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.

Data Pipeline 100

Data Pipeline Data Lake Data Warehouse Designing 100

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?

Java 111

Java Cloud AWS Government 111

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.

AWS 52

AWS Kafka Data Ingestion Data 52

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.

Data Science 52

Data Science Engineering Data Building 52

Advertisement

Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

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.

BI 52

BI Cloud Computing Data Warehouse Big Data 52

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.

Cloud 60

Cloud IT 60

Teradata

DECEMBER 20, 2018

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

45

45