Applying DevOps concepts can have great benefits to any big data initiatives, but the analytics teams still choose not to use these methodologies. Applications based on the components of big data ecosystem need to be hardened in order to run in production, and DevOps can be included as an important part of that.
What is DevOps?
The idea behind DevOps is to tear down the barriers that stand between IT infrastructure administrators and software developers, in order to make sure that everyone’s focused on a singular goal. This requires a bit of cross-training from both sides so the used terminology is understood by everyone. After the completion of training, clear lines of direction and communication can be established, with a clear aim of continuous improvement. Both ends will be able to bring software features and fixes to end users faster, as DevOps enables them to work in tandem to tune production infrastructure components and test environments to meet new software requirements.
Big data analysts know how tough and complex it is to extract meaningful and accurate answers from big data. Big data software developers lack coordination in many enterprises, which often makes things more challenging and big data projects remain siloed for different reasons. Today, we will present you all the benefits that DevOps can provide to big data project teams, as well as why they choose not to use its methodologies.
Why doesn’t Big Data use DevOps?
Many IT leaders have abandoned the DevOps methodologies, procedures, and processes that they use with other apps the department supports, due to the complexity of the analytical sciences part of big data. For the in-house data analysts, this field of data science is foreign to many IT professionals, so big data developers and analysts formed their own group, separated from the operations side of their companies. The big data trends are shifting, but in this aspect they still operate in accordance with this separation of functions.
How can Big Data benefit from DevOps?
The same bottlenecks and inefficiencies that were managed to get solved with DevOps practices in other applications are unveiling in big data projects, because of this separation of departments. However, the issues are becoming compounded, because IT leaders are feeling more pressure to produce results as big data projects are more challenging than expected. Analytics scientists are thus forced to revamp their algorithms, which pushes different infrastructure requirements in terms of resources than it was planned for originally.
The operations team kept out of the process, without proper collaboration, until the last minute. The lag in resource allocation coordination and in communication eventually slow down progress when the infrastructure change requests finally come in from the software developers. Big data analytics can provide a potential competitive advantage, which is affected by this slowdown. DevOps methodologies are thus needed for preventing this from happening.
Integrating DevOps and Big Data “ The challenges
You should understand the challenges that you might face along the way, if case you decide to integrate DevOps with your big data projects.
The operations section of the company must learn about the ways analytics models are implemented and gain more profound knowledge on big data platforms. Also, as opposed to data engineers, analytics professionals perceive themselves as social engineers, so they must learn some new things as well.
In regard to network and compute resources, the magnitude of potential scalability can be enormous, never before seen in another production application. Resource coordination is going to be of extreme importance if speed is an important part of your DevOps plan.
Also, one should know that, in order to make additional big data DevOps run at maximum efficiency, additional human resources will be required. Cloud computing is also important for improving efficiency, as these services allow IT departments to shift their focus away from patching operating systems, provisioning hardware, and other types of commodity work, and spend it on other tasks aimed at adding value to the business.
Integration challenges are outweighed by the benefits of integrating DevOps and big data. DevOps stresses the integration and collaboration between operation professional and developers, but it’s still not in the vocabulary of business data scientists. The testing of the performance of analytic models in production-grade environments will need to be more thorough and faster due to the intensifying performance requirements on advanced analytics.
The needs are ever changing and ever growing, so the mismatches in practice and perspective between IT administrators (who are all about performance) and data scientists (who place performance lower on their list of priorities) will turn more acute.
