Hadoop’s significance in data warehousing is progressing rapidly as a transitory platform for extract, transform, and load (ETL) processing. Mention about ETL and eyes glaze over Hadoop as a logical platform for data preparation and transformation as it allows them to manage huge volume, variety and velocity of data flawlessly. Hadoop is extensively talked about as the best platform for ETL because it is considered as an all-purpose staging area and landing zone for enterprise big data. To understand the significance of big data and hadoop for ETL professionals read this article to endorse the awareness on why is this the best time to pursue a career in big data hadoop for all data warehousing and ETL professionals.
The spurt of internet users and the adoption of technology by all the conceivable industries through the past two decades began generating data in exponentially expanding volumes. As the data kept growing, owners realized a need to analyze it and thus originated an entirely new domain of Data Warehousing. That laid the foundation for an entirely new domain of ETL (acronym for Extract Transform Load) – a field which continues to dominate the data warehousing to this date.
Data is the foundation of any Information Technology (IT) system and as long as we are prepared to manipulate and consume it, we will keep adding value to the organization. The modern technological ecosystem is run and managed by interconnected systems that can read, copy, aggregate, transform and re – load data from one another. While the initial era of ETL ignited enough sparks and got everyone to sit up, take notice and applaud its capabilities, its usability in the era of Big Data is increasingly coming under the scanner as the CIOs start taking note of its limitations.
Hadoop for ETL platform
Extract, transform and load processes form the backbone of all the data warehousing tools. This has been the way to parse through huge volumes and data and prepare it for analysis. That notion has been challenged of late, with the rise of Hadoop. A number of Hadoop advocates argue that the only way to manage data in the future is to learn Hadoop. The conventional ETL software and server set up are plagued by problems related to scalability and cost overruns, which are ably addressed by Hadoop.
Industry experts place a great emphasis on individuals to learn Hadoop. Josh Rogers, President of Syncsort, global business operations and sales lead says, “Data integration and more specifically, Extraction, Transformation and Loading (ETL), represents a natural application of Hadoop and a precedent to achieving the ultimate promise of Big Data – new insights. But perhaps most importantly at this point in the adoption curve, it represents an excellent starting point for leveraging Hadoop to tackle Big Data challenges.”
Though industry experts are still divided over the advantages and disadvantages of one over the other, we take a look at the top five reasons why ETL professionals should learn Hadoop.
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Reason One: Wider Career Path
The ETL vs. Hadoop debate is gathering momentum by the day and there is no clear cut winner in sight in the near future. They both offer their own set of advantages and disadvantages. There is no generalized solution and the preference of one over the other is often a matter of choice and both the approaches are holding their ground firmly.
If you encounter Big Data on a regular basis, the limitations of the traditional ETL tools in terms of storage, efficiency and cost is likely to force you to learn Hadoop. Thus, why not take the lead and prepare yourself to tackle any situation in the future? As things stand currently, both the technologies are here to stay in the near future. There can be requirement specific situations where one is preferred over the other and at times both would be required to work in sync to achieve optimal results.
Even if ETL fades into oblivion, it will not be a binary change. Rather, it would be a journey and you will need a combination of traditional ETL and Hadoop for most part of it.
"Hadoop is a key ingredient in allowing LinkedIn to build many of our most computationally difficult features, allowing us to harness our incredible data about the professional world for our users," said Jay Kreps, Principal Engineer, LinkedIn.
Reason Two: Handle Big Data Efficiently
The emergence of needs and tools of ETL proceeded the Big Data era. As data volumes continued to grow in the traditional ETL systems, it required a proportional increase in the people, skills, software and resources. With the passage of time the huge volume of data began pressurizing the resources and the performance parameters started taking a dip. A number of bottlenecks surfaced in the traditionally smooth ETL processes. As ETL involves reading data from one system, copying and transferring it over the network and writing in another system, the growing volumes of data started adversely affecting the performance parameters.
Systems that contain the data are often not the ones that consume it and Hadoop is changing that concept. It is a data hub in enterprise architecture and presents an inexpensive, extreme performance storage environment to transform and consume data without the need to migrate large chunks of it over the network systems.
At times, all ETL does is to just extract data from one system, perform minor aggregation functions and load it into another system. A majority of it only causes systematic bottlenecks and often does not add any value and for an activity that is essentially non – value add, the costs and time spent is becoming unmanageable.
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Reason Three: Handle Unstructured Data
As organizations across all industries continue growing at breakneck pace, they generate high volume, complex and unstructured data which are exposing the limitations of the traditional ETL systems. It is increasingly becoming a humongous task for data management professionals to accurately handle the large scale data. The growth in data has been so abrupt that even the existing warehousing platforms are unable to absorb, aggregate, transform and analyze it within the resource constraints. To add to the trouble, the limited ability of the traditional ETL tools to handle unstructured and semi – structured data does not bode well for any 21 st century business. One option to stay abreast with the data chaos is to learn Hadoop – and an increasing number of organizations are taking that route because upgrading the traditional data warehousing infrastructure is not a permanent solution, not to mention hours of processing time required by them.
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Reason Four: Need to Synchronize Traditional ETL and Hadoop
A lot of recent discussions have been projected as an ETL vs. Hadoop one, which is not an accurate analysis. At least at present, these are not mutually exclusive and there is a very strong possibility of both of them co – existing. Having said that, the data professionals cannot afford to rest on their existing expertise of one or more of the ETL tools. Hadoop is catching on and a number of analysts strongly recommend its adoption, especially for projects dealing with voluminous, semi structured and unstructured data on a regular basis. Both the technologies have their set of pros and cons and ETL tools are not going to disappear any time soon, even with the adoption of Hadoop. Offloading the transformation processing to a platform like Hadoop frees up considerable capacity in the data warehouse – thus making it a viable alternative to an expensive expansion or upgrade to make room for exponential expansion in data volumes.
Hadoop is capable of virtually unlimited scalability at a cost which is over 50 times less than the traditional data warehousing solutions. It also makes a strong case for data archiving as it enables analytics on archived data. Though it will not replace the traditional RDBMS systems any time soon, its superior price to performance ratio has presented organizations a realistic option to lower their costs while maintaining the existing level of performances.
Reason Five: Open Source, One Stop Solution
Traditional ETL systems have mushroomed in the past two decades and there is no uniformity in the products. A vast variety of data warehousing solutions are available to choose from which can be quite confusing. Once you learn Hadoop, you discover that it is a one stop, open source solution to the existing solutions related to unstructured data, process time and scalability. All data warehousing professionals are expected to possess the skills of querying, troubleshooting and data processing, which cover all the pre – requisites to learn Hadoop. It enables you to flawlessly manage volume, variety and velocity of data in comparably less time than the traditional ETL solutions.
Statistics from Gartner Business Intelligence Summit Statistics (2013) reveals the following statistics which further strengthen the case to learn Hadoop:
75% of current data warehouses will not scale to meet the new velocity and complexity of data demands
86% of companies cannot deliver the right information at the right time
These findings are reason enough for professionals and organizations to start investing their time and money to learn Hadoop
Hadoop is at a very interesting stage of growth at the moment. As of now it is primarily being looked at as an all – purpose landing zone and storage area for enterprise data. It has emerged as the next logical platform for transforming and preparing data. Having said that, companies are not going to move all their data to the Hadoop platform all of a sudden, but it surely will be a gradual process, which will require the analyst programmer to know both sides of the story, i.e. ETL and Hadoop.
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