What is DataOps?

DataOps is a methodology that combines technology, processes, principles, and personnel to automate data orchestration throughout an organization.

By merging agile development, DevOps, personnel, and data management technology, DataOps offers a flexible data framework that provides the right data, at the right time, to the right stakeholder.

From data analysts, to marketers, to salespeople, every employee must now drive results with data. But today’s under-resourced data and BI teams are overwhelmed by these growing expectations. That’s where DataOps comes in.

DataOps delivers high-quality, on-demand data to organizational customers by speeding up the development and deployment of automated data workflows.

What is the Value of DataOps?

This is the mission of DataOps is to create agile, scalable, and controllable data workflows for DataOps teams.

DataOps feeds data consumers, internal and external stakeholders, and customers the data they need, when they need it. This provides companies with several competitive edges in the data economy, including:

1. Data Democratization

From executives, to SDRs, to warehouse workers, DataOps unlocks data for employees across the entire company. By enriching every part of an organization with data, DataOps enhances results, ROI, and competitiveness across the organization.

2. Faster Speeds to Data Insights

DataOps equips stakeholders with critical insights faster, allowing stakeholders and teams to move with superior speed in a rapidly-moving market.

3. Incisive Decision Making 

DataOps puts the right data into anyone-and-everyone’s hand, leading to more informed and effective decision making at every level of the company.

4. Improved Data Productivity 

The agile framework of DataOps enables data professionals to make quick, targeted data pipeline deployments and changes, eliminating manual and cumbersome processes.

This boosts productivity not only for the data team, but also for data consumers, who no longer need to wait for data to complete tasks.

5. Sharper Data Insights, Sharper Results

The DataOps framework builds feedback from data consumers into pipeline development, producing the customized insights that stakeholders need to increase revenue.

Who Leads DataOps?

By design, DataOps teams will include temporary stakeholders during the sprint process. However, a permanent group of data professionals must power every DataOps team, and often include:

  • The Executive (CDO, CTO, etc.) – The executive drives the team to produce business-ready data for data consumers and leadership. He/she guarantees the security, quality, governance, and life cycle of all data products.
  • The Data Steward – The data steward builds a data governance framework for the organization’s data systems, in order to manage data ingestion, storage, processing, and transmission. This framework forms the backbone of the DataOps initiative.
  • The Data Quality Analyst – The data quality analyst improves the quality and reliability of data for consumers. Higher data quality translates into better results and decision making for data consumers.
  • The Data Engineer – The data engineer builds, deploys, and maintains the organization’s data infrastructure, such as all data pipelines, including SQL transformations. The data infrastructure ingests, transforms, and delivers data from source systems to the right stakeholder.
  • The Data/BI Analyst – The data/BI analyst manipulates, models, and visualizes data for data consumers. He/she discovers and interprets data so stakeholders can make strategic business decisions.
  • The Data Scientist – The data scientist produces advanced analytics and predictive insights for data consumers. These enhanced insights enable stakeholders to improve decision making.

Are DataOps and Data Operations the Same?

DataOps is a collaborative data management practice whose goal is faster delivery of insights and data by automating the data delivery design and management. The decision-making data teams and applications get the data at real-time speeds.

On the other hand, data operations has a different, much wider approach to data. Looking at the data and the data pipeline, it considers the data availability, operational needs, the hybrid infrastructure where data resides, and security. The process also covers the integrity and performance that maximizes the data potential.

The goal of data operations is to maximize the value of the data and the pipeline. The infrastructure within the pipeline requires tuning, securing, testing, performance monitoring, and much more.

What is Data Operations Management?

Data management is a challenging task for all data teams and data scientists. Data management ensures acquiring, validating, storing, and protecting data in a certain standardized way.

At the same time, DataOps is a collaborative data management practice that improves the integration, communication, and automation of data flows.

Recognized as an innovation trigger, DataOps allows the maximization of the data business value and infrastructure. There has been a rise in DataOps to boost the data processes of the business.

