Using DataOps for Data Pipeline Engineering Quality

Organizations need a way to get extremely fast insight from any-size data and share it with co-workers of varying skill sets – wherever they happen to be.Look no further! SAS Visual Analytics lets everyone, even those without significant analytics skills, explore and analyze data. It allows users to quickly see connections and correlations, then immediately share results via the Microsoft Office tools you use every day.

What the biggest changes and surprises are in the Magic Quadrant this year.How you can interpret the Magic Quadrant and use it to develop and mature your analytics strategy.This year's BI and Analytics Magic Quadrant includes new players, as well as movement in who are leaders, visionaries, and niche players. You'll hear from the authors on the biggest changes, how to interpret the Quadrant, and how best to use it to develop your BI strategy and get a glimpse at the next wave of disruption.

Big data pipelines can span many cloud and on-premises storage and compute resources and have many complex scheduling dependencies. It can be hard to monitor and troubleshoot all of the different activities you may have running with Azure Data Lake, Azure SQL DW, HDInsight and other services. In this webinar, see the brand new Azure Data Factory application. Learn how to deploy, monitor and manage complex data pipelines with one simple interface and consistently deliver refined data to feed your BI and other analytics tools.

No matter what industry or department you work in, almost any BI and Analytics solution requires a similar set of key requirements at the core. Those requirements combine the capabilities of both business users and technical users that are critical to create a successful solution with higher adoption rates and greater impact on the business. As the key requirements and expectations changed dramatically with new modern self-service capabilities, many organizations are still struggling to deliver on all of those requirements at the pace the business requires. Often the failure to deliver is rooted in the fact that too many tools are required to deliver on all capabilities, making the process too complex and time consuming.