The need for DevOps in Big Data and Data Science
Big Data and DevOps don’t relate much. Therefore it’s obvious for people working in DevOps domain to think that Big Data doesn’t have much to do with them — and vice versa. But the boundary line between the two fields is becoming obscure. Many businesses are accepting the need to implement DevOps in Big Data.
What is DevOps?
People dealing with Big Data or Data Science must have a very vague idea of the concepts of DevOps. Let’s discuss what exactly is DevOps.
DevOps is the advanced standard of software development and delivery. It improves the communication and collaboration between development and operation teams. Collaboration and communication are crucial for DevOps and QA (Quality Assurance). It is essential for effective communication of the Dev and Ops team.
DevOps involve agile development and it evolves collaboration between the software developers who build and test applications and the Operation teams that are responsible for deploying and maintaining IT systems and operations. DevOps can increase the speed of application delivery of an organization dramatically.
What is Big Data?
Big Data contains massive and complex data sets. Generally, the traditional data processing methods and software are inefficient to deal with them. Top Big Data challenges include data capturing, analysis, searching, sharing and visualization.
With the goal to increase the speed of data ingestion from a variety of data sources — mainframes, relational database management systems. It needs the right sets of tools and methodologies for the data ingestion and transformation that can be tested thoroughly to provide expected business results.
Need for DevOps in Big Data
Gaining an accurate and thorough understanding of Big Data project is very challenging. In most companies, due to lack of communication between Big Data developers and Operation teams, it becomes more difficult. Due to the lack of collaboration among the teams, it becomes quite difficult to deliver quality results. IT operations team is involved at the last moment which makes things more cumbersome and effects the whole productivity.
Click below to read more about the benefits of DevOps in dealing with Big Data applications…
Conclusion
Through DevOps, software and services can be delivered faster. Still, it is not considered as the key approach for Software Development by many of the worldwide organizations. Large scale companies are still following the old approaches because of the fear of transition failure.
