Sat.Oct 27, 2018 - Fri.Nov 02, 2018

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Using Notebooks As The Unifying Layer For Data Roles At Netflix with Matthew Seal - Episode 54

Data Engineering Podcast

Summary Jupyter notebooks have gained popularity among data scientists as an easy way to do exploratory analysis and build interactive reports. However, this can cause difficulties when trying to move the work of the data scientist into a more standard production environment, due to the translation efforts that are necessary. At Netflix they had the crazy idea that perhaps that last step isn’t necessary, and the production workflows can just run the notebooks directly.

Scala 100
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Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware. At Uber, cluster management … The post Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads appeared first on Uber Engineering Blog.

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#NoEstimates

Zalando Engineering

Why I advocate a practice of no estimates as a software engineer Before I get to the topic, I would like to clarify one thing: I don’t want to ban estimations generally from software development, as there are good and solid reasons for it. In a nutshell, business needs to be predictable. I want to show a software developer's view on how to reduce or even get rid of endless estimations meetings with doubtful outcomes.

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Cloud Native: What It Means in the Data World

Rockset

Prior to Rockset, I spent eight years at Facebook building out their big data infrastructure and online data infrastructure. All the software we wrote was deployed in Facebook's private data centers, so it was not till I started building on the public cloud that I fully appreciated its true potential. Facebook may be the very definition of a web-scale company, but getting hardware still required huge lead times and extensive capacity planning.

Cloud 40
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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Netflix MediaDatabase?—?Media Timeline Data Model

Netflix Tech

Netflix Media Database?—?the Media Timeline Data Model In the previous post in this series, we described some important Netflix business needs as well as traits of the media data system?—?called “ N etflix M edia D ata B ase” (NMDB) that is used to address them. The curious reader might have noticed that a majority of these characteristics relate to properties of the data managed by NMDB.

Media 52
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Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber Engineering

Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware. At Uber, cluster management … The post Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads appeared first on Uber Engineering Blog.

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Dynamic Typing in SQL

Rockset

As Peter Bailis put it in his post , querying unstructured data using SQL is a painful process. Moreover, developers frequently prefer dynamic programming languages, so interacting with the strict type system of SQL is a barrier. We at Rockset have built the first schemaless SQL data platform. In this post and a few others that follow, we'd like to introduce you to our approach.

SQL 40
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Doing a 180 on Customer 360 – The Preferred Path to Customer Insights

Cloudera

451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms ( watch the replay here ). In this blog post, Sheryl outlines how next-gen CIP applications are delivering a better customer experience, and why businesses are relying on CIPs as their preferred path to customer insights.

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Why SQL on Raw Data?

Rockset

Over a decade after the inception of the Hadoop project, the amount of unstructured data available to modern applications continues to increase. Moreover, despite forecasts to the contrary, SQL remains the lingua franca of data processing; today's NoSQL and Big Data infrastructure platform usage often involves some form of SQL-based querying. This longevity is a testament to the community of analysts and data practitioners who are familiar with SQL as well as the mature ecosystem of tools around