50 Open Source Replacements for Proprietary Business Intelligence Software.

In a recent Gartner survey, CIOs picked business intelligence and analytics as their top technology priority for 2012. The market research firm predicts that enterprises will spend more than $12 billion on business intelligence (BI), analytics and performance management software this year alone.

As the market for business intelligence solutions continues to grow, the open source community is responding with a growing number of applications designed to help companies store and analyze key business data. In fact, many of the best tools in the field are available under an open source license. And enterprises that need commercial support or other services will find many options available.

This month, we've put together a list of 50 of the top open source business intelligence tools that can replace proprietary solutions. It includes complete business intelligence platforms, data warehouses and databases, data mining and reporting tools, ERP suites with built-in BI capabilities and even spreadsheets. If we've overlooked any tools that you feel should be on the list, please feel free to note them in the comments section below.

Data Modeling ,moving from SQL to NoSQL in the enterprise lecture

Very interesting lecture about data modeling and moving MySQL to NoSQL

Summary
Kenneth M. Anderson shares some of the data modeling issues encountered while transitioning from a relational database to NoSQL.
http://www.infoq.com/presentations/MySQL-NoSQL-Data-Modeling

SQL is what’s next for Hadoop: Here’s who’s doing it.

SUMMARY:

More and more companies and open source projects are trying to let users run SQL queries from inside Hadoop itself. Here’s a list of what’s available and, on a high level, how they work.

Installing and comparing MySQL/MariaDB, MongoDB, Vertica, Hive and Impala (Part 1)

A common thing a data analyst does in his day to day job is to run aggregations of data by generally summing and

averaging columns using different filters. When tables start to grow to hundreds of millions or billions of rows, these operations become extremely expensive and the choice of a database engine is crucial. Indeed, the more queries an analyst can run during the day, the better he can be at understanding the data.

SQL, NoSQL, BigData in Data Architecture

All about how to build "Data Architecture" using SQL, NoSQL and BigData technologies and how to evaluate them.

SQL, NoSQL, BigData in Data Architecture from Venu Anuganti

Intro to NoSQL

What is NoSQL?

Relational databases were introduced into the 1970s to allow applications to store data through a standard data modeling and query language (Structured Query Language, or SQL). At the time, storage was expensive and data schemas were fairly simple and straightforward. Since the rise of the web, the volume of data stored about users, objects, products and events has exploded. Data is also accessed more frequently, and is processed more intensively – for example, social networks create hundreds of millions of customized, real-time activity feeds for users based on their connections' activities.