Big Data, practical methods and solutions for data analysis Training

Understanding the concepts and challenges of Big Data

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

Big Data technologies

• Description of the architecture and components of the Hadoop platform.
• Storage methods (NoSQL, HDFS).
• Operating principles of MapReduce.
• Overview of the main distributions on the market and additional tools (Hortonworks, Cloudera, MapR, Aster).
• Installing a Hadoop platform.
• Overview of Big Dataspecific technologies (Talend, Tableau, Qlikview, etc.).

Exercise
Installing a full Big Data platform via Cloudera and its components.

Managing structured and unstructured data

• 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.

Exercise
Implementing massive data flows

Data analysis methods for Big Data

• Exploration methods.
• Segmentation and classification.
• Estimating and prediction.
• Implementing models.

Exercise
Setting up analyses with the software R.

Data visualization and concrete use cases

• Offtheshelf visualization tools
• Report formatting methodology
• Benefits of Big Data for “Social Business”.
• Measuring the ereputation and fame of a brand.
• Measuring customer satisfaction and experience, optimizing the customer's path.

Exercise
Installing and using a Data Visualization tool to create dynamic analyses, retrieving data from social media, and creating ereputation analyses.

Conclusion

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