Over the past few decades, use of the internet has increased exponentially. Nowadays, the need to build quality web applications and managing its huge data effectively is a huge concern of any organization. Thus, there has been continuous exploration for finding better methods of software development and data management.
To meet the need of today’s internet users, many technology companies have stepped up and are continuously utilizing their resources to find more efficient ways of developing web applications in less time and with better quality, and are finding more effective ways to manage and analyze its data.
To achieve the same in application development and data pipelining management, many big organizations have started following a set of practices—DevOps and DataOps respectively.
In this article, we’ll take a look at what DevOps and DataOps are, and then we will focus on the ultimate question: Are they really worth to implement in an organization, and will following the same set of practices really improve the application development and data management—and consequently the growth—of an organization?
DevOps and DataOps: An Introduction
In simple words, DevOps and DataOps are a set of practices for development of software and management of data respectively, for a particular project or to follow at an organization level.
DevOps emphasizes on automation of processes of various teams—software development team, quality assurance team and IT operational team—so as they can complete the relevant tasks (i.e. plan, code, build, test, release, deploy, operate and monitor) of application development quickly. Also, the these teams will be able to maintain the higher quality of software application by delivering/publishing software in smaller parts with higher deployment frequency while keeping the cost low, thus following the agile methodology more seamlessly.
DataOps, in comparison, can be defined as DevOps with data analytics.
As mentioned earlier, we know that DevOps emphasizes the collaboration of software development team (including testers) with system administrators (operational team). In a similar manner, DataOps weighs on collaborating the teams of data engineers, data scientists and other data professionals who are handling the data-related processes (i.e. store data, analyze data, deriving decisions from data, archive data or deliver data).
The concept emphasizes both communication and collaboration between various teams of the same organization. The combined team can have more visibility of the project and come up with faster results. It also will improve the morale of the organization by proving the fact that people will be always above processes and tools when it comes to management of projects.
| DevOps | DataOps |
| Automation and Monitoring of Software Development | Automation and Monitoring of Data Pipelining/Management |
| Combination of – Software Development – Quality Assurance – Technology Operations | Combination of – Data Engineering – Data Integration – Data Quality – Data Security/Privacy |
| Collaboration of – Software Engineers (Developers) – System Administrators (Operational Team) – Testers | Collaboration of – Data Engineers – Data Scientists – Data Analysts – Data Professionals |
| Typically aims at – Shorter development cycles – Increased deployment frequency – Quickly moving from Dev – to/from Test – to Production – More dependable releases | Typically aims at – Automation of Storage Workflows – Monitoring the Operational Flow – Optimization of Data Analytics – Creating Infrastructure that will enforce data quality rules |
Now that we’ve gone through the basic details regarding DevOps and DataOps and what they are doing for software development and data management respectively, let’s take a look at some of their potential benefits to see if they can really act as a stimulator for an organizational transformation.
Benefits of DevOps and DataOps to adapting Organization
There are numerous benefits of adapting the defined processes of DevOps and DataOps in project execution and running the organization. Here are some of the key benefits for applying the practices of DevOps for applications development in an organization.
Benefits of DevOps
- Transparent Processes and Culture across Organization – By continuous communication and collaboration between the development team, QA team and IT operations team for every deployment cycle, each employee will be updated regarding latest information of project and will be more aware regarding the processes of other departments.
- Quicker and Higher Quality Deployment Cycles – By applying the set of practices of DevOps, especially the shorter development cycle, will boost the frequency of deployment. As the focus of teams will be limited—i.e. on small task/module—for a deployment cycle, quality of each deployment will improve.
- Faster Resolution of Bugs – As tasks are smaller and continuously deployed on relevant servers, the development team will be given feedback very quickly from the QA team, thus enabling the development team to provide much quicker resolution of any non-working functionality. Application stability will be a byproduct of this process, as the reason behind any error can be identified very easily (because particular deployment cycle of an error is known).
- Flexibility to Handle Unplanned Work – As agile methodology insists on the handling of the raised change request, teams streamlined with DevOps will be able to handle such unplanned work more efficiently.
Benefits of DataOps
- Faster Processing of Data – As various teams are getting collaborated, communicating continuously and working on micro databases rather than all at a time, processing of data at various process stages are done much quicker.
- Quicker Solution of Problems – Similarly to DevOps, in DataOps data are being processed in bunches rather than at once. This approach can help to identify issues pertaining to a bunch of data more quickly, and their resolution can be derived more quickly, as the focus is limited.
- Better Data Security – As the data security professionals are in continuous touch with data integrators and data analysts, securing data at each process level becomes easier.
- Improved Resource Output – When the team adheres to the fundamentals of DataOps, seamless communication and transparency between the various teams will aid in the performance of each team member and will lead to better resource output.
Implementation Procedure of DevOps and DataOps
Defining the processes for implementing DevOps and DataOps is simple, but implementing same processes do require tremendous dedication of teams. Let’s focus first on the general steps for implementing DevOps processes:
- Collaboration, communication/seamless integration between formerly siloed teams, i.e. software development team, quality assurance team and IT operations team
- Breaking down the major modules/big development tasks in particular smaller tasks
- Processes for DevOps should be followed for each deployment cycle/each smaller tasks
- Tools – There are various tools available in the market to accomplish processes of DevOps, and there are some techniques for continuous development and performing agile operations‚ such as Kanban, Scrum, etc., which should be followed for seamless application deployment.
With DataOps, there are no specific software tools available in the market to implement DevOps. But there are some frameworks and its supporting tools that can assist the DataOps approach for agile-based project management.
- Data storage and analytics infrastructure should be redefined, i.e. cluster-based or redundant data storage approach is recommended. Also some latest data analytics techniques should be adopted.
- Use of microservices-based architecture and software that can allow blending of various type of data, i.e. structured and unstructured, are also recommended
- Implement ‘built for purpose’ database engines – By doing such, accessing and analyzing large quantities of data become much easier (these are the same type of database engines that drive Google Search, YouTube etc.)
- Supporting tools – Tools that aid to agility of project and collaboration between teams i.e. ETL/ELT tools, data curation and cataloging tools and log analysers should be used. For data blending, software tools such as Map Reduce, HDFS, Kafka, Hive and Spark can be used
Conclusion
All IT organizations have been managing software application development and its relevant data for a long time. But right now, we are in an era where managing the same with velocity and quality can make your organization stand above the rest of peer companies. Adopting DevOps and DataOps demand sheer dedication and focus of the teams, but the output after implementation will be very positive and such followed standards will definitely transform the organization constructively.
