5 Mar 2020
What Would Be the Difference Between DevOps And DataOps?
Judelyn Gomes
#IT | 5 min read
What Would Be the Difference Between DevOps And DataOps?
Judelyn Gomes

There has been an exponential increase in the use of the internet across the planet over the last decade. The requirement of building efficient web applications and managing its expansive data effectively tends to be a great area of concern among contemporary businesses. To adequately solve these concerns, there has been a consistent exploration of the search for superior methods of software development and data management in recent years.  


DevOps and DataOps: Increasing importance


In order to adequately meet the needs of the internet users of today, as well as to develop premium web applications within a lesser time period, many organizations have started to follow DevOps and DataOps practices. Software development companies like HashedIn enable their clients to create synergies while shortening delivery time while facilitating smooth application development and data pipelining management through services of DevOps and DataOps.

DevOps has especially emerged to be crucial in shaping the world of software, and as per many experts, it is predicted to reach its peak in 2020. While DevOps is largely related to the software needed to run the operational side of the business, DataOps applies to the data site of the firm. This includes both managing and versioning of data, data models, as well as queries that are used to generate business intelligence. All of these aspects depend on data availability at a specific point in time. 


How do DevOps and DataOps differ from one another?


DataOps is gradually altering the way people develop data products, similar to how DevOps has changed the manner, how software is created. Even though both of these are based on a distinct agile-framework, DataOps can ultimately be a bit more complex to tackle as it involves every element working with data, and it ideally needs superior iteration, as it is prone to changes.

By choosing to leverage DevOps methodologies and practices, companies can achieve superior quality, flexibility, and speed when it comes to developing and maintaining software products. Even though DataOps largely have similar goals in mind for creating data products, their Delivery Pipeline tends to differ when it comes to the building, testing, and release of the product.

While many think that DataOps is just DevOps that is applied to data analytics, so is not the case. Even though both of these key methodologies and practices do have the common ground of establishing new, streamlined collaboration, DevOps does respond to organizational changes involved in developing and continuously deploying applications. While on the other hand, DataOps, even while responding to similar challenges, tends to be majorly linked to collaborative development of data flows, as well as the continuous use of data across an organization. The processes involved in this are superiorly complex and sophisticated in comparison.

 DevOps facilitates collaboration between operations and developers, where the developers are provided with the capability and creative freedom to innovate while ensuring that their code runs in an accurate, controlled and safe environment that the operations strive to achieve. In a similar fashion, DataOps must ensure the freedom and data innovations of discerning business teams while providing assurance of accuracy, consistency, auditability, and governance for the technical teams.


DevOps Vs DataOps: Use case  


While both of these methodologies drive the best operational practices, both of them have their distinct place in a business. For example, it is common to access an in-house application for the purpose of looking up customer information or performance metrics. Now in the best-case scenario, an application would be easy-to-use and navigate, and provide the users with the service or information they desire. This is where the aspect of DevOps comes to play and makes sure of the satisfaction of the end-user with an application that optimizes user experience continuously.

Taking into consideration the best-case scenario, one should delve into the second component. While people might have got the information they need from the application, it is imperative to determine its accurateness.  In a perfect scenario, the data and metrics used for the information would be governed, trusted and have had passed a certain quality check. This is where DataOps is at work, and aids in making sure that all relevant data is ready to be used in real-time, while also being actionable for insights and expansive projects—like Artificial Intelligence.

The above-mentioned example was a simple setting that shows how the two practices are different from each other but can work together.


Bringing DevOps to data analytics


In simple terms, it can be said that DataOps is what one gets when data analytics is combined with DevOps. It involves streamlining the processes involved in storing, interpreting, as well as deriving value from big data. It focuses on breaking down the siloes that traditionally have separated diverse teams from one another in the domain of data storage and analytics.  DataOps can be the perfect way to optimize the data analytics and storage workflow of a business in a manner similar to how DevOps does the same for application development.

To make the most of DevOps, companies ideally have to migrate to a microservices-based workflow where a number of agile technologies like containers are used. Companies would also need to hire engineers and admins who can leverage next-generation data technologies when developing their analytics and data storage.

DataOps infrastructure needs to be equipped with the capacity to adequately accommodate diverse workloads to achieve agility similar to that of a DevOps delivery pipeline. The key to achieving this agility would include building data processing toolset comprised of distinct solutions like big data analytics tools like Hadoop and Spark, as well as log aggregators like Splunk and Sumo Logic.


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