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How to Use Big Data with Right Tools

Published on 06 February 15
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Online tips to help you reap the benefits of Business Intelligence


The term Big Data has become very popular in the last few years in the context of business analytics. It refers to extremely large data sets, for example: enterprise resource planning (ERP) or customer relationship management (CRM) databases, or documents, images and video. Big Data is now the most important component of Business Intelligence (BI). Organizations and businesses are finding it increasingly difficult to collect and manage information. In order to implement an effective BI strategy, an enterprise needs specialized tools, software, and methods to process ever increasing and constantly changing data.


The management of an enterprise is faced with an uphill challenge: How to find the most important and appropriate tools for their enterprise. If you are new to BI, you may find it more confusing because there are so many Big Data tools with specific capabilities. The question is: Are you now ready to discover the perfect solution?


In order to solve this enigma, first you need to complete a set of questionnaires. Asking the right questions will go a long way in helping you find the most appropriate tool for your business analysis projects. It should be noted that every new BI implementation project may require a new set of Big Data tools. The very first question one should ask is: Are new Big Data tools really required? Be clear about whether data issues are because of wrong technologies being used or because of wrong implementation methods using those tools? In your new BI project there may be many problems that can be easily solved with your current Big Data tools if they are used properly. Another consideration is the cost of solving the issues of a new project with the same tools.


If you are convinced that using existing tools for a new BI implementation project is not reasonable, then start contemplating what you need. At this point, concentrating on your BI objectives and data science will prove to be helpful. You should also focus on your data engineering objectives. Determine what type of reports or models are required to be built on the Big Data and how that huge data will be managed and accessed. Another consideration should be how fast you need to address the problem. Preparing a thorough requirements inventory to start and continue the process will help. After this, you will have to ascertain that the designed solution will provide all the requirements. At the same time, you will need to analyze the capacity of data skills and application development in the project and whether any specific training will be required for our resources. Will you need new resources for the new BI implementation project?


Next, analyze all categories of Big Data tools to decide what new tools you need for the new project. For data storage and batch processing engine, Hadoop is the most effective Big Data tool. While it is easy to load Big Data using Hadoop, you will find it slow to respond. Some new SQL applications on Hadoop may reduce response time. Nevertheless, Hadoop is a batch engine which shouldnât be used for real time processing of Big Data.


The second category of tools is the huge variety of New SQL or NoSQL tools. These tools load faster and offer swift access to particular records. However, these tools will fail to offer swift in-database processing or aggregation. One may increase swiftness of loading and response for accessing particular records individually by using a better type of NoSQL tools. MongoDB is one of the popularly used NoSQL database systems.


The third category of tools is Analytic databases and MPP (Massively Parallel Processing). These tools are used for attaining very swift in-data processing or aggregation of Big Data. But, often, these tools fail to load data swiftly. One of the very successful open source tools for distributed database management is Apache Cassandra. Choose Pentaho for better visualization including charts presentation, analysis and predictions.


As you can see, there is a huge range of Big Data tools offering different capabilities. Consider your requirements very carefully to decide on the optimum combination. Selecting specific tools from the vast pool of tools in each category is daunting. Check your requirements inventory thoroughly. Ask the Big Data tools vendors how their products can help your enterprise. Seek the help of an independent online consulting company offering advice and education regarding best practices for BI implementation.











Online tips to help you reap the benefits of Business Intelligence

The term Big Data has become very popular in the last few years in the context of business analytics. It refers to extremely large data sets, for example: enterprise resource planning (ERP) or customer relationship management (CRM) databases, or documents, images and video. Big Data is now the most important component of Business Intelligence (BI). Organizations and businesses are finding it increasingly difficult to collect and manage information. In order to implement an effective BI strategy, an enterprise needs specialized tools, software, and methods to process ever increasing and constantly changing data.

The management of an enterprise is faced with an uphill challenge: How to find the most important and appropriate tools for their enterprise. If you are new to BI, you may find it more confusing because there are so many Big Data tools with specific capabilities. The question is: Are you now ready to discover the perfect solution?

In order to solve this enigma, first you need to complete a set of questionnaires. Asking the right questions will go a long way in helping you find the most appropriate tool for your business analysis projects. It should be noted that every new BI implementation project may require a new set of Big Data tools. The very first question one should ask is: Are new Big Data tools really required? Be clear about whether data issues are because of wrong technologies being used or because of wrong implementation methods using those tools? In your new BI project there may be many problems that can be easily solved with your current Big Data tools if they are used properly. Another consideration is the cost of solving the issues of a new project with the same tools.

If you are convinced that using existing tools for a new BI implementation project is not reasonable, then start contemplating what you need. At this point, concentrating on your BI objectives and data science will prove to be helpful. You should also focus on your data engineering objectives. Determine what type of reports or models are required to be built on the Big Data and how that huge data will be managed and accessed. Another consideration should be how fast you need to address the problem. Preparing a thorough requirements inventory to start and continue the process will help. After this, you will have to ascertain that the designed solution will provide all the requirements. At the same time, you will need to analyze the capacity of data skills and application development in the project and whether any specific training will be required for our resources. Will you need new resources for the new BI implementation project?

Next, analyze all categories of Big Data tools to decide what new tools you need for the new project. For data storage and batch processing engine, Hadoop is the most effective Big Data tool. While it is easy to load Big Data using Hadoop, you will find it slow to respond. Some new SQL applications on Hadoop may reduce response time. Nevertheless, Hadoop is a batch engine which shouldnât be used for real time processing of Big Data.

The second category of tools is the huge variety of New SQL or NoSQL tools. These tools load faster and offer swift access to particular records. However, these tools will fail to offer swift in-database processing or aggregation. One may increase swiftness of loading and response for accessing particular records individually by using a better type of NoSQL tools. MongoDB is one of the popularly used NoSQL database systems.

The third category of tools is Analytic databases and MPP (Massively Parallel Processing). These tools are used for attaining very swift in-data processing or aggregation of Big Data. But, often, these tools fail to load data swiftly. One of the very successful open source tools for distributed database management is Apache Cassandra. Choose Pentaho for better visualization including charts presentation, analysis and predictions.

As you can see, there is a huge range of Big Data tools offering different capabilities. Consider your requirements very carefully to decide on the optimum combination. Selecting specific tools from the vast pool of tools in each category is daunting. Check your requirements inventory thoroughly. Ask the Big Data tools vendors how their products can help your enterprise. Seek the help of an independent online consulting company offering advice and education regarding best practices for BI implementation.

This blog is listed under Development & Implementations and Data & Information Management Community

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