learning, but it needs to be interfaced carefully with streaming technologies, such as Kafka, which are predominantly written in Java and Scala.
 
-Quix Streams provides Python and C# developers with a client library that abstracts the complexities of building streaming applications.
-
-For Python developers, Quix Streams can provide streaming data packaged in a data frame, so you can write data processing logic and connect it directly to the abstracted broker. Developers can read about the most important streaming concepts in the [Quix Streams introduction](../quix-streams/client-library-intro.md).
+For Python developers, Quix Streams can provide streaming data packaged in a data frame, so you can write data processing logic and connect it directly to the abstracted broker. Developers can read about the most important streaming concepts in the [Quix Streams introduction](../quix-streams/quix-streams-intro.md).
 
 ## Building with Quix
 
@@ -143,7 +165,7 @@ These are described in more detail in the following sections.
 
 ### Pipelines
 
-Event stream processing is implemented by building pipelines consisting of a series of applications deployed to Kafka and Kubernetes clusters. These processing pipelines are now described by a single YAML file, `quix.yaml`. With just this file, you can reconstruct any pipeline. 
+Python stream processing applications are implemented by building pipelines consisting of a series of applications deployed to Kafka and Kubernetes clusters. These stream processing pipelines are described by a single YAML file, `quix.yaml`. With just this file, you can reconstruct any pipeline. 
 
 Further, changes in this file in one environment can be merged into another environment, giving you the ability to test changes in one environment, before deploying into another, while the change history is retained in Git. 
 
@@ -158,16 +180,17 @@ You can see how to build a simple pipeline in the [Quix Tour](./quixtour/overvie
 
 ### Multiple environments
 
-In Quix you create a project to contain your event stream processing pipeline. A project corresponds to  a Git repository, either hosted by Quix, or alternatively using an external Git provider such as GitHub. Within a project you can create multiple environments, containing your event stream processing pipelines. Each environment is associated with a Git branch, so that you can embrace the full Git workflow, having for example, production, staging and development branches. You can also configure your preferred Kafka hosting option for the environment too, for example you can choose Quix-hosted Kafka, self-hosted Kafka, or Confluent Cloud.
+In Quix you create a project to contain your Python stream processing application. A project corresponds to  a Git repository, either hosted by Quix, or alternatively using an external Git provider such as GitHub. Within a project you can create multiple environments, containing your stream processing pipelines. Each environment is associated with a Git branch, so that you can embrace the full Git workflow, having for example, production, staging and development branches. You can also configure your preferred Kafka hosting option for the environment too, for example you can choose Quix-hosted Kafka, self-hosted Kafka, or Confluent Cloud.
 
-### Monitoring and managing your data
+## Monitoring and managing your data
 
 Quix provides a suite of tools to enable you to monitor and manage your data. These include:
 
 * Data Explorer - The Data Explorer enables you to view your data graphically in real time. Graph, table and messages views.
 * Logs - Real-time logging information is displayed in a console tab. You also have the option of downloading your logs.
 * CPU monitor - you can monitor the CPU utilization of your deployment in real time.
 * Memory monitor - you can monitor the memory usage of the deployment in real time.
+* CLI - a powerful command line interface (see the [CLI documentation](../get-started/cli.md)).
 
 [See the Data Explorer in action](https://www.loom.com/share/0e3c24fb5f8c48038fe5cf02859b7ebc?sid=743fbdf7-fad5-4c26-831d-b6dad78b9b06).