Big Data Hadoop is everywhere. A majority of the companies are already invested in Hadoop and things can only get better in the future. Hadoop job market is on fire and salaries are going through the roof. Hadoop is the software framework of choice that is used to work with Big Data and make sense of it all to derive valuable business insights.
Hadoop started out as a project to distribute data and processing power on to different computer hardware in order to get work done faster and more efficiently. It was named after the toy elephant of the son of one of the founders of Hadoop – Doug Cutting.
Currently the Hadoop set of technologies and ecosystem is being managed by a global non-profit organization called the Apache Software Foundation. The Apache Foundation dictates the standards and norms and regularly comes up with new open sources technologies, tools and platforms that can work seamlessly with Hadoop. This Foundation is being maintained by an exclusive group of software programmers and contributors who work for the love of technology and with an aim to change the world for good.
- Average Hadoop Developer Salary in the US is around $112,000 which is 95% higher than average salaries for all job postings nationwide.
- The Top Hadoop Salary goes to the Hadoop Administrator at $123,000 per annum. Learn more about Intellipaat Big Data Hadoop Training Course & See Your Career Grow!
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What Makes Hadoop So Valuable to the Big Data Universe?
Poor Data Quality Costs US Businesses up to $600 Billion Annually! – wikibon.org
There are some characteristic features of Hadoop that make it one of the best frameworks to deploy for Big Data applications.
Highly scalable: Since Hadoop works on commodity hardware it can easily scale and accommodate any amount of processing requirements without any issues. This is especially suited for extremely vast amounts of data like the ones coming from social media and also the next-generation devices connected through IoT.
Extremely powerful: Hadoop is parallel computing taken to the limit. It is a highly powerful computing platform that can deliver results in an amount of time that is just not possible on any other database platforms for that matter.
Completely resilient: The various nodes share the workload and as such there is no single point of failure. Upon the detection of failure at any node the work will immediately be transferred to another node and there will be no disruption whatsoever in the Hadoop system.
Vastly economical: The open source nature of Hadoop is highly beneficial since organizations don’t have to shell big bucks as licensing fees. The second reason for extreme low cost of Hadoop is that it uses commodity hardware which is extremely cheap and available in abundance.
Utmost flexibility: Hadoop is an extremely flexible database system as it can work with a variety of data sets – structured, unstructured and semi-structured. So there is no specific rule on storing the data unlike other systems where it is crucial to store the data in a particular way before processing it.
Lower administration: Hadoop does not need extreme administration and monitoring since it works in a resilient manner and does not have any issues regarding scaling the system as and when the situation demands.
Why Organizations Need Commercial Hadoop Vendors?
Hadoop is essentially an open source framework but deploying it for various business applications needs a considerable amount of effort and understanding of the core concepts and procedures. There are numerous specialized vendors who are packaging and streamlining the Hadoop platform so that it can be easily deployed for mission-critical applications.
Packaging: Hadoop might be open source but it needs the expertise of a Hadoop Vendor to make it complete in every aspect. Different companies have different requirements from their Hadoop systems and this might include extra services or add-on tools which the Vendor will provide for a nominal fee.
Assistance: Today Hadoop is deployed across the board and most of these organizations don’t have clear understanding of how exactly Hadoop works. This calls for support and assistance in order to manage mission-critical applications. The Hadoop Vendor brings with it all the technical experience and knowledge in order to ensure everything is working smoothly at all times with regards to Hadoop.
Security: Even though Hadoop is highly resilient and fault-tolerant it still needs the security backing from the experts who know the ins and outs of Hadoop. There might be some bug or glitch that needs to be fixed, or software patches and upgrades that need to be installed. Hence the Hadoop Vendor provides such valuable services.
Here’s a list of some of the Top Hadoop Vendors:
- Cloudera
- Hortonworks
- IBM
- Microsoft
- MapR
Hortonworks is a pure play Hadoop Vendor that is committed to providing extremely powerful and innovative Hadoop solutions. It actively partners with IT enterprises and non-profits in order to delivery Hadoop services across the board.
Cloudera is a market leader among the Hadoop Vendors worldwide. It is being supported by some of the biggest IT players in the world like Oracle, IBM and HP. It has over 350 customers including the United States Army. Some of its customers are using over 1000 nodes on a Hadoop cluster in order to crunch huge amounts of data.
MapR is also one of the most active and innovative Vendors of Hadoop. It is constantly pushing the envelope when it comes to ensuring its Hadoop solutions and services. MapR offers enterprise grade infrastructure, data protection, and a secure and reliable environment for Hadoop implementation.
IBM effortlessly combines its Hadoop offerings with its proprietary enterprise solutions in order to give the customers a complete package. The best part of having IBM as the Hadoop Vendor is that organizations can be up and running in no time thanks to advanced Big Data Analytics incorporated within the holistic package.
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How Hadoop is being deployed in real world business scenarios today?
94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data – wikibon.org
Today there is no excuse for enterprises for not exploiting the vast amounts of data that is available to them. There are enough tools that can convert data into insights. But still something is amiss. According to a Forrester Report, on an average between 60% and 73% of all data in an organization is not used for lack of the right business analytics and business intelligence tool. Hadoop attempts to change all that.
The natural solution available is deploying Hadoop as the framework of choice for all analytical applications. Since Hadoop is open source, enterprises can save big amounts of money in licensing fees on proprietary software that was hitherto spent on data warehousing and business intelligence applications. Hadoop can efficiently handle workloads using both SQL and NoSQL formats.
Sometimes it is next to impossible to work with huge volumes of data that gets created at breakneck speeds both within the organization and outside. Due to this most enterprises are creating huge Data Lakes thanks to Hadoop. A Data Lake is more informal than a data warehouse. Data Lake affords you the privilege of dumping all your data in one place regardless of its types. So you don’t have to worry whether you have to store it in SQL or NoSQL format.
Organizations can deploy Hadoop for storing data in Data Lakes and then once valuable insights within the data is discovered it can be upgraded to be stored into a data warehouse. But some organizations are completely taking data warehouse out of the equation by using the Data Lakes for all purposes regarding data storage. This option can of course lead to huge monetary savings.
Since Hadoop is absolutely versatile it can be used for a wide variety of business applications. By decking it with the right set of supporting software and applications it can be extensively used for business intelligence and analytics purposes as well. Most traditional Business Intelligence tools hardly go beyond creating reports, charts, data visualization and working with dashboards for coming up with business analytics.
Hadoop can go much further by incorporating machine learning systems like Apache Mahout for advanced analytics. This is really important in a world where enormous amounts of data will be generated from machine-to-machine interactions thanks to IoT in the not so distant future.
Hadoop can be deployed for mission-critical applications for working with unstructured data, coming up with real-time analytics, building predictive models, and deriving insights that have a very short shelf life and hence the need to convert it into Business Intelligence at the earliest. The best part of Hadoop is that it can be easily combined with a lot of proprietary and open source technologies for creating a tailor-made solution for any business enterprise.
Source- TDWI Best Practice Report
One such example is the extensive use of Apache Spark instead of MapReduce as the processing platform of choice. MapReduce was developed at Google for a specific purpose – parsing web pages. But today’s data exigencies demand a lot more than MapReduce can deliver and hence Apache Spark seems to be the natural replacement. Spark can even be 100 times faster than MapReduce. Spark is more resilient and is a solution that is made-to-order for Big Data requirements of the 21st century!
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