Data Intelligence for Growth
Get fast analytics with the power of a data warehouse in the cloud
News and Events
- BitYota Introduces Breakthrough Data Warehouse TechnologyData Warehouse Service (DWS) provider BitYota recently announced latest release of its flagship DWS for Big Data analytics. This update delivers the platform’s data collection framework, an in-database processing pipeline for ELT (extract-load-transform), enhanced resource management and platform-specific improvements to further boost analytics performance. The new capabilities provide greater power, versatility and convenience to one of the industry’s leading platforms for multi-structured data analytics.
- BitYota cloud data warehouse gets a boost from Microsoft AzureHoping to enlarge its pool of corporate customers to include the enterprise market, startup BitYota will soon offer its data warehouse service on the Microsoft Azure cloud, thanks to a partnership between the two companies.
- Microsoft Azure: more partnerships, more databasesThere’s more on the Azure relational database front, but this time in the MPP data warehouse (DW) department. Cloud DW vendor BitYota announced last week the availability of its Data Warehouse Services (DWS) on Microsoft Azure. That gives the Redmond cloud its first competitive story against the Amazon Web Services (AWS) Redshift MPP cloud data warehouse offering.
From the Blog
- Partitioning Your Data in BitYota’s DWS For PerformanceWhen dealing with data, particularly Big Data, the performance of your queries can be greatly improved by organizing the data inside BitYota’s data warehouse to meet your typical data access patterns. BitYota enables you to do this data organization using partitioning. Partitioning decomposes very large tables into smaller and more manageable pieces or partitions.In BitYota’s DWS, partitioning is equally applicable to structured or semi-structured data. This means you can partition your JSON data (loaded as native JSON, not as text) using individual keys embedded in your JSON document without having to first transform every key into a physical column (unlike other traditional data warehouses like Redshift ). This is one of our core tenets for big data exploration - having to impose an a priori structure on varying or unexplored semi-structured data is detrimental to the velocity and flexibility of analytics and should be avoided.
- Providing your own schema for loading data into BitYota’s DWSBefore you load data into a BitYota DWS table, you need to specify a table schema - BitYota assists you by sampling your data before load and suggesting a schema; but for users who know the table structure they want, we offer the ability to override the suggested schema in one step. Another good reason for specifying your own schema is if you have semi-structured JSON data and want to project a few frequently accessed elements into columns for query performance. You may also want to created computed columns and add them to your table.
- “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