When it comes to data, size is always relative.
True, the number of data sources and the amount of information that can be stored and analyzed have increased significantly over the past several years. This increase coincided with the entry of the term big data into the popular lexicon.
Yet it’s not as though enough large data sets didn’t exist until we started talking about big data. What we call big data today may involve more data than the data sets and workloads of the past, but it may not. Again, it’s all relative.
What Really Defines Big Data
If you can’t distinguish big data from traditional data sets in terms of size, then what does define big data?
The answer lies in how the data is used. The processes, tools, goals, and strategies that are deployed when working with big data are what set big data apart from traditional data.
Specifically, big data is defined by the following features:
Highly scalable analytics processes
Big data platforms like Hadoop and Spark have become popular due in large part to their ability to scale. The amount of data that they can analyze without a degradation in performance is virtually unlimited. This is what sets these big data tools apart from traditional methods of investigating data, such as basic SQL queries. The latter doesn’t scale unless you integrate them into a larger analytics framework.
Big data is flexible data. Whereas in the past all of your data might have been stored in a specific type of database using consistent data structures, today’s datasets come in many forms. Effective big data analytics strategies are designed to be highly flexible and to handle any type of data that is thrown at them. Fast data transformation is an essential part of big data, as is the ability to work with unstructured data.
Traditionally, organizations could afford to wait for data analytics results. In the world of big data, however, maximizing value means gaining insights in real time. After all, when you are using big data for tasks like fraud detection, results received after the fact are of little value.
Machine learning applications
Machine learning is not the only way to leverage big data. It is, however, an increasingly important application in the big data world. Machine learning use cases set big data apart from traditional data, which was very rarely used to power machine learning.
Scale-out storage systems
Traditionally, data was stored on conventional tape and disk drives. Today, big data often relies on software-defined scale-out storage systems that abstract data away from the underlying storage hardware. Of course, not all big data is stored on modern storage platforms, which is why the ability to move data quickly between traditional storage and next-generation storage remains important for big data applications.
Data quality is important in any context. With the increasing complexity of big data, however, has come greater attention to the importance of ensuring data quality within complex data sets and analytics operations. Attention to data quality is a core feature of any effective big data workflow.
If you’re not striving to achieve these features in your big data, you’re not making the most of your data.
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