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Journal articles on the topic 'Data Lakes'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Mathis, Christian. "Data Lakes." Datenbank-Spektrum 17, no. 3 (2017): 289–93. http://dx.doi.org/10.1007/s13222-017-0272-7.

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Abhijit, Joshi. "The Rise of Data Lakes: Best Practices for Architecture and Value Extraction." Journal of Scientific and Engineering Research 5, no. 12 (2018): 342–47. https://doi.org/10.5281/zenodo.11667542.

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This whitepaper aims to elucidate the concept and strategic importance of data lakes, providing an in-depth technical exploration suitable for executives and technical stakeholders alike. Drawing upon established best practices derived from industry leaders and seasoned data experts at Cloudera, this document intends to demystify data lakes, advocating for their adoption based on proven principles rather than transient trends. Amidst the complexity and burgeoning volumes of business and analytical data, clarity on optimal data lake architectures and actionable strategies for extracting value i

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Sainath, Muvva. "The Role of Data Lake and Delta Lake in Big Data, Machine Learning, and Artificial Intelligence." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 10, no. 6 (2022): 1–6. https://doi.org/10.5281/zenodo.14535503.

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Data Lakes and Delta Lakes are changing how we store and manage big data. Data Lakes are like huge digital buckets that can hold all kinds of information. Delta Lakes make these buckets even better by adding special features. These features help keep data organized and easy to use.This paper looks at how these new ways of handling data have grown over time. This paper compares what Data Lakes and Delta Lakes can do, especially for smart computer programs that learn and make decisions. We also look at how different businesses use these tools.By looking at the good points, tricky parts, and real

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Veernapu, Kiran. "AI Enhanced Data Quality in Data Warehouses and Data Lakes for Efficient Data-Driven Intelligence." International Scientific Journal of Engineering and Management 03, no. 07 (2024): 1–6. https://doi.org/10.55041/isjem02160.

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Data quality is paramount in data-driven decision-making processes, especially when dealing with large volumes of data in environments like data warehouses and data lakes. These systems store vast amounts of raw and processed data from multiple sources, making data management and quality assurance complex yet critical. With the growing adoption of Artificial Intelligence (AI), new techniques and tools have emerged that can significantly enhance data quality. This paper discusses how AI can improve the quality of data within both data warehouses and data lakes by automating data cleansing, vali

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Ganachari, Girish. "DATA GOVERNANCE FOR ENTERPRISE DATA LAKES." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 1 (2023): 958–61. http://dx.doi.org/10.51219/jaimld/girish-ganachari/228.

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Nandish Shivaprasad. "Strategies for Data Lakes in Financial Data Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2033–50. https://doi.org/10.32628/cseit2410612413.

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The deployment and optimization of data lakes in financial data management is investigated in this research article. Concerning an ever-growing volume and diversity of data, conventional data management technologies are showing insufficient capability for financial organizations. Providing a scalable and flexible infrastructure for storing and evaluating enormous volumes of organized and unstructured data, data lakes provide a good answer. With an eye on data governance, security, and analytics, this paper investigates many approaches for building, running, and managing data lakes in the finan

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Janelidze, Gulnara, Ia Aptsiauri, and Lela Tsitashvili. "Data lakes: Opportunities, Challenges, Threats and Ways to Mitigate Them." Journal of Technical Science and Technologies 8, no. 2 (2024): 31–35. https://doi.org/10.31578/jtst.v8i2.159.

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Data lakes, which collect and store huge amounts of structured and unstructured data, are currently one of the most important technological tools. Their structure differs from traditional databases, as they are more flexible and allow organizations to store diverse data in a single repository for further processing and analysis. Their use is advisable in many fields, ranging from business and science to public administration. However, the rapid development of data lakes presents new challenges. The paper presents the key characteristics of data lakes and data warehouses, along with a comparati

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Chandrakanth, Lekkala. "Implementing Efficient Data Versioning and Lineage Tracking in Data Lakes." Journal of Scientific and Engineering Research 10, no. 8 (2023): 117–23. https://doi.org/10.5281/zenodo.12792488.

