Data Center

The Data Center module acts as a central hub for streamlined management of various data sources.
Its native connectors ensure seamless connectivity, facilitating smooth operations.
Structured data collections simplify data retrieval processes, while the BDB Data Store provides continuous storage with integrated metadata for efficient integration.
The user-friendly Data Sheet minimizes data isolation, while the Data Sandbox allows controlled experiments.
The swift data discovery aids decision-making, and a combination of secure sharing and ML techniques ensures data quality throughout.

Pre-Build Components

leveraging specialized native connectors designed for various data sources such as relational databases (SQL and NoSQL), APIs (web services), files (CSV, Excel), and other formats can significantly enhance the efficiency and effectiveness of your data access and management processes.
Data connector in the BDB platform enable seamless and efficient communication between the BDB platform and various data sources.

Quickly get hold of well-organized data in a particular format that's great for easy analysis.
You can also share these datasets with your teammates while keeping a grip on who can access them. And guess what? The whole process supports keeping different versions of the data using Git.
This way, you can check out all the nitty-gritty details about each version when you grab the data.
Plus, this data smoothly fits into the Data Preparation step, and you can easily open up the Widget Visualizer to see the data in a more understandable way.

BDB Data Store functions as a versatile and reliable data repository tailored for the secure storage of various data collections
These encompass a variety of data sources, including conventional databases, APIs, and flat files.

Data store metadata is essential for strong data governance, smooth data integration, and valuable data analysis.
It helps users quickly find, understand, and use data effectively.
There are two main ways to create data store metadata: manual definition, where data attributes and context are documented carefully, and automated extraction, which uses tools to identify data features
Users can also share specific metadata stores with others for collaboration.
The "Refresh Synonyms" option ensures that metadata tables stay up-to-date with the latest changes, keeping data accurate and relevant.

The Data Sheet provides a user-friendly and visually appealing way to store data in a spreadsheet format.
It functions as a powerful Data Sheet serves as a convenient and visually appealing data storage solution presented in a spreadsheet format.
This platform functions as a robust database that's particularly effective in dismantling data silos, facilitating the storage, and enabling the sharing of well-structured data across various contexts.
The Data Sheet provides a user-friendly and visually appealing way to store data in a spreadsheet format.
It functions as a powerful Data Sheet serves as a convenient and visually appealing data storage solution presented in a spreadsheet format.
This platform functions as a robust database that's particularly effective in dismantling data silos, facilitating the storage, and enabling the sharing of well-structured data across various contexts.

The Data Sandbox creates a separate space to carefully expose data, making it useful for conducting data science experiments.
Data can be added either manually or through pipelines, and the page offers guidance for accessing the Data Preparation workspace dedicated to a chosen Data Sandbox file.
Users can quickly create and store Views using the widget visualizer.
The Widget page also has a Delete option, letting users remove both regular and saved Widgets.
Users can name Datastores and reupload Sandbox files as required for flexibility and management.

The Data Catalog feature in the BDB Platform's Data Center allows efficient data asset management with tags and data stewardship, saving time.
Additionally, users easily share dashboards, reports, and more, fostering collaboration and informed decision-making.
BDB supports Data lineage, which allows users to track the origin and history of data as it moves through the platform, making it easier to manage and understand big data.

Data as API means making data available through easy-to-use interfaces called APIs.
These APIs let different systems, apps, or developers interact smoothly with data.
This way, data providers can offer structured data that external parties can use programmatically.
Data as API changes how we share data by putting data and its functions into clear API frameworks
This lets me work with data using standard API methods, without needing to worry about where or how it's stored.
This approach makes working with data simpler, so I can focus on analysis and insights.

Quickly identify anomalies, eliminate unnecessary data using Machine-Learning techniques, and export for analysis.
Access Data Grid view via Data Preparation icon; displayed data adapts based on changes.
BDB Data Preparation's Grid visualizes data, showing sample/full data, displaying 10K rows, with Data Quality Bar indicating quality via colors.
Offers 100+ transformations for efficient data modification.

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