Data Intelligence for Growth
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News and Events
- Understanding the Costs of Data WarehousesDespite the utility of data warehousing, the technology has a high total cost of ownership, including direct and indirect costs. The component parts of the TCO include the costs of software, hardware and personnel, all of which must be addressed before a business can begin analyzing the first row of data.
- IoT Takes Location Based Services to the Next LevelCarl Ford & BitYota's Dev Patel discuss how advertising and spefically location base services was going to be impacted by IoT.
- 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.
- How to Innovate Using Multi-Source, Multi-Structured DataThe future of big data lies in the advent of tools and techniques that derive value out of multi-source, semi-structured data. Developers need to innovate with these solutions that support ad hoc queries with multiple values, conditions and ranges—the kind of intelligent questions made possible when “sum of all knowledge” systems have finally arrived.
From the Blog
- Announcing the Public Preview of BitYota DWS on Microsoft AzureOver the past few months we have been working closely with Microsoft to bring BitYota Data Warehouse Service (DWS) to Microsoft Azure, and today I am pleased to announce our Public Preview of BitYota the DWS in Azure’s US & Europe datacenters.We are particularly excited about the Enterprise Big Data analytics opportunity we see on Microsoft Azure. We are very impressed with the breadth and speed at which Microsoft Azure is rolling out capabilities in both IaaS (e.g. VMs for every type of workload) and PaaS (e.g. SQL-as-a-Service).
- 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.