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Google BigQuery Architecture for Data Engineers

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Google’s BigQuery is an enterprise-grade cloud-native data warehouse. BigQuery was first launched as a service in 2010, with general availability in November 2011.

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7 Resources to Becoming a Data Engineer

KDnuggets

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

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Top 9 AI conferences and events in USA – 2023

Data Science Dojo

A Glimpse into the future : Want to be like a scientist who predicted the rise of machine learning back in 2010? Data Observability : It emphasizes the concept of data observability, which involves monitoring and managing data systems to ensure reliability and optimal performance. Link to event -> Live!

AI 244
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Unlock ML insights using the Amazon SageMaker Feature Store Feature Processor

AWS Machine Learning Blog

It lets engineers provide simple data transformation functions, then handles running them at scale on Spark and managing the underlying infrastructure. This enables data scientists and data engineers to focus on the feature engineering logic rather than implementation details. Group by model_year_status.

ML 112
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How Do I Integrate Snowflake Security With My Enterprise Security Strategy?

phData

The OAuth framework was initially created and supported by Twitter, Google, and a few other companies in 2010 and subsequently underwent a substantial revision to OAuth 2.0 This allows you to define what your user’s resources should look like and automatically generate (and execute) the Snowflake SQL necessary to create those users.

SQL 52
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Analyzing the history of Tableau innovation

Tableau

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 145
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Analyzing the history of Tableau innovation

Tableau

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Another key data computation moment was Hyper in v10.5 (Jan

Tableau 98