You may not believe that every day more than 305 billion emails are sent all over the world. There are over 3.5 billion search queries on Google every day. This tells us that a large amount of data is being generated by humans every day. According to statistics, human beings produce 2.5 quintillion data bytes every day. Imagine the large chunks of data that companies need to store, manage and process efficiently. It is a mammoth task.
Therefore, scientists and engineers focus on developing new platforms, technologies, and software to efficiently manage large amounts of data. These technologies also help companies to filter relevant data and use it for generating revenues. One such technology is MapReduce in Big Data.
What is MapReduce?
MapReduce is an algorithm or programming model used in the Hadoop software that is a platform to manage big data. It splits Big data clusters in the Hadoop File System (HDFS) into small sets.
As the name suggests, the MapReduce model uses two methods – map and reduce. The entire process is done in three stages; splitting, applying and combining.
During the mapping process, the algorithm divides the input data into smaller segments. Then, the data is mapped to perform the required action and creates key-value pairs. In the next step, these key-value pairs are brought together. This is known as merging or combination. It is commonly called the shuffling stage. These key-value pairs are sorted by bringing together inputs with the same set of keys and removing duplicate data.
Next is the reduction stage, at which input is received from the merging and sorting stage. During this step, different sets of data are reduced and combined into a single output. It is the summary stage.
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What is the use of MapReduce in BigData?
Big Data is available both in structured and unstructured form. While it is easier for companies to process structured data, unstructured data poses a concern for companies. This is where MapReduce in Big Data comes to the rescue. Here are some of the benefits of MapReduce in Hadoop software.
1. Converts Big Data Into Useful Form
Big Data is usually available in raw form that needs to be converted or processed into useful information. However, it becomes nearly impossible to convert Big data through traditional software due to the sheer volume. MapReduce processes Big data and converts it into key-value pairs that add value to businesses and companies.
MapReduce is beneficial for various sectors. For instance, the use of MapReduce in the medical industry will assist in going through huge files and previous records and processing the patients’ medical history. Thus, it saves time and aids early treatment of patients, especially in critical ailments. Similarly, the eCommerce sector helps to process essential data, including customer orders, payments, inventory stocks, etc.
2. Decreases Risk
Big Data is available across connected servers. Therefore, even a slight breach in security can result in a big loss to companies. Companies can prevent data loss and cyber breaches with several layers of data encryption. The MapReduce algorithm decreases the chances of data breaches. Since MapReduce is a parallel technology, it performs several functions simultaneously and adds a layer of security because it becomes difficult to track all the tasks carried out together. Also, MapReduce converts data into key-value pairs that serve as a layer of encryption.
3. Detects Duplicate Data
One of the significant benefits of MapReduce is the deduplication of data which is identifying duplicate and redundant data and getting rid of it. The MD5 marker in the MapReduce algorithm finds duplicate data in key-value pairs and eliminates it.
Since Hadoop has a cloud storage facility, it is cost-effective for companies compared to other platforms where companies need to spend on additional cloud storage. Hadoop. MapReduce breaks down large data sets and into small parts that are easy to store.
What is the Career Scope of MapReduce in Big Data?
It is expected that the amount of data produced by humans per day will reach 463 exabytes by 2025. Therefore, in the next few years, the market growth of MapReduce is likely to grow at a tremendous speed. This will eventually increase the number of job opportunities in the MapReduce industry.
The market size of Hadoop is expected to increase exponentially by 2026. In 2019, the Hadoop market size was $26.74 billion. It is predicted that the market will grow at a CAGR of 37.5% by 2027 and will reach over $340 million.
Various factors are contributing to the exponential rise of Hadoop and MapReduce services. The growth in competition due to the increasing number of businesses and enterprises is the driving factor. Even the small and medium sector enterprises (SMEs) are also adopting Hadoop. In addition, rising investment in the data analytics sector is another factor driving the growth of Hadoop and MapReduce.
Also, since Hadoop is not confined to a particular sector, you get an opportunity to choose your desired field. You can get into finance and banking, media and entertainment, transportation, healthcare, energy, and education.
