Big Intelligence- SAP HANA-Hadoop – The Reasons behind the Success of Big Data

Big data has come up with its three pillar model to make sure data analytics working with Hadoop or SAP HANA face no major lags.

According to expert market analysts, global investment and spending on big data rapidly increased to 4.5 billion Euros in the year 2012. In fact, the big data market is supposedly the strongest segments that have been growing steadily. It has fast become the driving force of economic activities in the IT business.

Conventional BI

A conventional BI solution for enterprise is all that you needed while dealing with the retrograde analysis of structured data from different business applications such as CRM or ERP systems. In such cases, data is to be stored in a typical data warehouse. Analytical systems can transform data into information efficiently. Data can be accessed through BI port like SAP Business Objects or MS SQL server.

Setting up the enterprise data warehouse as well as BI platforms is the key component of BI. Preferably, these settings are based on business intelligence process models. BPM, or Business Performance Management, is also needed. BPM clearly defined methods, processes and tools to improve the profitability and performance of a company. It also covers forward-thinking processes such as prognoses and planning. BPM is specifically imperative for audits, compliance, controls, financial reporting, corporate governance and accounting standards.

SAP HANA and Real-Time Reporting

SAP HANA is an in-memory technology that supplements BI solution. It can also help companies analyze some structured data in real time, which is very important. In-memory solutions, unlike enterprise BI, can store data straightaway in working memory rather than on hard drive, thus making data instantly accessible for further processing, evaluation as well as use.

However, technology is not just good enough. It is very important to combine technology with process know-how to create added value and increase performance by almost hundred times.

SAP HANA is definitely a high performance appliance that does not store all the data in the memory of the server. Such appliances are mainly based on shared nothing architecture, which divided up the total data amount into different packets of the same size and further stores the same on various nodes. It is the sub-data packets that get processed first and locally, and then the data is compiled to generate good result. In fact, this is one approach that can also be scaled to meet rising demands.

Hadoop for Unstructured Information Analyses

Companies are now using Hadoop and not just SQL technologies to process very high volumes of both structured as well as unstructured data. One of the major advantages of Hadoop is that organizations can collect their data and later analyze the same data on the basis of their targeted queries.

Hadoop, a Java-based software framework, helps users in handling compute-intensive processes on different server clusters having large volumes of data. It also allocated complex computational jobs with massive data volumes in Petabytes range to over thousands of nodes.

NoSQL is basically the foundation for new generation databases. It can manage high volume of unstructured data, which is very important. The main function of NoSQL is to make sure that growing number of poly-structured data can be distributed equally among several computers like data from social network.

Data Analytics Service Provider – Choosing the Right One

To analyze data, you need large number of computers. All these computers should be able to cluster massive amount of data into multiple smaller packets of data and process them during the same time. IT service providers having adequate cloud computing resources who meet governance, compliance regulations and risk is usually beneficial in this regard. Providers also should meet the higher security needs for big data solutions with services like disaster recovery, backup and high availability guarantee.