Apache Spark Supply Chain Analytics - Locus IT Services

Apache Spark Supply Chain Analytics

Locus IT ServicesSupply Chain AnalyticsApache Spark Supply Chain Analytics

Apache Spark Supply Chain Analytics

There has been a big boom in the field of Apache Spark Supply Chain Analytics.

With almost all the processes related to supply chain are being carried out digitally the data has been expanded to a limit where in it has to handled meticulously. Extracting and handling such an intricate widespread information big data analytics is the only rescue to resort to.

Big data analysis runs on the Apache Hadoop framework so far which allows an organization to store large quantity of data on disc and also process it.

Apache Spark Supply Chain Analytics in Big Data

  • Supply chain management is simply the transport management or flow of goods and services, it also includes
    shelf life, storage, analysis of goods procured and goods sold, logistics etc.
  • Supply chain management helps in executing and planning various supply chain activities of a particular organization so as to build up a net value of the organization, determining the current market trend related to the supply and demand of any goods or services and also synchronizing the same for measuring the performance of the organization

Apache Spark Framework for Big Data Analytics

  • With the existing techniques and growing number of demand in this case data analytics consumes the more time in a given time frame.
  • To cut speed and time things ‘APACHE SPARK’ was the framework that was released initially 18 months ago.
  • It’s basically an open source computing framework which allows user programs to load data into a cluster’s memory ad query it repeatedly.

Apache Spark Features

  • Earlier Spark was released all the big data analytics was done with the help of another open source known as Hadoop which processed vast amounts of data using cheap off the shelf hardware.
  • As like Hadoop, Spark processes huge amount of data but it does it with a speed 100 times faster than that of Hadoop. This main difference is because Hadoop is supported on the backend by MapReduce which makes processing data relatively cumbersome.
  • According to an ad network InMobi, it would take around seven months to develop a machine learning model using Hadoop whereas, currently using Spark they complete building about 4 models a day. Thus, the demand for Spark has recently surged

Some of the main features of Apache Spark are as follows:


As mentioned before Apache Spark runs 100 times faster than Hadoop. It has an advanced DAG execution engine that’ll supports cyclic flow of data and in-memory computing.

Ease of Use

Spark offers over 80 high level operators that makes it very easy to build parallel applications which makes one use it interactively from the Scala, R shells and Python.

Runs Everywhere

Spark runs on Mesos, Hadoop, Standalone or in the cloud. It can also access diverse data sources including Cassandra, HDFS, HBase and Amazon S3

Apache Spark benefits for Supply Chain Management

  • Supply chain management comes in multiple terabytes of data every second on a global scale. This data is not only important but also very sensitive and should be processed as quick as possible because, it actually determines how efficient and how well versed the organization is with its business.
  • Quicker big data analytics leads to quicker decision making scenarios which indeed lead to faster growth of the organization and when we consider the current market: fast is what sells, slow is no longer an option.
  • As mentioned before Big data related to supply chain management consists of information like: orders, demand and supply, customer services etc. addressing such an information so as to come up with an efficient solution should be a fast process so as to beat the competition at ever level.

In short, we can term Apache Spark as the standard data processing framework. Apart from its aggressive speed Apache Spark also incorporates a few of the Big Data highlights:

  • Spark SQL and SQL for structured data processing
  • MLlib for machine learning
  • GraphX for graph processing
  • Spark streaming, for streaming data

Understanding the business volatility of the supply chain Analytics, Locus IT has helped many companies to adopt Apache Spark Supply Chain Analytics and we provide business, operational, customer and community-related insights to supply chain operations.

We provide Apache Spark SupplyChain training, Apache Spark SupplyChain support, Apache Spark SupplyChain implementation and much more. For more details please contact us we’ll be happy to consult with you.

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