Solutions: Refurbish whole section

docs/_include/card/timeseries-dask.md

@@ -2,7 +2,7 @@
 
 :::{grid-item}
 :columns: auto 9 9 9
-**Notebook: How to Build Time Series Applications with CrateDB**
+**Notebook: How to build time series applications with CrateDB**
 
 This notebook illustrates how to import and work with time series data using
 CrateDB and [Dask DataFrame]s.

docs/_include/card/timeseries-explore.md

@@ -2,7 +2,7 @@
 
 :::{grid-item}
 :columns: auto 9 9 9
-**CrateDB for Time Series Modeling, Exploration, and Visualization**
+**CrateDB for time series modeling, exploration, and visualization**
 
 Access time series data from CrateDB via SQL, load it into pandas DataFrames,
 and visualize it using Plotly.

docs/_include/card/timeseries-intro.md

@@ -3,8 +3,8 @@
 :padding: 0
 :gutter: 2
 
-::::{grid-item-card} {material-outlined}`topic;2em` Time Series: Device Readings with Metadata
-:link: guide:timeseries-objects
+::::{grid-item-card} {material-outlined}`topic;2em` Time series: Device readings with metadata
+:link: timeseries-objects
 :link-type: ref
 :class-footer: text-smaller
 
@@ -19,11 +19,11 @@ for fast aggregations.
 - Relational JOIN operations.
 - Common table expressions (CTEs).
 +++
-Combine time series data with document data: CrateDB is all you need.
+Combine time series with document data: CrateDB is all you need.
 ::::
 
-::::{grid-item-card} {material-outlined}`lightbulb;2em` Time Series: Analyzing Weather Data
-:link: guide:timeseries-analysis-weather
+::::{grid-item-card} {material-outlined}`lightbulb;2em` Time series: Analyzing weather data
+:link: timeseries-analysis-weather
 :link-type: ref
 :class-footer: text-smaller
 CrateDB provides advanced SQL features for querying time series data.
@@ -40,5 +40,22 @@ CrateDB provides advanced SQL features for querying time series data.
 Advanced queries on time series data: CrateDB is all you need.
 ::::
 
+::::{grid-item-card} {material-outlined}`area_chart;2em` Time series: Process financial data
+:link: pandas-tutorial-jupyter
+:link-type: ref
+:class-footer: text-smaller
+Acquire and store historical data from S&P-500 companies into CrateDB
+using Python.
+
+:::{rubric} What's Inside
+:::
+- Acquire historical stock ticker data from the Yahoo! Finance API.
+
+- Store data into CrateDB.
+
+- Query back data from CrateDB.
++++
+Custom ETL tasks: CrateDB is all you need.
+::::
 
 :::::

docs/admin/sharding-partitioning.md

@@ -1,6 +1,6 @@
 (sharding-partitioning)=
 
-# Sharding and Partitioning 101
+# Sharding and partitioning 101
 
 ## Introduction
 

docs/feature/query/index.md

@@ -53,7 +53,7 @@ FROM OrderedData
 ORDER BY location, timestamp;
 :::
 
-{{ '{}(#timeseries-analysis-advanced)'.format(tutorial) }}
+{{ '{}(#timeseries-analysis-weather)'.format(tutorial) }}
 ::::
 
 ::::{grid-item}

docs/handbook/index.md

@@ -225,26 +225,29 @@ Load data from many sources into CrateDB.
 :link: solutions
 :link-type: ref
 :link-alt: Solutions built with CrateDB
-Learn about solutions built with CrateDB and
-how others are using CrateDB successfully.
+Learn how to use CrateDB for time series use-cases,
+about industry solutions built with CrateDB and
+how others are using CrateDB successfully with
+both standard software components and in
+proprietary system landscapes.
 +++
 **What's inside:**
-Full-text and semantic search, real-time raw-data analytics,
-industrial data, machine learning, data migrations.
+Time series data. Industrial big data.
+Real-time raw-data analytics. Machine learning.
 :::
 
