DevOps was designed to reduce software deployment time by pooling the skills of development teams and operations teams. It relies, among other things, on the Agile method. DataOps has built on the same principles and leverages Data Science to improve the efficiency of the process.
DevOps: what is it?
DevOps is the originator of all the practices that reduce the time it takes to write and deploy software and improve its efficiency. The process requires the intervention of a team of operating specialists and developers. It is a process of development and operation that leads to better execution allowing a company to become more competitive.
The operations and development teams are involved in each phase of software creation. They are also responsible for its development and deployment. The application undergoes several tests, after which the DevOps team approves its move into production. The DevOps helped to put an end to the frictions that existed previously between the two teams. It managed to build a bridge between them.
The purpose of DevOps
The term DevOps is a contraction from the English words development and operations. Its goal is to shorten development cycles and ensure more frequent deployments. DevOps is based on continuous integration (CI) and continuous development (CD).
CI is the process by which new codes are built, integrated, and tested repeatedly and automatically. Any problems are then identified and resolved quickly. As for the CD, it automates the delivery and deployment of software. The DevOps also aims to automate as much as possible until the final completion and administration of the software.
The progress and promise of Big Data has so far failed to achieve great success in leading to rapid production. Hence, the need to adopt a more efficient approach capable of accompanying the development of software in all stages.
The Agile Principle
The Agile method is based among other things on streaming and testing and learning. The goal is to get to use quickly, while appealing to the different teams that establish a bond of mutual trust. DevOps is frequently linked to Agile. Based on Agile, it is possible to provide software on an ongoing basis. User feedback is also an integral part of the process.
DataOps: the heir
DataOps is considered the heir to DevOps. It is based on the latter by perfecting its advantages. It is always about improving collaboration between teams and better ensuring the links between technologies and processes. Data trades and data consumers are involved.
DataOps requires the involvement of several stakeholders so that all work in harmony. The team includes, for example:
- Data Engineers who create and maintain databases and big data processing;
- Data Scientists, who are responsible for cross-referencing and interpreting the data.
The various teams also include Business Analysts, Data Architects and Data Stewards.
Using the DevOps model, DataOps is based on collaboration and the Lean and Agile principles. Several practices are common to DevOps and DataOps. Both call for continuous integration and development. Both also use unit testing, environmental management, version management and monitoring. All these practices allow us to share the qualities of all the members of the different teams.
DataOps also has several improvements compared to the traditional DevOps. It is not as effective when it comes to creating applications that can process and analyze flows on an ongoing basis. DataOps is recommended to build and maintain a Data Pipeline, that is, a data stream designed from its creation to its use. Batches are grouped together and travel from the point of origin to their destination.
Le Data Pipeline
The Data Pipeline is linked to the Ops aspect of the system. Another advantage of DataOps is related to the creation of Data Science, or data science projects. Data Science applies pure science to computer science. It uses data collected to draw lessons and trends from it in order to design predictive models. It is a way to optimize decision-making.
DataOps is used to automate the pipeline and gradually integrates new data that is constantly being added. The first analysis of the raw data is less refined. After this first exploration, data begins to be cleaned up. The original models are analysed and refined. They are validated when they are deemed ready for production. The models are then directed to production and made available to the consumer.
Le DataOps and Data Science
DataOps provides more benefit from the analyses provided by Data Science, particularly in the reproducibility of results. Data Scientists also play an important role in monitoring model performance. The model itself may change, or the situation may evolve to the point where the model itself becomes obsolete. For end-users, it is also important to make the models available in an application.
The analyses and approaches promoted by Data Science are based on a very large volume of data. This data needs to be constantly reassessed and reinterpreted. The emphasis is on the continuous development of the model. The DataOps principle ensures that, despite the volume of data, implementation is faster.
Data Ops also uses the Agile method to provide better data analysis in a very short period of time.
The best way to optimize the performance of applications and software is to opt for an approach that combines DataOps and DevOps. To achieve this, it is important to use a technology orchestrator.
The technology orchestrator
This orchestrator organizes and coordinates systems and software from different processes. It relies on automation and is a valuable help in managing data at all stages, from collection to consumption. All Data and Analytics projects can be entrusted to it so that they can be implemented more quickly and efficiently.
Data and Analytics analyzes raw data to identify trends that promote their use in the context of business needs. It is based above all on inference as well as on the process and establishes from there a relevant conclusion. It can also determine the degree to which the data is well protected.
All technologies are used to manage all the steps. These include Python, HDFS, Impala, Hive, Drill, Java, Scala, Jupyter and Mongo DB. For example, Python is used to automate multiple repetitive tasks. The latest versions are supported.
In conclusion, observers agree that DataOps is in its infancy and that it remains to define its frameworks. It is undeniable that this is a path to be taken for the future and if you too want to make the most of your data, Ryax accompanies you every step of the way.
La Ryax Team.