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Gartner lists RAW Labs in its Market Guide for Analytics Query Accelerators

RAW Labs was mentioned as a Representative Vendor in Gartner’s Market Guide for Analytics Query Accelerators1. According to Gartner, “Data and analytics leaders continue to struggle with getting value from data lake initiatives that have grown to be unwieldy or that cannot deliver adequate performance as they have evolved. Analytics query accelerators provide a means of making data in semantically flexible data stores more accessible for production and exploratory use. For those data lakes that store some of their data in semi-structured or structured and understood form, the accelerators provide a means of accessing the data in situ.”

RAW Data Fusion is a data management platform for data engineers, data scientists and data analyst to seamlessly query heterogenous operational data sources in situ and in real-time, and transform them into new data sets for consumption by analytics tools, ML-Models and enterprise applications.

Says Miguel Branco, RAW Labs CTO: “Being mentioned by Gartner as Query Analytics Accelerator is a great testament to our efforts of delivering a ground breaking data management platform that helps companies get more value of their data lakes, data warehouse and operational systems.”

“Analytics query accelerators provide optimization on top of semantically flexible data stores, typically associated with data lake architectures. Data and analytics leaders should use these offerings to accelerate the time to value of their data lake initiatives as they move toward operational production delivery.”

1) “Gartner Market Guide for Analytics Accelerators,” by Analysts Adam Ronthal, Merv Adrian, Henry Cook, December 9, 2020

https://www.gartner.com/en/documents/3994139/market-guide-for-analytics-query-accelerators

Gartner Disclaimer
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

RAW Labs mentioned for the first time by Gartner in its Feb 2019 report

RAW Labs have been mentioned by Gartner as a vendor to consider for data management solutions in its “Other Vendors to Consider for Data Management Solutions for Analytics. 

RAW Labs addresses the growing need for companies to have a single platform to manage heterogenous data at scale: “Being able to quickly access, integrate, join and transform any type of data – whether it comes from internal sources, or external sources – is the basis for any digital transformation”, says Lars Farnstrom, CCO at RAW Labs.

RAW Labs makes it possible to create enhanced sets of data in real time, regardless of data format and complexity, and use these data sets to build data driven applications without having to store the data in a database. Seamless integration to data science tools, allows the user to further enhance the data sets. The enhanced data sets in RAW can then be used to create new data driven applications such as predicting diabetes in patients or can be made available as a service for consumption by a company’s existing enterprise solutions. By accessing data at source and caching it, RAW eliminates data duplication and the need for heavy ETL processes before the data can be used, thus providing unprecedented time to value, lost cost and minimal IT overhead.

Gartner Disclaimer

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About RAW Labs

Founded in 2015 as a spin-off from the renowned Ecole Polytechnique Federale de Lausanne in Switzerland, RAW Labs enable companies rapidly build and deploy data driven applications and micro services without having to invest time and money in new and expensive data lakes and data warehouses. RAW Labs platform is available in the cloud and on premise.

Butler Analytics Review: “This is very interesting technology…”

Butler Analytics Review of RAW Labs NoDB

The recent developments in database architectures and storage methods have not necessarily made life any easier. We can store data in various encoded forms, as arrays, as hierarchies, in large quantities, and so on. The core problem of bringing data together for analysis has not necessarily been helped, despite the current fascination for data lakes and other mechanisms for merging data sources. The problem with all schema-on-store mechanisms is that the scope of queries that can be made against the schema is limited. Schema-on-read is the holy grail here, but for other than modest data sets, this largely remains a dream.

RAW Labs uses some very interesting technology and techniques to provide a workable schema-on-read solution to the needs of analysis. The company could quite legitimately call its technology AI, although the solid academic origins of the platform mean it is presented in a rather soberer manner. The claim is that RAW infers a data schema based on the query that is launched against diverse, and multiple data sources. It does this using some very advanced and novel mathematical techniques that come from Category Theory. The net result is that RAW learns how data is being used, and through smart caching optimizes access to data. Obviously, if we have gigabytes or even terabytes of previously unseen, unindexed data then it will take a while to figure things out on the first pass. Subsequent queries will perform with much greater speed, and as the data is used so mathematical models are built and caches configured accordingly.

The platform, which can be on-premises or hosted in the cloud, is already being put to good use by a number of large organizations. It supports just-in-time analytics – the ability to query data as the business demands, rather than how the data structures will allow. RAW transparently accesses most data stores including CSV, noSQL, XML/JSON, RDBMS and log files.

The platform also supports complex queries such as arbitrarily nested queries as well as the ability to join the data from the variety of underlying source files in a single query. E.g. joining machine logs with asset information from excel files and maintenance history from a relational data base.

Use cases include the conversion of unstructured Word documents into structured data, the discovery of unusual items of data in very large data volumes, consolidation of disparate data sources, and many others. In fact, once a business has access to a platform that can handle high volumes of data from diverse sources the applications become numerous.

This is a very interesting technology, and not least because it employs techniques that are wholly new in this domain. The problems associated with the diversity and volume of data are common to all analysis platforms. RAW could certainly be positioned as a universal back-end for analytical tools. It would also make a very good acquisition target for one of the large analysis platform vendors.

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