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Massive Data Workloads with Open Source Software

 

Massive Data Workloads with Open Source Software

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Massive Data Workloads with Open Source Software Tips, Tools and Techniques for Data Aggregation, Storage, Processing, Analysis & Visualization with Open Source Software
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Be the first to respond. 8 Open Source Big Data Tools to use in 2018. Vladimir Fedak. Apache Hadoop. The long-standing champion in the field of Big Data processing, well-known for its capabilities for huge-scale data processing. Apache Spark. Apache Storm. Apache Cassandra. MongoDB. R Programming Environment. Neo4j.

What you'll learn

  • Tips, tools, techniques and strategies for working with massive data workloads using open source software
  • Tools and strategies for aggregating data using open source software
  • Strategies for selecting open source storage solutions
  • Tools and strategies for processing real time and batch workloads with open source software
  • Strategies for analyzing and visualizing
  • Optimizing on performance, reliability, security and costs

Requirements

  • A computer with internet access is required

Description

The process of selecting the right tools, technologies and strategies for aggregating, processing and making sense of high-velocity, high-volume application log data from tens, hundreds or sometimes thousands of sources can be very overwhelming, expensive, intimidating, stressful and frustrating. This course offers a complete, hands-on instruction on how to aggregate, process, search and visualize massive log data using open source software tools, frameworks and platforms available today to solve these challenges.


Who this course is for:

  • Software Engineers, Data Engineers, Data Analysts, Data Scientists and Operations Engineers


Israel Ekpo is a senior cloud solutions architect based in the United States. Container orchestration, DevOps, high-performance computing, real-time analytics, and event processing with open source software are his areas of speciality. In his day job, he guides customers developing solutions on the platform to select pragmatic tools, technologies, and strategies in the implementation of their solutions.

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