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Data Lakes
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Author(s): Anjani Kumar (University of Nebraska at Omaha, USA)and Parvathi Chundi (University of Nebraska at Omaha, USA)
Copyright: 2023
Pages: 15
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch025
PurchaseView Data Lakes on the publisher's website for pricing and purchasing information. |
Abstract
Data lake (DL) technology is popular for its flexibility to handle different raw data formats at the ingestion time as well as at the time of retrieval from the data lake. It typically includes the following five layers data ingestion, staging, processed data, storage and visualization, and analytics. These five layers together provide access to seemingly infinite computation and storage resources for democratizing data access and for supporting a wide variety of analytics tasks in an enterprise. This work is going to explain the four steps approach for doing the analysis task. It will describe the three pillars for building a DL. Then, it will give a brief history of the evolution from Excel Sheet to DL. It will explain the five layers: data ingestion, staging, processed data, storage and visualization, and analytics. It will briefly explain three DL systems, Snowflake, Databricks, and Redshift, and then nine important metrics for these three DL systems will be compared.
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