Big Data Training Fees

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₹ 15,000 to ₹ 16,000 per month

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Prashanth K

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Besant Technologies

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Trending discussions on Big Data Training

Asked on 31 May IT Courses/Big Data

Firstly, Congratulations on scoring 8 bands. Yes, 2 months is more than enough for PTE to score required... read more

Firstly, Congratulations on scoring 8 bands.

Yes, 2 months is more than enough for PTE to score required score with proper guidance and practice. Assess yourself in PTE modules in which you are lacking initially and plan accordingly.

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Answer

Lesson Posted on 22 May IT Courses/Big Data

Case Study : Tibco

Tecksphere

We are taking this chance to introduce you the Tecksphere Training and Consulting Services, an US based...

OVERVIEW Our Client is a leading shoe retailer, dwelling over 3,500 shops across the United States of America. The retailer proffers an assorted variety of fashion accessories and footwear products by enforcing modernism and dynamic response. The client holds many warehouse amenities and supply chain... read more

OVERVIEW

Our Client is a leading shoe retailer, dwelling over 3,500 shops across the United States of America. The retailer proffers an assorted variety of fashion accessories and footwear products by enforcing modernism and dynamic response. The client holds many warehouse amenities and supply chain maintained by a Warehouse Management System.

BACKGROUND

Initially, the Client practised the traditional batch processing solution for the execution of outstanding orders. The mainframe system handles the data, and a time-frame is scheduled during early morning or night and was fed into Warehouse Management System. Moreover, E-Commerce orders are also processed which increased the volume of batch processing data. This imbalance in load leads to several time and processing constraints in the Warehouse Management System. In addition to this, the issues aroused with joining of the data as it is needed to join various distribution centres data. For the client, there was a necessity for the solutions to be reliable and should be architecturally compatible between the Eastern Distribution Center and the Western Distribution Center solutions. The Client approached TeckSphere and asked us to integrate the incoming ticket requests from the mainframe system to real time system to process the outstanding orders quickly.

TECKSPHERE SOLUTION

TeckSphere recommended a real time solution by employing TIBCO suite to integrate the enterprise data management products. The solution was built and enforced individually for the two Warehouse Management Systems and deployed to a central enterprise integration management and administration application. While processing E-Commerce orders, the Mainframe system generates a pick ticket file, and the data in the pick ticket file was populated into the Warehouse Management System staging table after the ETL procedure.

Our team has employed Publisher-Subscriber Architecture to collect, examine and transforms the incoming data before publishing it into Java Messaging Service (JMS) Server and finally upload the data to the target system. TIBCO adapter is used in file transfer operation to assure reliability. TIBCO File Server is used to parse the transaction, generates the order message, transform the message into canonical format and publish the individual transactions. The subscribing Business Information Warehouse converts the message into Eastern Distribution Center, and the Western Distribution Center format and places the transactions into their corresponding Warehouse Management System staging tables and the mainframe system is alerted for the process completion by inducing a trigger.

Based on the business needs, our TeckSphere experts have explored a complete data transformation and integration solution. The solution holds reusable property and adheres the standard of Retail Industry.

Outcome

  • The retail orders are processed under real time scenario. 
  • Enhanced order management support. 
  • Well Organized and Centralized distribution operations. 
  • Improved system scalability and data consolidation 
  • Reduced overall system Usage by avoiding the usage of Warehouse Management System

    TECHNOLOGIES USED

    TIBCO BusinessWorks

    TIBCO DataExchange

    TIBCO Rendezvous

    TIBCO EMS

     TIBCO Object Star

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Lesson Posted on 28 Apr IT Courses/Big Data IT Courses/Apache Spark IT Courses/Hadoop

Lets look at Apache Spark's Competitors. Who are the top Competitors to Apache Spark today.

Biswanath Banerjee

We have trained more than 1000 students on Big Data Technologies - Hadoop ecosystem, Apache Spark, Tableau,...

Apache Spark is the most popular open source product today to work with Big Data. More and more Big Data developers are using Spark to generate solutions for Big Data problems. It is the de-facto standard tool today. But are there any tools/products which can claim as a close competitor to Apache Spark?... read more

Apache Spark is the most popular open source product today to work with Big Data. More and more Big Data developers are using Spark to generate solutions for Big Data problems. It is the de-facto standard tool today. But are there any tools/products which can claim as a close competitor to Apache Spark? Putting the question in another way - If I am given a choice, can I as a Big Data Architect can think beyond using Apache Spark as a tool which I can use for all my Big Data tasks?

I would like to analyse this question taking different use cases.

Firstly, the data which is to be considered. In this case, the data scientist gets data from some source. The data scientist or the user gets the data from somewhere, understands the data, cleans it up, correlate the data with other sources.The size of the data determines a lot here. If the data is few gigabytes (GBs), we have the option of choosing between R, MySQL, SQL Lite or a python notebook with Pandas. Spark is more useful when data is too large to process. Apache Spark is best for huge data, AWS Athena or Google BigQuery can be good competitors for Spark, but Spark has more enriched features. In such case, Spark steals over other competitors.

Secondly, for Data Visualization and creating Dashboards that provide monitoring and insights based on data streams. Here Spark does not come up to that level for this use case. BI tools like Tableau and SiSense provide much better support than Spark for streaming data within a certain range of the data set which is being used.

Thirdly as an ETL tool Spark works well especially when the data does scale up pretty high. But the user has to do a lot of work around Spark to make sure that everything is working smoothly. This usually means that when Spark is used for ETL, data is considerably delayed by several hours or even a day. Apache Flink and Spark streaming are two other alternatives for this use case, but the user needs to code a lot and manage the cluster.

Fourth and last when talking about Machine Learning as a use case to determine other alternatives for Apache Spark, we can analyse the entire process into the following steps-
1. Preparing your data set
2. Building your models and
3. Using your models in a production environment.

Spark is considered very good for the first two jobs - preparing the data sets and building the models. Apache Spark scores high over other tools on data discovery and manipulating the data. Spark has rich Machine learning libraries for building models. However key-value data store like Cassandra also required here which increases the complexity of the solution and running these data models in production for real-time predictions gives Spark the bumps and the process usually falls apart. Few alternatives to Spark for this particular use case are Google's Tensorflow and ScikitLearn.

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