Data Intelligence for Growth
Get fast analytics with the power of a data warehouse in the cloud
News and Events
- BitYota Seeks Out Gov API Opportunities for its Data Warehouse as a ServiceData-warehouse-as-a-service BitYota is using APIs to make data analytics in the cloud scalable and elastic. Already working with marketing customers who need to draw in semi-structured data from multiple source streams in order to create business intelligence, they now have their sights set on city and government use cases where streaming in multiple data sources are also central to performing any sort of meaningful analysis.
- Fight the Firehose: Four Steps to Keep Your IoT Data from Becoming OverwhelmingMassive amounts of user data are coming in at unimaginable speeds, and in any number of languages and formats. In order to process the data, there need to be certain metrics or translation methods that can keep up with the flood, and make decisions on the spot about how to categorize the raw information into usable packets, then aggregate the rest for future use
- How You Use Attribution Analysis for Stronger Marketing InsightAttribution analysis can be a very quick and easy way to bring together data from a number of internal and third-party sources, and make sense of that data in terms that you very specifically determine. You’ll gain the insights you need to clearly define and meet your marketing objectives, then hone your strategy to achieve the highest ROI possible on every marketing dollar spent.
- 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.
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.