Big Data and Hadoop are closely related. The Hadoop ecosystem of tools has been the industry standard for Big Data analytics for many years. While Hadoop is one of the oldest Big Data tools still in active use, it can still be an effective tool for processing Big Data for organizations of all sizes.
This post will explain what Hadoop is and why it has been such a popular Big Data tool with data scientists and data analysts for so long. While businesses have many more Big Data options to choose from today besides Hadoop and relational databases, Hadoop is still widely used.
What Is Hadoop?
Hadoop is an open-source framework that enables users to store, process, and analyze large amounts of structured data and unstructured data. Hadoop’s origins date back to the early 2000’s.
Hadoop was initially developed to help with search engine indexing, but after the launch of Google, the focus pivoted to Big Data. This open-source solution is not only very accessible for organizations of all sizes and needs, but it is also very versatile.
In addition, Hadoop provides a low cost of entry and eliminates the need to run Big Data analytics through expensive in-house hardware. As a result, Hadoop became an attractive option for businesses that wanted to process data and store large datasets.
Today, this open-source Big Data framework is supported and maintained by the Apache Software Foundation, so you may hear it called Apache Hadoop. However, many people still refer to it as simply Hadoop.
The core of Hadoop consists of three primary components:
- Hadoop Distributed File System
- Hadoop MapReduce
- Hadoop YARN
Hadoop Distributed File System
The Hadoop Distributed File System (HDFS) is the data storage component of the Hadoop system. HDFS takes data stored on the system and splits it into smaller, more manageable chunks that are then stored in data nodes across the Hadoop cluster.
The Hadoop Distributed File System can effectively and efficiently reduce large datasets to more manageable sizes. All data can still be accessed simultaneously from each data node to perform data tasks and analysis.
MapReduce is the primary programming component of the Hadoop system. Using Hadoop MapReduce, Big Data tasks can be split into miniature tasks that are simultaneously performed across the entire Hadoop cluster.
Parallel processing greatly reduces the time it takes to process data and reduces the chances of a catastrophic computer failure. MapReduce and HDFS are closely intertwined and rely upon one another to function properly within the system.
Hadoop YARN (Yet Another Resource Negotiator) is responsible for managing computing resources across the system. YARN schedules and allocates tasks across the Hadoop cluster.
YARN isn’t quite the data processing engine of the system, but it is the system’s overarching task and resource manager.
Hadoop Common is not part of the original three components of Hadoop. Hadoop Common is a fourth component added by Apache. Common is a Java library that includes additional utilities and applications like HBase, Hive, Apache Spark, Sqoop, and Flume.
All of these are add-on features that improve the performance and capabilities of the Hadoop ecosystem.
The Benefits of Using Hadoop for Big Data
While Hadoop is one of the oldest Big Data solutions on the market, there are still benefits to using this system. The top benefits of Hadoop include:
- Fault tolerance
One of the most important factors for any business technology is cost. One of the most significant benefits of Hadoop is the cost associated with using it. The Hadoop system is open-source, so your business does not have to pay for expensive software solutions.
In addition, Hadoop uses commodity hardware to store data, which ensures that your business doesn’t have to invest in expensive hardware infrastructure. Hadoop is probably the most cost-effective for businesses of all the Big Data solutions.
Hadoop’s distributed computing resource model enables businesses to process Big Data fast. Distributed computing allows your organization to access more computing resources than it has on-site.
With Hadoop, the more data nodes your business uses, the more powerful its processing capabilities are. You won’t find a more powerful Big Data system at the price point of Hadoop.
Businesses that want to fully utilize their data and process it fast can benefit from the computing power Hadoop offers.
Distributed computing is beneficial beyond the vast computing power it offers organizations. Hadoop’s distributed computing model also ensures that your data processes are protected in the event of hardware failure.
If a node goes offline due to hardware failure, all data tasks are automatically rerouted to an active node, and data operations continue without interruption. Businesses should love the benefit of fault tolerance.
We all know how valuable data is. You can’t afford to lose valuable data or have processing operations offline because of hardware failure. Hadoop’s distributed model offers strong fault tolerance that can give businesses confidence in their Big Data operations.
Hadoop offers businesses significant flexibility when it comes to storing data. With traditional relational databases, all data must be preprocessed before storage to ensure all data formats are consistent.
Hadoop makes storing data from multiple sources easy because it doesn’t have to be preprocessed or structured in any set manner. Collect data of all types and store it however your business wants. Hadoop enables your business to store data and decide how it will be used later.
In addition, Hadoop is extremely scalable, which is the ultimate form of flexibility. If you need more storage space or more computing power, it is easy to add additional nodes and improve performance levels with little administrative work.
Hadoop might not be the newest or shiniest Big Data tool, but it is still a major part of Big Data. If your business is interested in utilizing a Big Data solution, Hadoop is a great place to start because it is open-source, easy to use, and, most of all, powerful.
If you want to learn more about Hadoop and Big Data, contact a data expert like Koombea for guidance and insight.