Data Intelligence for Growth
Get fast analytics with the power of a data warehouse in the cloud
News and Events
- Bityota Enables Unified Data Collection and Processing from Third-Party APIs, NoSQL DatabasesFlagship data warehouse service delivers streamlined analytics.
- Instant Data Warehousing in the CloudAs companies look to crunch their accumulating mounds of big data coming from multiple sources and at multiple velocities, data warehousing-as-a-service, or DWaaS, promises to relieve companies of all the technological heavy lifting, allowing them to focus on the data itself. While big names like Teradata and Amazon have been attacking the space, one of the hottest names in the category is BitYota a two-year-old startup backed by venture firms such as Andreessen Horowitz, and Crosslink Capital, as well as individual investors like Maynard Webb and Jerry Yang.
- 5 Ways Data Warehousing Is ChangingIt’s a new era for data warehousing. Enterprises now draw upon multi-source, semi-structured data to facilitate new and more useful products and services. These diverse data sets also enable business models built on a deeper understanding of customers and constituents, problems and phenomena. Everyone, from data scientists and engineers to SQL-savvy analysts and businesspeople at enterprises of all sizes, needs a data warehouse that allows them to understand their data, build better products and services, and create new avenues of growth and productivity. By instituting a data warehousing strategy that supports the rise in Big Data, enterprises can stay in step -- and simultaneously, create opportunities that benefit us all
From the Blog
- “Share and Enjoy”… or How to Use instance groups in your BitYota Data WarehouseSimple, easy segregation of your BitYota DWS cluster resources, using storage and compute instance groups leads to better utilization, easier capacity planning and happier users
- Big Data – a strong case for ELT (and not ETL)Big data is forcing us to revisit data pipeline processing from ETL/ELT to data discovery and BI metrics. Lets discuss why ELT should be the preferred technique for Big Data and not ETL. Some argue that the difference is merely semantic – its a matter of where processing happens and the same end result can be achieved in both methods. However, its about what you want from the data, how quickly, availability of system resources, data architecture and economics.