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Data lakes are now the most prevalent solution for storing and managing large data volumes in unstructured, semi-structured, and structured formats. However, the data science problem is not associated with the data lake growth and its size and complexity because it may become difficult to guarantee data reproducibility, traceability and governance. Data versioning and lineage tracking stand right at the heart of an effectively managed data lake, providing organizations with a way to track revisions within datasets, retain the record of changes that transform data, and maintain rules of data re

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Mandala, Nishanth Reddy. "ETL in Data Lakes vs. Data Warehouses." ESP Journal of Engineering & Technology Advancements 1, no. 2 (2021): 224–30. https://doi.org/10.56472/25832646/jeta-v1i2p123.

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Xiong, Runqun, Shiyuan Zhao, Ciyuan Chen, and Zhuqing Xu. "Optimizing Multimodal Data Queries in Data Lakes." Tsinghua Science and Technology 30, no. 6 (2025): 2625–37. https://doi.org/10.26599/tst.2025.9010022.

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Randhi, Kiran, and Srinivas Reddy Bandarapu. "Building Cognitive Data Lakes on Cloud: Integrating NLP and AI to Make Data Lakes Smart." International Journal of Scientific Research and Management (IJSRM) 12, no. 03 (2024): 1151–61. http://dx.doi.org/10.18535/ijsrm/v12i03.ec19.

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The enormous increase in the volume of digital data in all industries has made organizations look for more efficient storage and processing techniques for data which has provided further impetus for the change from conventional data lakes to cognitive data lakes. In addition to being a structured or unstructured data pool, cognitive data lakes have AI and NLP strategic built-in features to offer real-time intelligent data analytics to support the organization’s strategic decisions and plans (Smith et al., 2023). Consequently, they provide a more effective method for data utilization enabling e

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Fauzi, M., A. Hendrizal, and B. Amin. "Morphometric Surface Dimension Analysis of Three Different Oxbow Lakes in Lubuk Siam Village." IOP Conference Series: Earth and Environmental Science 1118, no. 1 (2022): 012045. http://dx.doi.org/10.1088/1755-1315/1118/1/012045.

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Abstract Oxbow lake is usually formed in a meandering river. Lubuk Siam village is one of the places passed by the Kampar River, which has a meander shape. In this study, there are three oxbow lakes to be studied: Lubuk Siam, Selat Panjang, and Putus. Morphometric Surface Dimension data was collected by using geographical information system (GIS). It was then analyzed using GIS data processing software. The results showed that Lake Putus has the most significant area compared to the other two lakes adjacent to each other. The size of Lake Putus is 22.99 Ha. The results showed that three of the

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Lubick, Naomi. "Great Lakes health data hidden." Environmental Science & Technology 42, no. 8 (2008): 2716–17. http://dx.doi.org/10.1021/es0870505.

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Abu, Hasan, Andrey Kirienko, and Anatoliy Homonenko. "Method of Transition from Data Warehouses to Geographic Information System Data Lakes Based on Lambda Architecture." Intellectual Technologies on Transport, no. 1 (April 14, 2024): 45–55. http://dx.doi.org/10.20295/2413-2527-2024-137-45-55.

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This paper discusses the transition from traditional data warehouses to data lakes in geographic information systems using Lambda architecture. Provides an overview of the key transition steps, including planning, data collection and processing, data querying, data analytics, and metadata management. Particular attention is paid to the interaction of data lakes and GIS, as well as sample big data processing code based on Lambda architecture. The advantages of using data lakes in GIS and the possibilities o integrating modern data processing technologies are considered.

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Du, Baolong, Liping Zhu, Jianting Ju, Junbo Wang, Qingfeng Ma, and Qiangqiang Kou. "A Quantification of Heat Storage Change-Based Evaporation Behavior in Middle–Large-Sized Lakes in the Inland of the Tibetan Plateau and Their Temporal and Spatial Variations." Remote Sensing 15, no. 14 (2023): 3460. http://dx.doi.org/10.3390/rs15143460.

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A large number of different-sized lakes exist in the inland area of the Tibetan Plateau (TP), which are examples of the important connection between the atmosphere and hydrosphere through the analysis of lake surface convergence and evaporation processes. The evaporation level changes that occur in middle–large-sized lakes (surface area > 50 km2) in the area directly influence the regional mass and energy balance values, atmospheric boundary layer heat and humidity structures, and weather processes occurring in the lower-reach areas. The studies conducted in the literature at present, conce

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Yuan, Lester L., and John R. Jones. "Modeling hypolimnetic dissolved oxygen depletion using monitoring data." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 5 (2020): 814–23. http://dx.doi.org/10.1139/cjfas-2019-0294.

