Modern data platforms are built with a dizzying number of technologies. At the same time, the expectations of your customers, analysts, data engineers, and data scientists are getting higher every day. Cloud DataOps is a managed service that helps you architect, operate, automate, and support your data platform to meet the needs of your customers.
Business teams that expect access to reliable, up-to-date data.
Product owners who are building products using data and AI.
Data scientists who want reliable access to data in all its forms.
Governance and security teams that need to eliminate data sprawl and centralize management.
Cloud DataOps is one of our most flexible services. No two customers are exactly alike, but below we list some examples of what is typically covered and not covered.
Our typical Cloud DataOps Managed Services customer is a large enterprise using many different cloud-based data technologies. Pricing for these services typically starts at about $150,000 per year and increases based on the support hours and complexity of the platform. Our smaller customers with less complexity typically opt for Snowflake Administration. All of our managed services use elastic, usage-based pricing. You pay only for the parts of the service that you use.
DataOps and CloudOps engineers have plenty in common. Both use DevOps principles of continuous integration and continuous deployment (CI/CD). Both are involved in management of infrastructure, creating best practices for automation, deploying infrastructure, configuration management, resource management, and ensuring SLAs are met.
DataOps engineers are specialists in applying CloudOps principles to next-generation technologies for analytics, machine learning, and data storage. Since data teams have specific best practices and skills for provisioning, monitoring, automation, and policies, DataOps has emerged as a specialty.
As an example, DataOps engineers often help support the entire organization to succeed with data. This means making recommendations on analytical file-formats, Spark sizing, troubleshooting and debugging pipelines, and configuration management for data warehouses. They also help to choose and manage common tooling and automation like self-service onboarding, automated access management, or centralized logging and alerting.
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