What is a Data Lake? Understanding the Buzzword

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In summary, a data lake is a system or repository that stores data in its natural/raw format and can include various types of data such as structured, semi-structured, unstructured, and binary data. It can be used for reporting, visualization, advanced analytics, and machine learning. However, there is no clear consensus on what exactly a data lake is and it can be interpreted differently by different authors. Some see it as a synonym for ETL, others as a distributed file system or NoSQL database with additional tools. It is often considered a buzzword and can have vague or misleading definitions, making it difficult to fully understand.
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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 :)
 
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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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What is a Data Lake?

A Data Lake is a centralized repository where large amounts of raw data are stored in its native format. It is designed to hold vast quantities of structured, semi-structured, and unstructured data, making it a flexible storage solution for Big Data.

Why is it called a Data Lake?

It is called a Data Lake because it is designed to store data in its raw form, without any transformations or alterations. This is similar to a lake where water flows in and out, and it is up to the person using it to determine its purpose. Similarly, in a Data Lake, data can be stored, processed, and analyzed according to the user's needs.

What is the difference between a Data Lake and a Data Warehouse?

A Data Lake and a Data Warehouse serve different purposes. A Data Warehouse is used to store structured data that has already been processed for analysis. On the other hand, a Data Lake stores both structured and unstructured data in its raw form, making it more flexible for different types of data analysis. Additionally, a Data Lake is typically less expensive to set up and maintain than a Data Warehouse.

How is data stored in a Data Lake?

Data is stored in a Data Lake in its native format, without any pre-defined structure or organization. This allows for more flexibility in storing different types of data. Data Lakes also use distributed file systems, such as Hadoop, to store data across multiple servers, making it easier to scale and handle large amounts of data.

What are the benefits of using a Data Lake?

Some of the benefits of using a Data Lake include the ability to store and analyze large amounts of data in its raw form, making it more flexible for different types of analysis. It also allows for easier data integration from different sources, and the use of distributed file systems allows for scalability and cost-effectiveness. Additionally, Data Lakes can be used for advanced analytics, such as machine learning and artificial intelligence, to gain valuable insights from the data.

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