Remove Designing Remove Metadata Remove Relational Database Remove Structured Data
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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

This data can be structured, semi-structured, or unstructured and comes from various sources such as databases, IoT devices, log files, etc. What are Data Modeling Methodologies, and Why Are They Important for a Data Lake? Want to learn more about data governance?

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

AltexSoft

The goal is to provide a comprehensive guide that can be a navigational tool for all specialists plotting their course in today’s data-driven world. What is a data lake? A data lake is a centralized repository designed to hold vast volumes of data in its native, raw format — be it structured, semi-structured, or unstructured.

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Top 10 Hadoop Tools to Learn in Big Data Career 2024

Knowledge Hut

NoSQL This database management system has been designed in a way that it can store and handle huge amounts of semi-structured or unstructured data. Avro creates binary data which can be both compressed as well as split. Avro creates a file that stores all the data and saves the schema in the metadata section.

Hadoop 52
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Data Engineering Glossary

Silectis

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of structured data.

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How Windward Built Real-Time Logistics Tracking and AI Insights for the Maritime Industry

Rockset

This enrichment data has changing schemas and new data providers are constantly being added to enhance the insights, making it challenging for Windward to support using relational databases with strict schemas. They used MongoDB as their metadata store to capture vessel and company data.

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A Definitive Guide to Using BigQuery Efficiently

Towards Data Science

Summary ∘ Embrace data modeling best practices ∘ Master data operations for cost-effectiveness ∘ Design for efficiency and avoid unnecessary data persistence Disclaimer : BigQuery is a product which is constantly being developed, pricing might change at any time and this article is based on my own experience.

Bytes 72
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An Engineering Guide to Data Creation - A Data Contract perspective - Part 1

Data Engineering Weekly

Architectural patterns for Data Creation There are three types of architecture patterns in data creation. Event Sourcing Change Data Capture [CDC] Outbox pattern 1. Event Sourcing Event sourcing is a system design pattern that writes the current state of the business process into a journal of records.