Training courses on Big Data for Companies

torus-BIG-DATA

Torusware expands its training offer with four new courses in Big Data, covering a wide range of training needs, from business opportunities in Big Data to the deployment and management of advanced tools (technologies in the Hadoop ecosystem, R and NoSQL solutions).

These courses are key part of our new project, Torus Academy, whose objetive is to reinforce our activity in training and knowledge transfer of Torusware technologies (microservices, performance, Big Data). Torusware has an large experience in training (more than 3 years forming professionals from more than 15 companies, with highly positive evaluations).

This training, focused in the practical area and to develop in the customer premises, is given by Torusware professionals with a wide experience in each area. The concepts key developed in our portfolio of courses in Big Data are detailed below. There is also the option of training plans tailored to each client’s specific interests.

For more information, please contact: academy@torusware.com

-Opportunities of business in Big Data

  • What is and what isn’t Big Data?
  • Examples of Big Data projects
  • Business opportunities in Big Data. Specific sector approaches 
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    • Marketing
    • Public administration
    • Financial services and insurance
    • Health
    • Retail
    • Telcos
    • Industry 4.0
    • R & D

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  • Use cases
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    • Single view of customer. CRM 360
    • Relationship discovery  in data
    • Predictive analytics
    • Optimization of IT infrastructure
    • Technologies Big Data open source. Hadoop Ecosystem

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-Technology Big Data: Hadoop Ecosystem

  • What is and what isn’t Big Data?
  • Big Data modules and tools
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    • Modeling (formats, compression, diagram design)
    • Intake (periodicity, transformations, tools)
    • Storage (HDFS)
    • Processing (Batch, real-time)
    • Orchestration (Oozie)
    • Analysis (SQL, Machine Learning, Graphs, UI)
    • Governance (Atlas, Falcon)
    • Integration with BI (display: Pentaho, PowerBI)

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  • The centerpiece of the Big Data: Hadoop
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    • HDFS (description, characteristics, performance)
    • MapReduce (description, characteristics, performance)
    • YARN (description, characteristics, performance)

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  • Data processing
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    • MapReduce, complexion, Pig, Hive, Spark, Storm

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  • Data analysis
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    • SQL (Impala, Spark SQL, Data Lake Analytics,…)
    • Machine Learning (MLib, Mahout, R and RStudio, Azure ML)
    • Other (Spark GraphX,…)

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  • Examples of projects Big Data
  • Business opportunities in Big Data.

-Introduction to the NoSQL databases

  • Principles of NoSQL: CAP and BASE
  • Classification of NoSQL Databases (key-value, columnar, document-oriented and graph)
  • MongoDB
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    • Installation, configuration and implementation
    • Data model
    • Example of use

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  • Cassandra
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    • Installation, configuration and implementation
    • Data model
    • Cassandra Query Language
    • Example of use

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  • Neo4j
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    • Installation, configuration and implementation
    • Data model
    • Cypher
    • Example of use

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  • REDIS
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    • Installation, configuration and implementation
    • Example of use

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-Execution of R in HPC and Big Data environments

  • Distributed and parallel computing
  • HPC environments (supercomputing, HPC clusters, clouds).
  • Big Data (supercomputing center, proprietary systems, clouds) environments
  • RStudio Server: installation, configuration, and deployment of R codes
  • Spark-R: installation, configuration and deployment of R codes
  • Use of R with notebooks (Jupyter, Zeppelin)
  • Integration with HPC (parallel/distributed execution) and Big Data (Hadoop)

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