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Use cases

Database for Digital Twins

CrateDB enhances digital twin implementations by managing real-time data from multiple sources with precision and speed. Its scalable ingestion engine and automatic indexing allow instant analysis of complex datasets, ensuring that digital twins stay up-to-date and reflective of real-world systems.

Digital twins offer a way to bridge the gap between the physical and digital worlds. Whether it’s for predictive maintenance, performance optimization, simulation and testing or product lifecycle management, digital twins offer huge potential to improve operational efficiency and position enterprises for future growth.

CrateDB is a perfect database to underpin your digital twin initiative and significantly enhances the effectiveness and capabilities of digital twin implementations while reducing development efforts and optimizing total cost of ownership.

Comprehensive data collection and flexible data modeling

CrateDB can collect and store a wide range of data from various sources: real-time sensor data, historical data, geospatial data, operational parameters, environmental conditions, and other relevant information about the physical entity being modeled.

CrateDB offers the capabilities to store complex objects before even knowing what you want to model. New data types and formats can be added on the fly without any need for human intervention, removing the need of having multiple databases to synchronize.

Read more about flexible data modeling in CrateDB >

Read more about CrateDB for IoT >

Read more about CrateDB for geospatial tracking >

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Scalability and Performance

CrateDB is scalable from one to hundreds of nodes and can handle huge volumes of information. CrateDB also provides high-performance capabilities with query response time in milliseconds to process and analyze the data efficiently - including querying the twins and their relationships - ensuring real-time insights and responsiveness. There is no need to downsample or pre-aggregate the data.

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Data integration

CrateDB offers easy 3rd party integration with many solutions for ingestion, visualization, reporting, and analysis thanks to native SQL and the PostgreSQL Wire Protocol, drivers and libraries for many programming languages, and its REST API.

View a sample list of CrateDB integrations >

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Time-Series Data Management

CrateDB offers advanced time-series capabilities, including instant access to data regardless of the volume of data, thanks to its distributed architecture with efficient sharding and partitioning mechanisms. It supports efficient storage, retrieval, and querying of temporal data to enable trend analysis, forecasting, and historical comparisons.

Read more about CrateDB for time-series >

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Metadata and Contextual Information

CrateDB offers a unique repository to store and retrieve metadata associated with digital twins. This includes information about the physical entity, data sources, data quality and modeling assumptions. Time-series data can be contextualized with this information in real-time. This way, you can easily switch from a technical view to a business view.

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Data Analytics and AI Integration

CrateDB facilitates the integration of data analytics and AI technologies. It supports running complex algorithms, machine learning models, and statistical analysis directly on the stored data. CrateDB also provides APIs, drivers and the PostgreSQL Wire Protocol to connect with external analytics tools and platforms.

Read more about CrateDB for AI/ML >

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Data Innovation Summit 2024

Digital Twins and Generative AI: How TGW Revolutionizes Warehouse Operations with CrateDB's Combination of Time Series, Documents, and Vectors

In this talk, TGW Logistics showcases their use of CrateDB to optimize distribution centers. With up to half a million items handled daily, they focus on automation and data-driven decisions.

Webinar: Digital Twins & Gen AI on Azure

Explore how TGW, a global leader in logistics automation, digitally transformed warehouse operations using Azure. This session delves into the creation of automated warehouses and LLM-based internal Q&A system, answering general questions of employees, providing deep insights based on technical documentation and support tickets, and streamlining sales support. 

Watch the recording

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Today's warehouses are complex systems with a very high degree of automation: TGW simplifies the aggregation of massive volumes of diverse data with CrateDB, gaining valuable insights to improve customer experience and competitive advantage.

"Data comes in many different formats, much of it unstructured, and it is spread across multiple systems, databases, spreadsheets, and documents. Also, our customer warehouse sites are distributed all over the world, filled with robotic and mechatronic devices, along with material flow control and warehouse management software, all feeding us data. It is extraordinarily complex."
Alexander Mann Digital Core TGW
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The key to the successful operation of these warehouses lies in having a holistic digital view of the entire system based on data from various components like sensors, PLCs, embedded controllers and software systems. These components can be seen as "data silos" distributed across the entire site - each of them storing just some pieces of information in various data structures and different ways to access it.

It’s not just time-series data, not just measurements, but also events with many tags and a lot of structured data.

The TGW team is building a cloud platform to connect all customer warehouse sites around the world, acquire data from them, and apply advanced analytics and AI to gain valuable insights and enable more proactive support through data-driven "digital assistants".

