What is Big Data and How Does It Work?

The landscape of data handling has evolved over the last few decades, becoming a complex stream of information integrated heavily into our daily lives; Sensor outputs, social media, mobile communication, and web interfaces are just a few examples from the exhaustive list of data outlets we consume. What was once deemed “data processing” now has taken on many names, one of which is has been coined “Big Data”. But when does data become “Big”? The term itself is not necessarily just indicative of size, but rather the ingestion, manipulation, storage, and structure applied to large data sets that may not be suitable for typical relational database solutions.

Understanding Big Data

To properly understand Big Data, you must first understand that it is comprised of data sets, which are essentially “groups” of structured data that is interrelated, often organized in database tables. To be considered Big Data, typically the size of the data sets must be large enough to surpass the capabilities of an environment’s relational database management systems, although a common benchmark for defining a need for big data is if it meets the criteria of the “3Vs” model: volume, variety and velocity. Volume, of course, is how much data is being processed. Variety defines the many forms and formats of data that are collected. Velocity is the speed at which the data is being collected through various platforms.

A common reference to big data usage is platforms like Facebook or Twitter, each of which respectively handle hundreds of millions of active users, with massive collections of data being processed and shared daily. While these are both excellent examples of use cases for big data, there are many more applications that are not quite as apparent. As mentioned previously, big data can be described as large data sets that surpass your environment’s capabilities. As each environment is different, so are the bottlenecks that may necessitate a big data solution. It’s important to note that big data, in most cases, will not replace your relational database systems, but rather be utilized to complement them. So even if your organization is not regularly processing petabytes of high-velocity data, like Facebook and Twitter, a big data solution could be a valuable asset for offloading resource-intensive tasks that free up availability for other processes while possibly mitigating the need for upgrading your hardware resources.

Big Data Platforms And How They Work – Introduction to Hadoop

big data hadoopOrganizations must continually process the big data they collect, then organize, index, analyze and visualize it. To handle the volume and velocity of large datasets with relational database systems, parallelized software is used on a multitude of servers. However, big data solutions take a completely different approach; their architecture allows them to run as separate instances on each server, with no requirement for them to share resources or memory with each other.

Hadoop is currently the most popular platform for handling and managing big data. Facebook, along with many other data-centric organizations such as Yahoo, ebay, Amazon, and LinkedIn utilize Hadoop as their big data solution. The reason why these organizations choose Hadoop is likely because of its efficient architecture, environment flexibility, and maturity. Even though it is an open-source software, Hadoop is backed by the Apache Software foundation, and supported by a collaborative team of technical experts continually working to upgrade and improve the software. Despite being an Apache product, Hadoop’s flexibility includes the ability to be deployed on both Windows/IIS and Linux/UNIX/Apache platforms. Hadoop does require a Java Runtime Environment, but since it ingests and releases data independently from coexisting systems, it can still be a valuable addition to your technology stack regardless of platform preferences.

From an architectural perspective, Hadoop excels by consuming data sets and deconstructing them into smaller pieces; it then distributes this data across the server cluster. This indexed data is then queried across the servers and returned the same as if it were all stored in one place. Hadoop’s MapReduce framework model is the catalyst that enables it to map your program across the Hadoop cluster and reduce the results to your requested output.

To fully realize Hadoop’s potential, implementations will include other components (which are usually also open-source) to perform pertinent tasks. An example workflow would begin with a data processor/pre-processor, ingestion and aggregation (often through Apache Flume) and distribution through a HDFS Channel (Hadoop Distributed File System). The steps of the processes as data is disseminated can be controlled through a workflow scheduler such as Apache Oozie. After completion, the data is allocated and ready for analyzed. Additional components such as Hive or Cloudera Impala can then be utilized to query the data and add structure it back into an application consumable format. Connectivity can also be applied to business intelligence platforms for deeper analytics and data visualization.

Aptude Consulting Big Data Solutions

Big data solutions can be a valuable asset when utilized to their full potential. Data-centric organizations like Google, Facebook, and Yahoo have realized the usefulness of big data, and your organization doesn’t need to ingest as much high-volume data as them to reap the benefits. Bottlenecks reached from either software or hardware shortcomings could potentially be addressed with a big data solution like Hadoop. Whether planning your technology roadmap or reacting to an escalated issue in your environment, consider how big data can help you rise to the challenge and scalability that is demanded of technology platforms these days.

As a leader in IT consulting for application development, business intelligence, and big data, Aptude has an expertise in architecting and delivering solutions across multiple facets of information technology. To view an example of how we customize big data implementations to fit our client’s needs, visit our Hadoop implementation case study for a transportation and logistics leader.