The term Big Data is today on everyone’s lips. It refers to both structured and unstructured data generated and obtained by a business as a result of its transactions on a day-to-day basis and through everyday activities. Expert analysts unanimously tend to claim that it’s not the amount of data which plays a major role but the way businesses deal with it and use it. Big data is always full of potential for insights and hidden facts about the business performance and marketing environment.
In this article, we would love to go deeper into the subject of big data analytics with the help of two BI technologies: OLAP and Hadoop.
What Is Big Data?
Big data is considered to be too vast and intricate to be processed with traditional software applications, The main challenges of big data analytics encompass such issues as data storage, it's capturing, analysis, transfer, sharing, querying, visualization, updates, and many others. And although big data may cause certain impediments on the way to successful and prompt business and data management, its amount is relevant depending on the organization. This way for some organizations hundred gigabytes of data may provoke a trouble forcing them to look for new data management tools, whereas for the others the same amount of data may be absolutely fine to keep them confident and satisfied with the current software tools (such as OLAP for instance).
Why Use It?
Big data is mainly applied to user behavior analysis, predictive analysis, and other sophisticated data analytics techniques which contribute to attaining value from day-to-day information exchange. Due to big data, it’s possible to discover parallels in business trends, marketing flows, the perception of a brand, and customers behavior. End-users are free to use it for what-if analysis, reporting, forecasting, etc.
The data can be taken from any sources available; after its extraction, it can be examined to help solve the issues connected with costs, terms, product development, new offers, and smart decisions. Big data analytics is also a great key to the aspects such as:
- actual reasons for breakdowns in business;
- finding the best way to attract customers when it comes to the brand launch or promotion;
- relocation, decentralization, and other significant changes and shifts within a company;
- disclosure of dishonesty and counterfeiting in and out of the company;
- complex reporting available in minutes without burdening multiple specialists or departments.
Does OLAP Suit?
Nowadays the variety of data sources has significantly expanded. Data today may encompass multiple types such as pdf, txt, doc, jpg, mp3, mp4 files and others all being possible to sort, classify, and analyze. When it comes to choosing a tool for all that, OLAP is considered to be one of the main technologies to handle data with regard to analysis and reporting. OLAP is still in high demand as it successfully responds to the needs of budgeting, forecasting, what-if analysis, planning, and others.
Although a traditional OLAP solution isn’t able to handle Big Data. Particularly, OLAP is not scalable enough, there are connector cartridge ins and outs, dimension processing limitations, distinct count aspects, etc. Thereby multiple organizations today are willing to offload their data issues connected to data sources and types by tying OLAP and Hadoop.
Accelerating OLAP with Hadoop
What Is Hadoop?
Apache Hadoop is a group of software tools aimed to assist in the progress of computer problem-solving processes. It delivers a software framework processing Big Data and distributing its storage. The modules of Hadoop are developed with an idea that hardware breakdowns are commonplaces which should be managed by the framework.
What Is Hadoop’s Principle of Work?
Hadoop divides files into big chunks and allocates them across several nodes in a cluster. Afterward, it transmits JAR files into nodes so that to process the data simultaneously. Such steps taken ensure locality of reference (also known as the principle of locality), high processing speed, and steep efficiency.
OLAP on Hadoop As an Enhanced BI Tool
Now numerous businesses and organizations are implementing the OLAP technology on Hadoop where the former is required with a view to ensuring the right level of scalability, granularity, and flexibility. Due to such an approach to big data management, end-users have an opportunity to examine and analyze large volumes of the data, draw up reports on it over big periods of time with better detalization and through combining all possible data sources and types rather than while using merely OLAP. Consequently, there is a bigger probability to gain meaningful business insights. OLAP on Hadoop provides seamless and transparent data analytics without a need to terminate day-to-day activities. Thereby, owing to OLAP tied to Hadoop, end-users don’t have to change the tool they are used to for reporting.
Most commonly it is usual to pull the data from Hadoop into a data mart and examine it already there. Even though data transferring from one database to another often brings about delays and scalability limitations especially when comes to the data volume to be handled.
There is today a plenty of solutions appearing on the market intended to tackle this issue about the data transfer. For instance, there are tools which exploit an approach where it is not needed to move the data into an extended data mart as it is possible to provide the very data with analytical capabilities.
From our perspective, the best and most efficient way to accomplish it is to deliver the OLAP technology precisely on Big Data, harnessing the advantages of Hadoop infrastructure which facilitates the load for OLAP analytics. The tools exploited by end-users can be linked to the OLAP layer which will boost performance.
The good thing about connecting Hadoop to OLAP is that Hadoop’s environment is being developed more and more these days and a lot of useful and helpful features are becoming available. This ensures the return on the organization’s investment and substantiates the adoption of the technology.