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We transform raw data into actionable insights using analytics & Data Science

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We migrate existing application environments from traditional data warehousing/BI platforms to the Hadoop Ecosystem

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Latest From Our Blog

Written by Spry

This post is a follow-up to our originally published Apache Kafka overview blog (An Overview of Apache Kafka).  Here, we will provide an example of how to leverage Kafka's fairly robust client APIs, with the general use case being the integration of Kafka functionality into custom applications.  Kafka offers a diverse lineup of client API's, with two of the most mature being the Java and Python client API's.  This overview will center around a Python implementation because of its interpretive simplicity and suitability for integration with many application layers.

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Written by Spry

Spry was recently given the opportunity to be a guest author for the Hortonworks blog. The post is available in its entirety here. A sneak peek of the blog is given below!

In early 2014, Spry developed a solution that heavily utilized Hive for data transformations. When the project was complete, three distinct data sources were integrated through a series of HiveQL queries using Hive 0.11 on HDP 2.0. While the project was ultimately successful, the workflow itself took an astounding two full days to execute, with one query taking 11 hours.

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Written by Spry

What is Kafka?

Apache Kafka is a distributive commit log service. It leverages a language independent TCP protocol to provide functionality as a messaging system over partitioned and replicated feeds called "topics". The partitioned logs are the object of distribution, as each active node constitutes a Kafka server and remains responsible for processing data and requests for a section of the partitions.

This post will provide an overview of these concepts and give you more insight into how Kafka functions.

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About Spry

We are a high standards Data Science & Hadoop firm solving complex problems for Fortune 500 companies

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