-:::{grid-item-card} {material-outlined}`numbers;2em` Topics
+:::{grid-item-card} {material-outlined}`numbers;2em` Categories / Topics
 :link: topics
 :link-type: ref
 :link-alt: CrateDB topics overview
-Learn how to apply CrateDB's features to optimally cover use-cases
-across different application and topic domains.
+Learn how to apply CrateDB's features to optimally cover
+different application categories and topic domains.
 For example, connect CrateDB with third-party
 software applications, libraries, and frameworks.
 +++
 **What's inside:**
-Business intelligence, data lineage, data visualization,
-programming frameworks, software testing, time series data.
+Business intelligence, data lineage, data migrations, data visualization,
+programming frameworks, software testing.
 :::
 
 ::::

docs/integrate/pandas/tutorial-jupyter.md

@@ -1,5 +1,5 @@
 (pandas-tutorial-jupyter)=
-# Automating financial data collection and storage in CrateDB with Python and pandas
+# Process financial data using CrateDB, Jupyter, and pandas
 
 :::{article-info}
 ---
@@ -27,11 +27,14 @@ Before anything else, I must make sure I have my setup ready.
 
 So, let’s get started.
 
-## Setting up CrateDB, Jupyter, and Python
+## Prerequisites
+
+You will need access to a CrateDB cluster and a Jupyter environment with
+pandas and the psycopg2 packages installed.
 
 ### CrateDB
 
-If you’re new to CrateDB and want to get started quickly and easily, a great option is to try the **Free Tier** in CrateDB Cloud. With the **Free Tier**, you have a limited Cluster that is free forever; no payment method is required. Now, if you are ready to experience the full power of CrateDB Cloud, take advantage of the 200$ in free credits to try the cluster of your dreams.
+If you’re new to CrateDB and want to get started quickly and easily, a great option is to try the **Free Tier** in CrateDB Cloud. With the **Free Tier**, you have a limited Cluster that is free forever; no payment method is required. Now, if you are ready to experience the full power of CrateDB Cloud, take advantage of $200 in free credits to explore CrateDB Cloud's full capabilities.
 
 To start with CrateDB Cloud, [navigate to the CrateDB website](https://cratedb.com/download?hsCtaTracking=caa20047-f2b6-4e8c-b7f9-63fbf818b17f%7Cf1ad6eaa-39ac-49cd-8115-ed7d5dac4d63) and follow the steps to create your CrateDB Cloud account. Once you log in to the CrateDB Cloud UI, select **Deploy Cluster** to create your free cluster, and you are ready to go!
 
@@ -55,9 +58,9 @@ The [Jupyter Notebook](https://jupyter.org/) is an open-source web application t
 
 A Jupyter Notebook is an excellent environment for this project. It contains executable documents (the code) and human-readable documents (tables, figures, etc.) in the same place!
 
-I follow the [Jupiter Installation tutorial](https://jupyter.org/install.html) for the Notebook, which is quickly done with Python and the terminal command
+I follow the [Jupyter Installation tutorial](https://jupyter.org/install.html) for the Notebook, which is quickly done with Python and the terminal command
 `pip3 install notebook`
-and now I run the Notebook (using Jupyter 1.0.0) with the command
+and now I run the Notebook with the command
 `jupyter notebook`
 
 Setup done!
@@ -204,7 +207,7 @@ and it looks like this:
 
 ## Connecting to CrateDB
 
-In the **Overview** tab of my CrateDB Cloud Cluster I find several ways to connect to CrateDB with CLI, Python, JavaScript, among others. So I select the **Python** option and choose one of the variants, such as **psycopg2**(version 2.9.1).
+In the **Overview** tab of my CrateDB Cloud Cluster I find several ways to connect to CrateDB with CLI, Python, JavaScript, among others. So I select the **Python** option and choose one of the variants, such as **psycopg2**.
 
