Implementing Oracle Database-as-a-Service for Cloud-Like Agility

In this webinar we will provide an overview and demonstration of new IBM Watson Explorer for Data Science Experience features that enable data science teams to more productively discover and use insight from document collections and other text data to achieve new outcomes.

As part of its platform, GoodData provides a fault-tolerant, high performance and scalable system for data integration. While built for large-scale analytic applications, it is a metadata-driven, modular system that can start small and grow with your business. In this session, Cameron demonstrates how to set up and schedule regular data extraction from SQL databases and other sources. He also covers some of the issues requiring attention in data extraction such as data merging and incremental loads. A future session will cover transformations and data enrichment along with data distribution.

Join data management guru Dave Wells for this educational webinar on the data catalogs role, and how to get the best fit for your enterprise.Managing data in the age of big data and self-service analytics is a complex job. Managing data without a data catalog is difficult and fraught with risk. Data cataloging is the new gold standard for metadata and an essential component of modern data management. Data catalogs support data curation and data stewardship with intelligent and automated tagging, profiling, and linking to business glossary. They help business analysts and data scientists to quickly find data and evaluate the fit for their specific use cases. Strategically, cataloging is at the core of data asset management with direct impact for data governance, regulatory compliance, and analytic quality and efficiency.

Artificial intelligence (AI) and big data technologies are driving major changes in how organizations think about business intelligence and analytics. Rather than be limited to querying and reporting on just what is in traditional BI systems or data warehouses, many business users and analysts want to tap a fuller range of data in systems running Apache Hadoop, Apache Spark, or on cloud data platforms and storage. At the same time, AI practices and technologies (in particular machine learning and natural language processing) are changing how users explore, analyze, and interact with data and the types of insights they can generate.