For someone who has never dealt with it before, Big Data may seem like a complex concept best left to big enterprises and incorporated companies. This is especially so if you’re to consider the financial and administrative implications of adopting a Big Data platform. The truth of the matter is that Big Data has become so ubiquitous both as a concept and in practice that even SMEs can reap the benefits it has to offer.

Businesses that deal with large amounts of data but haven’t felt the need for adopting a platform like, say, Hadoop, are also well-placed to make gains in faster decision making, better architecture utilization, lower overhead costs and more.

Businessman using big data platform

What is a Big Data Platform and is It Necessary?

A lot of firms are able to get by just fine without Big Data platforms, so is it really necessary? Take Uber, for example. For the first few years of their existence, they survived on nothing other than online transactional databases (in this case, MySQL and Postgres) and did just fine. With their rapid expansion around the world in the company’s post-2014 era, things took a different turn.

SQL is a very versatile solution for most problems, but it just doesn’t scale to the extent companies as Uber require. Dealing with over 3 million drivers, who serve over 15 million rides a day, they produce over 100 petabytes of data. This has to be cleaned, stored and served with minimum latency. SQL platforms utilize ACID-based transactions.

ACID makes it very efficient at querying data, but unfortunately, sacrifice processing speed in favor of more complex operations. Big Data platforms like Redshift have been benchmarked to perform as much as 1,000 times faster for very large datasets as compared to transactional databases like Postgres.

Ultimately, a company should decide whether the tradeoffs of adopting a Big Data platform will be worth the benefits to be gained.

Are There Downsides to Using Big Data?

Big Data has a lot of advantages that businesses could use, but this will come with a number of overhead costs you should be aware of. For most people and corporations, it comes down to Apache Spark vs Hadoop.

Hadoop is the software credited for starting off the Big Data revolution, and is still in use in companies like Expedia, and to a lesser degree, Google. Many consider it a legacy technology, since Big Data needs have been transformed to speed-optimized needs that enable real-time communication and processing. Spark has been quickly gaining momentum where Hadoop has faltered and is currently considered the de facto Big Data Platform.

Both of these frameworks have one thing in common – they are expensive to set up and run, albeit for different reasons. Hadoop is expensive because it needs several nodes if the performance is going to be respectable. Spark runs most of its data processing in-memory, rather than disk-based processing the same way Hadoop does. It needs at least 8GB worth of RAM, of which 75% has to be dedicated to Spark alone.

Data analysis using big data platform

Benefits of Big Data Platforms

1. Advanced Data Analytics

The first major benefit of having a big data platform is the ability to carry out advanced data analytics. Advanced data analytics gives businesses the ability to project future events and develop statistical models that allow them to future-proof their operations.

In short, the main goals of data analytics are:

  • Collecting data that’s to be used to achieve the goals the business orients itself with.
  • Finding existing insights that are easy to miss using normal methods of data analysis.
  • Eliminating biases that are also easy to come by if conventional ways of data analysis are used.
  • Creating connections between new and existing data points.

2. Big Data Allows Organizations to Put All Their Data to Good Use

Businesses connect all kinds of data on a daily basis. Structured data includes easy-to-process file types like JSON files and URL encoded data from forms.

Unstructured data is data that’s not necessarily easy to find, and is often embedded within other documents. For example, extracting a set of dates or emails from a HTML document. Transactional databases limit you to working with data in a predefined form, unlike Big Data platforms.

One of the consequences of this is that it allows businesses to put data that would have otherwise been dismissed as unusable. This creates value for the data itself, and, should it result in usable information, add value to the company, too.

3. Security and Risk Management

The final and increasingly more significant use case for a Big Data platform is to enable better security and risk management. A Big Data platform by itself doesn’t exist to replace traditional security systems, but complements them instead.

For example, don’t expect Hadoop or Spark to take the place of a server firewall. Using Big Data, it’s possible, however, to train machine learning models that recognize threats in the form of DDOS attacks, fraud and even intrusive visitors.

Analyzing big data

Will a Big Data Platform Replace Your Current Architecture?

The most common use case for Big Data platforms is informing better decision making and reinforcing a business’ security. To that effect, software such as Hadoop was not intended to serve those specific purposes. Rather, Hadoop complements existing data processing, security and analytics reporting systems.

Hadoop was, for example, built so that it’s possible to incorporate related software into it, rather than replace the ones you already use. Spark has been touted as the ‘Hadoop Killer’ but many people ignore the fact that the two software can be used together, for instance. Businesses that heavily rely on Excel will also be happy to find that Excel data can be imported directly into Hadoop.

What Big Data platforms do replace is MPP (massively parallel processing) databases. These are multi-core and multi-processor systems that rely on their own OS and systems to process massive amounts of data, and are usually very expensive.

Data lakes have also fallen out of favor of the tech industry due to their tendency to transform into useless data swamps, a problem that modern Big Data platforms solve.