Sat.Jun 01, 2019 - Fri.Jun 07, 2019

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Evolving An ETL Pipeline For Better Productivity

Data Engineering Podcast

Summary Building an ETL pipeline can be a significant undertaking, and sometimes it needs to be rebuilt when a better option becomes available. In this episode Aaron Gibralter, director of engineering at Greenhouse, joins Raghu Murthy, founder and CEO of DataCoral, to discuss the journey that he and his team took from an in-house ETL pipeline built out of open source components onto a paid service.

Media 100
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Predictive CPU isolation of containers at Netflix

Netflix Tech

By Benoit Rostykus, Gabriel Hartmann Noisy Neighbors We’ve all had noisy neighbors at one point in our life. Whether it’s at a cafe or through a wall of an apartment, it is always disruptive. The need for good manners in shared spaces turns out to be important not just for people, but for your Docker containers too. When you’re running in the cloud your containers are in a shared space; in particular they share the CPU’s memory hierarchy of the host instance.

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Reliable, Fast Access to On-Chain Data Insights

Confluent

At TokenAnalyst , we are building the core infrastructure to integrate, clean, and analyze blockchain data. Data on a blockchain is also known as on-chain data. We offer both historical and low-latency data streams of on-chain data across multiple blockchains. How we use Apache Kafka and the Confluent Platform. Apache Kafka ® is the central data hub of our company.

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Improving Multi-tenancy with Virtual Private Clusters

Cloudera

Noisy Neighbors in Large, Multi-Tenant Clusters. The typical Cloudera Enterprise Data Hub Cluster starts with a few dozen nodes in the customer’s datacenter hosting a variety of distributed services. Once configured and secured, the cluster administrator (admin) gives access to a few individuals to onboard their workloads. Over time, workloads start processing more data, tenants start onboarding more workloads, and administrators (admins) start onboarding more tenants.

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15 Modern Use Cases for Enterprise Business Intelligence

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?

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Why Hadoop Failed and Where We Go from Here

Teradata

Chad Meley delves into the demise of Hadoop distribution vendors and how they got there.

Hadoop 110
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IValue: efficient representation of dynamic types in C++

Rockset

Introduction In traditional SQL systems, a column's type is determined when the table is created, and never changes while executing a query. If you create a table with an integer-valued column, the values in that column will always be integers (or possibly NULL ). Rockset, however, is dynamically typed , which means that we often don't know the type of a value until we actually execute the query.

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