Having all this data available and accessible in a digital twin application, users can correlate different data series to perform detailed error analysis, for example. Every time there is a high error rate, users can go back in time, see all relevant machine data and draw their conclusions on how to avoid errors in the future.

TGW Logistics Group is one of the leading international suppliers of material handling solutions. As systems integrator, TGW plans, produces and implements complex logistics centers, from mechatronic products and robots to control systems and software. 

Using CrateDB, TGW accelerates data aggregation and access from warehouse systems worldwide, resulting in increased database performance. The system can handle over 100,000 messages every few seconds.

"CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL".

Alexander Mann
Owner Connected Warehouse Architecture
TGW Logistics Group

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Additional digital twins resources

White Paper

TGW Logistics Redefines Warehouse Intelligence Using CrateDB

TGW simplifies aggregating massive volumes of diverse data with CrateDB, gaining valuable insights to improve customer experience and competitive advantage

Video

Not All Time-Series Are Equal: Challenges of Storing and Analyzing Industrial Data

In this presentation, we demonstrate how TGW leverages CrateDB to build digital twins of physical warehouses around the world. 

Digital twins are virtual representations of physical objects, processes, or systems that exist in the digital realm. They combine real-time data, analytics, and simulation models to create a dynamic, virtual counterpart or mirror image of a physical entity. Digital twins enable organizations to gain deep insights into their physical assets and processes, leading to improved performance, reduced costs, and enhanced decision-making capabilities.

  • Predictive Maintenance: By monitoring real-time data from a physical asset, a digital twin can detect anomalies and predict maintenance needs, optimizing asset performance and reducing downtime.
  • Performance Optimization: Digital twins enable continuous monitoring and analysis of various parameters, allowing for optimization of processes, systems, or products to enhance efficiency and effectiveness.
  • Simulation and Testing: Digital twins can be used for simulating and testing scenarios, allowing for experimentation and evaluation without the need for physical prototypes.
  • Product Lifecycle Management: From design and manufacturing to operation and maintenance, digital twins can provide valuable insights throughout a product's lifecycle, facilitating decision-making and improving overall performance.

FAQ

Digital Twins collect data such as operational status, environmental conditions, and user interactions. This data often comes from sensors, user input, and other data sources. CrateDB handles multiple data models seamlessly within the same database, including structured (table), unstructured (vector, text), semi-structured (JSON), and binary (BLOB).

Digital Twins collect data through interconnected sensors and devices. These devices continuously send real-time data to the digital twin, reflecting the physical object's state. CrateDB can collect and store a wide range of data from various sources, including real-time sensor data, historical data, geospatial data, operational parameters, and environmental conditions, and other relevant information about the physical entity being modeled. 

The data requirements for a Digital Twin include real-time and historical data, high data accuracy, and diverse data types (sensor data, operational data, environmental data). CrateDB offers the capability to store complex objects before even knowing what you want to model. New data types and formats can be added on the fly without any need for human intervention, removing the need for multiple databases to synchronize. 

The database components of a Digital Twin system typically include a data ingestion layer (for data collection), a data processing layer (for data analysis), and a data storage layer (for data archiving). CrateDB significantly enhances the effectiveness and capabilities of Digital Twin implementations while reducing development efforts and optimizing total cost of ownership.

Use cases for Digital Twin include predictive maintenance, process optimization, product development, and real-time monitoring in industries like manufacturing, healthcare, and smart cities.

  • Predictive Maintenance: By monitoring real-time data from a physical asset, a digital twin can detect anomalies and predict maintenance needs, optimizing asset performance and reducing downtime. CrateDB’s real-time data processing capabilities ensure quick detection and response to anomalies. Learn how TGW Logistics uses CrateDB to revolutionize its warehouse operations > 
  • Performance Optimization: Digital twins enable continuous monitoring and analysis of various parameters, allowing for optimization of processes, systems, or products to enhance efficiency and effectiveness. CrateDB’s scalability and performance capabilities ensure efficient handling of large data volumes and complex queries.
  • Simulation and Testing: Digital twins can be used for simulating and testing scenarios, allowing for experimentation and evaluation without the need for physical prototypes.
  • Product Lifecycle Management: From design and manufacturing to operation and maintenance, digital twins can provide valuable insights throughout a product's lifecycle, facilitating decision-making and improving overall performance.