from 200€/month (excl. tax)
A data hub platform for data engineering, AI and transactional applications.
Rapid.Space BDH is a single platform which supports all data needs including high performance relational store, distributed object store, metadata management and recurring data processing tasks.
Rapid.Space BDH supports the real-time collection of data from multiple sources: machines, sensors, websites, e-commerce, clients and suppliers.
All raw data (stream, batches, ndarrays, etc.) is stored in a distributed transactional object database (NEO).
All structured data (tables, relational indicies) is stored in a high performance clustered relational database (MariaDB).
Rapid.Space BDH unifies data engineering through the usage of Python on both the analysis environment and the production cluster.
Data engineers can choose from more than 100 ready-made plugins for different web services and databases thanks to the integration of ARM's open-source data collection solutions Fluentd (for streaming) and Embulk (for batch data).
Collected raw data can then be aggregated and structured with PyData libraries such as Pandas and SciPy and finally be analysed automatically with machine learning tools such as scikit-learn or TensorFlow.
The business process-oriented approach of managing data analysis operations makes Rapid.Space BDH a perfect fit for unification and automation of recurring data science tasks on a production system.
Native out-of-core access to persistent NumPy ndarrays provided by the wendelin.core library allows for scalable analytics. Analysis operations can be implemented without restrictions on the available memory, and they do not need to be recompiled for running on the production cluster.
The complete NumPy API is available when accessing out-of-core ndarrays, unlike other technologies which depend on a compatibility layer.
Rapid.Space BDH can be deployed on Rapid.Space VPS, on third party cloud or both.
Automate Rapid.Space BDH provisioning and configuration thanks to Rapid.Space REST API.