DataOps is one of the recent developments in the field of IT. This is a technology, which has been linked with Big Data Analytics. There are a set of tools and practices, which are used to improve the quality of data analytics and DataOps deals with the integration and automation of the various processes, which are usually done by data scientists and data engineers. It is a way of data management that helps in connecting teams and systems together as well as helps in the process of data analyzing.
Therefore, before digging in deeper in DataOps, you must first know the answer to the question “What is DataOps?” It will give you a clear understanding of the process and techniques used in DataOps. DataOps is a data analysis technique, which is primarily used to process the data in an automated method. There are numerous advantages of DataOps, which include reduced data analytics cycle time and enhanced quality of data analytics which help to save a lot of time. However, many people who work in the IT industry are still puzzled by the question “What is DataOps?” To be precise, DataOps is primarily the data version of DevOps. DevOps is a unique and new way of software production, which stresses on the importance of collaboration between developers, testers and IT operation teams who are the people involved in designing, compiling and managing any software. It is based on the fact that the software can be produced and managed more effectively once the collaboration between the developers and testers is established. DataOps is exactly based on the same concepts except for the fact that it stresses on the need for collaboration between people who set up a database and people who manage the database. The people who manage the databases might also include the security team who ensure that the data stored in the database is safe and complies with various policies, which have been set up by the Government or the organization, which maintains the database.
DataOps is not actually new to the world of data analytics. It is exactly similar to DevOps but it focusses on the importance of coordination between people who handle the data. Hence, it is popularly known as the data version of DevOps. It is one of the very few approaches, which focusses on the way the data that is handled by data analytics companies. This technique can greatly provide companies with valuable insights who rely on the big data technology. Since the evolution of big data, companies have got smart and a revolutionary technology like DataOps it can be ensured that all the people who handle the precious data are on the same page. Also, there are various features like the visualization of data, modeling, and statistics which help the data engineers to interpret the data in an effective manner. Apart from this, many databases do not channelize the data flow and this makes it difficult to automate the process. However, DataOps also helps in channelizing the flow of data to the database infrastructure, thereby helping in the automation of the entire process instead of the conventional and mundane manual procedures.
The Tools Used
DataOps is associated with rapid processing and analysis of data, which makes it necessary for using the right tools. There are several DataOps tools, which also include a wide range of tools, which belong to the open source framework. The other tools include log analyzers, monitors, and other tools might help in the curation of data. Microservice architectures, which help in the blending of structured and unstructured data also form a part of the tools used for DataOps. The DataOps tools are designed to handle in a precise and effective manner, thereby enhancing the quality of data analytics through features like a rapid response, which are recent developments in the field of data analytics.
There are numerous benefits of DataOps and these benefits are the reason for the popularity of DataOps. Some of the benefits offered by DataOps are related to factors such as time, security, problem-solving, and communication. Due to the streamlined communication within the entire team, the time required to work on the data is greatly reduced. This helps the entire team of data engineers to focus on various aspects of the problem instead of a single data engineer banging his head to solve all problems. Also, DataOps helps in distributing the work equally among different members of the team for faster processing. However, to distribute the work, it is necessary that all the members of the team have a perfect understanding of the data in the database. This is easily achieved through DataOps. Also since all the members of the team receive the same level of information, there are higher chances of identifying a big flaw in the system at a very initial stage. Once such issues are taken care of at such early stages itself, the chances of encountering technical issues at a later stage of the project are almost negligible. Also, it saves a lot of time for your employees as they don’t have to repeat the same information about the data to different employees to keep them informed. DataOps takes care of this and your employees will probably have a lot more time to focus on other aspects of the project.
Data does not belong to IT. In fact, it belongs to everyone in the business. So, your tolls have to help the employees to create their own analyses and share their discoveries with their colleagues.