Data Modeling ETL Events 
Data Science Dojo
OCTOBER 31, 2024
Key Skills Proficiency in SQL is essential, along with experience in data visualization tools such as Tableau or Power BI. Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes.
AWS Machine Learning Blog
FEBRUARY 21, 2025
Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data. Data is stored in HDFS and is accessed via Hive, which provides a tabular interface to the data and integrates with Spark SQL.
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Dataconomy
SEPTEMBER 4, 2023
Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.
phData
SEPTEMBER 27, 2024
Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.
Mlearning.ai
MAY 16, 2023
This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.
AWS Machine Learning Blog
JANUARY 5, 2024
MLOps – The architecture implements a SageMaker model monitoring pipeline for continuous model quality governance by validating data and model drift as required by the defined schedule. Whenever drift is detected, an event is launched to notify the respective teams to take action or initiate model retraining.
ODSC - Open Data Science
SEPTEMBER 27, 2023
In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach. Interested in attending an ODSC event?
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