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Five Ways A Modern Data Architecture Can Reduce Costs in Telco

Cloudera

The way to achieve this balance is by moving to a modern data architecture (MDA) that makes it easier to manage, integrate, and govern large volumes of distributed data. When you deploy a platform that supports MDA you can consolidate other systems, like legacy data mediation and disparate data storage solutions.

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Data Warehouses vs. Data Lakes vs. Data Marts: Need Help Deciding?

KDnuggets

A comparative overview of data warehouses, data lakes, and data marts to help you make informed decisions on data storage solutions for your data architecture.

Data Lake 117
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Thoughts on Amazon Express One and its impact in Data Infrastructure

Data Engineering Weekly

The Current State of the Data Architecture S3 intelligent tiered storage provides a fine balance between the cost and the duration of the data retention. However, the real-time insight on accessing the recent data remains a big challenge. The combination of stream processing + OLAP storage like Pinot.

IT 85
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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. As a result, they can be slow, inefficient, and prone to errors.

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Open-Source Data Warehousing – Druid, Apache Airflow & Superset

Simon Späti

In my recent blog, I researched OLAP technologies, for this post I chose some open-source technologies and used them together to build a full data architecture for a Data Warehouse system. I went with Apache Druid for data storage, Apache Superset for querying and Apache Airflow as a task orchestrator.

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How Much Data Do We Need? Balancing Machine Learning with Security Considerations

Towards Data Science

I am the first senior machine learning engineer at DataGrail, a company that provides a suite of B2B services helping companies secure and manage their customer data. Data that isn’t interpretable generates little value if any, because you can’t effectively learn from data you don’t understand. Do you keep all data forever?

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

Knowledge Hut

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.