Data reliability engineering (DRE) is recently becoming the catchphrase for prominent leaders. Not only the tech executives but also leaders from top industries like finance, media, logistics, etc., are also paying attention. Everyone wants to maximize value from collected data. This is where data quality engineering comes in. Cost efficiency, errorless results, a variety of data tools, and interdependent teams are the reason behind DRE’s popularity. In this article at DZone, Alvin Lee shares how DRE is getting traction across industries.
The Dawn of Data Reliability Engineering
Is Data Reliability Engineering Necessary?
When Google created the site reliability engineering (SRE) team in 2003, its chief aim was to produce software that would be scalable with low downtime and latency. The team has received even more accountability since its inception. The same holds true for data reliability engineering. Yes, cloud warehouses and extract-load-transform (ELT) tools have indeed reduced the workload for data engineers. But when it comes to ‘lineage tracking, change management, pipeline monitoring, and cross-team communication’, data reliability engineering comes to the forefront.
How Does It Work?
Let’s say that you have a machine learning (ML) model where data is constantly fed for analysis. However, if the data system crashes, the model will show the analysis based on the data collected before the crash. With reliability engineering, you can constantly run queries to check how old the data is. For example, if the data is older than two days, an alert will be sent to you to address the issue.
What Must a DRE Do?
You must be wondering what a DRE will do after the initial queries are set. Well, systems fail every day. So, the engineer will constantly look for any issues or figure out ways to optimize the queries. A DRE must have the following skills:
- Warehousing and applications
- Concepts regarding data governance
- Knowledgeable about orchestration and monitoring tools
- Cloud computing capabilities
- Provisioning for infrastructure
- Expertise in IP networking, firewalls, VPNs, DNS, load balancing, etc.
Additionally, unit and regression testing can help the teams detect existing or discover potential issues.
Are DRE, SRE, and DevOps Linked?
SRE and DevOps are a developer’s domain and are integral to any company. Data reliability engineering is going to take center stage soon. SRE and DRE have similar approaches in that their success relies heavily on teamwork. However, DRE has an added advantage. Its method focuses on recovering from failure.
When to Invest in DRE
- You have a dedicated team for AI or ML-decision-making
- The company considers investing in data to boost business
- Clients do not have access to data because another team is handling it
To view the original article in full, visit the following link: https://dzone.com/articles/the-rise-of-the-data-reliability-engineer