Let us see the most desired roles in the Hadoop Industry!
1. Big Data Engineer
This is a prominent position in any company. Big data engineers have to build solutions for companies that can effectively collect, process, and analyze big data. The average salary of a big data engineer in India is INR 8 lakhs per annum.
2. Hadoop Developer
The role of a Hadoop Developer is similar to a software developer. The foremost responsibility of a Hadoop Developer is to code or program Hadoop Applications and write codes to interact with MapReduce. A Hadoop Developer is responsible for building and operating the application and troubleshooting errors. It is essential to know Java, SQL, Linux, and other coding languages. The average base salary of a Hadoop Developer in India is INR 7,55,000.
3. Big Data Analyst
As the name suggests, the job description of a Big data analyst is to analyze the Big data and convert it into useful information for companies. A Data Analyst interprets the data to find patterns. The essential skills required to become a Big data analyst are data mining and data auditing.
A Big Data Analyst is one of the highest-paying profiles in India. The average salary of an entry-level data analyst is six lakhs, whereas an experienced Big data analyst can earn up to INR 1 million per year.
4. Big Data Architect
This job includes facilitating the entire Hadoop process. A Big data architect’s job is to oversee Hadoop deployment. He plans, designs, and comes up with strategies about how an organization can scale with the help of Hadoop. The annual salary of an experienced Big data architect in India is nearly 20 lakhs per year.
How Can You Learn MapReduce Skills?
With plenty of jobs in the market, the number of job seekers in Hadoop is also high. Therefore, you must learn relevant skills to gain a competitive edge.
The most desired skills to build a career in MapReduce are data analytics, Java, Python, and Scala. You can learn the intricacies of Big Data, Hadoop Software, and MapReduce by pursuing a certificate course in Big Data.
upGrad’s Advanced Certificate Programme in Big Data helps you acquire real-time learning of data processing and warehousing, MapReduce, cloud processing, and more. This program is best suited for working professionals who wish to switch their careers in Big Data or enhance their skills for growth. upGrad also offers career support to all the learners like mock interviews and job affairs.
Hadoop is one of the most coveted careers today. With the increasing production of data with every passing day, plenty of growth opportunities will be available in Hadoop and MapReduce areas in the next few years. If you are looking for a challenging and high-paying role, you can consider a job in the Hadoop industry. For this, you will need to learn various skills that will give you an added advantage.
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What are the disadvantages of MapReduce?
There are many reasons why MapReduce might not work out for you. One of the prominent ones includes real-time processing. Additionally, MapReduce has a rigid framework and isn’t flexible at all. Furthermore, with MapReduce, the need to perform coding manually is very extensive; therefore simple operations like joins, projection, sorting, and distinct require a lot of effort solving. Semantics is the next disadvantage as it hides the map which makes it difficult to optimize and implement operations.
What are some of the benefits of using MapReduce with Hadoop?
There are tons of advantages to why MapReduce and Hadoop are a good blend. MapReduce allows parallel processing, i.e., tasks can be subdivided and executed simultaneously. Using Hadoop, which is a very scalable platform, helps in large chunks of data distribution across multiple servers. The next reason would be how cost-effective Hadoop is. The constant need for data keeps on rising, and with Hadoop, all the data-storage requirements are effectively managed. Another benefit is the simplicity of the Hadoop programming model which makes it a preferred choice. Plus, Hadoop’s security is structured with HBase and has an upper hand over other platforms. Considering these benefits, MapReduce with Hadoop will have unlimited future prospects.
What is the future scope of using MapReduce?
With Spark taking over, it won’t be an accurate conclusion to say that the end of the road for Hadoop and MapReduce has come. They will stick together in the upcoming time; however, people’s inclination toward spark could make their survival challenging. MapReduce and Hadoop are widely being used in trading and financial companies in the form of mainframe systems. Nonetheless, Spark’s invention has taken the limelight away from MapReduce and Hadoop and shall continue to do so in the coming time. Shortly, the advanced inventions to look out for will see Apache Spark, Apache Kafka, and Apache Storm instead of Hadoop and MapReduce.