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

AltexSoft

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. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.

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Demystifying Modern Data Platforms

Cloudera

Mark: While most discussions of modern data platforms focus on comparing the key components, it is important to understand how they all fit together. The high-level architecture shown below forms the backdrop for the exploration. The data products are packaged around the business needs and in support of the business use cases.

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Modernizing Data Warehousing with Snowflake and Hybrid Data Vault

Snowflake

With Snowflake’s support for multiple data models such as dimensional data modeling and Data Vault, as well as support for a variety of data types including semi-structured and unstructured data, organizations can accommodate a variety of sources to support their different business use cases.

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Moving Past ETL and ELT: Understanding the EtLT Approach

Ascend.io

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

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15+ Must Have Data Engineer Skills in 2023

Knowledge Hut

With a plethora of new technology tools on the market, data engineers should update their skill set with continuous learning and data engineer certification programs. What do Data Engineers Do? Technical Data Engineer Skills 1.Python Knowing how to work with key-value pairs and object formats is still necessary.

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5 Reasons Data Discovery Platforms Are Best For Data Lakes

Monte Carlo

Data Catalogs Can Drown in a Data Lake Although exceptionally flexible and scalable, data lakes lack the organization necessary to facilitate proper metadata management and data governance. Data discovery tools and platforms can help. Interested in learning how to scale data discovery across your data lake?

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How to Become a Big Data Engineer in 2023

ProjectPro

Becoming a Big Data Engineer - The Next Steps Big Data Engineer - The Market Demand An organization’s data science capabilities require data warehousing and mining, modeling, data infrastructure, and metadata management. Most of these are performed by Data Engineers.