Skip to content

Commit b73c681

Browse files
committed
Solutions: Refurbish whole section
1 parent 235500b commit b73c681

File tree

33 files changed

+994
-663
lines changed

33 files changed

+994
-663
lines changed

docs/_include/card/timeseries-dask.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22

33
:::{grid-item}
44
:columns: auto 9 9 9
5-
**Notebook: How to Build Time Series Applications with CrateDB**
5+
**Notebook: How to build time series applications with CrateDB**
66

77
This notebook illustrates how to import and work with time series data using
88
CrateDB and [Dask DataFrame]s.

docs/_include/card/timeseries-explore.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@
22

33
:::{grid-item}
44
:columns: auto 9 9 9
5-
**CrateDB for Time Series Modeling, Exploration, and Visualization**
5+
**CrateDB for time series modeling, exploration, and visualization**
66

77
Access time series data from CrateDB via SQL, load it into pandas DataFrames,
88
and visualize it using Plotly.

docs/_include/card/timeseries-intro.md

Lines changed: 22 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -3,8 +3,8 @@
33
:padding: 0
44
:gutter: 2
55

6-
::::{grid-item-card} {material-outlined}`topic;2em` Time Series: Device Readings with Metadata
7-
:link: guide:timeseries-objects
6+
::::{grid-item-card} {material-outlined}`topic;2em` Time series: Device readings with metadata
7+
:link: timeseries-objects
88
:link-type: ref
99
:class-footer: text-smaller
1010

@@ -19,11 +19,11 @@ for fast aggregations.
1919
- Relational JOIN operations.
2020
- Common table expressions (CTEs).
2121
+++
22-
Combine time series data with document data: CrateDB is all you need.
22+
Combine time series with document data: CrateDB is all you need.
2323
::::
2424

25-
::::{grid-item-card} {material-outlined}`lightbulb;2em` Time Series: Analyzing Weather Data
26-
:link: guide:timeseries-analysis-weather
25+
::::{grid-item-card} {material-outlined}`lightbulb;2em` Time series: Analyzing weather data
26+
:link: timeseries-analysis-weather
2727
:link-type: ref
2828
:class-footer: text-smaller
2929
CrateDB provides advanced SQL features for querying time series data.
@@ -40,5 +40,22 @@ CrateDB provides advanced SQL features for querying time series data.
4040
Advanced queries on time series data: CrateDB is all you need.
4141
::::
4242

43+
::::{grid-item-card} {material-outlined}`area_chart;2em` Time series: Process financial data
44+
:link: pandas-tutorial-jupyter
45+
:link-type: ref
46+
:class-footer: text-smaller
47+
Acquire and store historical data from S&P-500 companies into CrateDB
48+
using Python.
49+
50+
:::{rubric} What's Inside
51+
:::
52+
- Acquire historical stock ticker data from the Yahoo! Finance API.
53+
54+
- Store data into CrateDB.
55+
56+
- Query back data from CrateDB.
57+
+++
58+
Custom ETL tasks: CrateDB is all you need.
59+
::::
4360

4461
:::::

docs/admin/sharding-partitioning.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,6 @@
11
(sharding-partitioning)=
22

3-
# Sharding and Partitioning 101
3+
# Sharding and partitioning 101
44

55
## Introduction
66

docs/feature/query/index.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -53,7 +53,7 @@ FROM OrderedData
5353
ORDER BY location, timestamp;
5454
:::
5555

56-
{{ '{}(#timeseries-analysis-advanced)'.format(tutorial) }}
56+
{{ '{}(#timeseries-analysis-weather)'.format(tutorial) }}
5757
::::
5858

