Databases Vs Data Warehouses Vs Data Lakes - Key Differences and Importance

Name: Databases Vs Data Warehouses Vs Data Lakes - Key Differences and Importance
Uploaded: 2023-06-20 11:00:16
Description: Databases, data warehouses, and data lakes serve different purposes, with databases being efficient for transactions and data warehouses and data lakes being designed for analytics, making it challenging for systems to effectively handle both functions.

This article is a summary of a YouTube video "Databases Vs Data Warehouses Vs Data Lakes - What Is The Difference And Why Should You Care?" by Seattle Data Guy

TLDR Databases, data warehouses, and data lakes serve different purposes, with databases being efficient for transactions and data warehouses and data lakes being designed for analytics, making it challenging for systems to effectively handle both functions.

Key insights

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Understanding the differences between databases, data warehouses, and data lakes is crucial in selecting the right tool for specific needs and tasks.
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Data warehouses, on the other hand, are designed to store data in a columnar format, which is more suitable for analytical queries and data aggregation.
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Bill Inman defined a data warehouse as a subject-oriented, non-volatile, integrated, time variant collection of data in support of management decisions.
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A data warehouse allows you to connect different domains and answer questions that span across various data sources, providing a broader understanding of your business.
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"If you're looking for a data warehouse specific system, you can also look at something like teradata or vertica if you're doing something more on-prem."
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Data warehouses like datavaults offer more flexibility compared to traditional databases, allowing for easier data changes without the need for alter statements.
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Data warehouses transform messy and complex data from traditional databases into a more structured and simplified format, making it easier for analysts to work with and generate reports and dashboards.
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"None are necessarily better than the others, they really all serve different purposes."

Timestamped Summary

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00:00
Databases are for transactions, while data warehouses and data lakes are for analytics.
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00:58
Databases are efficient for transactions but face challenges when used for analytics.
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03:09
Data warehouses are collections of data used for management decisions, with multiple sources feeding into them, while running SQL directly on production databases can be risky due to potential slowdowns and lack of historical information.
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04:30
A data warehouse is a centralized location that integrates databases and applications, enabling comprehensive reporting and the ability to answer cross-domain questions, while databases only store current data, which can be limiting for analytical purposes.
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06:56
Data warehouses use a snowflake or Star schema model, store data in columns for improved performance, and can be built on platforms like Postgres or Microsoft SQL server, or more specific options like Redshift, Snowflake, Teradata, or Vertica.
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08:47
Data warehouses are the core production environment for transforming messy data into a format that is easy to work with and understand, enabling reporting and data accessibility for analysts and leadership.
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11:24
Data lakes are a flexible storage solution for constantly changing data, allowing for immediate access and processing before potentially transferring it to a data warehouse, with different users at each level and the need for technical skills to access unstructured data.
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13:43
Databases, data warehouses, and data lakes all serve different purposes, with databases being geared for transactions and analytical systems designed for analytics, making it difficult for systems to do both effectively.