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

phData: Data Engineering

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

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How to learn data engineering

Christophe Blefari

formats — This is a huge part of data engineering. Picking the right format for your data storage. The main difference between both is the fact that your computation resides in your warehouse with SQL rather than outside with a programming language loading data in memory. workflows (Airflow, Prefect, Dagster, etc.)

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Introducing Vector Search on Rockset: How to run semantic search with OpenAI and Rockset

Rockset

In the demo, you’ll see how Rockset delivers search results in 15 milliseconds over thousands of documents. Organizations have continued to accumulate large quantities of unstructured data, ranging from text documents to multimedia content to machine and sensor data. Why use vector search?

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dbt Core, Snowflake, and GitHub Actions: pet project for Data Engineers

Towards Data Science

Storage —  Snowflake Snowflake, a cloud-based data warehouse tailored for analytical needs, will serve as our data storage solution. The data volume we will deal with is small, so we will not try to overkill with data partitioning, time travel, Snowpark, and other Snowflake advanced capabilities.

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Accenture’s Smart Data Transition Toolkit Now Available for Cloudera Data Platform

Cloudera

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

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Azure Data Engineer (DP-203) Certification Cost in 2023

Knowledge Hut

The latest Azure exam from Microsoft is structured as follows: Design and implement data storage: Creating and implementing a storage structure, a partition, and a serving layer are tested in this portion (40–45%). Microsoft learning platform: Azure data engineering training is officially documented by Microsoft.

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How to Build an End to End Machine Learning Pipeline?

ProjectPro

Data Ingestion Data Processing Data Splitting Model Training Model Evaluation Model Deployment Monitoring Model Performance Machine Learning Pipeline Tools Machine Learning Pipeline Deployment on Different Platforms FAQs What tools exist for managing data science and machine learning pipelines?