Data Marketplace
Data has value but often isn’t available in an easily consumed manner. A data marketplace is your shop window for data, but you will need complementary data delivery machinery.
RAW for your data marketplace. What type do you have?
- Internal data marketplace. Sharing data between different groups within an organization. The focus here is on fostering greater data collaboration and efficiencies.
- Open data marketplace, e.g. government or city data, where focus is on opening up the data to as many people as possble.
- Paid data marketplace, where focus is on controlling access to specific resources, slices of data, and often in highly secure environments between organizations.
No matter which type of data marketplace you’re working with, you will want to:
- Turn both raw and refined data into accessible data quickly and at low effort.
- Test both the marketplace and the market with the data product value proposition.
- Get feedback and iterate rapidly.
- Deliver data in the most optimal way for different end user needs and access patterns.
That’s where RAW comes in to deliver data as APIs, quickly and efficiently.
RAW Features and Benefits
DataOps Inside
Reduce time-to-market, allowing you to test the proposition quicklySQL on Databases, Files & APIs
Query raw and refined data sources and output in multiple formatsNo ETL - Query data at source
Reduce cost and complexity, whilst enabling greater agilityAPIs-as-a-Service
Spend all your effort on business logic and not infrastructureSmart Caching
Faster results for users without complex configurationLearn More
Analysing the News with RAW
Use RAW with Web APIs to produce powerful analysis results on web pages. This example shows analysing web pages in RSS feeds, using metadata extraction and Google's language entity extraction.
Introducing the RAW Data Product Platform
Data Products as a Service: A collaborative DataOps platform as a service for data APIs. Create and Share data faster.
Data as a Product
Do we really manage our data as a product? What does that mean for customers? Is a data product really that different from a traditional product? And what can we learn from other more established industries about their products, and apply them back to our fledgling data product space?