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Future of Big data and Hadoop

Updated on October 23, 2018
Robinson Ng profile image

is a Computer Science Engineer. He is also a part-time Technical writer and has a great interest in trendy technologies.

Importance of Big Data

By now most professionals in big companies should be familiar with big data and analytics and its importance in business or IT world. Executives, managers, and professionals need to have a clear idea of how big data and analytics will impact the growth of a company. It would be foolish if the significance of big data is not clear to the sharpest, most experienced employee in a company by now.

Data is the key to understanding the economic performance of the company and what drives it every day. For growth in business and analytics, the most important data are generally the data that companies have had for years like customers data, financial data, old and new transactional data, etc. Big data is a form of unstructured digital contents such as pictures, video clips, text messages, and document images. From a business perspective, big data is simply another potential source of useful information and digital contents that might be useful for analytical purposes aimed at improving the business processes. Analysing form past decades, to increase the growth of a company's business data warehouses, Business Intelligence and traditional analytics tools are utilized. But, many successful companies still need to do more to fully exploit their data.

Future of big data:

The big question is what will be the position of big data in the coming 10 years. It is obvious that with the rapid growth in technologies the size of data will only continue to increase rapidly. Considering the 21st century, we have already created ten times more data in the history of mankind.


Some predictions that can be made for big data in coming decades are-
1. Many new and simpler big data technologies, analysis tools, and applications will become available.
2. Business growth will hugely depend on investment in big data analysis.
3. There is a huge gap between the increasing growth in big data companies and lack of Analysts. So there will be high demand for Data Scientists and Analysts with very high salaries.
4. Big Data integration in Machine learning, IoT and automation will grow rapidly.
5. Drastic growth in the data volume of big data with the growth of internet technologies.
6. With the increase of data in bytes, there will also be privacy and security challenges which will also include numerous violations in data utilization.
7. Set of new policies and rules will be established when dealing with big data.
8. Companies will demand more algorithm rather than programs for analyzing their own data efficiently.
9. Real-time streaming insights into data will be in high demand; business decisions will be made in real time with programs like Kafka and Spark.
10. Data Business or Big Data Market will become a common thing for decades to come. Data will be sold or purchased between big data companies.
11. A new position of officials called Chief Data Officer (CDO) will be appointed in big data companies.
12. Predictive Analytics and Prescriptive Analytics will be built into business analytics software. This will help businesses to make smart decisions at the right time.

It should be clear by now that data and analytics are totally different from the past. The traditional database systems like Excel, RDBMS, MS Access, dBase, MySQL etc will not able to handle the increasing volume of data in the future. It would cost more if an organization has to accommodate with the old database system. So, professionals are looking for better, efficient and cost-effective data analysis software or programs. Some of the most popular tools and programming languages used by data analysis include Hadoop, R, Python, Splunk, Data Manager, D3, Tableau, etc. Now, professionals find it difficult to decide which tools to adopt which will best benefit their business strategies.

Hadoop

Hadoop is a game-changing technology to use for this purpose. You'll find that Hadoop is the tool that is most known for handling big data. Hadoop was initially released in December 2011. It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts where each part handles different tasks. It is a solution for data which is scalable for any amount of data. Normally, in traditional file management systems, a single machine finds it hard to handle big data so they try to construct the data into a smaller dataset from the full dataset. Hadoop has the ability to run different data analysis tasks utilizing different Hadoop tools on the full dataset, but it should also be taken into account that Hadoop is able to work on a smaller dataset too. The Hadoop framework is used by major companies like Facebook, IBM, Google, Yahoo etc. It is also widely used for applications concerning search engines and advertising. Hadoop has become a must know technology for professionals such as Data Scientists, Developers, Business Intelligence Professionals, Graduates, Data Management Professionals, Data Mining Professionals, etc.

Features of Hadoop

1. It is an open source framework.
2. It is Java-based.
3. It is part of the Apache project sponsored by the Apache Software Foundation (ASF).
4. It consists of the Hadoop kernel, MapReduce, the Hadoop Distributed File System (HDFS), and a number of related projects such as Apache Hive, HBase, and Zookeeper.
5. Windows and Linux are the preferred operating systems, but Hadoop can also work with BSD and OS X.

Why Hadoop?

1. Hadoop framework has HDFS and MapReduce algorithm.
(HDFS) Hadoop Distributed File System is a scalable storage space with a distributed filesystem.
MapReduce algorithm is best suitable for distributed computing. It is a design that takes data and divides the data and store at different servers that has available space and resources in a cluster of computers. A processor in the cluster will be entitled as Master which controls the other Slaves processors present in the cluster. This feature helps in providing low latency in processing.
2. Hadoop is capable of identifying computers which are closest to the data it wants to access at any time. It also keeps track of all the files and makes it available with minimal response time. This vastly lower the network traffic while searching a required data.
3. Hadoop framework has different tools for people with different skills. Some of the examples are-
Hadoop framework is built on Java so people with Java skills can understand Hadoop.

Hadoop has its own distributed database model HBase which is an open source, non-relational.

Hadoop has Apache Pig where you can write scripts to process the data.
Hadoop has Hive which is similar to SQL.

Future of Hadoop

1. The implementation of new filesystem and investments of time and capital to find people with the required skill set to run the new filesystem is a tedious task. Various companies such as Facebook, Amazon, Google, eBay, Etsy, yelp, twitter, Salesforce, etc. have started using the Hadoop for storing and processing their big data. So, in the near future, these companies are not likely to change their filesystems but only provide room for improvement in the Hadoop framework.
2. As there is only growth in utilization of the Hadoop framework, professionals and graduated with Hadoop skills will be on high demands in the near future.
3. A room for improvement in the Hadoop framework will always be required so that it can stay competitive with new technologies in the future.

© 2018 Ngangom Robinson Meitei

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