Using DevOps to improve the operation of a data lake
Our client, a Quebec leader in the insurance industry, has implemented a data lake to centralize a huge volume of structured and unstructured data and strengthen their business intelligence. The operation of a data lake requires different technologies, different skills, and our client found that their teams tended to work in silos. How can we foster good team collaboration and thus maximize the input of the data lake?
Our challenge: breaking the silos
From development to data consumption, teams must work hand in hand, which is not always spontaneous in data lake operation. The DevOps methodology aims to get teams of experts to collaborate and bring value from the data lake more quickly. By applying the DevOps methodology, we were involved in :
- The selection of tools used by different teams (Cloud, Snowflake, Azure and Power BI),
- The implementation of good practices and methods of collaboration between departments,
- Process automation.
The Key to Success: Choosing the right tools and best practices
The DevOps methodology's successful application is mainly based on the teams' willingness to go further and get out of their comfort zone. It is by identifying the right tools and best practices that this collaboration is made possible. And the success of this mandate was also reinforced by the contribution of Cloud technologies and the agile methodology.
You could compare the different technical teams in a company to bricks laid side by side. DevOps would be like the cement that unifies these bricks with a set of tools, methods, and best practices.
The benefits of the DevOps methodology
DevOps is a facilitator, as part of the operation of a data lake, it contributes to:
- Reduced delivery time,
- Faster delivery of value,
- A better solution for your needs,
- Automation and continuous improvement of the processes and the solution,
- Improved reliability.
Technological environment
- Azure DevOps
- Terraform
- Snowchange
- Snowflake