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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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A Data Mesh Implementation: Expediting Value Extraction from ERP/CRM Systems

Towards Data Science

ERP and CRM systems are designed and built to fulfil a broad range of business processes and functions. This generalisation makes their data models complex and cryptic and require domain expertise. As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly.

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Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape.

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Monte Carlo Announces Delta Lake, Unity Catalog Integrations To Bring End-to-End Data Observability to Databricks

Monte Carlo

To help organizations realize the full potential of their data lake and lakehouse investments, Monte Carlo, the data observability leader, is proud to announce integrations with Delta Lake and Databricks’ Unity Catalog for full data observability coverage. billion in 2020 to 17.60 billion in 2020 to 17.60

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Data Engineering Zoomcamp – Data Ingestion (Week 2)

Hepta Analytics

We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. When the business intelligence needs change, they can go query the raw data again. The purpose of the data is not determined.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

Faster data processing: By automating data workflows and leveraging modern data processing technologies, DataOps architecture accelerates data ingestion, transformation, and analysis. They include the various databases, applications, APIs, and external systems from which data is collected and ingested.

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Demystifying Modern Data Platforms

Cloudera

The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms.