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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 Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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How to Choose the Right Data Management Solution

The Modern Data Company

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

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How to Choose the Right Data Management Solution

The Modern Data Company

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

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How to Choose the Right Data Management Solution

The Modern Data Company

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Data lakes offer a scalable and cost-effective solution.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

DataOps Architecture Legacy data architectures, which have been widely used for decades, are often characterized by their rigidity and complexity. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

Data pipelines can handle both batch and streaming data, and at a high-level, the methods for measuring data quality for either type of asset are much the same. In many ways, the cloud makes data easier to manage, more accessible to a wider variety of users, and far faster to process.