A Quick Look at Data Lake Architecture

Are you struggling with managing and analyzing large amounts of data? Look no further than data lake architecture- A centralized repository.

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What is Data Lake?

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– Simplify data analysis & generate insights. – Offer fast query processing, scalability, & cleaner data for BI tools. – Handle various types of data (images, videos, traditional sources like ERP).

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A step-by-step process for designing and maintaining a data lake to retain data without a predefined structure, with emphasis on monitoring and security.

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1. Ingestion Layer Responsible for processing raw data into data inside the data lake, without modifying the raw data. It can be either a front-end or back-end depending on the application requirements.

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2. Distillation Layer Transforms structured data into an ingestible form for Ingestion Layer, known as data cleansing or purging. Once transformed, data is clear-cut & formatted, ready for business users.

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3. Processing Layer It involves designing data stores & analytical tools, determining which parts of the system will execute complex queries, & transforming structured data into usable information.

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How does Data Lake differ from Data warehouse?

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A Quick Look at Data Lake Architecture

Unleash the power of data...

Are you struggling with managing and analyzing large amounts of data? Look no further than data lake architecture- A centralized repository.

What is Data Lake?

A modern approach to data management that allows you to store large amounts of structured and unstructured data in its native format, making it easier to analyze and access.

– Simplify data analysis & generate insights. – Offer fast query processing, scalability, & cleaner data for BI tools. – Handle various types of data (images, videos, traditional sources like ERP).

Why Data Lake?

A step-by-step process for designing and maintaining a data lake to retain data without a predefined structure, with emphasis on monitoring and security.

Data Lake Architecture

1. Ingestion Layer Responsible for processing raw data into data inside the data lake, without modifying the raw data. It can be either a front-end or back-end depending on the application requirements.

Key Components of Data Lake Architecture

2. Distillation Layer Transforms structured data into an ingestible form for Ingestion Layer, known as data cleansing or purging. Once transformed, data is clear-cut & formatted, ready for business users.

3. Processing Layer It involves designing data stores & analytical tools, determining which parts of the system will execute complex queries, & transforming structured data into usable information.

How does Data Lake differ from Data warehouse?

Check out our detailed guide for a deep dive into the world of Data Lakes.

Don't miss out on the chance to upskill yourself with IntervewBit's engaging web stories.

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