Real-time Supply Chain Analytics

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This document discusses using Mesos and Spark together to build a fault tolerant data pipeline. It outlines the architecture of connecting data sources to the pipeline, implementing multiple pipelines, ensuring high availability through Mesos, and performance monitoring. Learnings and a conclusion are also presented.

Software

• Spark => In-memory Execution Engine
• Mesos => Resource Manager
• Mesos + Spark => ??

Connecting Sources to Pipeline
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By Pradeepto K. Bhattacharya, MSys-Clogeny So it is release crunch time and your developers are hacking away. Your Jenkins CI build queue is getting longer because of lack of free slaves. What do you do? Allocate more dedicated hardware? OK, but what happens when the crunch time is over? Those slave instances will then be sitting idle. Not good! Do you wish that nodes in your infrastructure could be better utilized? Is your infrastructure a victim of "static partitioning"? How awesome would it be if there was a way to scale to multiple Jenkins slaves automatically, whenever needed - and then also scale back down when the work is done? This talk is about solving these problems. Pradeepto will cover all the concepts necessary to understand Apache Mesos, its architecture, what it does and how it does it. He will then demo Jenkins running over Apache Mesos and demonstrate the ability to scale up and down as needed. Code will be shared for better understanding of the solution and the concepts behind it.

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• Distributed datasets loaded into named columns (similar to relational DBs or Python DataFrames). • Can be constructed from existing RDDs or external data sources. • Can scale from small datasets to TBs/PBs on multi-node Spark clusters. • APIs available in Python, Java, Scala and R. • Bytecode generation and optimization using Catalyst Optimizer. • Simpler DSL to perform complex and data heavy operations. • Faster runtime performance than vanilla RDDs.

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