RAW Labs presenting its innovative data at the HPE Innovation event

RAW Labs will be presenting its innovative data platform for digital transformation on September 24th in Geneva to HPE’s Swiss customers and partners.

RAW Labs makes it easy for companies to bring together heterogenous data from multiple sources, enhance it, and make it available for consumption in other systems, or directly to end-users via its data driven applications.

“This is a great opportunity for companies to learn more about how RAW Labs can help companies accelerate digital transformation”, says Lars Farnstrom, CCO.

RAW Labs optimized software platform for HPE infrastructure and services offer a unique combination of time to value and performance.

RAW Labs CEO and co-founder Anastasia Ailamaki featured in CNNMoney Switzerland

When Raw Labs co-founder Anastasia Ailamaki and her team set out to win over new clients to the start-up’s pioneering data-management software platform, one of the greatest challenges was getting people to trust a small company and to choose them over a better-known brand. But the EPFL-spin-off is fast-becoming a major player in its own right thanks to its tools that help harness big data. 

RAW Labs joins startup program of HPE Switzerland to help companies accelerate digital transformation.

RAW Labs is pleased to announce it has been selected to join the startup program of Hewlett Packard Enterprise (HPE) in Switzerland. As part of the program, RAW Labs’s software platform for data driven applications will optimized for HPE hardware and services. 

“This is a unique opportunity for RAW Labs to benefit from HPE’s market reach and expertise in cloud computing, IoT, AI and Edge computing”, says Lars Farnstrom, CCO of RAW Labs. “By optimizing our software platform for HPE infrastructure and services, we will be able to offer a unique combination of time to value and performance.”

RAW Labs CEO Anastasia Ailamaki wins prestigious ACM SIGMOD Edgar F. Codd Innovation Award

We are proud to announce RAW Labs CEO and co-founder Anastasia Ailamaki has received the prestigious ACM SIGMOD Edgar F. Codd Innovation award for her pioneering work on the architecture of database systems, its interaction with computer architecture, and scientific data management. 

Read full article here.

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.

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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.

RAW Labs and Serial SA form partnership

RAW Labs is pleased to announce that Serial SA has chosen to become a RAW Labs Consulting Partner. Founded in 1986, Serial SA is the leading Swiss system integrator for the French speaking part of Switzerland. Together, RAW Labs and Serial SA will be offering a unique combination of technology and expertise to help companies accelerete their digital transformations.

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RAW Labs participates at the HPE / Innosquare IOT Innovation event

RAW Labs was selected to participate at the Innosquare IoT Innovation event hosted by Hewlett Packard Enterprise at their EMEA HQ in Meyrin. Switzerland. The event which had more than 200 participants and national press coverage was a show case for innovation in the area of IoT and big data. This provided a great opportunity for RAW Labs to demonstrate its new and groundbreaking platform for data driven applications to multiple large and well known enterprises.

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RAW Labs presents to the CIO’s of French speaking part of Switzerland

RAW Labs presented its virtual data lake solution to the CIO’s of the French speaking part of Switzerland. The event was organised by the “Digital Circle” – a group founded by the ICT Journal which brings together CIOs to discuss digital strategies and technologies. 

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RAW Labs to Participate in “SmartDataLake: Sustainable Data Lakes for Extreme-Scale Analytics” EU Initiative

RAW Labs SA has been awarded a contract to participate in the highly visible SmartDataLake project funded by the EU. RAW Labs will provide its NoDB Platform for the project. “This is an exciting moment for RAW Labs. Through this project we will be able to expose our unique NoDB technology to industry leaders and researchers”, says Anastasia Ailamaki, Co-Founder and CEO of RAW Labs. 

“SmartDataLake: Sustainable Data Lakes for Extreme-Scale Analytics”, abstract:

Data lakes are raw data ecosystems, where large amounts of diverse data are retained and coexist. They facilitate selfservice analytics for flexible, fast, ad hoc decision making. SmartDataLake enables extreme-scale analytics over sustainable big data lakes. It provides an adaptive, scalable and elastic data lake management system that offers: (a) data virtualization for abstracting and optimizing access and queries over heterogeneous data, (b) data synopses for approximate query answering and analytics to enable interactive response times, and (c) automated placement of data in different storage tiers based on data characteristics and access patterns to reduce costs. The data lake’s contents are modelled and organised as a heterogeneous information network, containing multiple types of entities and relations.

Efficient and scalable algorithms are provided for: (a) similarity search and exploration for discovering relevant information, (b) entity resolution and ranking for identifying and selecting important and representative entities across sources, (c) link prediction and clustering for unveiling hidden associations and patterns among entities, and (d) change detection and incremental update of analysis results to enable faster analysis of new data. Finally, interactive and scalable visual analytics are provided to include and empower the data scientist in the knowledge extraction loop. This includes functionalities for: (a) visually exploring and tuning the space of features, models and parameters, and (b) enabling large-scale visualizations of spatial, temporal and network data. The results of the project are evaluated in real-world use cases from the business intelligence domain, including scenarios for portfolio recommendation, production planning and pricing, and investment decision making. SmartDataLake will foster innovation and enable European SMEs to capitalize on the value of their own data lakes.

Brief description of RAW Labs’ role in the project

RAW Labs SA has designed a software stack that permits efficient and scalable execution of analytic queries directly on raw data files (i.e., without pre-formatting and importing them in a database). Therefore, it has extensive experience in the following two areas: (a) integration of heterogeneous data into one data model, and (b) optimization of query execution against these data, in particular in the context of distributed storage/computing. Accordingly, RAW Labs SA will participate in WP2, in particular leading the efforts on distribution and elasticity.

The presence of RAW Labs SA will bring in not only the strong technical expertise but also a robust codebase of its flagship product called RAW, a distributed query execution engine for raw data. Starting from a mature and robust query execution engine codebase will enable the project participants to focus on the innovative aspects of SmartDataLake. In addition, RAW Labs SA will participate in the pilot testing and will contribute to the activities for dissemination and exploitation, particularly within its network of clients and collaborators.

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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.

Read original article here

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