Intelligence artificielle et données

Apache Spark is a powerful open-source framework designed for efficient and unified processing of big data. It dramatically enhances performance, executing applications on Hadoop clusters up to 100 times faster in memory and supports SQL, data streaming, machine learning, and graph processing capabilities. Designed around Resilient Distributed Datasets (RDDs), it optimizes iterative computations and leverages lazy evaluation for workflow efficiency. Additionally, Spark's ecosystem includes specialized libraries like Spark Streaming, Spark SQL, MLlib, and GraphX for advanced data workflows.

This document is an accelerated Python tutorial designed as part of the Master in Business Analytics & Data Science program. It introduces foundational Python concepts, including virtual environments, libraries, and syntax essentials. Core sections cover Python data structures, conditional logic, loops, and functions, alongside advanced topics like sorting and list comprehensions. The tutorial emphasizes Python's capabilities for data manipulation and machine learning with Scikit Learn.

This document provides an overview of data interrogation languages used in Big Data environments, with a focus on PIG and HIVE. It aims to equip readers with the knowledge necessary to process and analyze large datasets effectively using these tools. The methodologies discussed include structured querying through HIVE and scripting approaches via PIG. The document emphasizes practical applications in data analytics using Hadoop ecosystems.

This document is an educational resource on Big Data, focusing on HDFS and MapReduce frameworks. It explains HDFS architecture, emphasizing data replication and fault tolerance mechanisms in a master/slave setup with NameNode and DataNode roles. Similarly, it explores the MapReduce processing model, detailing the task distribution and fault tolerance strategies. Both methods prioritize efficient, redundant, and reliable data storage and processing distributed across large clusters.

The document provides an overview of Big Data, explaining its core characteristics (4Vs: Volume, Velocity, Variety, Veracity) and discussing its applications and challenges. It introduces Hadoop as an open-source framework optimized for distributed computing, enabling storage and processing of massive datasets. Key technologies such as MapReduce, HDFS, and Hadoop's ecosystem are detailed, emphasizing cost-efficiency and scalability. Challenges like system heterogeneity, fault tolerance, and concurrency in distributed computing are identified, with potential solutions outlined.

This document delves into the implementation of OLAP for analyzing shoe sales data at "Au bon pied." A star-schema relational data structure is developed, enabling multidimensional reporting on dimensions such as Time, Model, and Store while calculating measures like sales volume and revenue. Advanced hierarchical configurations, on-the-fly calculations, and temporal trend evaluations allow for efficient analysis and decision-making. Sparse data optimization strategies further enhance storage and processing of high-dimensional sales data across thousands of references and stores.

This document introduces Online Analytical Processing (OLAP), contrasting it with Online Transaction Processing (OLTP). It details key OLAP operations, such as roll-up, drill-down, slice, and dice, as well as the underlying architecture, including data storage methods (ROLAP, MOLAP, HOLAP). Additionally, various OLAP analysis examples highlight the methodology in sales data interpretation using multi-dimensional schemas and SQL queries. Lastly, it covers challenges of physical storage, sparsity issues, and operational restructuring of cubes for better analytical insights.

This document provides an in-depth analysis of Apache Spark, a unified analytics engine for big data processing. Spark overcomes limitations of MapReduce such as high I/O latency and inefficiency in multi-pass computations. With its in-memory data processing and lazy evaluation, it achieves up to 100x faster performance in memory and up to 10x on disk compared to Hadoop. Spark allows for batch and streaming data processing, supports RDDs, DataFrames, and Datasets, and includes additional libraries for Machine Learning, graph processing, and SQL queries. Its ecosystem is expansive, with mult...

This document introduces MapReduce as a paradigm for distributed computing, focusing on its practical advantages over traditional methods for processing large datasets. It explains the methodology through key concepts such as the map and reduce operations, the division of tasks into smaller fragments, and their parallel execution across machines in a distributed cluster. Several concrete examples, such as word frequency analysis and shared-friend calculation in social networks, illustrate the practical utility of MapReduce. These examples, coupled with systematic pseudo-codes, demonstrate h...

The report outlines an internship experience at IDsoft, a software company specializing in real estate applications. The author's main project involved contributing to the development and enhancement of the reporting capabilities of IDsoft's SaaS-based ERP solution, IDimmo. The project incorporated Business Intelligence (BI) tools such as SQL Server Integration Services (SSIS), Analysis Services (SSAS), and Reporting Services (SSRS), which were utilized for efficient data handling and reporting. The work facilitated improved data aggregation, visualization, and decision-making processes for...