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Data Warehouse vs Data Lake vs Data Lakehouse: Definitions, Similarities, and Differences

Monte Carlo

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

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Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

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Now in Public Preview: Processing Files and Unstructured Data with Snowpark for Python

Snowflake

Previously, working with these large and complex files would require a unique set of tools, creating data silos. Now, with unstructured data processing natively supported in Snowflake, we can process netCDF file types, thereby unifying our data pipeline. Mike Tuck, Air Pollution Specialist Why unstructured data?

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How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

Data Loading : Load transformed data into the target system, such as a data warehouse or data lake. In batch processing, this occurs at scheduled intervals, whereas real-time processing involves continuous loading, maintaining up-to-date data availability. A typical data ingestion flow.

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The Evolution of Table Formats

Monte Carlo

Depending on the quantity of data flowing through an organization’s pipeline — or the format the data typically takes — the right modern table format can help to make workflows more efficient, increase access, extend functionality, and even offer new opportunities to activate your unstructured data.

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Data Engineering Weekly #161

Data Engineering Weekly

link] Zendesk: dbt at Zendesk The Zendesk team shares their journey of migrating legacy data pipelines to dbt, focusing on making them more reliable, efficient, and scalable. The article also highlights sink-specific improvements and operator-specific enhancements that contribute to the overall performance boost.