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.

Why DataOps is Important?

In the present time, when the world of technology is dealing with data at every moment, DataOps in business matters a lot.

• Rapid Experimentation and Innovation: It enables quick experimentation and invention.
• Collaboration Across the Data Lifecycle: It helps in collaborating throughout the entire data life cycle of the organization.
• Excellent Data Quality and Low Error Rates: It enables very excellent data quality and very low error rates.
• Establishing Data Transparency: It helps in establishing data transparency while maintaining security.
• Simplified Processes: Processes are made simpler with DataOps, which also ensures continuous insight delivery.

Flow of DataOps

Working Process of DataOps:

1. Combining DevOps and Agile: The goal of DataOps is to combine DevOps and Agile methodologies which manages data in alignment with business goals. Agile processes are used for data governance and analytic development while DevOps processes are used for optimization code, product builds and delivery.
2. Statistical Process Control (SPC): Building code is only one part of DataOps as streamlining and improving the data warehouse is equally efficient.  It utilizes Statistical Process Control (SPC) to monitor and control the data analytics pipeline. With the SPC around the place, data flowing through an operational system is constantly monitored and verified to be working.
3. Technology-Agnostic Approach: On the other hand, it’s acknowledged that DataOps is not tied with a particular technology, architecture, tool, language or framework. Tools support DataOps promotes collaboration, security, quality, access, and ease of use.
4. Data Validation: DataOps validates the data entering the system, as well as the inputs, outputs, and business logic at each step of transformation. Quality and uptime for data pipelines rise sharply, well above targets.
5. Automated Testing: Automated tests validate the data entering the system with outputs and business logic at each step of transformation. The process and workflow for developing new analytics are streamlined and now operate effortlessly.
6. Virtual Workspaces: The virtual workspace provides developers with their own data and tools environments so that they work independently without impacting operations. DataOps utilizes process and workflow automation to improve and facilitate and communicate with coordinates within a team and between the groups in the data organization.

Pros of DataOps:

• Improved Cooperation: The development, operations, and data teams work together more often thanks to data operations. Everyone participating in the data lifecycle will work together smoothly to achieve shared objectives thanks to this cooperative approach.
• Enhanced Effectiveness: DataOps increases total data management efficiency by automating repetitive jobs and optimizing procedures. Development cycles and insight delivery are accelerated as a result.
• Better-Quality Data: Higher data quality is a result of testing and validation of the data across the pipeline. DataOps reduces errors and improves data reliability by spotting and fixing problems early in the process.
• Improved Use of Resources: By streamlining resource allocation and automating repetitive operations, DataOps maximizes resource utilization. Costs are reduced, and human resources are used effectively as a result.

Cons of DataOps:

• Difficulties with Technology Integration: It can be difficult to integrate different tools and technologies into a DataOps architecture. There may be compatibility problems and a need for new tool training.
• Data Security Issues: Data security issues could arise from DataOps enhanced automation and teamwork. Sensitive data must be protected by strong security measures implemented by organizations.
• Implementation Complexity: DataOps deployment can be challenging, requiring a thorough reorganization of current procedures and technological frameworks. There can be difficulties because of this intricacy throughout the transfer.
• Initial Investment: An upfront investment in technology, training, and organizational adjustments may be necessary for the successful implementation of DataOps. Some organizations may find it difficult to afford the upfront expenses.

Tips for better DataOps:

While data operations are getting complicated in modern forms, which pose numerous challenges, in small teams. It keeps track of a lot of hidden ways for things to go wrong. In the DataOps approach, data pipelines are an essential component that is resilient, scalable, reliable and has high performance and throughput.

• Create collaboration, Cross-functional teams.
• Centralize your data sources.
• Design data pipelines flexibility.
• Log everything and store it.
• Containerize your efforts.
• Automates version control.
• Learning to use DataOps for Advancement.

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.