Back in 2014, Rob Bearden, CEO of Hortonworks, stated in his keynote speech at the Hadoop Summit in San Jose, that:
“The data volume in the enterprise is going to grow 50x year-over-year between now and 2020. I think the most important thing to recognize is that 85% of that data is coming from net-new data sources.”
The “net-new sources” he talked about include smartphones, social media, and IoT. As more and more advanced sources keep adding to this list, the amount of data generated every second continues to pile up at an unprecedented speed. Furthermore, ever since businesses and organizations have entered the Big Data game, the importance of data has increased manifold. Today, data is generated from a vast range of disparate sources, including mobiles, social media, emails, IoT, and machine data, transactional data, and business data.
Since data now pours in from every which way, organizations have to adopt advanced Big Data tools – case in point, Hadoop – to transform the raw data into meaningful insights. Businesses and organizations can use these insights to promote data-driven decision making and gain a competitive advantage in the market. One of the best tools to capitalize Big Data is Hadoop.
Apache Hadoop is an open-source Big Data framework used for storing and processing Big Data and also for developing data processing applications in a distributed computing environment. Hadoop-based applications run on large datasets that are spread across clusters of commodity computers which are cheap and inexpensive. So, you get the computational power of an extensive cluster network at an economically feasible cost. Hadoop’s distributed file system structure allows for concurrent processing and fault tolerance.
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Features Of Hadoop
- It is best-suited for Big Data analysis
Typically, Big Data has an unstructured and distributed nature. This is what makes Hadoop clusters best suited for Big Data analysis. Hadoop functions on the ‘data locality’ concept, which means that instead of the actual data, the processing logic flows to the computing nodes, thereby consuming less network bandwidth. This increases the efficiency of Hadoop applications.
- It is scalable
The best thing about Hadoop clusters is that you can scale them to any extent by adding additional cluster nodes to the network without incorporating any modifications to application logic. So, as the Big Data volume, variety, and velocity increase, you can also scale the Hadoop cluster to accommodate the growing data needs.
- It is fault-tolerant
In the Hadoop ecosystem, there’s a provision to replicate the input data to other cluster nodes as well. Thus, if ever a cluster node fails, data processing will not come to a standstill as another cluster node can replace the failed node and continue the process.
Hadoop Applications in the real-world
- Security and Law Enforcement
Yes, Hadoop is now used as an active tool in Law enforcement. Thanks to its speedy and reliable Big Data analysis, Hadoop is helping Law enforcement agencies (like the police department) to become more proactive, efficient, and accountable. For instance, the national security agency of the USA uses Hadoop to prevent terrorist attacks. Since Hadoop can help detect security breaches and suspicious activities in real-time, it has become an effective tool to predict criminal activity and catch criminals.
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- Enhance customer satisfaction and monitor online reputation
Businesses are now using Hadoop to analyze sales data and compare it against many other factors to determine when and at which time a specific product sells best. By continually monitoring sales data, business owners can find out why certain products sell better on particular days or hours or season. In the same way, Hadoop can also mine social media and online conversations to see what your customers (both existing and potential) are saying about you on online platforms. It monitors the sentiments behind the comments and feedback of the customers. This insight helps marketers and business owners to analyze customer pain points and what they expect from the brand. All of this vital information can be used by businesses and companies to enhance the quality of their products, boost customer satisfaction quotient, and improve their online reputation.
- Monitor patient vitals
Many hospitals have started leveraging Hadoop to make their staff more productive in their work process. Healthcare systems and machines generate large volumes of unstructured data. Conventional data processing systems cannot process and analyze such large quantities of raw data. However, Hadoop can. An excellent case in point is when the Children’s Healthcare of Atlanta fitted a sensor beside the bed of its ICU units to continually track the vital of child patients such as blood pressure, heartbeat, and respiratory rate. The primary aim was to store and analyze these critical signs and be alerted if ever there occurred any change in the patterns. This allowed the healthcare provider to promptly send a team of doctors and medical assistants to check on patients in need. This was made possible using the core components of the Hadoop ecosystem components – Hive, Flume, Impala, Spark, and Sqoop.
- Healthcare Intelligence
Healthcare insurance companies usually combine all the associated costs (including the risks involved) and equally divide it by the total number of members in a particular group. Naturally, the outcomes are always dynamic since they keep changing. This is where Hadoop’s scalable and inexpensive feature can be highly useful. Hadoop can efficiently accommodate dynamic data and scale according to the ever-changing needs. By using Hadoop-based healthcare intelligence apps, both healthcare providers and healthcare insurance companies can devise smart business solutions at an affordable cost.
