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Tips to Build a Robust Data Lake Infrastructure

DareData

Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals. In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently.

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

Knowledge Hut

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB. GDPR, HIPAA), and industry standards.

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Forge Your Career Path with Best Data Engineering Certifications

ProjectPro

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

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Top 14 Azure Tools You Must Know in 2023

Knowledge Hut

It is one of the best Azure Dev tools for teaching that offers advanced contextual visualization for Azure-based applications. Be it accessing users in the Azure role-based access control, governing user actions, or merely monitoring user profiles – Serverless360 can do it all. Pros Best User Interface.

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Data Vault on Snowflake: Feature Engineering and Business Vault

Snowflake

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

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Turning Streams Into Data Products

Cloudera

Faster data ingestion: streaming ingestion pipelines. Reduce ingest latency and complexity: Multiple point solutions were needed to move data from different data sources to downstream systems. Without context, streaming data is useless.” What is Kafka blindness? Who is affected?

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