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Big Data: Architecture & infrastructure

Description

Big Data is seen today as the oil of the 21st century. Fueled by the massive use of social networks, the boom in connected objects, the adoption of the quantified self and the digital transformation of businesses, the big data market continues to grow. By 2020, the value of the personal data of European consumers could approach 1 trillion euros according to the Boston Consulting Group. This training will allow you to consider the implementation of big data in your company.

Course plan

  1. Introduction to Big Data

    1. Trainer and participants presentations

    2. Big Data principles and challenges

    3. The key numbers

    4. The challenges (ROI / Confidentiality)

    5. Big Data Architecture Concept

  2. Big Data technologies

    1. Market tools (MapR, Watson, Oracle Exalead, Microsoft, Teradata, Talend, Qlik Sense, AWS...)

    2. Storage methods

    3. Hot data

    4. Lukewarm data

    5. Archiving

  3. Languages:

    Python, Matlab, R, …

  4. State of the art of Big Data

    1. The main actors

    2. The different professions of Big Data

  5. Hardware Architecture

    1. Storage

    2. CPU/GPU

    3. Memory

    4. Network

    5. Distributed Systems

  6. Technical Architecture

    1. Data Quality: use of an Open Data flow operation with Talend Data Quality

    2. Data storage: Hadoop (HDFS), NoSQL (Cassandra, MongoDB)

    3. Data dissemination

      1. Streaming with Apache Kafka, Amazon AWS Kinesis

    4. Real-time or deferred processing

      1. Apache Spark

      2. ELK

        1. ElasticSearch

        2. Logstash

        3. Kibana

    5. Distributed architectures: Hadoop clustering

    6. Platform supervision: App Dynamix, Ambari

  7. Application Architecture (case study)

    1. The different stages of data management

    2. Ingestion from the source

    3. The transformation

    4. Restitution / visualization

    5. Predictive analysis

  8. Workshop: 360 customer analysis

  9. Business applications (use cases)

    1. Text Analytics

    2. Fraud detection

    3. Customer targeting

  10. Risks of the Big Data project

    1. Business risks related to the framing of the scope

    2. Strategic risks and lack of sponsorship

    3. The KPIs

    4. The maturity of market solutions

  11. Safety, ethics & legal issues

    1. Ensure data protection

      1. The anonymization of data

      2. Integrity check

      3. Encryption of data

    2. What is the blockchain?

    3. Use case: Bitcoin

  12. Conclusion (global debrief)

duration
  • 5 days
Level
Beginner
Audience
Technical ArchitectsDevelopersSysOpsTechnical Project ManagersBusiness Intelligence Consultants
Prerequisite
Have a working knowledge of Shell Linux, networking basics, relational databases and distributed architectures.
Educational goals
-Exploit Big Data architectures -Design the integration of data flows
Educational Method
Alternation of lectures (70%) and practical work and use cases (30%).
Interested to attend this training?

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