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Pentaho Reporting is a suite (collection of tools) for creating relational and analytical reports. It can be used to transform data into meaningful information. Pentaho allows generating reports in HTML, Excel, PDF, Text, CSV, and xml. This tutorial provides a basic understanding of how to generate professional reports using Pentaho Report Designer.

This tutorial is designed for all those readers who want to create, read, write, and modify Dynamic Reports using Java. In addition, it will also be quite useful for those readers who would like to become a Data Analyst.

Before you start proceeding with this tutorial, we assume that you have prior exposure to Core Java, Database Concepts, and SQL Queries.

MapReduce is a programming paradigm that runs in the background of Hadoop to provide scalability and easy data-processing solutions. This tutorial explains the features of MapReduce and how it works to analyze Big Data.

This tutorial has been prepared for professionals aspiring to learn the basics of Big Data Analytics using the Hadoop Framework and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.

It is expected that the readers of this tutorial have a good understanding of the basics of Core Java and that they have prior exposure to any of the Linux operating system flavors.

Impala is the open source, native analytic database for Apache Hadoop. It is shipped by vendors such as Cloudera, MapR, Oracle, and Amazon. The examples provided in this tutorial have been developing using Cloudera Impala

This tutorial is intended for those who want to learn Impala. Impala is used to process huge volumes of data at lightning-fast speed using traditional SQL knowledge.

To make the most of this tutorial, you should have a good understanding of the basics of Hadoop and HDFS commands. It is also recommended to have a basic knowledge of SQL before going through this tutorial.

DAX functions play an important role in the usage of DAX for data modeling and reporting. It is an inbuilt function provided in the DAX language that helps you perform commonly used data calculations on the Data Model. In this tutorial, you will learn data modeling and analysis using DAX, based on the Power Pivot data model. As you proceed, you will learn about the different facets of Power Pivot, DAX and DAX functions in data modeling and analysis. By the end of the tutorial, you will be able to perform data modeling and analysis with DAX for any context at hand.

This tutorial has been designed for all those readers who depend heavily on MS-Excel to prepare charts, tables, and professional reports that involve complex data. It will help all those readers who use MS-Excel regularly to analyze data. Professionals who use data modeling and data analysis for reporting and decision-making purposes will benefit from this.

To proceed with this tutorial, it is a good idea to first go through the following tutorials −

IBM Cognos Business intelligence is a web based reporting and analytic tool. It is used to perform data aggregation and create user friendly detailed reports. IBM Cognos provides a wide range of features and can be considered as an enterprise software to provide flexible reporting environment and can be used for large and medium enterprise. Cognos also provides you an option to export the report in XML or PDF format or you can view the reports in XML format.

IBM Cognos provides a wide range of features and can be considered as an enterprise software to provide flexible reporting environment and can be used for large and medium enterprises. It meets the needs of Power Users, Analysts, Business Managers and Company Executives.

Power users and analysts want to create ad-hoc reports and can create multiple views of the same data. Business Executives want to see summarize data in dashboard styles, cross tabs and visualizations. Cognos allows both the options for all set of users.

IBM Cognos Business Intelligence is an advanced topic. Even though the content has been prepared keeping in mind the requirements of a beginner, the reader should be familiar with the fundamentals of running and viewing reports or manage schedules, portal layouts, and other users' permissions before starting with this tutorial.

Cassandra is a distributed database from Apache that is highly scalable and designed to manage very large amounts of structured data. It provides high availability with no single point of failure.

The tutorial starts off with a basic introduction of Cassandra followed by its architecture, installation, and important classes and interfaces. Thereafter, it proceeds to cover how to perform operations such as create, alter, update, and delete on keyspaces, tables, and indexes using CQLSH as well as Java API. The tutorial also has dedicated chapters to explain the data types and collections available in CQL and how to make use of user-defined data types.

This tutorial will be extremely useful for software professionals in particular who aspire to learn the ropes of Cassandra and implement it in practice.

It is an elementary tutorial and you can easily understand the concepts explained here with a basic knowledge of Java programming. However, it will help if you have some prior exposure to database concepts and any of the Linux flavors.

The volume of data that one has to deal has exploded to unimaginable levels in the past decade, and at the same time, the price of data storage has systematically reduced. Private companies and research institutions capture terabytes of data about their users’ interactions, business, social media, and also sensors from devices such as mobile phones and automobiles. The challenge of this era is to make sense of this sea of data.

This is where big data analytics comes into picture. Big Data Analytics largely involves collecting data from different sources, munge it in a way that it becomes available to be consumed by analysts and finally deliver data products useful to the organization business. The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Big Data Analytics. In this tutorial, we will discuss the most fundamental concepts and methods of Big Data Analytics.

This tutorial has been prepared for software professionals aspiring to learn the basics of Big Data Analytics. Professionals who are into analytics in general may as well use this tutorial to good effect.

Before you start proceeding with this tutorial, we assume that you have prior exposure to handling huge volumes of unprocessed data at an organizational level.