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, helping you reduce costs, optimize revenue and mitigate risks. 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. DataHub is also a streaming platform designed for building real-time data pipelines and streaming applications and is particularly well-suited for handling large volumes of data streams in a reliable and efficient manner.
Why is a data platform for artificial intelligence so important?
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Data Collection and Preparation: AI models heavily rely on data for training, validation, and testing. A robust data platform allows for efficient collection, storage, and preparation of large datasets.
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Scalability: The volumes of data used in AI can be massive. A dedicated AI data platform should be able to scale up or down as required to handle varying data sizes and traffic patterns.
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Data Security and Privacy: Secure storage and handling of data, especially personal information, is critical. A good data platform considers data security, encryption, and access controls.
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Availability and Distribution: AI solutions are often used across organizations and geographical locations. A solid data platform ensures data is accessible where it's needed, with low latency and high reliability.
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Iterative Development: AI models often go through many iterations before they are optimal. A data platform provides tools for version control, experimentation, and comparison of model versions.
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Integration: AI solutions often need to integrate with other systems, whether it's to gather data or to deliver predictions. A good data platform offers tools and interfaces for smooth integration.
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Performance Monitoring and Maintenance: Over time, the performance of AI models may degrade, especially if the underlying data changes. A data platform should provide monitoring tools to track model performance and alert when maintenance or retraining is required.
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Cost-effectiveness: Running AI models, especially deep learning models, can be costly in terms of computational resources. A dedicated AI data platform can optimize resource usage and thus reduce costs.
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Support for Various Model Types and Frameworks: There are many different AI frameworks and model types. A flexible data platform should be able to support a wide range of these, giving developers the freedom to choose the best solution for their needs.
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Collaboration: AI development is often an interdisciplinary field involving data scientists, engineers, domain experts, and business analysts. A good data platform promotes collaboration among these stakeholders by providing common tools and interfaces.
DataHub comes with data flexibiliy
Unlike relational databases that typically require a set schema before data ingestion, DataHub can evolve over time. As new types of entities or relationships emerge, they can be added without overhauling your entire structure.
The reason for this is that you can store your data in a graph network that handles complex relationships. 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.
Screenshot of multi-dimensional relationships between assets.
But do not worry, you can still store data in columns and rows, timeseries and events prefer that kind of data structure. DataHub provide you the flexibility in terms of data storage and retrieval. 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, Pandas and machine learning frameworks, to extract insights and value from the stored data. DataHub is particularly valuable in situations where data volumes are large, data types are diverse, and there is a need for flexibility in data analysis and exploration.
This means DataHub is designed to ingest, process, store, and analyze data in real-time or near-real-time as it is generated or produced. Streaming data platforms are essential for organizations that need to handle and derive insights from large volumes of continuously flowing data, such as event data, sensor data, log files, social media updates, and more. These platforms enable businesses to make immediate decisions, monitor real-time events, and gain deeper insights from their data.