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.
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.
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.
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.
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.
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 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:
- Data orchestration automates entire data workflows, from ingestion, to transformation, to delivery
- Auto-syncing developer source code with main repository
- Data pipelines pushed live into production with one-click deployments
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.
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.
Questions about DataOps or what it can do for your company?
Rivery is a fully-managed DataOps platform for all your organizational data. Automate, manage, and transform data so it can be fed back to stakeholders as meaningful insights. Rivery equips your organization with all the capabilities you need to conquer the modern data landscape, including: