Introduction to DataOps

In the current data analytics and data science scenarios, we can see the emergence of one new member named as DataOps. When we work around MLOps(machine learning operations), we definitely use practices defined inside DataOps, which is also a set of rules, practices and processes but aims to improve data communication, integration and automation. As data is the new fuel, any organization processing based on data needs a higher quality data processing to run it appropriately. Practices of DataOps can establish better data collaboration and improve the data flow speed and data quality across any organization. So Let’s take an introductory dive into the subject and understand it.

What is DataOps?

In simple words, we can define DataOps as a set of practices, processes, and technologies to efficiently design, implement and maintain data distribution architecture in any data workflow so that higher business values can be obtained from big data. Generally, the implementation of these practices includes a wide range of open-source tools to make the data accurately flow in the direction of production.

We can also think of DataOps as DevOps of the data pipeline, where these practices strive to increase the speed of applications that work around big data frameworks. The main objective of opting DataOps should be leveraging data silos efficiently with the data management, IT operation and software teams. This ensures that data is being used in the most flexible, quickest and most effective manner to gain higher business values from the data.

When going deeper into this topic, we find that this newer technology includes so many technology components in the data lifecycle to bridge many technology disciplines like data development, data quality management, data extraction and transformation, data governance and centre capacity control, and data access.

One big difference between DevOps and DataOps is that almost zero software or tool helps in DataOps. So to build a complete DataOps process, the collaboration of tools like ETL tools, cataloguing tools, system monitors and data curation tools are required. With the help of the well-structured architecture of these tools, we can increase the agility of the system.

Practices Behind DataOps

Here are a few best practices associated behind the DataOps implementation strategy:

1. Establishment of performance measures and benchmarks at every stage of the data lifecycle.
2. Predefine the rules for data and metadata before applying them to any process.
3. Use monitoring and feedback loops to maintain the quality of data.
4. Use tools and technology to automate the process as much as possible.
5. Usage of optimization processes for better dealing with bottlenecks such as data silos and constraint data warehouses.
6. Ensure the scalability, growth and adaptability of the program before implementing it.
7. Treat the process as lean manufacturing that focuses on constant improvements to efficiency.

Benefits of DataOps

As we discussed, DataOps is a set of practices aiming at the betterment of collaboration, improving the speed and quality of data flowing and processing across any organization. This set of practices includes the following seven main benefits for organizations:

1. Collaboration: DataOps helps in improving the collaboration between the different teams like data engineers, data scientists, business stakeholders, and operations teams, which also helps to speed up the data flow with sustainable quality.
2. Automation: data automation is one of the key benefits of DataOps as it helps avoid manual and repetitive data processes like data ingestion, cleaning, processing, and deployment.
3. Continuous Integration and Continuous Deployment (CI/CD): It leverages a better CI/CD environment around data products, including data pipelines, machine learning models and many more, and enables rapid iteration and deployment.
4. Monitoring and Feedback: this set of practices encourages the importance of monitoring and Feedback. It loops them to detect and resolve issues in real time, which leads to continuous improvement of data products.
5. Data Quality: the main focus of DataOps is to improve the quality by using the practices such as data validation, profiling, and governance
6. Data Security: DataOps helps easily take control over data encryption, data access control, and data masking so that data security can be ensured.
7. Data Governance: DataOps includes practices that ensure data is managed nicely and used ethically. This part of the benefits can be achieved using processes like data stewardship, metadata management, and data lineage tracking.

How DataOps Works

As discussed above, DataOps is a set of practices that aim to streamline and strengthen the collaboration and communication of data processes and data flows. As a set of practices, it takes a team involving members of different roles and responsibilities, such as data engineers, data scientists, and business analysts in team works and follows this set of practices to work more efficiently and effectively. Making data pipeline integration and delivering continuity, making data validation easy, and real-time data monitoring are the primary motives of DataOps, while also increasing the quality of data and reducing errors.

We can also think of Dataops similarly to lean manufacturing, focusing on minimizing the waste within manufacturing processes. In many places, we see the use of SPC(statistical process control) to constantly monitor and validate the data analytics consistency. The SPC is used to ensure that statistics remain within optimized ranges, higher data processing efficiency and improved data quality.

Evolution from ETL to DataOps

We all know that it all started by ingesting data on-premises into databases where self-built data connectors helped in ingestion, and because of being slow, ETL tools came into the picture.

Being hardware, issues such as less scalability, transformation and continued deficiencies came with the data connectors.

In the second phase, cloud data warehouses got introduced in the field and eliminated the classic hardware with scalability issues. Here ETL platforms started reducing the number of data connectors in the pipeline. With these developments, data ingestion becomes easy, but data transformation remains similar.

Soon cloud data warehouses started providing facilities for data transformation inside them, and this is how the concert of data lakes came into the picture and offered unlimited insights from endlessly queryable data.

Nowadays, we can see that companies are not facing the issue of data generation but delivering it appropriately is a constant one. Also, various traditional ETL platforms are still working, leading to data silos and distribution between authorized users.

About DSW

DSW, specializing in Artificial Intelligence and Data Science, provides platforms and solutions for leveraging data through AI and advanced analytics. With offices located in Mumbai, India, and Dublin, Ireland, the company serves a broad range of customers across the globe.

Our mission is to democratize AI and Data Science, empowering customers with informed decision-making. Through fostering the AI ecosystem with data-driven, open-source technology solutions, we aim to benefit businesses, customers, and stakeholders and make AI available for everyone.

Our flagship platform ‘UnifyAI’ aims to streamline the data engineering process, provide a unified pipeline, and integrate AI capabilities to support businesses in transitioning from experimentation to full-scale production, ultimately enhancing operational efficiency and driving growth.