Remove Amazon Web Services Remove Cloud Storage Remove Data Ingestion Remove Structured Data
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Most important Data Engineering Concepts and Tools for Data Scientists

DareData

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. Airflow is written in Python and has a web-based user interface for managing and monitoring pipelines.

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

We continuously hear data professionals describe the advantage of the Snowflake platform as “it just works.” Snowpipe and other features makes Snowflake’s inclusion in this top data lake vendors list a no-brainer. AWS is one of the most popular data lake vendors. A picture of their Lake Formation architecture.

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15+ Best Data Engineering Tools to Explore in 2023

Knowledge Hut

It provides a flexible data model that can handle different types of data, including unstructured and semi-structured data. Key features: Flexible data modeling High scalability Support for real-time analytics 4. Key features: Instant elasticity Support for semi-structured data Built-in data security 5.

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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

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

ProjectPro

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes: people_positive_cases_count county_name case_type data_source Language Used: Python 3.7 Machines and humans are both sources of structured data.