Ways of using Big Data Discovery

In your organization, use BDD as the center of your data lab, as a unified environment for navigating and exploring all of your data sources in Hadoop, and to create projects and BDD applications.

Using BDD in the data lab

The data lab is a center of innovation that makes analytics creativity possible for everyone who works with data. It supports a large portfolio of data projects, and is integrated with the production environment for commercialization and feedback.

The data lab is a complete set of descriptive, diagnostic, predictive, and prescriptive analytics solutions. It is a place for collaboration between a select group of analysts who work together as a team and have easy access to multiple data sets. The data lab serves as an easy sandbox for ad-hoc data experiments.

When used in the data lab, Big Data Discovery lets you:
  • Define projects in BDD and share them with others in your research data lab.
  • Use BDD to quickly develop ideas, build prototypes and models, and invent ways of deriving value from data. For example, you can try out new approaches, quickly discard the ones that are not working, and move on to try new ways of working with your data.
  • Shape data in your projects in multiple ways, from making easy single-row changes, such as trimming, editing, splitting, or null-filling, to advanced data shaping techniques, such as aggregations, joins, and custom transformations.
  • Let others in your organization consume models created in the data lab, and publish insights to decision-making groups inside your organization.

Using BDD to navigate and explore data sets in Hadoop

When used as the navigator on top of your data sources in Hadoop, BDD visually represents all data available to you in your environment. You see all the data in Studio's Catalog and can find interesting data sets quickly. You can filter, edit metadata on data sets, and create new data sets for others in your team.

Using BDD to create BDD applications

BDD lets you create BDD applications for well-established and known problems, to suit your business needs. For example, you can:
  • Create BDD projects from scratch, improve them, and share them with wider groups of users in the organization.
  • Serve as the author of all discovery solution elements: data sets, information models, discovery applications, and transformation scripts.
  • Run discovery repeatedly and transparently to others in your group, and on different large-scale data sets arriving periodically from various sources.