BOULDER, CO -- (Marketwire) -- Precog, the leading developer of infrastructure for data warehousing and analysis, announced the launch of its Precog platform in public beta. Designed for developers and data scientists, the Precog platform combines the scalability of big data platforms with the number-crunching power of statistical tools. As a result, development teams can quickly build big data applications without the headache and time commitment of custom data infrastructure development and maintenance, while data scientists can aggregate, massage, analyze, and model large amounts of data without needing a separate ETL process into a small-data statistical tool.
Broadly defined as the collection and analysis of large amounts of data to create a competitive advantage, big data has quickly become a challenge for any application that generates high volumes of data or multi-structured data that cannot be captured by standard database systems. By simplifying big data capture, storage and analysis through cloud APIs, Precog empowers developers to build highly sophisticated big data and analysis features into their applications. Precog eliminates the need for development teams to learn Hadoop and related complex data storage and analysis technologies, freeing them to focus on core application functionality. Statistical models, aggregations, or complicated analytical calculations developed by a data scientist can be transparently run in production by developers, allowing new features to be added to products or allowing automation of workflows.
"Modern applications, whether web, mobile or desktop, have grown increasingly sophisticated in their ability to increase interconnectivity, drive efficiency and generate valuable data. Where web applications often fall short is in their ability to create meaningful, actionable insight through the deep analysis of the massive volumes of data they are often creating," said John A. De Goes, CEO, Precog. "By making it easy for development teams to build advanced big data capabilities into their applications, Precog is powering a new generation of highly sophisticated applications that are poised to unlock the big data puzzle."
Widely adopted online sales and support tool SnapEngage uses the Precog platform to provide its customers with deep insight into the support process and its effects on conversion and retention. Before adopting Precog, SnapEngage used a data warehousing and analytics solution to store interaction data, but the cumbersome data model and lack of first-class support for advanced analytics made it difficult and expensive to iterate based on customer feedback, or look at data in new ways.
"SnapEngage has quickly become the de-facto live chat platform for any company interested in engaging with their customers in real-time. As we grew our install base, we realized that there was an opportunity to give our customers the ability to access end-user interaction data and analyze it in new, powerful ways," said Chris Vieville, Community Manager, SnapEngage. "Precog helps us capture all interactions between support agents and website visitors and perform sophisticated analytics on that data to identify patterns, trends, and correlations that help support managers improve conversion and retention."
The Precog platform offers an end-to-end solution for programmatic big data analysis: from capture and storage, to cleaning and enrichment, to deep analysis designed to power intelligent, insightful features inside applications. Precog is ideal for heterogeneous data, normalized and denormalized data, whole data analysis, complicated analysis and data integration.
Precog key features include:
- Warehousing: Precog is the primary authority for measured data. Precog does not need to rely on connections to external data sources, which is commonplace for many data analysis offerings.
- Analysis: Precog provides very deep data analysis, including advanced analytics and statistics. This functionality supersedes analysis features of standard warehousing offerings, which typically are limited to rollups and aggregations, and tend to rely on extract, transform and load (ETL) to import a subset of data into a more capable analysis product.
- Measured Data: Precog is designed for capturing event-oriented data, such as behavioral interaction records, transactional records, historical aggregates, individual sensor measurements or any data set that is traditionally stored in a fact table under a relational database management system (RDBMS).
All Precog features are exposed via embeddable Precog APIs, which include a data ingest API, data analysis API, data integration API, security API and accounts API. Precog APIs make it easy and secure to package Precog-powered analysis into third-party applications and to load Precog with data from third-party sources, including external APIs (Twitter, Facebook, Salesforce), CSV files, websites, and transactional data stored in existing databases (SQL, Hadoop, MongoDB).
Precog will be showcasing its family of warehousing, analysis and reporting products at DataWeek, taking place September 24-27 in San Francisco, CA. Developers and data scientists interested in learning more about the Precog platform from the company's Web site.
Precog is the leading developer of data warehousing and analysis infrastructure designed to help developers build sophisticated big data capabilities into their applications. Precog's family of warehousing, analysis and reporting products empower developers to quickly build big data applications without the headache of custom data infrastructure development and maintenance. Precog is the author of the Quirrel declarative query language and has developed the industry's most complete and heavily optimized execution stack for Quirrel. A 2011 TechStars graduate, Precog is backed by RTP Ventures, Resonant Venture Partners, David Cohen and several notable individual investors.