1

Understanding the concepts and challenges of Big Data

• Origins and definition of Big Data.
• Key figures in the international and French markets.
• The challenges of Big Data: ROI, organization, data privacy.
• An example of Big Data architecture.

2

Big Data technologies

• Description of the architecture and components of the Hadoop platform.
• Storage methods (NoSQL, HDFS).
• Operating principles of MapReduce, Spark, Storm, etc.
• Most popular distributions on the market (Hortonworks, Cloudera, MapR, Elastic Map Reduce, Biginsights).
• Installing a Hadoop platform.
• Technologies for the data scientist.

3

Installing a Hadoop Big Data platform (via Cloudera Quickstart or other software).

• Operating principles of the Hadoop Distributed File System (HDFS).
• Importing outside data into HDFS.
• Creating SQL requests with HIVE.
• Using PIG to process the data.
• Using an ETL to industrialize the creation of massive data flows.
• Overview of Talend For Big Data.

4

Importing outside data into HDFS.

• Creating SQL requests with HIVE.
• Using PIG to process the data.
• The principle of ETL (Talend, etc.).
• Managing massive data streaming (NIFI, Kafka, Spark, Storm, etc.)

5

Big Data Analytics techniques and methods

• Machine Learning: A component of artificial intelligence.
• Discovering the three families: Regression, Classification, and Clustering.
• Data preparation, feature engineering.
• Generating models in R or Python.
• Ensemble Learning.

6

Setting up analyses with the tools studied.

• Takeaways.
• Summary of best practices.
• Bibliography.