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Who Should Manage Your Hadoop Environment? Featured

Who Should Manage Your Hadoop Environment? Kylie Haulk

Hadoop is the leading open source technology for the management of data. In any discussion about big data and its distributions, you will never fail to come across Hadoop in the middle of the talks. As outlined in a technical paper by Google in 2006, it was designed as a distributed processing framework back in 2006. It was first adopted by Yahoo in the same year, followed by other tech giants such as Facebook, Twitter, and LinkedIn.  During this time, Hadoop evolved significantly into one of the most complex big data infrastructure known today.

Over the years, the platform has evolved significantly to encompass various open-source components and modules. These modules help in the capture, processing, managing, and analyzing large data volumes, that are supported by many technologies. The main components of Hadoop include the Hadoop Distributed File System (HDFS), YARN (Yet Another Resource Negotiator), MapReduce, Hadoop Common, The Hadoop Ozone, and Hadoop Submarine.

With the usefulness of this platform, big data management of Hadoop is becoming a critical aspect. It is important to understand that the best performance of Hadoop depends on the proper coordination of IT professionals who will collaborate in various parts of management. The areas that must be managed include planning of architecture, development, design, and testing.  Other areas include the ongoing operations and maintenance, that are meant to ensure good performance.

The IT team that will manage Hadoop will include the requirements analysts whose role will be to assess the system performance requirements based on the applications that will operate on the Hadoop environment. The system architects will evaluate performance requirements and hardware design and configurations while the system engineers will manage the installations, configurations, and tuning of the Hadoop software stack. The work of application developers will be to design and implement apps. On the other hand, the data managers prepare and run the integration of data, create layouts, and carry out other data management duties. System managers are also a critical part in the management of Hadoop. They ensure that the system is operational and manages maintenance. Similarly, the project managers are responsible for overseeing the implementation of the Hadoop environment and prioritization and development and deployment of apps.

Once Hadoop has been deployed, those in charge within the organization must always ensure that it runs with low latency and processes data in real-time. It must also be able to support data parallelism and ensure high faster computation. Doing so ensures that the platform handles analytics tasks that are needed without failing or without requiring further server customization, more space, and financial resources. Furthermore, the Hadoop framework should be used to improve server utilization and load balancing by the IT managers. They should also ensure data ingestion is optimized for the integrity of data. Furthermore, they must also carry out regular maintenance on different nodes in each cluster, replace and upgrade the nodes, and replace and update operating systems whenever possible.

Hadoop is an open-source platform; this means that it is free. While this is the case, it is important to note that deployment, customization, and optimization can raise the costs of using it. While this is true, any company can offer Hadoop-based products and services. Some of the companies that provide robust Hadoop-based services include Amazon Web Services (AWS), Cloudera, and Hortonworks, among others. The evolution that has been realized by Hadoop has transformed the business intelligence and analytics industry. As such, analytics that the user organizations can run as well as the types of data that can be gathered by and analyzed by applications have been expanded.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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