Let’s assume that a healthcare insurance company wishes to find the age in a region where people below a certain age limit aren’t prone to a specific disease. This is to be done to help the company to calculate the approximate cost of the insurance policy. However, to gather the age data of the people in the region, the company will have to invest a large sum of money in processing and analyzing vast volumes of datasets to extract relevant information regarding the disease in question, its symptoms, its target victims, and so on. This is where Hadoop components like Pig, Hive, and MapReduce can come in handy – these can process large datasets at relatively low costs.
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- Track clickstream data
Essentially, Hadoop’s primary function is to store, process, and analyze massive volumes of data, including clickstream data. Hadoop can successfully capture the following:
- Where did a visitor originate from before reaching a particular website?
- What search term did the visitor use that lead to the website?
- Which webpage did the visitor open first?
- What are the other webpages that interested the visitor?
- How much time did the visitor spend on each page?
- What product/service did the visitor decide to buy?
By helping you find the answers to all such questions, Hadoop offers an analysis of the user engagement and website performance. Thus, by leveraging Hadoop, companies of all shapes and sizes can conduct clickstream analysis to optimize the user-path and predict what product/service the customer is likely to buy next, and where to allocate their web resources.
- Track geolocation data
Smartphones have become a crucial part of our lives now. With the number of smartphone users around the world increasing as we speak, these tiny devices are the heartbeat of the digital world. So, why not capitalize on this opportunity and use smartphones to your advantage? Businesses can use Hadoop to track the geolocation data on smartphones and tablets to track customers’ movements, behavior patterns, purchases, and predict their next move. Not just that, Hadoop clusters can also streamline massive amounts of geolocation data and help organizations to identify the challenges in their business and operation processes.
7. Track sensor data
Today, electronic gadgets and machines are using sensors to enhance the user experience and more importantly, to harvest customer data. The growing trend toward incorporating sensors has become more pronounced following the increasing adoption of IoT devices. In fact, sensor data is among the fastest-growing data types now. Devices and machines are infused with advanced sensors that can monitor and track a host of features like temperature, speed, pressure, proximity, location, image, price, motion, and much more. Since sensor data tends to become overwhelming with time, Hadoop is the best and most effective solution to track, store, and analyze sensor data. By tracking and monitoring sensor data, companies can obtain operational insights into their business and improve their processes accordingly.
- Strengthen security and compliance
Hadoop can efficiently analyze server-log data and respond to a security breach in real-time. Server-logs are nothing but computer-generated logs that capture network data operations, particularly the security and regulatory compliance data. Server-log provides companies and organizations important insights pertaining to network usage, security threats and compliance. Hadoop is the perfect fit for staging and analyzing this data. It is an excellent tool to extract errors or detect the occurrence of any suspicious event in a system (example, login failures). By loading the server logs into Hadoop, network admins can identify the cause of the security breach and fix the issue promptly.
Although these are only a handful of Hadoop applications in the real-world scenario, many more are yet to come. As the Big Data use cases expand and Hadoop technology matures, we will see more of such pioneering applications of Hadoop.
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Learn more about Hadoop Future Scope
Hadoop is a technology of the future. Sure, it might not be an integral part of the curriculum, but it is and will be an integral part of the workings of an E-commerce, finance, insurance, IT, healthcare are some of the starting points. So, waste no time in catching this wave; a prosperous and fulfilling career awaits you at the end of the time. Good luck!
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Is Hadoop the same as the Cloud?
Hadoop is an open-source programming platform based on Java that processes enormous volumes of data across a distributed computing environment using simple programming models. On the other hand, cloud computing uses various computational concepts and is applied across networks of computers which are connected in real-time. Cloud computing models are designed to offer scalability and adaptability and to meet on-demand requirements. Hadoop helps extract worth out of velocity, variety and volume. Cloud MapReduce is an alternative to MapReduce, which is part of Hadoop. However, Cloud MapReduce does not come with implementations of its own but relies on other cloud service providers for support.
What are the uses of Hadoop in real life?
Apache Hadoop is employed by various organizations across different industry sectors. From finance, security and retail to healthcare and entertainment, Hadoop has found an extensive range of applications. It is used to detect and prevent fraudulent activities, risk mitigation, identify suspicious patterns, and perform many more financial sector tasks. Hadoop is also known to be used by US security forces to prevent terrorist attacks and cyber-attacks. Hadoop helps identify consumer buying patterns and understand market demands in the retail industry. Healthcare service providers rely on Hadoop for the overall betterment of public health from their health records.
Is SQL better than Hadoop for data management?
SQL or Structural Query Language is a programming language strictly used to handle data contained in relational databases. On the other hand, Hadoop is an open-source ecosystem that is designed to handle massive volumes of data present in a distributed non-relational database system. SQL and Hadoop are both useful for managing data, but in ways that are very different. While Hadoop is a framework of several software modules, SQL is essentially a programming language. Hadoop is better at handling vast amounts of data but writes only once. But SQL is easier to implement but not suitable for huge data sets. So the choice depends on specific business needs rather than their features only.