Hadoop: How It Is Used and Its Benefits to Business

Hadoop is an open-source, Java-based framework that is used to share and process big data. An innovative project that opened up big data horizons for many businesses, Hadoop can store terabytes of data inexpensively on commodity servers that run as clusters. There are also cloud options with Hadoop; its distributed FileSystem is designed to enable greater fault tolerance and concurrent processing.This increase in tolerance and processing speed enables larger quantities of data to be processed more quickly, improving the timeliness of data insights and the level of detailed analysis possible. 

Hadoop rose to the forefront due to its capacity for processing massive amounts of data and recent innovations have made it even more efficient and useful. Hadoop makes it possible to access database content quickly and efficiently while storing petabytes of information — far beyond what capabilities may be available in a company’s internal database.

While all of this may seem very technical, there is a practical business side to Hadoop usage. Specifically, Hadoop remains one of the most important tools that you can have as a data professional. A solid understanding of Hadoop will help develop your data science skills and start you down the path to becoming an adept data science professional.

What is Hadoop?

Hadoop was first created by Apache and was built in the early 2000s from projects designed to respond to the growth of search engines like Yahoo and Google. Developed by Doug Cutting and Michael Cafarella, the project took its name from one developer’s child’s toy elephant. Hadoop was first released as an open-source project in 2008 and then in 2012 by the Apache Software Foundation. It breaks down large structured or unstructured data, scaling reliably to handle terabytes or petabytes of data. Today, Hadoop is composed of open-source libraries intended to process large data sets over thousands of clustered computers.

What are the main features of Hadoop?

Hadoop is considered an ecosystem that includes core modules, as well as related sub-modules, that can expand or customize Hadoop’s usability for many data managers. Some of the core Hadoop modules include the following:

  • Hadoop Common: These common utilities are used across all modules and libraries to support the project.
  • Hadoop Distributed FileSystem (HDFS): This Java-based system provides the framework to store massive data sets across distributed nodes and clusters.
  • MapReduce: This is the original programming model and processing engine used by Hadoop. Later versions of Hadoop have included support for other execution engines.
  • Yet Another Resource Negotiator (YARN): Since Hadoop 2, YARN manages resources and applications for scheduling and monitoring processing across clusters.

There is also a range of supplementary components widely used as part of the Hadoop ecosystem, including Hive, Pig, Flume, Sqoop, ZooKeeper, Kafka and HBase.

How is Hadoop used in business?

Hadoop is used to manage, access and process massive stores of data using open-source technology on inexpensive cloud servers. It provides significant cost savings over many proprietary database models. By collecting and obtaining insights from large volumes of data generated by customers and the public at large, businesses can make better decisions about marketing, processes and operations. Today’s digital marketing decisions are driven by the outcomes of big data processing handled by Hadoop and similar tools. Big data is an in-demand sector in the marketplace, with many people pursuing a data science degree or supplementary data science education through a tech bootcamp. Data science and big data programming and processing are key to how to become an online marketer today.

Why Use Hadoop: 5 Key Benefits for Any Business

Big data is an industry in itself, but it is also at the core of many industries’ and companies’ strategies for improved customer management, marketing and development. When considering how to be a digital marketer, tools like Hadoop are essential to managing the inflow of data for a broad-scale approach to marketing. 

In the past several years, some concerns about the future of Hadoop have been shared — especially as more companies rely on massive cloud storage from Amazon, Google and Microsoft Azure. The shift to cloud storage has changed the economics of database structures for many companies, opening up more economical paths to interrogate big data stores. 

However, the shift to cloud storage has not rendered Hadoop obsolete. On the contrary, newer modules have been added to the Hadoop ecosystem. Not only is Hadoop at the center of many businesses’ big data systems, but it also continues to power a wide range of cloud-based applications and technologies. 

To this point, Hadoop is still used by companies — not only in the tech sector, but also in a wide variety of industries to manage data and better understand customers and communities. Here are some of the main benefits that Hadoop provides businesses in any industry:

1. Speed: Fast storage and data retrieval

Hadoop allows parallel processing across a data set, with tasks split and run concurrently across distributed servers. Hadoop’s framework allows for dramatically improved processing speed over prior types of data analysis, whether on local servers or in the cloud.

2. Fault tolerance: Replication allows for resilience

Because data stored in any particular node is also replicated elsewhere in the cluster, Hadoop has a high level of fault tolerance to handle one node going down or some type of corrupted data. Hadoop also helps to keep data secure and constantly accessible.

