Data Engineering Examples Metrics 
Xebia
DECEMBER 23, 2024
For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.
Altexsoft
JUNE 25, 2019
If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.
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Xebia
DECEMBER 23, 2024
For example, a retailer might scale up compute resources during the holiday season to manage a spike in sales data or scale down during quieter months to save on costs. For example, data scientists might focus on building complex machine learning models, requiring significant compute resources.
CIO
NOVEMBER 27, 2024
Not cleaning your data enough causes obvious problems, but context is key. “A A lot of organizations spend a lot of time discarding or improving zip codes, but for most data science, the subsection in the zip code doesn’t matter,” says Kashalikar. That’s a classic example of too much good is wasted.”
Xebia
MARCH 2, 2025
Deployment isolation: Handling multiple users and environments During the development of a new data pipeline, it is common to make tests to check if all dependencies are working correctly. Let’s see through an example. Therefore, we can just run databricks bundle deploy command, to deploy on dev target. x-cpu-ml-scala2.12
Cloudera
SEPTEMBER 17, 2020
With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.
CIO
FEBRUARY 17, 2025
For example, mapping the time taken for tasks such as rate case submissions can pinpoint where AI can streamline processes. By evaluating metrics like lead time (time to start an action) and cycle time (time spent on productive work), utilities can identify repetitive tasks that can be automated.
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