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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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How to Design a Modern, Robust Data Ingestion Architecture

Monte Carlo

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

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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 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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Reflections On Designing A Data Platform From Scratch

Data Engineering Podcast

Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. Visit them today at dataengineeringpodcast.com/timescale RudderStack helps you build a customer data platform on your warehouse or data lake. That’s Timescale. That’s Timescale.

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How CMS Evaluated and Implemented Its Security Data Lake Strategy with Snowflake

Snowflake

There were often parallel efforts to ingest, store, and normalize the same data in multiple ways. These inefficiencies created duplicative, non-universal ways to process various security data streams and resulted in security visibility issues, extra data storage costs, employee hours spent on ETL pipelines, and more.

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Unify your data: AI and Analytics in an Open Lakehouse

Cloudera

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses.