The European manufacturing industry needs revolutionary methodologies and tools to optimize operations, improve efficiency as well as product quality at a reduced production and distribution cost. Nowadays, many manufacturers collect massive amounts of data from different sources (e.g. machines, production lines, sensors, etc.) and they need to centralize storage of data across complex global and often fragmented supply chains. That’s where Big Data analytics and Apache Hadoop come in to cover these needs.

Apache Hadoop is an open-source distributed processing framework that manages data processing and storage for big data applications in scalable clusters of servers. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The framework itself is designed to detect and handle failures at the application layer rather than relying on hardware to deliver high availability. Hadoop aims to address the limitations of conventional Relational Database Management Systems in terms of storing large datasets, handling data of different formats and processing data at high speed.

But like any framework, Hadoop has both some advantages and disadvantages, and here’s a quick comparison of that:

Hadoop advantagesHadoop disadvantages
Quick processing of huge volume of dataIssue with small files
Supports a variety of data sources including structured and unstructured dataVulnerable by nature
Fault toleranceProcessing overhead
Scalability and High throughputSupports only batch processing

In fact, Hadoop is a collection of multiple tools and frameworks for big data management, storage, processing and analysis. There a 3 major components of the Hadoop ecosystem:

  • Hadoop Distributed File System (HDFS). This is the storage unit of Hadoop. HDFS splits the data unit into smaller units that are called blocks and stores them in a distributed manner.
  • Hadoop MapReduce. This is the processing unit of Hadoop allowing you to write applications for processing big data. MapReduce runs these applications in parallel on a cluster of low-end machines. It does so in a reliable and fault-tolerant manner.
  • Hadoop Yet Another Resource Negotiator (YARN). This is the resource management unit of Hadoop. It allocates to applications RAM, and other resources depending on their requirements. The basic principle behind YARN is to separate resource management and job scheduling/monitoring function into separate daemons.

Are you interested in taking a course on Apache Hadoop for Big Data Analytics on Advanced Manufacturing? Follow our news section and also us on our social media to make sure you don’t miss the big news: we are currently developing the training courses of the DTAM project and we plan to finalize them at the beginning of 2022.

Resources:

[1] Apache Hadoop

[2] What Is Hadoop? | Simplilearn (YouTube video)

Featured image credit: GeeksForGeeks

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