Big Data vs. Hadoop: The Differences Explained

Hadoop and big data are closely intertwined and you’ll often see them mentioned together, if not at least near each other. When it comes to big data, nearly everything can be interrelated due to wide-reaching data implications. Big data is quickly emerging as a field to contend with in today’s digital world and Hadoop is just one more way to find answers within that data.

What is Hadoop?

Hadoop is an open-source framework meant to tackle all the components of storing and parsing massive amounts of data. It’s a software library architecture that is versatile and accessible. Its low cost of entry and ability to analyze as you go make it an attractive way to process big data. Hadoop’s beginnings date back to the early 2000s, when it was created as part of search engine indexing to create faster search results. At the same time, Google was forming. Google took off with innovative web searching, while Hadoop found other opportunities within its technology architecture and focused on the technical aspects of storing and processing data. The project was named after the creator’s son’s toy elephant: Hadoop.

What Hadoop does and why it’s everywhere

Hadoop is a collection of parts that work together to parse stored data. It consists of four models: Hadoop Common: the basic utilities that support most use cases Hadoop Distributed File System (HDFS): stores data in an easy to access format Hadoop MapReduce: processes data by mapping out a large set, then filtering through it for certain results Hadoop YARN: manages resources and scheduling Hadoop is prevalent because it’s accessible and easy to get into. It’s affordable and useful, with modules that allow for a lot of options. Hadoop can easily scale with multiple machines to accommodate just about any size data sets, and the way to stores and processes data makes an appealing enterprise solution for ever-scaling data storage.

Using Hadoop for low-cost analysis with hardware flexibility

The problem with storing a lot of data is that it gets pretty expensive to maintain the resources and hardware to handle the load. The reason Hadoop is so widespread and adopted is that it’s much more accessible and allows for a flexible use of hardware. Hadoop uses “commodity hardware,” meaning low-cost systems straight off the shelf. No proprietary systems or pricey custom hardware are needed to run Hadoop, making it inexpensive to operate. Instead of relying on expensive hardware in order to process data, Hadoop breaks down the processing power across multiple machines. The system can scale to accommodate just about any size data set. IT professionals are often the ones who most benefit from this structure, as Hadoop enables them to purchase the numbers and types of hardware that best suit the custom needs of IT.

Storing data in data warehouses versus data lakes

Hadoop not only breaks down the processing power, but it changes how the data is stored and analyzed. Traditionally, data has been stored in “data warehouses.” Like the name implies, these were large collections of data sets stored and organized according to their information. Analysts then access these newly-stored tables and datasets. They are structured and the data is packaged to be accessed on demand. This requires analyzing all of the data in order to file it appropriately and to be able to recall it when needed. While data warehouse systems are handy for users accessing specific tables, the upfront analysis and storage can be time-consuming and resource intensive. Plus, misused data warehouses can be inefficient: if some data doesn’t have an immediate use or apparent function, it may be forgotten or excluded in analysis. Since storage can grow to be expensive, data warehouses require intentional strategies to scale if analysts and IT professionals want to take advantage of the structural perks. Data lakes, on the other hand, are the opposite. Where a data warehouse is controlled and cataloged, data lakes are a giant free-flowing dump of all the data. All data is stored, whether it is analyzed or has a use or might even have a use at some point. Data is imported in its raw form and only analyzed when needed. Since Hadoop is fairly economical in its hardware, it’s easy to scale up as needed in order to store or parse larger amounts of data. This does, however, mean that it’s harder to keep pre-packaged tables and approved datasets at the ready—a core benefit of data warehouses. Scaling data lakes means scaling governance strategies and education. There are unique benefits to both ways of storing data, and companies will often use both warehouses and lakes for different kinds of data needs.

Hadoop’s role in IoT (Internet of Things)

One such solution that Hadoop offers is the storage and ability to parse through the incomprehensible amounts of data. Big data is only getting bigger. Five years ago, we were generating a little more than half the data we do now. Fifteen years ago, the amount of data we created in a 24 hour day is less than what we create now in about three minutes. A large reason for this massive uptick in data generation is the current technological wave called the “Internet of Things” or IoT for short. This is when ordinary physical objects are connected to and controlled through the internet. Smartphones, smart TVs, and alarm systems were the first steps. Now we’ve moved on to smart home appliances like internet-capable refrigerators, dishwashers, thermostats, light bulbs, coffee makers, security cameras, baby and pet monitors, door locks, vacuum robots, and more. While those appliances make your life more convenient, they also track and store data about their every action. IoT also extends to professional, enterprise, and government settings. Smart air-conditioning units keep buildings efficient, body cameras protect police officers and civilians, and environment-sensing devices help governments respond faster to natural disasters like earthquakes and wildfires. In aggregate, all of these devices record a staggering amount of data that demands flexible monitoring and affordable scalability. This is why systems like Hadoop are often the go-to solutions for storing IoT data. Hadoop isn’t the only option, but it certainly is the most prolific due to the ever-scaling demands of IoT.

Big data storage is only useful if you can put it to work

As big data grows, we not only need to be able to store it effectively, but we also need to make sure we’re using it effectively. We can store all the data in the world, but it doesn’t do you any good if you let it sit around collecting dust. While Hadoop has an edge over some other data storage methods, data storage isn’t a substitute for data analysis or business intelligence. With larger amounts of data collection, storage will simply get more expensive. And if you don’t use that data to derive insight and value, then you’ll have only wasted a lot of money on a beautiful but useless data collection and storage strategy. A helpful metaphor is thinking about data in terms of gold mining: If you buy a plot of land to mine, but don’t mine it… well, you’ve just spent an awful lot of money dirt. Employed well, systems like Hadoop just make the land a bit cheaper.