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.

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.

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.

Time series analytics on Vertica

Gap Filling and Interpolation (GFI)

A Swiss-Army Knife for Time Series Analytics

Gap Filling and Interpolation (GFI) is a set of patent-pending time series analytics features in Vertica . In this post, through additional use cases, we will show that GFI can enable Vertica users in a wide range of industry sectors to achieve a diverse set of goals.

Rolling Average with Oracle or Vertica analytical functions.

This little example will demonstrate how to use Oracle's or Vertica's analytical functions to get the rolling average. First you have to create and load a table that contains each month's average temperature in Edinburgh in the years 1764-1820.

Large-Scale Processing in Netezza.

Transitioning from ETL to ELT

CIO: Why is that uber-powered [commodity RDBMS] system running out of steam? Didn’t we just upgrade?

MANAGER: Yes, but the upgrade didn’t take.

CIO: Didn’t take? Sounds like a doctor transplanting an organ. Do you mean the CPUs rejected it? (laughing)

MANAGER: (soberly) No, just the users. Still too slow.

CIO: That hardware plant cost us [X] million dollars and it had better get it done or I’ll dismantle it for parts. I might dismantle your prima-donna architects with it!

Enhanced Aggregation, Cube, Grouping and Rollup.

(OLAP reporting embedded in SQL)

Much of the OLAP reporting feature embedded in Oracle SQL is ignored. People turn to expensive OLAP reporting tools in the market - even for simple reporting needs. This article outlines some of the common OLAP reporting needs and shows how to meet them by using the enhanced aggregation features of Oracle SQL.

Analytic functions by Example.

This article provides a clear, thorough concept of analytic functions and its various options by a series of simple yet concept building examples. The article is intended for SQL coders, who for might be not be using analytic functions due to unfamiliarity with its cryptic syntax or uncertainty about its logic of operation. Often I see that people tend to reinvent the feature provided by analytic functions by native join and sub-query SQL. This article assumes familiarity with basic Oracle SQL, sub-query, join and group function from the reader. Based on that familiarity, it builds the concept of analytic functions through a series of examples.