What is IntelliStream DataHub?

DataHub enables business agility and IT efficiency by providing innovative data management technology and services that transform data into a strategic asset, enabling cost reduction, revenue optimization, and risk mitigation.

You can think of DataHub as a centralized repository or storage system that allows organizations to store, manage, and analyze vast amounts of raw and unstructured data. Unlike traditional relational databases or structured data warehouses, DataHub is designed to handle a wide variety of data types, including structured data, semi-structured data, and unstructured data, without the need for predefined schemas or data transformations.

Why is DataHub essential for AI development?

DataHub simplifies data handling by optimizing the collection, storage, and preparation of large datasets. It is highly scalable, adjusting to varying data volumes and traffic patterns. Security is prioritized through access controls that protect data. Tools for iterative development facilitate experimentation and version control, supporting the continuous enhancement of AI models. The platform integrates seamlessly with external systems for smooth data exchange and prediction delivery. Embedded performance monitoring tracks AI model performance, triggering necessary maintenance. Its flexibility empowers data engineers and scientists to choose the best tools for their tasks. Finally, the platform promotes collaboration by offering shared tools and interfaces, enhancing teamwork among data scientists, engineers, and business analysts.

DataHub’s Flexibility

Unlike traditional databases, DataHub’s graph-based architecture handles complex relationships and evolving schemas, accommodating diverse data types and enabling advanced querying. For structured data like time series and events, traditional formats are still supported.

Graph networks naturally represent intricate relationships between entities. This means they can accommodate many-to-many relationships, hierarchical structures, and more, providing a level of flexibility not easily achieved in other data models.

A graph model, also offer flexible querying capabilities. Users can traverse the network, follow relationships, and extract complex patterns without needing to predefine a rigid join structure, as in relational databases.

datahub overview of asset management Screenshot of multi-dimensional relationships between assets.
This makes DataHub well-suited for exploratory data analysis and data science tasks. DataHub is used in conjunction with other data processing and analytics technologies, such as Apache Spark, Apache Nifi, Apache Beam, Pandas, Tensorflow, PyTorch and other machine learning frameworks, to extract insights and value from the stored data. DataHub is particularly valuable in situations where there is a need for flexibility in data analysis and exploration.