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Big Data Analytics Infrastructure

@machinelearnbot

Recent surveys suggest the number one investment area for both private and public organizations is the design and building of a modern data warehouse (DW) / business intelligence (BI) / data analytics architecture that provides a flexible, multi-faceted analytical ecosystem. The goal is to leverage both internal and external data to obtain valuable, actionable insights that allows the organization to make better decisions. Unfortunately, the amount of recent DW / BI / Data Analytics innovation, themes and paths is causing confusion. The "Big Data" and "Hadoop" hype is causing many organizations to roll-out Hadoop / MapReduce systems to dump data into - without a big-picture information management strategic plan or understanding how all the pieces of a data analytics ecosystem fit together to optimize decision making capabilities.


Big Data Analytics Infrastructure

@machinelearnbot

Recent surveys suggest the number one investment area for both private and public organizations is the design and building of a modern data warehouse (DW) / business intelligence (BI) / data analytics architecture that provides a flexible, multi-faceted analytical ecosystem. The goal is to leverage both internal and external data to obtain valuable, actionable insights that allows the organization to make better decisions. Unfortunately, the amount of recent DW / BI / Data Analytics innovation, themes and paths is causing confusion. The "Big Data" and "Hadoop" hype is causing many organizations to roll-out Hadoop / MapReduce systems to dump data into - without a big-picture information management strategic plan or understanding how all the pieces of a data analytics ecosystem fit together to optimize decision making capabilities. This has resulted in the creation of a new word: Hadump - meaning data dumped into Hadoop with no plan.


A Deep Dive Into Data Lakes

#artificialintelligence

In the age of Big Data, we've had to come up with new terms to describe large-scale data storage. We have databases, data warehouses and now data lakes. While they all contain data, these terms describe different ways of storing and using that data. Before we discuss data lakes and why they are important, let's examine how they differ from databases and data warehouses. Let's start here: A data warehouse is not a database.