In December, Microsoft acquired Metanautix, a Palo Alto startup founded by Google and Facebook veterans Theo Vassilakis and Toli Lerios that emerged from stealth with $7M in Series A funding in August 2014. Metanautix delivers a SQL interface for querying relational and non-relational datasets that dispenses with the need to integrate disparate data sources. Metanautix’s Quest platform brings the power of distributed computing alongside the simplicity of SQL to enable companies to concurrently ingest, analyze and visualize data from multiple datasets and data repositories. Metanautix’s platform shortens the time between data acquisition and data analysis by helping analysts understand the topography of the data within scope and subsequently enabling SQL queries to run against Hadoop, NoSQL and relational databases. The acquisition bolsters Microsoft’s portfolio of big data analytic tools and promises to fit into Microsoft’s SQL Server and Cortana Analytics Suite. Importantly, Microsoft’s acquisition of Metanautix illustrates the evolution of the concept of the traditional enterprise data warehouse given that platforms such as Metanautix empower customers to obtain 360 degree analytics by means of distributed, SQL-based queries, analytics and data visualizations, without the requirement to integrate and house all data within a single, unified warehouse or repository.
Category: Metanautix
Metanautix Releases Personal Quest To Enhance Access To Its Platform For Integrated Analytics For SQL, NoSQL and Hadoop Datasets
On Tuesday, Metanautix released Metanautix Personal Quest, a product that enables individuals to leverage the power of the Metanautix platform to perform queries on data stored in Hadoop, NoSQL and relational database formats. Individual users can use Personal Quest to perform integrated analytics on data stored in relational and non-relational formats to obtain an integrated view of data stored throughout an organization’s different applications and data repositories. Metanautix allows users to download Personal Quest to their machine and subsequently test the capabilities of the Metanautix data compute engine for an unlimited time period for data limited to a designated size and number of queries. Metanautix Quest’s distributed compute engine enables the joining of SQL and non-SQL data sources without complex ETL processes. The video below shows how the integration of Metanautix Quest and Tableau enables customers to join data from Teradata SQL data to MongoDB NoSQL data to obtain a more granular understanding of sales by product by means of a few simple drag and drop operations. The clip illustrates how Metanautix Quest can execute a distributed join to analyze store sales data stored in a Teradata database to product data stored within MongoDB to enable a comparative analysis of sales across product categories such as books, children, electronics and shoes by month. After a visual review of sales by product category in a Tableau workbook reveals that shoes had a significant impact on overall sales, users can perform another join to drill down on shoe sales by shoe type to learn that men’s shoes and athletic shoes were largely responsible for the spike in sales specific to the shoe category. The distributed join performed by Metanautix Quest on Teradata SQL data and MongoDB NoSQL data facilitates a speedy analysis by means of a user interface that requires neither ETL nor the migration of data to a centralized staging repository. As such, Metanautix Quest radically simplifies data analysis and data visualization given the proliferation of different kinds of datasets in small, mid-size and enterprise-level organizations alike. By giving individual users unlimited time-based access to Metanautix Personal Quest, Metanautix intends to underscore the power of its analytic engine for performing analysis on data stored in sources that include Hadoop, Teradata, MongoDB and other SQL and NoSQL data repositories.
Metanautix Emerges From Stealth With $7M In Series A Funding For Streamlined Big Data Processing And Analytics
Metanautix emerged from stealth today by announcing the finalization of $7M in Series A funding in a round led by Sequoia Capital. Additional investors include the Stanford University endowment fund and Shiva Shivakumar, former VP of Engineering at Google. Metanautix delivers a Big Data analytics platform composed of a SQL interface for querying Hadoop data in conjunction with data discovery functionality that empowers analysts to more easily navigate massive amounts of structured and unstructured data. The platform focuses on simplifying the data pipeline between data acquisition and the production of data analytics. As such, Metanautix removes the necessity of combining disparate data sources and thereby delivers the benefits of distributed computing alongside the simplicity of SQL. Users of Metanautix can perform analytics in parallel on structured and unstructured data by taking advantage of an interface that allows users to understand the topography of the data that they are navigating. Founded by veterans of Google and Facebook, Metanautix intervenes in the big data analytics space by allowing users to run analytics on multiple streams of Big Data as noted by CEO Theo Vassilakis below:
The modern enterprise operates on a plethora of data sources. There is great value in using all of these data sources and in providing superior access to ask questions of any data. We’ve made it fast and simple for anyone in an organization to work with any number of data sources at any scale and at a speed that enables rapid business decisions.
Vassilakis notes the ability of Metanautix to manage “any number” of datasets “at any scale” toward the larger end of delivering actionable business intelligence from disparate data sources. Given Vassilakis’s background at Google working on Dremel and the experience of Metanautix’s CTO Apostolos Lerios with processing frameworks for billions of photographic images during his tenure at Facebook, the industry can expect Metanautix to deliver a truly multivalent processing and analytics engine capable of managing heterogeneous data sources of all kinds. Expect more details about the platform to emerge in forthcoming months but, based on the experience of its founders, the Big Data space should brace for the entry of a disruptive Big Data analytics and processing engine that can deliver analytics on massive datasets by means of a radically streamlined operational process. That said, Metanautix will need to find its niche quickly in order to outshine competitors such as Pivotal and Infochimps, the former of which recently announced a collaboration with Hortonworks to enhance Apache Ambari.
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