Consult our trainings :
Big Data, practical methods and solutions for data analysis Training
- Participants / Prerequisite
This course will enable you to understand the issues and benefits of Big Data as well as the technologies to implement it. You'll learn how to integrate massive volumes of structured and unstructured data via an ETL, then to analyze them using statistical models and dynamic dashboards.
- Understand the concepts and benefits of Big Data with respect to business challenges
- Understand the technological ecosystem needed to carry out a Big Data project
- Acquire the technical skills to manage massive, unstructured, complex data flows
- Implement statistical analysis models to address business needs
- Learn about a data visualization tool for reporting dynamic analyses
- 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.
- 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.).
Installing a full Big Data platform via Cloudera and its components.
- 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.
Implementing massive data flows
- Exploration methods.
- Segmentation and classification.
- Estimating and prediction.
- Implementing models.
Setting up analyses with the software R.
- 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.
Installing and using a Data Visualization tool to create dynamic analyses, retrieving data from social media, and creating ereputation analyses.