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How To Future-Proof Your Data Pipelines

Ascend.io

Why Future-Proofing Your Data Pipelines Matters Data has become the backbone of decision-making in businesses across the globe. The ability to harness and analyze data effectively can make or break a company’s competitive edge. Resilience and adaptability are the cornerstones of a future-proof data pipeline.

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Data News — Week 24.11

Christophe Blefari

Understand how BigQuery inserts, deletes and updates — Once again Vu took time to deep dive into BigQuery internal, this time to explain how data management is done. Pandera, a data validation library for dataframes, now supports Polars. This is Croissant. It's inspirational.

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Streamline Data Pipelines: How to Use WhyLogs with PySpark for Data Profiling and Validation

Towards Data Science

Streamline Data Pipelines: How to Use WhyLogs with PySpark for Effective Data Profiling and Validation Photo by Evan Dennis on Unsplash Data pipelines, made by data engineers or machine learning engineers, do more than just prepare data for reports or training models. So let’s dive in!

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Complete Guide to Data Transformation: Basics to Advanced

Ascend.io

Filling in missing values could involve leveraging other company data sources or even third-party datasets. The cleaned data would then be stored in a centralized database, ready for further analysis. This ensures that the sales data is accurate, reliable, and ready for meaningful analysis.

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Data Engineering Weekly #206

Data Engineering Weekly

DeepSeek development involves a unique training recipe that generates a large dataset of long chain-of-thought reasoning examples, utilizes an interim high-quality reasoning model, and employs large-scale reinforcement learning (RL). Many articles explain how DeepSeek works, and I found the illustrated example much simpler to understand.

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The Challenge of Data Quality and Availability—And Why It’s Holding Back AI and Analytics

Striim

Many organizations struggle with: Inconsistent data formats : Different systems store data in varied structures, requiring extensive preprocessing before analysis. Siloed storage : Critical business data is often locked away in disconnected databases, preventing a unified view. Heres how they are tackling these issues: 1.

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Data Validation Testing: Techniques, Examples, & Tools

Monte Carlo

The Definitive Guide to Data Validation Testing Data validation testing ensures your data maintains its quality and integrity as it is transformed and moved from its source to its target destination. It’s also important to understand the limitations of data validation testing.