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Snowflake’s AWS re:Invent Highlights for Fast-Tracking ML, Gen AI and Application Innovations 

Snowflake

We had a jam-packed week alongside more than 60,000 attendees at Amazon Web Services (AWS) re:Invent, one of the largest hands-on conferences in the cloud computing industry. Engaging with partners and customers — and showcasing what’s new on the Snowflake product front — made for a dynamic time in Las Vegas.

AWS 101
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Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]

Knowledge Hut

They work together with stakeholders to get business requirements and develop scalable and efficient data architectures. Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance.

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Snowflake Architecture and It's Fundamental Concepts

ProjectPro

Traditional data preparation platforms, including Apache Spark, are unnecessarily complex and inefficient, resulting in fragile and costly data pipelines. Multi-Cloud Support- Snowflake is a fully managed data warehouse deployed across various clouds while maintaining the same intuitive user interface.

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How to Become a Big Data Engineer in 2023

ProjectPro

Big Data Engineer performs a multi-faceted role in an organization by identifying, extracting, and delivering the data sets in useful formats. A Big Data Engineer also constructs, tests, and maintains the Big Data architecture. Your organization will use internal and external sources to port the data.

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20 Solved End-to-End Big Data Projects with Source Code

ProjectPro

There are open data platforms in several regions (like data.gov in the U.S.). These open data sets are a fantastic resource if you're working on a personal project for fun. Data Preparation and Cleaning The data preparation step, which may consume up to 80% of the time allocated to any big data or data engineering project, comes next.

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50 Artificial Intelligence Interview Questions and Answers [2023]

ProjectPro

This would include the automation of a standard machine learning workflow which would include the steps of Gathering the data Preparing the Data Training Evaluation Testing Deployment and Prediction This includes the automation of tasks such as Hyperparameter Optimization, Model Selection, and Feature Selection.