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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

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Power BI Developer Roles and Responsibilities [2023 Updated]

Knowledge Hut

Data Transformation and ETL: Handle more complex data transformation and ETL (Extract, Transform, Load) processes, including handling data from multiple sources and dealing with complex data structures. Ensure compliance with data protection regulations. Define data architecture standards and best practices.

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ELT Explained: What You Need to Know

Ascend.io

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Extract The initial stage of the ELT process is the extraction of data from various source systems. Read More: Zero ETL: What’s Behind the Hype?

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

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50 Artificial Intelligence Interview Questions and Answers [2023]

ProjectPro

Data Integration at Scale Most data architectures rely on a single source of truth. Having multiple data integration routes helps optimize the operational as well as analytical use of data. Having multiple data integration routes helps optimize the operational as well as analytical use of data.