5959
::::{grid-item}

docs/handbook/index.md

Lines changed: 12 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -225,26 +225,29 @@ Load data from many sources into CrateDB.
225225
:link: solutions
226226
:link-type: ref
227227
:link-alt: Solutions built with CrateDB
228-
Learn about solutions built with CrateDB and
229-
how others are using CrateDB successfully.
228+
Learn how to use CrateDB for time series use-cases,
229+
about industry solutions built with CrateDB and
230+
how others are using CrateDB successfully with
231+
both standard software components and in
232+
proprietary system landscapes.
230233
+++
231234
**What's inside:**
232-
Full-text and semantic search, real-time raw-data analytics,
233-
industrial data, machine learning, data migrations.
235+
Time series data. Industrial big data.
236+
Real-time raw-data analytics. Machine learning.
234237
:::
235238

236-
:::{grid-item-card} {material-outlined}`numbers;2em` Topics
239+
:::{grid-item-card} {material-outlined}`numbers;2em` Categories / Topics
237240
:link: topics
238241
:link-type: ref
239242
:link-alt: CrateDB topics overview
240-
Learn how to apply CrateDB's features to optimally cover use-cases
241-
across different application and topic domains.
243+
Learn how to apply CrateDB's features to optimally cover
244+
different application categories and topic domains.
242245
For example, connect CrateDB with third-party
243246
software applications, libraries, and frameworks.
244247
+++
245248
**What's inside:**
246-
Business intelligence, data lineage, data visualization,
247-
programming frameworks, software testing, time series data.
249+
Business intelligence, data lineage, data migrations, data visualization,
250+
programming frameworks, software testing.
248251
:::
249252

250253
::::

docs/integrate/pandas/tutorial-jupyter.md

Lines changed: 9 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
11
(pandas-tutorial-jupyter)=
2-
# Automating financial data collection and storage in CrateDB with Python and pandas
2+
# Process financial data using CrateDB, Jupyter, and pandas
33

44
:::{article-info}
55
---
@@ -27,11 +27,14 @@ Before anything else, I must make sure I have my setup ready.
2727

2828
So, let’s get started.
2929

30-
## Setting up CrateDB, Jupyter, and Python
30+
## Prerequisites
31+
32+
You will need access to a CrateDB cluster and a Jupyter environment with
33+
pandas and the psycopg2 packages installed.
3134

3235
### CrateDB
3336

34-
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.
37+
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.
3538

3639
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!
3740

@@ -55,9 +58,9 @@ The [Jupyter Notebook](https://jupyter.org/) is an open-source web application t
5558

5659
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!
5760

58-
I follow the [Jupiter Installation tutorial](https://jupyter.org/install.html) for the Notebook, which is quickly done with Python and the terminal command
61+
I follow the [Jupyter Installation tutorial](https://jupyter.org/install.html) for the Notebook, which is quickly done with Python and the terminal command
5962
`pip3 install notebook`
60-
and now I run the Notebook (using Jupyter 1.0.0) with the command
63+
and now I run the Notebook with the command
6164
`jupyter notebook`
6265

6366
Setup done!
@@ -204,7 +207,7 @@ and it looks like this:
204207

205208
## Connecting to CrateDB
206209

207-
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).
210+
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**.
208211

209212
![connections-for-cratedb-cloud|690x386](https://us1.discourse-cdn.com/flex020/uploads/crate/original/1X/2891e21d7ad9cd34eed068153285530badb0dc66.png){w=800px}
210213

