Data Processing vs. Big Data: Who Beats Whom

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Data processing and Big Data are terms we’ve heard thrown around, but what exactly are they, and how do they compare to each other? Is Big Data needed for data processing? Or is data processing needed for Big Data?

In this article, I’m going to compare the two terms and how they relate to each other. To see who wins in the data world, keep reading.

Data Processing

Data processing, simply defined, is the preparation and conversion of data into a useable form. Only once data is in a usable form can it be applied to data analytics.

Although data processing and analytics are sometimes lumped together, analytics is the part of data science that relates to drawing conclusions from the data, whilst data processing is the preparation of data for analytics.

Data Processing Steps

Data processing moves through several functions and key steps, which may be applied independently, or as a complete solution.

Data processing steps may vary depending on the quality of the data available or needed and the type of database. A relational database, for example, requires more cleaning and validation than a non-relational database.

To begin with, data must be collected and stored.

Data collection

Compiling data sets through capturing of data in processes, data entry, or data measurement.

Storage of data

With larger data sets, storage becomes important to ensure data is not degraded. Storage is usually digital. Note, the conversion may take place before storage, and in some cases cleaning also, data can also be stored unstructured.

Once captured and stored, data processing requires some or all of the following steps to be performed on data sets to make them useful:

Conversion

Converting data to a common format or language so that different types can be used together, and so that the type needed can be used in the data processing.

An example of conversion would be ensuring all measurements were in metric, imagine the error of comparing 3m to 3ft.

Cleaning

Removing erroneous data and noise from data sets.

Validation

Ensuring that supplied data is clean, in the correct form, (quality control checking of the cleaning and conversion steps) and applicable or valid for the intended use.

An example of validation would be removing the entry of a dodge from a car database of European cars, although the form was converted, and clean, it was not valid.

Sorting

Separating data sets into sequences of subsets for relationships.

Summarization

Combining multiple sets of data into one set of data, compressing detailed data to its main points.

Aggregation

Arranging similar data sets, and drawing further information from multiple types of data bring added information.

Classification

Separating categories of data, a more detailed method of sorting, which may be applied after summarization and aggregation.

Presentation 

Preparing charts, tables, figures, text, maps, vectors, and graphics related to the data which present the data sets in an easy to analyze form for analysts, experts, and managers, or other interested parties.

Reporting

Compiling a summary of the data sets and processes applied, for analysts and other interested parties to review.

As you can see data processing is a comprehensive application for taking raw data to useful data.

Big Data

When data sets become so huge that they require multiple computer processing tools to use them, they are referred to as Big Data.

Big Data follows the 5Vs classification of data, volume, velocity, value, variety, veracity.

The increasing development of Big Data sets has driven the development of more new technology in data processing platforms.

Hadoop was created as an open-source platform to network computers, and harness the power of network processing primarily as a result of big data needs. This platform allows quicker processing times due to the networked resources which are required for big data but benefit all data processing. Many open source applications have been built on the Hadoop framework, such as Hive, Presto, Spark, and Cassandra.

Big Data has opened up the field of data processing and analytics to more niche applications, and more diverse analytics, such as non-relational storage, and in-memory processing.

Many technology companies leverage the unstructured big data created in applications such as social media to gain ground in new industries.

How Does Big Data Relate to Data Processing

It makes sense that the more information we have to start with, the more conclusions we can draw from it. The more data we have to process, the more meaningful information can be deducted from this. Big data is providing data analytics with the lifeblood it needs to expand.

Big Data brings with it a host of work for data processors, such as validating the data, building systems for handling the data, increasing processing capacities, and lots of cleaning and sorting. It needs to be handled by professionals. There are potential shortages of industry skills to work with the data, as the growth of data sets exceed training growth.

Big Data provides structured and unstructured data from multiple processes and sources. The data provides useful information that allows data processing with more work promoting more rapid growth and provides more results available from data processing to be analyzed when used properly by professionals.

How Does Data Processing Relate to Big Data

Big Data is usually in an unstructured or uncleaned, and essentially an unusable format when it’s first collected.

Its datasets may provide huge data analysis opportunities. However, they also can provide huge amounts of errors if they are not properly treated. By treated, we mean processed to remove errors, converted into useful formats, and separated or summarized. Essentially, all of the key elements of data processing need to be applied to a Big Data set before it can be effectively used in analytics.

As mentioned above, bigger data sets have the need for more specialized skills and more potential for error. While useful, Big Data is not used alone in raw format.

Which Wins? Big Data or Data Processing

To sum it up, essentially we can’t have useful information from Big Data without effective processing, so Big Data relies on effective data processing.

Data processing conversely, can benefit greatly from Big Data, that is without Big Data, data processing functions are limited to small regular data sets, and the information they produce is less comprehensive.

This might start to sound like a chicken and egg debate, but it’s not quite. It’s probably more like a chicken and the grain debate. Big Data is an important element used in data processing, among many elements. It is, however, dependent on data processing.

Big Data is relatively useless without data processing functions to draw useful information from the data sets, while data processing can exist for smaller applications and many routine processes, outside of Big Data.

So in my mind, that means, hands down, data processing wins!

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