Agile development, treatment of data as code, automation of data processes, friendly application development, minding storage, and efficient data distribution are just some of the best data management practices with DataOps.

Agile: The Speed Flexibility of DataOps

In DataOps frameworks, the principles of agile development are used to build data infrastructure, such as data pipelines. On a granular level, data infrastructure is just code, or “infrastructure as code” (IaC). IaC is the “software product” in agile terminology.

Within a DataOps framework, cross-functional teams execute “data sprints” that design data models and deliver analytics for targeted stakeholders. Each team is composed of data managers (data engineers, BI managers, etc.) and data consumers (salespeople, leadership, etc.).

Feedback from data consumers is incorporated continuously within the sprint process to quickly improve and update data assets.

What is DataOps? DataOps Framework

What is the Difference between DevOps and DataOps?

Both originating from agile project management, DataOps and DevOps are two distinct methods that often get confused. Although they both work around bringing clarity and focus, their main business areas are completely different. Nonetheless, they do have some similarities, as shown in the table below:

DataOps vs. DevOps Similarities

DataOps vs. DevOps Differences

Increased Collaboration

Outcome

Agile Connection

Feedback

Iterative cycles for value

Testing

Workflow

The difference between DevOps and DataOps is that DevOps combines software development and IT operations to automate software deployments, while DataOps automates the ingestion, transformation, and orchestration of data workflows.

Traditionally employed in software production, DevOps combines software development (Dev) with IT operations (Ops) to speed up the time-to-release of high-quality software. DevOps merges the processes of building, testing, and deploying software into a single automated practice.

Although DataOps derives its name from DevOps, DataOps is not simply DevOps for data. Instead of automating software deployments, DataOps automates and unifies the ingestion, transformation, and orchestration of data workflows.

One place where both methods work parallel is the iterative cycles. These are used to produce fast results and feedback to proceed with the process should the data meet the basic needs.
There is also an increased collaboration on both sides by prioritizing the key performance indicator. The collaborative data management practice, DataOps, enables the development of valuable insights to goals by working with business users, while DevOps quality assurance teams aim to develop better software.




Testing


Workflow


Feedback


Outcome


DataOps

Because the outcome is uncertain, normal testing and additional validation are required to ensure the accuracy of the information.

In addition to creating new pipelines, it's important to focus on ensuring data accuracy at any time by constant data pipeline monitoring.

End-user feedback is required to validate if the delivered solution meets the expectations and offers the best information

Deliver business information to users by focusing on creating data pipelines

DevOps

With the outcome defined in advance, the testing process is a rather straightforward evaluation to check if the application behaves as expected.

Use frequent deployments and releases to define application features in the development stages.

End-user feedback is valued but not required if the delivered solution is satisfactory for the customer.

Deliver new functionality to users by creating software applications.

DataOps unlocks key advantages by harnessing the practices of DevOps, including:

1. Source Control 

A centralized repository of all data ingestion processes, transformation logic, pipeline source code, and data delivery monitoring, in version control systems such as GitHub.

2. Continuous Data Integration 

Automatically merge developer source code with live pipelines to avoid “integration hell.”

3. Separate Test & Production Environments

Evaluate pipelines before launch in a test environment identical to production. Easily push changes live after Q&A diagnostics.

4. Continuous Delivery

Continually deploy incremental but frequent code updates to the production environment automatically.

 

DataOps Framework

What is DataOps? DataOps Framework
DataOps team members can develop data infrastructure in separate but nearly identical environments, and push changes live with point-and-click functionality after a predefined testing process.

DataOps, like DevOps, relies on automation to eliminate manual tasks and IT processes, for example:

Differences in Processes and Outcomes

DevOps and DataOps may share the approaches and framework, but their processes and outcomes differ. DevOps focuses on fast software delivery through customer-guided improvements. DataOps’ focus is on data stream creation for the delivery of information to end users.
DataOps is known to help align business objectives and data through accelerated data processing. DevOps, on the other hand, mainly focuses on product development. In short, here are some of the more detailed differences between DataOps and DevOps.

