7 Best Practices To Integrate Hadoop In An Enterprise

Every smart business around is striving to milk out some business value out of this hot trend known as big data. No wonder many of them are turning towards analytics as the primary route towards getting this value. Now, this is where Hadoop for enterprise chips in with the capability to not only handle exploding data volumes but to also afford organizations the chance to scale existing IT systems in the realm of content management, warehousing and even archiving. This is reasons enough to expect Hadoop adoption to take off over the next few years, at least if recent stats are to be trusted. In fact, the adoption is accelerating as a recent survey done by the TDWI established. The TDWI survey found that the number of Hadoop clusters in production has increased by a massive 60% over the past two years and judging by this, there is a bright future for the trend.

What is Hadoop?

In case the term is new to you then Hadoop is just a software library that allows processing of large data sets in clusters of computers albeit with simplified programming models. Apache Hadoop Project developed it and it has the following modules :

  • Hadoop Common – It is the module one that supports the other modules.
  • Hadoop Distributed File System(HDFS) – It is a file system that provides speedy access to application data.
  • Hadoop YARN – This is a framework for scheduling jobs and managing resources in the clusters.
  • Hadoop Map Reduce – This is a YARN-based system that enables parallel processing of data sets.

‘Hadoop’ is thus usually used to refer to the entire Hadoop family of products regardless of their open source or vendor origins.

Benefits of Hadoop in an Enterprise:

If you have not adopted Hadoop in your enterprise, then you are missing a lot! Well, many organizations that have not put Hadoop into production have had nothing-positive impacts.

  • Advanced Analytics

The real charm with Hadoop is that it supports complex analytics built on statistical concepts, data mining, SQL and much more! Talk of things like exploratory analytics and sophisticated data visualization techniques businesses want them.

  • Complimentary to Data Warehousing

Many enterprises that have tried using Hadoop fancy the way it compliments data warehouses not to mention how huge a data source it is for analytics. Hadoop is also quite difficult to match as a platform for transforming data.

  • Data Scalability

Every enterprise will jump into any business function that reduces costs and Hadoop is certainly one of those platforms. Think of it this way. Hadoop helps an enterprise capture enormous amounts of data whilst running on low-cost hardware and software.

  • Handling New and Exotic Data

Hadoop is a platform designed to capture a wide array of data and file types meaning that any enterprise can handle data types that were previously not useful. Therefore, if you have been churning out lots of machine data from robots, sensors, and other devices, then you will be covered if you integrate Hadoop!

  • Fine Tuning Business Applications

Hadoop is quite useful when it comes to fine-tuning other business applications and activities. In other words, Hadoop can help in getting insights on business opportunities, detecting fraud, understanding consumer behavior, getting good ROI for big data etc.

Best Practices to Integrate Hadoop in an Enterprise

Now that you know why your enterprise is thirsty for Hadoop, let us now look into the best practices to keep in your fingertips when integrating Hadoop into the enterprise. Naturally, you might try to experiment Hadoop and see what works for you but we have sieved through the dirt and have found seven of the best practices that are generically applicable to any enterprise whether small or large.

1.   Define Usage

The first step towards a successful Hadoop project is to define the initial use case. Of course, you may have planned for a large enterprise data bank but it is definitely not wise to start big but rather, go for a manageable scope that will tap into data ingestion, access, and processing. You should start with defining the data access where you will have to specify the kind of data users will need, coupled with the ways they will access the data  i.e. through visualizations, batch data extracts, request response, pre-canned reports etc. You will also have to data extraction methods and go as far as defining every relevant boundary.

2.   Take advantage of Existing Enterprise Frameworks

As you might have heard, in IT there is no need to reinvent the wheel! The beauty is there is a dozen of software frameworks that can smooth Hadoop adoption. In this regards, why not go for frameworks that can control and monitor functions for pipelines, communication, data access etc. Some of the options to go for are Spring, Camel, and Jax-RS among others. The advantage with going such frameworks is that developers can shift their focus to creating business logic rather than spending too much time on control processes.

3.   Data Quality

It is always vital to keep data quality in mind especially when you design processing in Hadoop development. If you have system monitoring and exception management tools in place then your Hadoop development should work hand in hand with the tools in a bid capture any exceptions. For example, you can use data reconciliation frameworks to take care of any data quality issues.

4.   Data Modelling

Many developers tend to imagine that because Hadoop can accept any file type, they can simply throw in data to the platform expecting optimal processing performances. This is the wrong way to do it! The right way to do it is to tailor data modelling to access patterns. Again, this calls for you to understand how the data in question will be exploited. This will entail aspects like data formats, data access methods etc.

5.   Data Lineage

It is also advisable to track your data lineage especially as your data sets grow. This can be done by simply adding metadata to any incoming data, a feat that will help:

  • Track data elements right from the source to destination.
  • Track on data quality
  • Assign data access rights
  • Catalogdata sets in Hadoop clusters

A palatable way to navigate around metadata models is to define them before implementation.

6.   Security

Security is one of the potential barriers to Hadoop adoption meaning that your implementation might be hampered without proper security functionalities. You can go for directory-based security like LDAP and Active Directory, which make security scalable and very manageable. This way, administrators can just define user-based security at a single place then they are simply spread to other layers. Apache sentry can help you to enforce this sort of security to data and metadata in clusters. For granular security, choose virtual approaches to data volumes.

7.   Hire and Train Skilled People

One of the most common challenges to Hadoop adoption in enterprises has to be the skill gap. Thus, you are advised to focus on filling this gap through training and hiring of data experts who can develop applications tailored for data science. You might be tempted to fill in such positions with application specialists but they might not deliver what you are looking for.


As things look, Hadoop is surely rising to the challenge of the broad enterprise as it evolves towards serving many industries around the globe. There is no question that this evolution has been driven by the evolving business and technological requirements. It looks like this is just the beginning as we anticipate an even better platform in the coming years! Until then, it is time to start by integrating Hadoop into your enterprise today!


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