 ![connections-for-cratedb-cloud|690x386](https://us1.discourse-cdn.com/flex020/uploads/crate/original/1X/2891e21d7ad9cd34eed068153285530badb0dc66.png){w=800px}
 

docs/solution/analytics/bitmovin.md

@@ -0,0 +1,127 @@
+(bitmovin)=
+# Bitmovin insights
+
+:::{div} sd-text-muted
+Multi-tenant data analytics on top of billions of records.
+:::
+
+:::{rubric} About
+:::
+
+Bitmovin is a leading video streaming company that built the world’s
+first commercial adaptive streaming player and deployed the first
+software-defined encoding service that runs on any cloud platform.
+
+The use-case of Bitmovin illustrates why traditional databases are
+incapable of handling so many data records while keeping them all
+available for querying in real time.
+
+> CrateDB enables use cases we couldn't satisfy with other
+> database systems, also with databases which are even stronger
+> focused on the time series domain.
+>
+> CrateDB is not your normal database!
+>
+> <small>-- Daniel Hölbling‑Inzko, Director of Engineering Analytics, Bitmovin</small>
+
+:::{rubric} See also
+:::
+
+:::{card} Bitmovin: Analyzing large volumes of video streaming events while reducing the cost of analytics
+:link: https://cratedb.com/stories/bitmovin
+:link-type: url
+CrateDB forms the backbone of Bitmovin's real-time video analytics platform.
+
+Bitmovin's database cluster includes 14 nodes, storing 140 terabytes worth
+of structured data such as user events and user interactions.
+The video analytics application adds around 2 billion new events per day,
+with the largest tables comprising around 60 billion playback events in total. 
+:::
+
+
+:::::{info-card}
+
+::::{grid-item}
+:columns: 6
+
+{material-outlined}`analytics;2em` &nbsp; **Real-time analytics on user events**
+
+<iframe height="300" src="https://www.youtube-nocookie.com/embed/4BPApD0Piyc?si=J0w5yG56Ld4fIXfm" title="YouTube: Bitmovin Real-time Analytics on User Events" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+<small>-- [Bitmovin: Improving the streaming experience with real-time analytics]</small>
+::::
+
+::::{grid-item}
+:columns: 6
+
+Bitmovin, as a leader in video codec algorithms and as a web-based video
+stream broadcasting provider, produces billions of rows of data and stores
+them in CrateDB, allowing their customers to do analytics on it.
+
+One of their product's subsystems, a video analytics component, required to
+serve real-time analytics on massive, fast-moving data, so they needed
+to find a performing database at the right cost.
+
+:::{article-info}
+---
+author: Daniel Hölbling‑Inzko, Georg Traar
+date: October 14, 2022
+read-time: 50 min watch
+class-container: sd-p-2 sd-outline-muted sd-rounded-1
+---
+:::
+::::
+
+:::::
+
+
+:::::{info-card}
+
+::::{grid-item}
+:columns: 6
+
+{material-outlined}`video_camera_back;2em` &nbsp; **Live video broadcasting campaigns**
+
+<iframe height="300" src="https://www.youtube-nocookie.com/embed/IR6hokaYv5g?si=J0w5yG56Ld4fIXfm" title="YouTube: Live Video Broadcasting with CrateDB" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+
+<small>-- [How Bitmovin uses CrateDB to monitor the biggest live video events]</small>
+::::
+
+::::{grid-item}
+:columns: 6
+
+Bitmovin produces billions of rows of data and stores it in CrateDB.
+In this talk, Daniel explains how Bitmovin uses CrateDB to monitor
+the most significant live video events and especially which features
+they are using to address their monitoring and scalability challenges.
+
+Learn also about their typical queries and how the support from Crate\.io
+helps them in their day-to-day data operations.
+
+:::{article-info}
+---
+author: Daniel Hölbling‑Inzko
+date: Nov 15, 2022
+read-time: 35 min watch
+class-container: sd-p-2 sd-outline-muted sd-rounded-1
+---
+:::
+::::
+
+:::::
+
+
+:Industry:
+  {tags-secondary}`Broadcasting`
+  {tags-secondary}`Media Transcoding`
+  {tags-secondary}`Streaming Media`
+
+:Tags:
+  {tags-primary}`Event Tracking`
+  {tags-primary}`Real-Time Analytics`
+  {tags-primary}`Multi Tenancy`
+  {tags-primary}`SaaS`
+
+
+[Bitmovin: Improving the streaming experience with real-time analytics]: https://youtu.be/4BPApD0Piyc?feature=shared
+[How Bitmovin uses CrateDB to monitor the biggest live video events]: https://youtu.be/IR6hokaYv5g?feature=shared