DataOps

DevOps

Data operations

Development operations

Use build, test, and release, and a few other steps in between

Uses just build, test, release

Involves a mix of IT operations teams, and data management

Involves a mix of IT operations teams and product development

Offers structured and accurate data

Offers faster deployment with least bugs and issues

Fast delivery of data analytic solutions

Fast development and release of software products

Automated data pipeline for companies

Companies are given a chance to release products faster

Consistent coordination of ever-changing data

A fairly limited coordination

What’s important are the data flows within the system

The software itself is important

Evolution from ETL to DataOps Platform

For years, companies ingested data into on-premise relational databases using self-built data connectors. However, this process was too slow and expensive, and ETL tools gradually emerged for data ingestion.

But issues with database scalability, data transformation, and continued deficiencies with data connectors limited the strength of the insights.

Years later, cloud data warehouses eliminated hardware scalability issues, and ETL platforms began to close the gap in terms of data connectors. Ingesting data was no longer the problem; transformation was.

But soon, ELT platforms began to transform data inside the cloud data warehouse, leading to the rise in data lakes and unlimited insights from endlessly queryable data.

ETL and DataOps

Today, the challenge facing data-driven companies is more about delivering data than generating it. Now, everyone in an organization needs data, and needs it in minutes, not in hours.

However, most traditional ETL platforms are still reinforcing an outdated framework that silos data and puts it only in the hands of a “chosen few.” That’s why DataOps platforms are built for this new era.

DataOps platforms do not just generate, but deliver, the right insights, at the right time, to the right stakeholder. With full data orchestration, DataOps platforms automate the democratization of data, from start to finish.

DataOps platforms eliminate the rigid, top-down data culture facilitated by traditional ETL platforms, for a bottom-up system that provides stakeholders in the trenches with the data they need.

FAQ

Q: How do I implement the DataOps methodology?
A: The idea behind the DataOps methodology is to enable an organization to build and expand data pipelines and analytics. Following data governance and the collaborative data management practice will enable the delivery of high-quality business data.

Q: What is the difference between ad-hoc data analysis and DataOps?
A: Ad-hoc analysis is a process during which data analysis is curated and created by users on demand or when needed. This produces highly customized reports which are created to answer highly specific questions. It was a popular technique back in the day, but the reports were only used for the intended purpose.
DataOps incorporates Agile Development practices to bring data suppliers and consumers closer to getting more accurate data. The end-to-end cycle of data analysis is shorter. The automated deployment of infrastructure, application code, and software requires continuous integration and delivery practices for data analytics, as well as a skilled and proficient team. It reduces the time of operational analytics and improves the current analytics with less effort.

Q: How does DataOps help deal with Business Analytics challenges?
A: DataOps processes record all ad-hoc workflows as code by continuously testing information. The information is validated and confirmed before it goes through the data pipelines and undergoes impact reviews. DataOps ensures the intellectual property is kept shared, reviewed, enhanced, and reused over again.
To put it shortly, DataOps requires less maintenance and rework. There is a quicker time-to-insight of ad-hoc prompts and enablement of business analytics, not a replacement.

Q: The difference between DataOps and Data Science?
A: Data Science is the study of data like numbers, text, etc., and its goal is to gain knowledge and insight from this data. It’s a combination of tools, algorithms, and learning principles to find patterns from raw data.
On the other hand, DataOps combines and maintains data and workflows. It aims to create data symbiosis and dependability.

Q: What is the role of DataOps in Data Science?
A: DataOps enables efficient foreseeing of data and eliminates time-consuming data science algorithms by automating the process. It pushes flexibility and reusability to fit new use cases, and design-governed data allows the automatic implementation of policies in new pipelines. DataOps prevents unforeseen issues because data consumption is secure and easy.

Questions about DataOps?

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