Big Data is an umbrella term used to refer to strategies of gathering, organizing and processing data that go beyond the realm of traditional Excel spreadsheet and require much more advanced tools and methods to not only collect and store such volume of data, but also to gain insights from it.

The 5 Vs of Big Data

The five Vs of Big Data are, in no particular order: volume, velocity, value, veracity and variety. Volume refers to the sheer volume of data that is generated every second. In fact, according to best-selling author Bernard Marr, if we were to take all of the data that was generated between the dawn of civilization and 2008, the same amount of data will soon be generated every minute. Needless to say, with such amounts of data, all traditional data storage methods are obsolete.

Variety refers to all of the types of data that’s out there. According to IBM, 30 billion pieces of content are shared on Facebook every month, 400 million tweets are sent out every day by 200 million active users and 4 billion hours of video are watched on YouTube every month. Thanks to new advancements in Big Data technology, we can now collect and analyze data. Data collected from social media, sensors and photos are referred to as unstructured data, but we can analyze large structured data, which is data inside large databases or datasets.

Velocity refers to how fast the data is being generated. We mentioned how much social media activity goes on every month, but, thanks to Big Data technology, all of this data can be analyzed in real-time without storing it in a database.

Veracity refers to just how trustworthy the data that you collected really is. You may also see “validity” or “volatility” substituted instead of “veracity”, but all of these terms refer to the same thing. The data that you have collected has no value if it is not accurate and the results of Big Data analyses are only as good as the data that is being analyzed. To put it in layman’s terms: junk in equals junk out.

This brings us to value. What’s the point of having a vast amount of data if you can’t get any value from it? In order to extract value from Big Data, companies are investing heavily in artificial intelligence, machine learning, natural language processing and other cutting-edge technologies. All of this is done to personalize the data and present a more accurate picture.

Clustering Software

As we stated above, one single computer is not capable of handling the vast amounts of data and, in order to address this issues, computer clusters were introduced. This offers a large number of benefits, most notably: pooling resources to provide storage space and CPU memory, provide the necessary fault tolerance level and prevents both hardware and software errors which can prevent access to data and analytics. This becomes crucial when dealing with real-time analytics. On a more practical note, clustering allows horizontal scalability without the need to add extra physical resources to the machine. Hadoop Yarn or Apache Mesos is often used to manage computer clusters, coordinate resource sharing and schedule work on individual nodes.

Data Ingestion

The data ingestion process is about taking the source data and adding to the system. The complexity about of data ingestion derives from the quality of the data source and the estimated accuracy of how much processing is needed to refine the data to its desired state.

The data ingesting through the tools such as Apache Sqoop extracts data from a relational database and transfers it to the Big Data system. – Apache and Apache Flume Chukwa – collect and import the server application records, while Apache Kafka, serves as an exchange interface between different data sources and the Big data system.

There are some streams of analysis, labeling, and classifications that happen behind the scenes of ingestion process. These are often referred to as ETL (extract, transform, load). Although this term is commonly used or obsolete data warehouses stores, modification of the data, labeling, formatting, sorting, and filtering are still relevant to data that funnels to the Big Data system.

As the Big Data evolves businesses are increasingly turning to Big Data to enrich their business intelligence.  Big Data systems are the comprehensive tools at their heart. They identify even the most complex models and gain valuable insights that conventional methods are simply incapable to uncover.

To learn more how to extract valuable insights from your data to empower your decision-making check our Big Data consulting page.