Using DataOps for Data Pipeline Engineering Quality

tdwi.org

Data pipelines facilitate information flows and data exchange for a growing number of operational scenarios, including data extraction, transformation, and loading ETL into data warehouses and data marts, data migrations, production of BI reports, and application interoperability. When data engineers develop data pipelines, they may devise a collection of tests to guide the development process, but ongoing tests are not often put in place once those pipelines are put into production.
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Spotlight

89% of enterprises believe that companies that do not adopt a data and analytics strategy in the next year risk losing market share and momentum. (Forbes) 99.5% of data collected is never analyzed The infrastructure over which data travels stretches from the data center to the branch office to the network edge to the cloud Optimize what you store and reach what you need Bring the volume and variety of data together into a unified view, no matter where it lives Connect analytics to data, in the data center and at the edge, so you can deliver insight and action when and where they are needed most 65% of data generated by connected devices.


OTHER ON-DEMAND WEBINARS

The Power of Collaboration: SAS® Visual Analytics and Microsoft Office

SAS

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.
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Key Findings from the 2017 BI and Analytics MQ

Gartner

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.
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How to Monitor & Manage Big Data Pipelines with Azure Data Factory

Microsoft

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.
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Visual Analytics - How to Deliver on Key Requirements – Webinars

Dundas Data Visualization Inc.

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.
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Spotlight

89% of enterprises believe that companies that do not adopt a data and analytics strategy in the next year risk losing market share and momentum. (Forbes) 99.5% of data collected is never analyzed The infrastructure over which data travels stretches from the data center to the branch office to the network edge to the cloud Optimize what you store and reach what you need Bring the volume and variety of data together into a unified view, no matter where it lives Connect analytics to data, in the data center and at the edge, so you can deliver insight and action when and where they are needed most 65% of data generated by connected devices.

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