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See clearly, spend wisely: The power of data platform observability

Xebia

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.

Data 130
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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

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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See clearly, spend wisely: The power of data platform observability

Xebia

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.

Data 130
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When is data too clean to be useful for enterprise AI?

CIO

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.”

Data 211
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Simplify your workflow deployment with Databricks Asset Bundles: Part II

Xebia

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

Resources 130
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Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

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.

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How GoDaddy built a category generation system at scale with batch inference for Amazon Bedrock

AWS Machine Learning - AI

This post was co-written with Vishal Singh, Data Engineering Leader at Data & Analytics team of GoDaddy Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) in these solutions has become increasingly popular.