Netflix Tech

DECEMBER 18, 2018

Netflix OSS and Spring Boot?—?Coming Full Circle Taylor Wicksell, Tom Cellucci, Howard Yuan, Asi Bross, Noel Yap, and David Liu In 2007, Netflix started on a long road towards fully operating in the cloud. Much of Netflix’s backend and mid-tier applications are built using Java, and as part of this effort Netflix engineering built several cloud infrastructure libraries and systems?

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Teradata

DECEMBER 17, 2018

Brian Wood explains how Teradata Vantage in the cloud has your back when it comes to analytic simplicity, control, effectiveness, and results.

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Cloudera

DECEMBER 19, 2018

Data analytics priorities have shifted this year. Growth factors and business priority are ever changing. Don’t blink or you might miss what leading organizations are doing to modernize their analytic and data warehousing environments. Business intelligence (BI), an umbrella term coined in 1989 by Howard Dresner, Chief Research Officer at Dresner Advisory Services, refers to the ability of end-users to access and analyze enterprise data.

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Rockset

DECEMBER 20, 2018

We live in a world where diverse systems—social networks, monitoring, stock exchanges, websites, IoT devices—all continuously generate volumes of data in the form of events, captured in systems like Apache Kafka and Amazon Kinesis. One can perform a wide variety of analyses, like aggregations, filtering, or sampling, on these event streams, either at the record level or over sliding time windows.

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline

Domino Data Lab: Data Engineering

DECEMBER 15, 2018

This blog post provides highlights and a full written transcript from the panel, “ Data Science Versus Engineering: Does It Really Have To Be This Way? ” with Amy Heineike , Paco Nathan , and Pete Warden at Domino HQ. Topics discussed include the current state of collaboration around building and deploying models, tension points that potentially arise, as well as practical advice on how to address these tension points.

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Teradata

DECEMBER 20, 2018

Monica Woolmer provides insight into determining the right analytic models to provide practical, actionable answers for business.

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Data Engineering Podcast

DECEMBER 16, 2018

Summary Every business needs a pipeline for their critical data, even if it is just pasting into a spreadsheet. As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed.

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