Big Data Operations Challenge

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Big Data – what is it all about and why is so important?

Pawel Szuszkiewicz
Delivery Manager in Data Services Team in Capgemini Poland

Big Data is described as a huge chunk of unstructured, semi-structured, and structured data, that accompanies a hardship for processing data using traditional methods. Data is all around us and significant amounts of it are generated every day by all sorts of smart appliances, sensors, cameras, and other electronic devices. The total data size has just exceeded 74 Zettabytes and is forecasted to reach 300 Zettabytes by end of this decade.

The question is no longer if the processing of all these data piles makes sense but how to get it done efficiently to bring the maximum possible value to the business. Read in Polish >>>

Manufacturers are committing significant investments towards data analytics as they believe it will not only help them remain competitive on the market but also expand their portfolios and grow their businesses. The great majority of data comes from previously unstructured information from both the public web and private networks (extranets). The classic approach for data processing like RDBMS makes it time inefficient to work on unstructured datasets and hundreds of Terabytes in size. Specialized, sewed to measure software is the only solution to harness the power of Big Data.This is where Hadoop comes to play with its various distributions like Apache Hadoop, Cloudera CDP, HPE MapR, Azure HD Insights, and Amazon Athena.

What is Hadoop?

Hadoop is an Apache open-source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. The Hadoop framework application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from a single server to thousands of machines, each offering local computation and storage.

More information about Hadoop can be found here

Big Data Operations – how is it done?

If you ask a random person about Hadoop the answer probably would be as follows: ‚This is yet another technology that is out there that does something fancy and all people behind it are superheroes with exceptional skills.’ Hadoop engineers are indeed highly skilled individuals but not all the skills are crucial throughout all stages of the project.

There are two profile types that are usually engaged in Big Data operations delivery:

Data Scientists
Individuals who do the actual data processing and to be effective need to have years of experience in various programming languages like Java or Python.

Operational Engineers
Whose fundamental competence is Linux, as all Big Data platforms have been built around a Linux base.

There is a widely acknowledged talent gap in Big Data Operations. It can be difficult to find an entry-level engineer who has sufficient Linux skills to be productive with Cloudera or HPE MapR.

The complexity of Big Data software mainly lies in distributed nature of the underlying file system and the variety of customs configurations needed to be managed by the operations team. What makes it even more challenging is that Big Data software does not have easy-to-use, full-feature tools for data management, data cleansing, governance, and metadata. Especially lacking are tools for data quality and standardization. Although the number of challenges seems to be hard to overcome, certain methods help Big Data initiatives become successful projects.

There is a short overview of the most important factors:

People

  • Engineers with strong Linux expertise focusing on distributed networking and storage. Hadoop was built around Linux so knowing Linux is considered a foundation of competence.

Tools

  • Dev OPS tooling: there is no efficient Big Data administration without Dev OPS tools. They positively affect operational efficiency, ensure quality and glue various dots and pieces in the complex project scenarios.

Automation

  • Ansible, Puppet, and Scripting: Hadoop‚s built-in automation portfolio is very limited so orchestration with additional tools is in many cases necessary to be able to deliver certain tasks on time.

Methodology

  • Agile Operations: focusing on customer satisfaction and end product delivery are key elements of the operations team’s strategy.

Conclusion

Big Data is one of the most popular technology concepts in the market today. Data has the immense potential to change the world around mankind by giving significant insights to improve customer experience, satisfaction and providing unseen aspects of human behavior in absolute light. Creating support scenarios can be very challenging but knowing what it takes beforehand allows to address some possible problems in advance.

If you are looking for a Big Data project to improve your end product or customer experience, you can opt for Big Data Development and Operations Services. The data scientists can help you analyze your data and get insights into crucial things. Infrastructure Engineers will take care of running and maintaining underlying platforms.