Lines changed: 127 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,127 @@
1+
(bitmovin)=
2+
# Bitmovin insights
3+
4+
:::{div} sd-text-muted
5+
Multi-tenant data analytics on top of billions of records.
6+
:::
7+
8+
:::{rubric} About
9+
:::
10+
11+
Bitmovin is a leading video streaming company that built the world’s
12+
first commercial adaptive streaming player and deployed the first
13+
software-defined encoding service that runs on any cloud platform.
14+
15+
The use-case of Bitmovin illustrates why traditional databases are
16+
incapable of handling so many data records while keeping them all
17+
available for querying in real time.
18+
19+
> CrateDB enables use cases we couldn't satisfy with other
20+
> database systems, also with databases which are even stronger
21+
> focused on the time series domain.
22+
>
23+
> CrateDB is not your normal database!
24+
>
25+
> <small>-- Daniel Hölbling‑Inzko, Director of Engineering Analytics, Bitmovin</small>
26+
27+
:::{rubric} See also
28+
:::
29+
30+
:::{card} Bitmovin: Analyzing large volumes of video streaming events while reducing the cost of analytics
31+
:link: https://cratedb.com/stories/bitmovin
32+
:link-type: url
33+
CrateDB forms the backbone of Bitmovin's real-time video analytics platform.
34+
35+
Bitmovin's database cluster includes 14 nodes, storing 140 terabytes worth
36+
of structured data such as user events and user interactions.
37+
The video analytics application adds around 2 billion new events per day,
38+
with the largest tables comprising around 60 billion playback events in total.
39+
:::
40+
41+
42+
:::::{info-card}
43+
44+
::::{grid-item}
45+
:columns: 6
46+
47+
{material-outlined}`analytics;2em` &nbsp; **Real-time analytics on user events**
48+
49+
<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>
50+
51+
<small>-- [Bitmovin: Improving the streaming experience with real-time analytics]</small>
52+
::::
53+
54+
::::{grid-item}
55+
:columns: 6
56+
57+
Bitmovin, as a leader in video codec algorithms and as a web-based video
58+
stream broadcasting provider, produces billions of rows of data and stores
59+
them in CrateDB, allowing their customers to do analytics on it.
60+
61+
One of their product's subsystems, a video analytics component, required to
62+
serve real-time analytics on massive, fast-moving data, so they needed
63+
to find a performing database at the right cost.
64+
65+
:::{article-info}
66+
---
67+
author: Daniel Hölbling‑Inzko, Georg Traar
68+
date: October 14, 2022
69+
read-time: 50 min watch
70+
class-container: sd-p-2 sd-outline-muted sd-rounded-1
71+
---
72+
:::
73+
::::
74+
75+
:::::
76+
77+
78+
:::::{info-card}
79+
80+
::::{grid-item}
81+
:columns: 6
82+
83+
{material-outlined}`video_camera_back;2em` &nbsp; **Live video broadcasting campaigns**
84+
85+
<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>
86+
87+
<small>-- [How Bitmovin uses CrateDB to monitor the biggest live video events]</small>
88+
::::
89+
90+
::::{grid-item}
91+
:columns: 6
92+
93+
Bitmovin produces billions of rows of data and stores it in CrateDB.
94+
In this talk, Daniel explains how Bitmovin uses CrateDB to monitor
95+
the most significant live video events and especially which features
96+
they are using to address their monitoring and scalability challenges.
97+
98+
Learn also about their typical queries and how the support from Crate\.io
99+
helps them in their day-to-day data operations.
100+
101+
:::{article-info}
102+
---
103+
author: Daniel Hölbling‑Inzko
104+
date: Nov 15, 2022
105+
read-time: 35 min watch
106+
class-container: sd-p-2 sd-outline-muted sd-rounded-1
107+
---
108+
:::
109+
::::
110+
111+
:::::
112+
113+
114+
:Industry:
115+
{tags-secondary}`Broadcasting`
116+
{tags-secondary}`Media Transcoding`
117+
{tags-secondary}`Streaming Media`
118+
119+
:Tags:
120+
{tags-primary}`Event Tracking`
121+
{tags-primary}`Real-Time Analytics`
122+
{tags-primary}`Multi Tenancy`
123+
{tags-primary}`SaaS`
124+
125+
126+
[Bitmovin: Improving the streaming experience with real-time analytics]: https://youtu.be/4BPApD0Piyc?feature=shared
127+
[How Bitmovin uses CrateDB to monitor the biggest live video events]: https://youtu.be/IR6hokaYv5g?feature=shared

0 commit comments

Comments
 (0)