Data Lakes: A Survey of Concepts and Architectures
Abstract
:1. Introduction
- We provide a thorough explanation of the data-lake definition and concept.
- We provide a detailed overview of the differences between both data warehouses and data lakes.
- We review and categorize existing data-lake solutions based on their architectures.
- We construct a chronological timeline graph to illustrate the evolution of data-lake architectures.
- We explore the significance of data lakes in modern data architecture, the challenges they pose, and future data-lake development.
2. Review Methodology
- Research question formulation: We defined the primary research question as “What are the major types of data-lake architectures that have been proposed and implemented, and how have they evolved over time?”
- Literature search: A comprehensive search was conducted using academic databases including IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar. These databases were chosen for their extensive coverage of relevant literature:
- IEEE Xplore: Provides a vast collection of technical literature in electrical engineering, computer science, and electronics, essential for research on data-lake architectures.
- ACM Digital Library: Contains a comprehensive collection of full-text articles and bibliographic records in computing and information technology.
- ScienceDirect: Offers access to a wide range of scientific and technical research, including key journals and conference proceedings.
- Google Scholar: Ensures broad coverage across disciplines and indexes a variety of academic publications.
Search terms included “data lake architecture”, “data lake design”, “data lake implementation”, and related keywords. The search covered publications from 2008 to 2024 to capture the full evolution of data-lake concepts. The year 2008 was chosen as the starting point as it marks the early development of data-lake technology, and the period up to 2024 includes the latest advancements and implementations in the field. - Study selection: Inclusion criteria were applied to select relevant papers that specifically discussed data-lake architectural models, implementations, or evaluations. Exclusion criteria filtered out papers that only mentioned data lakes tangentially or did not provide substantive architectural details.
- Data extraction: Key information was extracted from each selected paper, including the proposed architecture type, key components, advantages, limitations, and use cases. A standardized data extraction form was used to ensure consistency.
- Architectural diagram standardization: To facilitate easy evaluation and comparison, the extracted architecture information was sketched into new diagrams using consistent notations across all architectures. This standardization process ensured that all architectural representations followed a uniform format, making it easier to identify similarities, differences, and trends across various data-lake designs.
- Quality assessment: The selected papers were evaluated for quality based on criteria such as clarity of architectural description, empirical evidence provided, and relevance to practical implementation.
- Data synthesis: The extracted information was synthesized to identify major categories of data-lake architectures, their defining characteristics, and trends in their development over time. A chronological analysis was conducted to map the evolution of architectural approaches.
- Critical analysis: The strengths, weaknesses, and applicability of different architectural models were critically analyzed. Comparisons were made between different approaches to highlight their relative merits and limitations.
- Findings compilation: The key findings from the analysis were compiled, including a classification of major data-lake architecture types, a timeline of architectural evolution, and insights into the drivers of architectural changes over time.
3. Data-Lake Definition and Characteristics
4. Literature Review
5. Findings
5.1. Distinguish Data Lake and Data Warehouse
5.2. Data Lake Architecture Classification
5.2.1. Mono-Zone Architecture
5.2.2. Lambda Architecture
5.2.3. Kappa Architecture
5.2.4. Data-Pond Architecture
5.2.5. Zone-Based Architecture
5.2.6. Multi-Zone Functional Architecture
5.2.7. Functional Data Lake architecture
5.2.8. Data Lakehouse architecture
6. Timeline of Data-Lake Architecture Development
6.1. Dl Architecture 1.0—Scalable Data Storage (2008–2010)
6.2. Dl Architecture 2.0—Real-Time Data Processing (2011–2014)
6.3. Dl Architecture 3.0—Data Organization & Governance (2015–2018)
6.4. Dl Architecture 4.0—Modern Data Platform (2019–Present)
7. Conclusions
7.1. Significance of Data Lakes in Modern Data Architecture
7.2. Data Lake Challenges
7.3. Future Data Lake Developments Trends
7.4. Future Research Directions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Attribute | Data Warehouse | Data Lake |
---|---|---|
Schema | Schema-on-read | Schema-on-write |
Data Type | Structured, processed data from operational databases, applications and transactional systems | Structured, Semi-Structured and Unstructured data from sensors, apps, websites, etc. |
Workload | Support batch processing as well as thousands of concurrent users performing interactive analytics | Support batch and stream processing, plus an improved capability over data warehouse to support big-data inquiries from users |
Data storage | Relational database | HDFS, NoSQL, relational database |
Data preparation | Aggregated | On the fly |
Data integration | Quality control, filtering | No treatment |
Agility | Less agile and has fixed configuration compared with data lakes | Highly agile and can configure and reconfigure as needed |
Data Granularity | Data at the summary or aggregated level of detail | Data at a low level of detail or granularity |
Cost/Efficiency | Efficiently uses CPU/IO but high storage and processing costs | Efficiently uses storage and processing capabilities at very low cost |
Tools | Mostly commercial tools | Can use open-source tools such as Hadoop or Map Reduce |
Architecture | Advantages | Disadvantages | Use Cases |
---|---|---|---|
Mono-zone Architecture [30] |
|
|
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Lambda Architecture [6] |
|
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Kappa Architecture [25] |
|
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Data-Pond Architecture [26] |
|
| |
Zone-Based Architecture [27,28,29,31,32] |
|
| |
Functional Data Lake Architecture [49] |
|
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Multi-Zone Functional Architecture [30] |
|
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Data Lakehouse Architecture [34] |
|
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Azzabi, S.; Alfughi, Z.; Ouda, A. Data Lakes: A Survey of Concepts and Architectures. Computers 2024, 13, 183. https://doi.org/10.3390/computers13070183
Azzabi S, Alfughi Z, Ouda A. Data Lakes: A Survey of Concepts and Architectures. Computers. 2024; 13(7):183. https://doi.org/10.3390/computers13070183
Chicago/Turabian StyleAzzabi, Sarah, Zakiya Alfughi, and Abdelkader Ouda. 2024. "Data Lakes: A Survey of Concepts and Architectures" Computers 13, no. 7: 183. https://doi.org/10.3390/computers13070183
APA StyleAzzabi, S., Alfughi, Z., & Ouda, A. (2024). Data Lakes: A Survey of Concepts and Architectures. Computers, 13(7), 183. https://doi.org/10.3390/computers13070183