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
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
Area of application
Basics of data exploration
What does Big data stand for?
Big data and Data mining
What is Hadoop
Architecture and configuration of Hadoop Distributed File System
MapReduce and Hadoop
Administration and configuration of Hadoop On Demand
Problems with Traditional Large-Scale Systems
What is Apache Spark?
Using the Spark Shell
Resilient Distributed Datasets (RDDs)
Functional Programming with Spark
Working with RDDs
Key-Value Pair RDDs
MapReduce and Pair RDD Operations
The Hadoop Distributed File System
Running Spark on a Cluster
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
Writing Spark Applications
Spark Applications vs. Spark Shell
Creating the SparkContext
Configuring Spark Properties
Building and Running a Spark Application
Spark, Hadoop, and the Enterprise Data Center
Spark and the Hadoop Ecosystem
Spark and MapReduce
Spark Streaming Overview
Example: Streaming Word Count
Other Streaming Operations
Sliding Window Operations
Developing Spark Streaming Applications
Common Spark Algorithms
Improving Spark Performance
Shared Variables: Broadcast Variables
Shared Variables: Accumulators
Common Performance Issues
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