Business Intelligence les formations

Business Intelligence Training

Formation Business Intelligence (BI)

Business Intelligence Course Outlines

Identifiant (ID) Nom Durée Vue d'ensemble
2625 Model MapReduce and Apache Hadoop 14 hours The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers. Data Mining and Business Intelligence Introduction Area of application Capabilities Basics of data exploration Big data What does Big data stand for? Big data and Data mining MapReduce Model basics Example application Stats Cluster model Hadoop What is Hadoop Installation Configuration Cluster settings Architecture and configuration of Hadoop Distributed File System Console tools DistCp tool MapReduce and Hadoop Streaming Administration and configuration of Hadoop On Demand Alternatives
2498 Business Intelligence in MS SQL Server 2008 14 hours Training is dedicated to the basics of create a data warehouse environment based on MS SQL Server 2008. Course participant gain the basis for the design and construction of a data warehouse that runs on MS SQL Server 2008. Gain knowledge of how to build a simple ETL process based on the SSIS and then design and implement a data cube using SSAS. The participant will be able to manage OLAP database: create and delete database OLAP Processing a partition changes on-line. The participant will acquire knowledge of scripting XML / A and MDX. basis, objectives and application of data warehouse, data warehouse server types base building ETL processes in SSIS basic design data cubes in an Analysis Services: measure group measure dimensions, hierarchies, attributes, development of the project data cubes: measures calculated, partitions, perspectives, translations, actions, KPIs, Build and deploy, processing a partition the base XML / A: Partitioning, processes and overall Incremental, delete partitions, processes of aggregation, base MDX language
287782 Apache Spark 14 hours Why Spark? Problems with Traditional Large-Scale Systems Introducing Spark Spark Basics What is Apache Spark? Using the Spark Shell Resilient Distributed Datasets (RDDs) Functional Programming with Spark Working with RDDs RDD Operations Key-Value Pair RDDs MapReduce and Pair RDD Operations The Hadoop Distributed File System Why HDFS? HDFS Architecture Using HDFS Running Spark on a Cluster Overview A Spark Standalone Cluster The Spark Standalone Web UI Parallel Programming with Spark RDD Partitions and HDFS Data Locality Working With Partitions Executing Parallel Operations Caching and Persistence RDD Lineage Caching Overview Distributed Persistence Writing Spark Applications Spark Applications vs. Spark Shell Creating the SparkContext Configuring Spark Properties Building and Running a Spark Application Logging Spark, Hadoop, and the Enterprise Data Center Overview Spark and the Hadoop Ecosystem Spark and MapReduce Spark Streaming Spark Streaming Overview Example: Streaming Word Count Other Streaming Operations Sliding Window Operations Developing Spark Streaming Applications Common Spark Algorithms Iterative Algorithms Graph Analysis Machine Learning Improving Spark Performance Shared Variables: Broadcast Variables Shared Variables: Accumulators Common Performance Issues

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