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Solutions > Industries

Technology Platforms

CrateDB empowers Technology Platforms with real-time analytics, scalable data storage, and predictive maintenance capabilities, enabling enhanced user experiences, optimized operations, and efficient IoT data management.

 

Real-time Data Analytics

CrateDB provides technology platforms with the ability to perform real-time analysis of large volumes of data from diverse sources. This enables platforms to gain actionable insights, detect trends, and make data-driven decisions to optimize their operations and enhance user satisfaction.

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Scalable Data Storage

CrateDB offers a scalable and flexible database solution for storing and managing structured and unstructured data. Technology platforms can use CrateDB to efficiently store and retrieve data, ensuring high performance and reliability as their user base and data volume grow.

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Predictive Maintenance

By leveraging CrateDB's capabilities for real-time data processing and predictive analytics, technology platforms can implement predictive maintenance strategies. This allows them to monitor the health and performance of their infrastructure and equipment, detect potential issues early, and schedule maintenance proactively to minimize downtime and maximize uptime.

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User Behavior Analysis

CrateDB enables technology platforms to analyze user behavior data, such as clicks, views, and interactions, in real-time. By understanding how users engage with their platforms, companies can optimize user experiences, personalize content and recommendations, and improve customer satisfaction and retention.
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IoT Data Management

For platforms that incorporate IoT devices and sensors, CrateDB provides a robust solution for managing and analyzing IoT data streams. This enables platforms to monitor and control connected devices, extract valuable insights from sensor data, and optimize IoT deployments for improved efficiency and performance.
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Case Study #1: ABB

Less than 20% of data generated by industrial companies is used and ABB's flagship digital solution wants to change that. ABB Ability Genix, launched in 2020, caters to multiple sectors, combining data-centric approaches with AI/ML and domain knowledge to deliver contextually rich data. It expands beyond sensors and devices to include engineering, design, and IT data, integrating them into the platform with pre-made adapters and ABB's domain knowledge.

Key objectives

  • Perform very fast time-series queries, both with hot and cold data.
  • Process different workloads with no impact on performance.
  • Detect real-time issues through window function aggregation.
 

Why CrateDB?

  • With horizontal scalability, ABB can adapt to every customer situation by adding as many nodes as needed.
  • Data Tiering (Warm/Hot/Cold) allows to optimize hardware costs
  • Multi-platform support to be deployed anywhere

OT-ET-IT-Industrial-Analytics-Industrial-AI

After exploring the market, we found out that CrateDB was the best fit, considering our requirements and use-cases.
Marko Sommarberg Lead, Digital Strategy and Business Development ABB
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1 Mill values/sec 30k to 120k event/sec
Data ingestion Event retrieval

Case Study #2: Bitmovin

Founded in 2013, 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.

Its portfolio includes services for scalable video encoding, the Bitmovin Player for playing videos on all platforms and devices, and a complete end-to-end streaming solution with Bitmovin Streams. The company offers a product for monitoring and analyzing video streaming data, which is used by the biggest media companies around the world to deliver high-quality video experiences to end users.

This function records certain metadata every time a video is played. This provides streaming providers with information on who watched the video, when it was started or stopped, and on which device it is running, among other things. Bitmovin produces billions and billions of lines of data, which streaming providers can use to monitor and analyze their customers’ user video experience.

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Challenge

Since the advent of video streaming, a lot has happened in this area. On the one hand, the quality of the streams is constantly increasing and, on the other hand, the number of companies and service providers offering video-on-demand is also growing. This situation increases the pressure on individual providers not only to offer their users the best content, but also to deliver customized content of the highest quality.

To meet these demands, streaming providers need to know their users, which is the only way they can optimize their offerings. This insight was the starting point for Bitmovin's video analytics solution.

Having already been very successful with its video encoding services and its streaming solutions including video players, Bitmovin started working on its new product, which aims to offer users – regardless of their video player – capacities for the collection and analysis of large and fast-moving data sets in real time.

