DataOps
Achieve data agility and eliminate data delivery bottlenecks
As data and analytics teams are crucial to supporting more diverse and complex business processes, many face challenges in delivering the data required to support a range of consumers and use cases. There is more pressure to deliver faster, with higher quality and resiliency in a continuously evolving digital landscape.
Traditional practices and tools are simply not able to deal with today’s explosive data growth. They create data bottlenecks and silos that are complex to manage, face compliance challenges and increase data management costs.
As data and analytics teams are crucial to supporting more diverse and complex business processes, many face challenges in delivering the data required to support a range of consumers and use cases. There is more pressure to deliver faster, with higher quality and resiliency in a continuously evolving digital landscape.
Traditional practices and tools are simply not able to deal with today’s explosive data growth. They create data bottlenecks and silos that are complex to manage, face compliance challenges and increase data management costs.
Bringing DevOps to the world of Data
DataOps is the evolution of data management and is defined as the alignment of people, processes, and technology to enable the fast, automated, and secure delivery and management of data within an organization.
DataOps incorporates the Agile methodology and DevOps principles to the collaborative development and maintenance of data and analytical pipelines in order to produce trusted, reusable and integrated data and analytical products.
It enables agile data collaboration driving speed and scale of operations and analytics throughout the data lifecycle, delivering purpose-fit data to data analysts, testers, developers and operations, applications and artificial intelligence fast. In short, it creates an environment where “faster, better, cheaper” is the norm, not the exception.
It enables agile data collaboration driving speed and scale of operations and analytics throughout the data lifecycle, delivering purpose-fit data to data analysts, testers, developers and operations, applications and artificial intelligence fast. In short, it creates an environment where “faster, better, cheaper” is the norm, not the exception.
DataOps is mainly defined by the following principles:
- ELT (extract, load, transform)
- Continuous Integration / Continuous Deployment (CI/CD) Orchestration
- Ease of use and maintenance
- Environment management
- Data Governance and change control
- Automated testing and monitoring
- Collaboration and Data Sharing
- Self-service, on demand access
Accelerate Releases with Fast, Secure, Quality Data
Validata’s DataOps and AI platform, ConnectIQ, capabilities help create an analytics foundation combining AI-enabled data-driven automation, data governance, data protection, and delivery of high-quality data across the business. These capabilities help increase efficiency, productivity and data quality, in order to provide a self-service data pipeline to the right people at the right time from any data source.
Get Your Data to the Right Place at the Right Time
Increased collaboration and data sharing
Collaboration between development, business and data teams will increase the agility of the business and avoid the creating inconsistent processes that are hard to maintain. Using DataOps we have seen a team of four people complete 200 cycles, 80 commits and 50 pushes to production in just one day.
Increased efficiencies
DataOps removes the need for operational teams’ manually managing development, test and production infrastructure.
Reduced costs
By shortening the time to production and recovery, businesses can reduce costs by more than 70% - and that’s before we measure the additional value from their data analytics.
Maintainability and total cost of ownership
Don't just think about the time and cost to implement a feature. Also think about the long-term support and maintainability of code and configuration and the effort needed to perform routine tasks. DataOps streamlines all of these, so Total Cost of Ownership (TCO) can be reduced by over 60%.
Data Quality
Improve the quality of the data you deliver to your business for better data-driven decision making.
Simplified orchestration and management:
You can store all data together, creating more flexible, more useful use cases at lower cost across your CI/CD pipeline.
Faster development and testing
Faster access to quality data, increases the agility of data processes and team productivity and reduces the long data provisioning times.
Build once and reuse
Data is created with clicks, not code and is reusable across use cases.
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