WHAT WE DO
Data Ops:
Improve trust in data
Introduce a stable and reliable data landscape
DataOps, or Data Operations, is an agile, process-oriented methodology aimed at improving the speed, quality, and reliability of data engineering and data analytics. It combines practices from DevOps, Agile development, and Lean manufacturing to create an integrated and collaborative approach to managing data workflows and infrastructure.
The key aspects of great Data Ops
DataOps aims to deliver high-quality data and analytics solutions quickly and efficiently, supporting better decision-making and business outcomes. The key aspects of Data Ops are:
Automation: Automating data pipelines to ensure consistent and error-free data processing and integration.
Collaboration: Promoting collaboration among data engineers, data scientists, and other stakeholders to streamline workflows and improve communication.
Continuous Integration and Continuous Deployment (CI/CD): Implementing CI/CD practices to ensure that changes in data and analytics models are tested and deployed rapidly and reliably.
Monitoring and Quality Control: Continuously monitoring data quality and system performance to quickly identify and resolve issues.
Scalability: Designing data workflows and infrastructure to scale efficiently with increasing data volumes and complexity.
Version Control: Using version control systems to manage changes in data schemas, transformation logic, and analytics models.
Feedback Loops: Incorporating feedback loops to learn from operational metrics and user feedback, enabling continuous improvement.
How Dataminded installs Data Ops
We help you implement technology to support your Data Ops needs, but more importantly, we design and install processes around Data Ops. We always introduce version control, even for notebooks. When leaving the experimentation phase, the first things that we will do is setting up CI/CD, and implementing a testing framework. We will guide your engineers not only to comply with these best-practices, but also to truly benefit from them.
When hitting production for the first time, monitoring and quality control gains importance. We will help you mature your data initiatives by offering data quality templates and establishing relevant monitoring of your data platform and data pipelines. Thinking of large scale use-cases? No worries, with our extensive knowledge on dynamic scaling, we will cover your needs.
From the start of the initial use-cases, we will introduce processes to continuously capture feedback. This is core to our data product mindset: we really believe understanding our consumers' needs is utmost important. When scaling to multiple use-cases, we risk becoming a bottleneck. That is not something we want to be. We therefor focus on collaboration with multiple profiles: data engineers, data scientists, end consumers. We enable all of them to become self-proficient.
Data Ops
Get started today
As of today, together with Dataminded, you can mature your data operations!