TGW insights¶
Use CrateDB to optimize distribution centers in warehouse logistics.
About
TGW Logistics Group implements advanced analytics for automated warehouses operating across the globe for clients like Amazon, Coop, and Zalando. Their systems collect a vast amount of data, apply AI to them, and support all kinds of data-driven applications.
Today’s warehouses are complex systems with a very high degree of automation. TGW simplifies aggregating massive volumes of diverse data with CrateDB.
CrateDB’s support for unstructured data, its fast query engine, scalability, and excellent support, is unparalleled.
CrateDB is a highly scalable database for time series and event data with a very fast query engine using standard SQL.
– Alexander Mann, Connected Warehouse Architecture TGW Logistics Group
TGW removed data silos with all different kinds of data formats, data structures from PLCs, databases, sensor information, etc.
NoSQL databases weren’t a sustainable solution for their use case. After trying multiple database systems, TGW Logistics selected CrateDB for its ability to aggregate different data formats and the ability to query this information instantly.
On the migration path, it was easy to start with CrateDB, and now it is at the heart of everything they are doing, and gives them peace of mind.
Alexander Mann
March 20, 2023
5 min watch
See also
Use case:
Accelerate aggregation and access to large volumes of diverse data collected in real-time from warehouse systems around the world.
Challenges:
Data is in many different formats spread across diverse systems worldwide
Classical and NoSQL databases cannot handle the volume of unstructured and object data
Data silos are difficult to combine and query, limiting analytics and modeling capabilities
TGW: Data acquisition in high-speed logistics
Storing, querying, and analyzing industrial IoT data and metadata without any hassle.
– TGW: Fixing data silos in a high-speed logistics environment
TGW Logistics Group implements key factors to the successful operation of these warehouses, by having a holistic view on the entire system acquiring data from various components like sensors, PLCs, embedded controllers, and software systems.
All the components can effectively be seen as individual “data silos”, distributed across the entire site. Each of them stores just some pieces of information in various data structures and different ways to access it.
Alexander Mann, Jan Weber
June 28, 2022
35 min watch
TGW: Challenges in storing and analyzing industrial data
Not all time series are equal: Challenges in storing and analyzing industrial data.
In the second presentation, you will learn how TGW leverages CrateDB to build digital twins of physical warehouses around the world. The unique set of features is suitable for storing and querying complex industrial big data with high variety, unstructured features, and at different data sampling rates.
What’s inside
The Complexity of IoT Data: An examination of the unique properties of industrial IoT data, including slow-moving structured information and high-frequency measurements.
Challenges and Solutions: Discussion of the difficulties in data storage, retention, and integration posed by this complexity, and how CrateDB provides a targeted solution.
Real-World Applications: Exploration of actual customer use cases to illustrate how CrateDB can be applied in various industrial scenarios.
Alexander Mann, Georg Traar
October 5, 2023
20 min watch
- Industry:
Logistics Shipping Warehouse Intelligence
- Tags:
Sensor Data Acquisition Digital Twin