Difference Between DevOps and DataOps
S.NO. |
DEVOPS |
DATAOPS |
---|---|---|
Definition | DevOps refers to transforming delivery capability by achieving speed, quality, and flexibility by employing a delivery pipeline seamlessly along with development and operation teams. | DataOps refers to transforming intelligence systems to end-users by building data pipelines by coordinating with ever-changing data and everyone who works with data across an entire business |
Focus | It focuses on the development of quality software. | It focuses on the extraction of high-quality data for faster and more reliable business intelligence. |
Automation | It automates versions and server configurations. | It automates data acquisition, modeling, integration, and curation. |
Value Delivery | For value delivery DevOps focuses on principles of Software Engineering. | For value delivery DataOps focuses on principles of Data Engineering. |
Quality Assurance | In DevOps for Quality Assurance they perform continuous testing, code reviews, and monitoring. | In DataOps for Quality Assurance(QA) they perform process control and data governance. |
Importance | In DevOps the code is the important thing. | While in DataOps the data is the important thing. |
Participants | In DevOps mostly technical people are involved. | In DataOps mostly business users and stakeholders are involved. |
Orchestration | In DevOps application code does not require complex orchestration. | But in DataOps data pipeline and analytics development orchestration are important components. |
Workflow | DevOps workflow depends on the continuous development of features with frequent releases and deployments. | DataOps workflow depends on continuous monitoring of data pipelines & building new pipelines. |
What is DataOps?
DataOps (Data Operation) is an Agile strategy for building and delivering end-to-end data pipeline operations. Its major objective is to use big data to generate commercial value. Similar to the DevOps trend, the DataOps approach aims to accelerate the development of applications that use big data.
While DataOps started out as a collection of best practices, it has evolved into a fresh iteration of an autonomous approach to data analytics. DataOps understands the interrelated nature of the development of data analytics in alignment with business goals and applies to the full data lifecycle, from data display through reporting.
- With the use of automated software testing and development processes, DevOps continuously focuses on delivering.
- Software engineering and deployment will be carried out at a faster rate, with better quality, predictability, and scalability.
- To improve data analytics, borrowing techniques from data operations are being used. Additionally, it makes use of statistical process control (SPC), which is used to particularly monitor and regulate the data analytics pipelines.
- The operational system is also continuously checked to ensure that it is operating as intended.