Hadoop distributions – Cloudera, HortonWorks, DataStax, MapR.

An introduction to Hadoop and the Hadoop Stack.

Beyond the Data Warehouse – Analytical databases, Hadoop and NoSQL DBMSs.

The new multi-platform Analytical Ecosystem.

This session looks at platforms and data storage options for big data analytics Module 3: Big Data Platforms and Storage Options

The Big Data Extended Analytical Ecosystem.

Key components in a Big Data Analytics environment.

Challenges when managing and analyzing big data.Traditional data warehousing and BI in the enterprise.It also looks at how to preserve existing investment in data management and BI tools across DW and Big Data platforms This session looks at Big Data Analytical workloads, the technology components involved and how you can integrate these with existing DW/BI systems in a new architecture for end-to-end analytics and to enrich business insight. Module 2: An Introduction to Big Data Analytics Popular patterns for Big Data technologies.Data Warehousing and BI versus Big Data.

Industry use cases – Popular big data analytic applications.

The need to analyze new more complex data sources.

It looks at Big Data use case studies and what the difference is between traditional BI and Data Warehousing versus Big Data This session defines Big Data and looks at business reasons for wanting to make use of this new area of technology.

How to make use of Big Data to deliver business value.

How to set up and organize Big Data Projects including skills.

How to clearly understand business use cases for different Big Data technologies.

How to integrate Big Data with traditional data warehouses and BI systems.

How to analyze un-modeled, multi-structured data using Hadoop, MapReduce & Spark.

Big Data analytical techniques and front-end tools.

Big Data technology platforms beyond the data warehouse.

How Big Data creates several new types of analytical workload.

Big Data Analytics: From Strategy to Implementation

Big data analytics ppt