However, the broadcast of large events, such as the World Cup, provide extremely high data throughput that traditional databases either cannot handle or can only handle at a massive cost. Moreover, Bitmovin did not want to make this solution available only to a small selection of hand-picked users but wanted to roll it out on a broad scale.

Bitmovin needed a database with certain characteristics:

  • A database suitable for huge volumes of data storage and real-time analytics
  • A database that could be operated cost-effectively
  • Easy to scale
  • Be able to process structured and semi-structured data (such as log files) in high quantities and at very high throughput speeds
  • SQL as query language

The databases used up to this point would have been beyond the cost of this project and would not have made sense from a business perspective, so Bitmovin began looking for an alternative database solution.

 

Solution

Bitmovin uses CrateDB as the backend database for its video analytics component. Unlike other CrateDB customers, the company has not opted for a Database-as-a-Service solution in the cloud. The reason is simple: Bitmovin has always managed its very complicated technology stack itself, and also handled the implementation of CrateDB. The CrateDB team provides ongoing support and the technology itself is easy to use from installation to querying. There is no need for a complex architecture or advanced knowledge of SQL to get started.
"Getting Crate's support was a key enabler for us, having their team's support is very helpful."
Daniel Hölbling-Inzko Senior Director of Engineering – Analytics Bitmovin
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The size of Bitmovin's backend is constantly increasing, with the number of nodes doubling since implementation. This is mainly because the system is exposed to a growing data load.

Currently, the database solution includes 14 nodes and 140 terabytes of storage, where the company stores structured data such as user events and user interactions. Every day, the video analytics tool adds around one billion new lines of code, with the largest tables comprising around 60 billion playback events. It is foreseeable that the database will continue to grow in the future.

 

Nodes Terabytes of storage Events in the largest tables New events per day
14 140,000 ~60 billion ~2 billion

 

Bitmovin works closely with the CrateDB team in times of great load peaks, to jointly integrate the insights gained from the increased data load into the product in a timely manner. With the support of CrateDB’s experts, Bitmovin is able to optimally adjust the configuration of the cluster ahead of events with high data load requirements.

 

Benefits

The most important and essential benefit of using CrateDB is that without it, Bitmovin would not be able to offer the video analytics tool at all. Alternative solutions are either not powerful enough or too expensive, so the analytics offering would not have been profitable. Thanks to CrateDB, the company has been able to increase its number of customers, as well as, its revenue. Furthermore, the database is stable and easy to manage, which frees up Bitmovin's developers to focus on more valuable tasks than building a scalable database system.

For Bitmovin, CrateDB is better than comparable products because:

  • The data aggregation performance required by the video analytics component would not be possible with other comparable solutions
  • The capability of scaling the clusters of the database itself
  • Bitmovin would like to accelerate this adaptation to peak loads even further by implementing logical replication, a new feature of CrateDB
  • With CrateDB, real-time video analysis and filling the dashboards with data is faster
"It is through the use of CrateDB that we are able to offer our large-scale video analytics component in the first place. Comparable products are either not capable of handling the large flood of data or they are simply too expensive."
Daniel Hölbling-Inzko Senior Director of Engineering – Analytics Bitmovin
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A perfect example in practice is that Bitmovin would install a second cluster for a large event, such as a global sport event like the World Cup, to handle the expected flood of data. Via logical replication, the collected data can then be transferred to the main cluster for storage and archiving. After the event, the company can then shut down the second cluster again.
"Thanks to our collaboration with the CrateDB team on events that cause peak loads, our customers can count on the reliability of our services, even with very large audiences."
Daniel Hölbling-Inzko Senior Director of Engineering – Analytics Bitmovin
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Real-time analytics use case

Bitmovin produces billions of rows of data and stores it in CrateDB. This allows their customers to do analytics on how their users use the video stream.

The speakers in this webinar share Bitmovin’s experience, challenges, and why they chose CrateDB as the best solution for their business, as well as insights about how businesses are dealing with an increasing need for storage and real-time analytics.

 

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