Are you looking for a solution that will help you process large datasets? If so, you’ve probably heard of Apache Hadoop.
If you’re on the fence about using Hadoop, this guide is for you. Below, you’ll find answers to essential questions — from “What is Hadoop?” to “How can I use Hadoop for Business Intelligence” — to help you decide if Hadoop is the right choice for you.
What Is Hadoop?
Apache Hadoop is an open-source software platform developed for “reliable, scalable, [and] distributed computing.”
Hadoop’s framework enables streamlined, distributed processing of large data sets.
It uses simple programming models to process these sets across clusters of computers, helping users scale up from relying on single servers to thousands of machines with their own local computation and storage features.
The Apache Hadoop project consists of the following modules:
- Hadoop Common: Common utilities that are needed to support other Hadoop modules
- Hadoop YARN: A framework used to schedule jobs and manage cluster resources
- Hadoop MapReduce: A YARN-based system that allows for parallel processing of large data sets
Data is also stored in a fourth module: The Hadoop Distributed File System (HDFS).
Hadoop’s Distributed File System offers superior reliability and resiliency. It replicates any node of the cluster to the other nodes, protecting against hardware or software failures.
Hadoop’s flexibility also allows users to store data in any format, including structured, semi-structured, or unstructured data.
Although it has storage features, it’s important to note that it is not designed for data storage or relational database. This tool is meant to process large amounts of data simultaneously and in real-time.
Hadoop’s code is primarily written in Java (although some native code is based in C language). Command-line utilities are often written as shell scripts, too.
How Does Hadoop Work?
Hadoop allows giant data sets to be distributed across clusters of commodity hardware. It uses parallel processing on multiple servers, helping organizations to store and process vast amounts of data quickly.
When using Hadoop, clients go through the following steps:
- Clients submit data and programs to Hadoop
- HDFS handles metadata and distributed file systems
- Hadoop MapReduce processes and converts input and output data
- YARN divides tasks across a cluster
Clients that utilize Hadoop experience increased efficiency, fast response times, and the ability to make the most out of big data.
There are lots of reasons why businesses might want to include this big boy in their tech stack, including the following:
It is an open-source platform (meaning that anyone can use it for free) that relies on cost-effective commodity software. It’s more affordable than many other Big Data processing solutions, including relational databases that require expensive hardware and high-end processors.
Hadoop’s distributed file system (HDFS) breaks large files into small file blocks and distributes them among available nodes in a particular cluster.
The platform uses parallel processing, making it one of the fastest options for Big Data processing. It allows you to access terabytes of data in minutes.
Hadoop relies on inexpensive commodity hardware systems, which can crash at any moment.
To combat potential data losses associated with system crashes, it replicates data on various Nodes within a cluster. The data is always copied or duplicated, meaning you don’t have to worry about losing anything along the way.
Hadoop’s distributed file system assigns various jobs to various nodes within a cluster. The parallel process allows for high throughput, meaning greater efficiency and productivity for you and your business.
Minimal Network Traffic
Hadoop divides each task into several small sub-tasks, each of which is assigned to an available data node in a cluster. Each node processes a small amount of data, allowing for less network traffic overall.
Despite its many benefits, it also presents some unique challenges. Here are some of the most significant cons to keep in mind:
Small File Difficulties
One of the biggest obstacles associated with this tool is that it sometimes struggles with small file sizes.
It works well for a small number of large-sized files. However, in the opposite situation, many small files can surcharge the Namenode (the master node in the HDFS Architecture) and interfere with its ability to work correctly.
Potential Security Issues
Hadoop is primarily written in Java, which is one of the world’s most well-known programming languages. The fact that it mainly uses Java makes it less secure since cyber-criminals will have an easier time figuring out and exploiting it.
Steep Learning Curve
The Hadoop framework can be complicated for end-users to understand and work with. The architecture also requires in-depth knowledge and significant resources to set up, maintain, and update.
How Can Hadoop Be Used for Business Intelligence?
Big data storage is one of the most significant challenges associated with Business Intelligence, along with handling unstructured data and advanced analytics.
BI professionals will experience significant benefits when they choose them over other solutions, including the following:
It also allows for faster and more effective scaling.
Because it stores and distributes huge data through multiple servers, it allows you to process massive amounts of data rapidly. The more efficiently you can process this data, the easier it is to grow and expand your business.
Hadoop is also a valuable option for BI professionals because of its cost-effectiveness.
Traditionally, it has been incredibly expensive to handle extraordinary data volumes. However, Hadoop has changed the game by using more cost-effective hardware.
Hadoop’s flexibility also makes it enticing to many BI professionals.
Hadoop can derive valuable data from numerous data resources, from social media to emails. It can also be used for multiple purposes, from marketing campaign analysis to fraud detection.
In the Business Intelligence world, there is no room for failure — especially when it comes to data processing and storage. Luckily, Hadoop’s fault tolerance ensures that a backup copy always exists, preventing the possibility of data loss.
Hadoop integrates with numerous databases, including PostgreSQL, Oracle, and MySQL, with the help of Apache Sqoop.
How Yurbi Can Help
Here’s the thing: Hadoop is closely tied to Apache Hive, and you cannot use Yurbi to directly access this. But the good news: Yurbi supports 3rd party ODBC drivers, so you can connect Yurbi to Hadoop via the Apache Hive ODBC driver (there are a few out there via Progress, CDATA, and others).
Once connected as a data source, you can use Yutbi’s other functions and features to do all the other things you need to accomplish for your business, from data visualization to embedded analytics, and anything related to modern business intelligence.
Yurbi is a self-service BI tool that you need to complete your BI tool arsenal.
Another selling point? Yurbi’s price is lower than most competition, so it is a great help to small and medium-sized business owners who want to scale up their business processes with the help of BI tools like Yurbi.