What is a Data Lake? Understanding the Buzzword

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SUMMARY

A data lake is defined as a repository that stores data in its raw format, encompassing various types such as structured, semi-structured, unstructured, and binary data. It serves as a single source for raw and transformed data utilized in reporting, visualization, advanced analytics, and machine learning. The term "data lake" is often misinterpreted, with some equating it to ETL processes, distributed file systems like Apache Hadoop HDFS, or NoSQL databases such as MongoDB. Ultimately, the definition of a data lake can vary significantly based on the author's perspective, leading to confusion and skepticism about its validity as a technology.

PREREQUISITES
  • Understanding of data storage formats (structured, semi-structured, unstructured)
  • Familiarity with data processing concepts, including ETL (Extract, Transform, Load)
  • Knowledge of distributed file systems, particularly Apache Hadoop HDFS
  • Basic comprehension of NoSQL databases, specifically MongoDB
NEXT STEPS
  • Research the architecture and use cases of data lakes in modern data analytics
  • Explore the differences between data lakes and traditional data warehouses
  • Learn about data ingestion techniques for data lakes, including batch and stream processing
  • Investigate tools for data lake management, such as AWS Lake Formation and Azure Data Lake Storage
USEFUL FOR

This discussion is beneficial for data engineers, data scientists, and IT professionals involved in data architecture and analytics, as well as anyone seeking to understand the implications of adopting data lake technologies in their organizations.

lomidrevo
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I think the basic idea is quite clear, as for example defined by wikipedia:
A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video).

But when I google more about this "technology", I am getting quite various ideas about what is considered as data lake. Some of them:
  • just a synonym to ETL approach to data processing
  • a distributed file system, like Apache Hadoop HDFS
  • NoSQL database with additional support of SQL, like for example MondogDB
  • or some proprietary architecture involving all of that and maybe some extra tools, like reporting, visualization and maybe machine learning?

How do you understand the term data lake? Is it just a buzzword?
 
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lomidrevo said:
Is it just a buzzword?
Yes. It can mean whatever the author wants it to mean.
 
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pbuk said:
Yes. It can mean whatever the author wants it to mean.
that is my current impression, thanks :)
 
Maybe the 'data lake' is the 'reservoir' that engenders and sustains the 'cloud' ##-## I think that such metaphors are used for enablement of non-rigorous semblances of understanding ##-## I have encountered use of such fanciful terms much more by marketers than by engineers.
 
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