• We make data available

    RAW is a platform for the rapid development and deployment of data-as-a-service 

    and data driven applications with minimal IT burden and cost.

    Request a demo
  • RAW Labs joins startup program of 
    HPE Switzerland to help companies accelerate digital transformation.

    Read More
  • No Extract-Transform-Load
    No Data Loading
    No Schema Creation
    No Index Creation
    No Database Tuning
    No Flattening of XMLs or JSONs
    No Weird SQL for Nested Data

  • RAW Labs CEO receives prestigeous award - read more

Harness the data value chain

Connect to any type of data

Connect to any data in its original format and in real-time. RAW code generates source specific adaptors for each source optimized for high performance data retrieval. RAW automatically detects the format, the schema and the encoding to make it transparent to the user. RAW supports both simple and complex data including CSV, Excel, Word, Amazon S3, machine logs, streams, JSON, HJSON, XML, AVRO, HDFS, HDF5, Parquet, NoSQL relational data bases, columnar databases, multi-dimensional arrays, APIs and more.

Transform data sources into data sets

RAW's extended version of SQL support all functions necessary to create workable data sets. Join, clean and transform data with different file formats all at the same time. Applying data cleansing algorithms to correct and improve the quality of your data. No need to create tables or perform heavy ETL processes. RAW supports very complex queries such arbitrarily nested queries allowing you to mix normally incompatible data.

Enhance your datasets with ML & AI

Use your favourite data science tools to turn your datasets into Smart Datasets. As a data scientist you can quickly bring together data, or use the queries created by your colleagues to run your experiments. Add new predictive data points directly from e.g. Python notebooks using Scikit Learn. Save the enhanced data as a Smart Dataset and make it automatically available in the RAW Virtual Lake. If your model works well, save it and share it with your colleagues. You can even embed your model directly in a RAW query and run it on the Edge.

Virtualize and share the data

Datasets are automatically virtualized in RAW either in near real-time, or through high performance caches. RAW's caching engine takes into consideration the structure and format of the datasets and optimizes the caching accordingly. A simple dataset based on CSV files will be cached different than a data set based on multidimensional arrays. RAW updates its caches as the datasets grow without having to refresh the entire cache, saving time and maintaining high response times. RAW allows the users to name and describes its datasets, thus providing a simple, yet effective to build on a repository of datasets that can then be shared with the appropriate user access rights across the organisation.

Deploy and create business value

Any dataset in RAW can be exposed in multiple formats: As RestAPIs, as csv files, excel, SQLLite, Python and in many other formats. Take your datasets and deploy it as a service to increase the value of your enterprise applications such as BI tools, supply chain system or marketing automation. Or use the Smart Datasets to create new data driven applications such as predictive maintenance, fraud detection or diabetes prediction for your end users.
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From concept to production in 3 weeks

RAW Labs helped the Swiss Broadcasting Corporation provide a single virtual view of all key programmatic content stored in regional databases and systems without duplicating the data. SRG SSR was able to integrate, clean and transform data from nested XLMs, JSONs and web services and deliver this a service to an enterprise search engine in just 3 weeks. 

Monetize all

enterprise data

  • Seamlessly integrate all data sources
  • Enhance with ML & AI
  • Break dow-data silos
  • Expose new data sets to existing systems
  • Create data-driven applications

Exceptional

time-to-value

  • Feels like SQL and easy to learn 
  • Significantly less data preparation time
  • Add new data sources on the fly
  • Automatic schema & format detection
  • No database tuning required



Significantly

reduced TCO

  • Avoid costly data duplication
  • No new data lake / data warehouse costs
  • No new ETL license and other tool cost
  • Reduced development costs
  • Minimal IT administration