Big data processing has a life cycle that includes a balance of ingestion, digestion and output. Of course, the life cycle of big data processing varies on a case by case basis. Of the 53 percent of companies that are using big data analytics, each have their own unique needs when it comes to data processing. Variety, volume and velocity all contribute to what a processing lifecycle looks like in different environments.
Each step in the big data processing life cycle is important because it ensures the quality of the data that is received. Data platforms that are being utilized for fraud prevention specifically rely on each of the steps in these processes in order to detect inconsistencies that point to threats.
Here are the basic components of a typical big data processing life cycle.
Evaluation
Big data processing platforms are set up with the specific goals and objectives in mind. This type of setup requires detailed evaluation and observation. Prior to setup, enterprises need to be able to determine what the results of their data analysis should be. The results should be clearly defined so that an enterprise is not only aware of what the processing platform is doing but why it is doing it as well.
Identification
Identifying the source of a set of data is the first step in being able to set up a way for it to be evaluated. The origin of every piece of data as well as the reliability the source should be identified. Data output can only be as reliable as the data input that is received.
Acquisition and Filtering
A platform can be set up so that data is acquired from a variety of different sources. These sources can be both external and internal. However, gathering data is just one part of the equation. Data must then be filtered to get rid of irrelevant or inaccurate findings. Filtering typically happens on a singular basis before it is transported to a larger data storage pool.
Extraction
Not every piece of data that is consumed by a system is compatible with the platform that is digesting it. In cases of incompatible data, alternate forms of analysis need to occur. Extraction is the process through which incompatible data is transformed into a format that can be processed and analyzed by a given platform. Extraction is an especially important step in big data analysis being done by agencies like the United Nations.
Validation and Cleansing
During the processing lifecycle, invalid data must be addressed. Invalid data skews results and creates inconsistencies in outputs. To ensure data validity, tests are run to audit data against a set of rules and conditions. This step enables enterprises to spot corrupted data and avoid invalid results.
Aggregation and Representation
When data is received from multiple locations, it must be joined at some point during the processing lifecycle. The task of data integration is a key step that must be performed by big data streaming systems. It allows for data cohesion and speeds up the timeline it takes to appropriately analyze a system’s data.
Visualization
Data visualization is the impressive output for end users in a data streaming platform. Visualization is the way through which results are communicated in a tangible and shareable way. Data visualization is often presented in the form of graphs, charts or heat maps.
Utilization of Results
This last stage a data processing lifecycle is the stage where the final product is used to analyze what the data is saying. After the data is understood, actionable decisions can be made. The utilization stage is the most enticing stage because it allows for a combination of data-driven information and human wisdom to enable an enterprise to make informed decisions.
Big data processing is a fascinating operation with unending possibilities. It streamlines analytics and provides a trustworthy means for turning large sets of data into meaningful information. When used correctly, big data processing can uncover nuances that would otherwise fall through the cracks.
Paul Petersen