Big Data Provisioning
Provisioning big data for meaningful use is the next step in creating actionable information.
- ODS, DW, and data mart DBs (pre-sorted)
- flat files for use in ETL tools, ELT appliances, and Hadoop
- piped, procedure, or brokered (MQTT/Kafka) in-memory data feeds
- 2D reports, federated views, or BIRT charts in Eclipse
- predictive analytic, data science, machine learning, and deep learning (AI) nodes in KNIME
- data wrangling for BI, data mining, and analytic tools like Cognos, OBIEE, Power BI, QlikView, R, Spotfire, Splunk, and Tableau
- masked production or synthetic test data targets
Voracity externalizes data preparation and provisioning in Unix, Linux, and Windows file systems or HDFS ... whatever you've configured will now be a faster, more cost-effective place to stage and scale your big data processing operations.
Voracity also offers a simplified form of data virtualization, allowing direct access by business people to the ultimate source of data. Because a single set of common metadata supports both virtualization and off-line processing, the alignment of cleansing and integration in different time frames is more easily achieved.
- Dr. Barry Devlin
At the same time, Voracity's simple, centralized metadata separates your data from your applications and becomes a Production Analytics Platform, which means:
No Impact on Production. Major Time and Cost Savings.
- Remove the heavy lifting of transformation from production database, application, and BI tools/layers Produce Insight As You Prepare Big Data
- Provide clean, secure, and pre-formatted data through files, tables, pipes, and procedures to the targets that need that data, when they need it
- Prevent the storage and synchronization problems associated with using multiple copies of data
- Surgically select and insert data using SQL-compatible /QUERY and /UPDATE commands inside Voracity jobs as another way to filter and increment data flows according to business rules
- Improve insight quality with fresher data
- Run your jobs on any platform, and on schedules subject to conditions you define and automate. Avoid the usual data warehouse and data mart update delays associated with manual provisioning.