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Data logs: The latest evolution in Meta’s access tools

Engineering at Meta

Here we explore initial system designs we considered, an overview of the current architecture, and some important principles Meta takes into account in making data accessible and easy to understand. Users have a variety of tools they can use to manage and access their information on Meta platforms. What are data logs?

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Simplifying BI pipelines with Snowflake dynamic tables

ThoughtSpot

When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. These tables provide a centralized location to host both your raw data and transformed datasets optimized for AI-powered analytics with ThoughtSpot. Set refresh schedules as needed.

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Data Integrity for AI: What’s Old is New Again

Precisely

(Not to mention the crazy stories about Gen AI making up answers without the data to back it up!) Are we allowed to use all the data, or are there copyright or privacy concerns? These are all big questions about the accessibility, quality, and governance of data being used by AI solutions today. A data lake!

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5 Big Data Challenges in 2024

Knowledge Hut

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

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Data News — Week 23.16

Christophe Blefari

Access — you will be able to namespace models with groups and visibility. Data Engineering at Adyen — "Data engineers at Adyen are responsible for creating high-quality, scalable, reusable and insightful datasets out of large volumes of raw data" This is a good definition of one of the possible responsibilities of DE.

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Future Proof Your Career With Data Skills

Knowledge Hut

It looks like this: Data collection This part deals with the collection of raw data from various resources. All this data needs to be collected and stored in a place which is easy to access while working with the data. Data cleaning This is considered as one of the most important steps in data science.

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

Monte Carlo

The inception of the data lakehouse came about as cloud warehouse providers began adding features ordinarily associated with lakes, as seen in platforms like Redshift Spectrum and Delta Lake. Conversely, data lakes began incorporating warehouse-like features, such as including SQL functionality and schema definitions.