3. Scalability and capacity: Large volumes of data with quick growth

The Hadoop Distributed FileSystem (HDFS) allows data to be split up and stored across commodity server clusters with simple hardware configurations. Cloud installations of Hadoop are also especially well suited to its distributed FileSystem. The setup can be easily and inexpensively expanded to embrace growing petabytes of data.

4. Cost savings: Open-source, accessible software

Hadoop is an open-source framework which means anyone with the programming knowledge and storage space can create a Hadoop system. No license is necessary to allow more staff to access Hadoop. For local hardware, commodity servers help to keep the system economical, while cloud storage space is now widely available at economical prices.

5. Managing diverse data: Many formats, many uses

The Hadoop FileSystem can store a wide variety of data formats in its data lakes, including unstructured data (such as videos), semi-structured data (such as XML files), or structured data such as that contained in SQL databases. Data accessed through Hadoop is not validated against a schema, so it can be parsed to fit into any schema. As a result, this flexible validation enables the analysis of the same data in different ways.

By learning Hadoop in an applicable boot camp, data professionals can capitalize on all the benefits while expanding their professional horizons. Ready to get started? Consider the Berkeley Data Analytics Boot Camp.

Why is Hadoop Important for Big Data?

Hadoop is, in many ways, the foundation for the modern cloud data lake. With its open-source nature, it democratized big data and mass computing power. Companies were able to change their approaches to digital marketing and embrace big data analysis due to the scalable, economical options provided by Hadoop. Before Hadoop, attempts at big data analysis outside the largest search engine enterprises largely depended on proprietary data warehouse options. Hadoop created the pathway to much of the current developments that have continued to advance big data innovation.

Indeed, companies tend to use big data infrastructure that relies on Hadoop, or its underlying distributed FileSystem, even if they do not use all of its components. Hadoop itself has continued to innovate, opening up new processing mechanisms (like Apache Spark) while relying on its FileSystem.

What Are Some Alternatives to Hadoop?

There are several other big data processing alternatives to explore when considering how to become a digital marketer. While these can be used independently of Hadoop, they can also work as part of the same ecosystem or over an HDFS basis. Some Hadoop alternatives may provide options other than MapReduce for processing data because it is less efficient for interactive queries and real-time processing, which have become more important with the rise of AI and other technologies. These alternatives may work in addition to Hadoop or as a completely different system, but experience with Hadoop is often useful in operating any type of big data infrastructure.

How is Hadoop different from Spark?

Apache Spark is one of the more prominent Hadoop alternatives. Spark is a data-processing engine that can be used on top of HDFS to make use of Hadoop’s storage and distribution capacities while using its libraries for streaming, real-time processing, graphs and SQL queries.

Speed

Spark is known for its speed and a variety of multi-feature APIs that allow data scientists to get rapid results from large data set queries. It also has a significant speed advantage over Hadoop’s MapReduce function.

Processing, not storage

While Hadoop is an entire ecosystem, Spark is a form of processing logic that can only work with stored data. For this reason, HDFS is often used to store the data processed by Spark.

Memory access

Although Spark is a faster option, it requires greater stores of RAM and other processing power compared to Hadoop. Where memory is a concern and businesses have more time to process, traditional Hadoop may be a better option.

How is Hadoop different from Storm?

Apache Storm is also an open-source tool used to process massive amounts of data and perform analytics. Like Spark, a Hadoop FileSystem can work well as an underlying layer to Storm data.

Real-time or batch processing

While Hadoop with MapReduce is designed to process data in batches, Storm is designed to do so in real-time, without a defined beginning or end. It is intended for streams of data and can be ideal for companies that need to constantly respond to new data input.

Fast data and big data

Like Adobe Spark, Adobe Storm does not store the data; it processes the data stored elsewhere. This can be data stored in another cloud framework or HDFS data. While Storm is designed to process data quickly, Hadoop can store large amounts of massive data as well as process it.

How is Hadoop different from Google BigQuery?

Google BigQuery is a data platform used for big data analysis. It operates using SQL — without managing the data infrastructure — because it relies on Google hardware, which is constantly being updated and upgraded.

Open-source vs. closed-source

While Google BigQuery offers constantly updated software and hardware, it is also a closed-source system that must run on Google’s servers. However, since Hadoop is an open-source framework, it can be utilized in any environment.

Proprietary approach

Similarly, the technology used in Google BigQuery is proprietary — rather than open to change or input from the community. While Hadoop has a stiffer learning curve, it has the benefit of being open-source and more quickly adapting to user requirements due to its robust community of users and developers.

Speed

Google BigQuery can process information in minutes or seconds, even information that would take hours to process in Hadoop. It is extremely responsive due to the quality of Google’s cloud servers.

Which Companies Use Hadoop?

Hadoop may be over a decade old, but it continues to cater to a diverse list of modern markets. Naturally, this has led to its use by a variety of popular companies. Many of these entities use Hadoop to manage large amounts of data, making it an integral part of consumer analytics, internal logistics and market forecasting. Major players like Facebook, Chevron, ebay and LinkedIn use the Hadoop ecosystem as an integral part of their data management.

Facebook

The massive social media platform uses Hive and Hadoop to generate data, allowing its advertisers to track and measure success. With Facebook advertising as a central profit-generating mechanism of the company, some of Facebook’s most revenue-intensive functions rely on Hadoop for analysis. Even more, Facebook’s Messenger service is based on the Hadoop-related NoSQL database, HBase.

Chevron

From loyalty cards to programs that promote fuel savings, Chevron uses Hadoop to measure energy consumption, gas sales, loyalty and other key customer metrics. It uses the analytics obtained through Hadoop to offer deals, services and savings incentives for customers. Such data-driven offerings can help instill consumer trust at a more personal level. 

eBay

For years, eBay has leveraged big data analytics to increase its overall business value and much of this infrastructure is rooted in Hadoop. When the popular e-commerce company revamped aspects of its technical strategy, it used Hadoop because of its convenient open-source functionality and scalability. As a result, the company has been able to enhance its user experience through the implementation of open-source components like Hue, Apache Zeppelin and Apache Knox. 

LinkedIn

LinkedIn also runs its vast data analytics initiatives through Hadoop, now storing one exabyte of total data across all of its Hadoop clusters. The social media outlet’s largest clusters contain data such as directories, blocks and user files. Like eBay, LinkedIn benefits from Hadoop’s scaling capabilities, as its clusters’ total data storage grew from 20 petabytes (PB) to 500 PB from 2015 to 2020.

What is the Best Way to Learn Hadoop and How to Implement It?

There are several different ways to learn Hadoop: you can learn on the job as a data science professional, pursue a degree in data science, teach yourself on your own time or learn Hadoop at a coding boot camp. While any option can be a path to learning for a skilled developer, a tech boot camp can provide a structured learning framework designed for beginners and programmers alike. Boot camps help to build and improve Hadoop capabilities, among other skills, in order to enter the data science field.

Learning Hadoop at a tech boot camp

If you want to advance your career and upskill your knowledge, Hadoop learning at a coding boo camp is one advanced skill that can help to power your qualifications. A coding boot camp teaching Hadoop is typically designed for swift upskilling and practical, job-relevant knowledge. While it is often helpful to have some coding experience or data science knowledge when learning Hadoop, many coding boot camps are able to teach beginners and more experienced learners effectively.

Upskill quickly with Hadoop

Coding boot camps provide a three- or six-month schedule for full-time or part-time learners. They also provide a structured curriculum that builds upon the degree or experience you may already have. This educational route can help people to make a career change or apply for a new job with more advanced data marketing and data science skills. The instructor-directed, intensive focus of a Hadoop boot camp provides knowledge of Hadoop and related technologies, with a program designed to allow you to hit the ground running in a new position or career.

To get started, consider the Berkeley Data Analytics Boot Camp.

Hadoop FAQ

Hadoop is a Java-based system and programmers get the most out of Hadoop by writing functions in Java. There can be a learning curve that makes Hadoop skills more in-demand and it can take time to become comfortable with the Hadoop ecosystems. In addition, pure Hadoop may offer lower performance due to frequent reading and writing to disk, so many Hadoop users may benefit from employing applications like Spark as well.

Hadoop skills are in demand. Hadoop continues to be used by a large number of big data companies and many newer big data products are built on top of the Hadoop approach and ecosystem. Hadoop skills indicate likely knowledge in data science and the job outlook for data scientists is bright, as there is a projected 31% increase in data science jobs in the next decade.

Hadoop developers should know Java and Linux — people with experience in programming languages and network administration can quickly pick up many of the concepts involved in Hadoop development. Those skilled in Python, Perl, C, Ruby and other languages can also quickly pick up Hadoop. A tech boot camp can give both beginner and experienced programmers the information they need to move into data science development.

Technology is always changing, but Hadoop continues to be used consistently and has a strong future. Even as alternatives to Hadoop’s MapReduce proliferate, the Hadoop distributed FileSystem and other aspects of the ecosystem continue to expand their use and exposure for cloud projects. Hadoop knowledge is a strong basis for a career in data science.

Data analytics and data science are major priorities for many organizations and learning Hadoop can also be an important gateway for people interested in the digital marketing field. Hadoop is a skill that will only increase your marketability in the workplace, as well as make you more productive in your chosen field.

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