DataOps is an emerging set of practices, processes, and technologies for building and enhancing data and analytics pipelines to better meet the needs of the business.

Join this presentation to explore the five key steps necessary to be successful with DataOps, including the process and cultural shift required. The discussion also covers the benefits of enabling DataOps’ success, such as improved productivity, streamlined and automated processes, increased output, and higher collaboration across teams. Learn how to better manage data flow across the data lifecycle—from ingestion to provisioning to analytics; derive tips from use cases involving data lakes, cloud, and data warehousing for better business insights; and increase collaboration, productivity, and business value.

The list of failed Big Data projects is long. They leave end users, data analysts, and data scientists frustrated with long lead times for changes. Bergh illustrates how to make changes to Big Data, models, and visualizations quickly, with high quality, using the tools teams love. Synthesizing techniques from DevOps, Demming, and direct experience shows you how to succeed with DataOps today.

The use of containers—which enable applications, data, dependencies, and runtimes to be housed within a portable environment to support greater flexibility—is becoming more widespread.

Container usage is now being adopted by organizations of all sizes, from small startups to companies with huge, established microservices platforms. This presentation is aimed at helping practitioners navigate the minefield of database containerization and avoid some of the major pitfalls that can occur. It covers considerations such as container configuration and homogeneous versus heterogeneous node types; data resilience, resources, and storage; cluster upscale, downscale, and upgrade; and data locality and networking.

We’re at a unique point in time when fundamental changes in data management in the enterprise, the emergence of new technologies, and the volume and value of enterprise data combine into a massive opportunity to create next-generation data engineering pathways. Marinelli highlights the converging factors that affect why DataOps is gaining traction in the marketplace today, how it’s different from earlier generations of data curation, and what the key components are to implementing DataOps.

Data wrangling skills and capabilities are critical and a true business imperative. Organizations that are unable to derive true business insights from data will be left behind in the ever-changing and fast-moving digital economy.

All the information that is needed to find and stop bad actors from entering our financial system already exists and is available to you today; it’s just buried in terabits of messy, unstructured data all over the internet. For those performing investigations and evaluating risk, this needle in a haystack-of-needles problem is huge and growing: Unstructured data already dominates the web (growing exponentially year over year), and the traditional technology these departments use cannot keep up. Recent developments in natural language processing technology (NLP), the field of AI that focuses on human language, have, for the first time, made it possible for automated systems to find and deliver identity-relevant intelligence hidden in unstructured textual data. These innovations unlock a new world of actionable insight, providing much-needed ammunition in the fight against fraud, money-laundering, financial crime, and terrorism.