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Eutrophication increases hypoxia in lakes and reservoirs, causing deleterious effects on biological communities. Quantitative models would help managers develop effective strategies to address hypoxia issues, but most existing models are limited in their applicability to lakes with temporally resolved dissolved oxygen data. We describe a hierarchical Bayesian model that predicts dissolved oxygen in lakes based on a mechanistic understanding of the factors that influence the development of hypoxia during summer stratification. These factors include the days elapsed since stratification, dissolv

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Li, Xue, Weibin Zeng, Zhibin Wang, et al. "GraphAr: An Efficient Storage Scheme for Graph Data in Data Lakes." Proceedings of the VLDB Endowment 18, no. 3 (2024): 530–43. https://doi.org/10.14778/3712221.3712223.

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Data lakes, increasingly adopted for their ability to store and analyze diverse types of data, commonly use columnar storage formats like Parquet and ORC for handling relational tables. However, these traditional setups fall short when it comes to efficiently managing graph data, particularly those conforming to the Labeled Property Graph (LPG) model. To address this gap, this paper introduces GraphAr, a specialized storage scheme designed to enhance existing data lakes for efficient graph data management. Leveraging the strengths of Parquet, GraphAr captures LPG semantics precisely and facili

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Zhang, Yi, Peter Baile Chen, and Zachary G. Ives. "Searching Data Lakes for Nested and Joined Data." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3346–59. http://dx.doi.org/10.14778/3681954.3682005.

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Exploratory data science is driving new platforms that assist data scientists with everyday tasks, such as integration and wrangling, to assemble training datasets. Such tools take scientists' work-in-progress data as a search object (table or JSON) and find relevant supplementary data from an organizational data lake , which can be unioned or joined with the current data. Existing data lake search tools find single , relational tables to match or join with a search object. Yet many data science applications revolve around hierarchical data, which can only be matched by creating views that sim

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Kuschewski, Maximilian, David Sauerwein, Adnan Alhomssi, and Viktor Leis. "BtrBlocks: Efficient Columnar Compression for Data Lakes." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–26. http://dx.doi.org/10.1145/3589263.

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Analytics is moving to the cloud and data is moving into data lakes. These reside on object storage services like S3 and enable seamless data sharing and system interoperability. To support this, many systems build on open storage formats like Apache Parquet. However, these formats are not optimized for remotely-accessed data lakes and today's high-throughput networks. Inefficient decompression makes scans CPU-bound and thus increases query time and cost. With this work we present BtrBlocks, an open columnar storage format designed for data lakes. BtrBlocks uses a set of lightweight encoding s

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Zhang, Chengming, Zeyong Gao, Jing Luo, et al. "Simulation and Prediction of Thermokarst Lake Surface Temperature Changes on the Qinghai–Tibet Plateau." Remote Sensing 16, no. 24 (2024): 4645. https://doi.org/10.3390/rs16244645.

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Thermokarst lakes are shallow bodies of freshwater that develop in permafrost regions, and they are an essential focus of international permafrost research. However, research regarding the mechanisms driving temperature fluctuations in thermokarst lakes and the factors that influence these changes is limited. We aimed to analyze seasonal variations in the surface water temperature, clarify historical trends in the phenological characteristics of lake ice, and predict future temperature changes in surface water of the thermokarst lakes using the air2water model. The results indicated that in co

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Strozzi, T., A. Wiesmann, A. Kääb, S. Joshi, and P. Mool. "Glacial lake mapping with very high resolution satellite SAR data." Natural Hazards and Earth System Sciences 12, no. 8 (2012): 2487–98. http://dx.doi.org/10.5194/nhess-12-2487-2012.

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Abstract. Floods resulting from the outbursts of glacial lakes are among the most far-reaching disasters in high mountain regions. Glacial lakes are typically located in remote areas and space-borne remote sensing data are an important source of information about the occurrence and development of such lakes. Here we show that very high resolution satellite Synthetic Aperture Radar (SAR) data can be employed for reliably mapping glacial lakes. Results in the Alps, Pamir and Himalaya using TerraSAR-X and Radarsat-2 data are discussed in comparison to in-situ information, and high-resolution sate

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Nandish Shivaprasad. "Integration of Business Intelligence in Data Lake Solutions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 1018–31. https://doi.org/10.32628/cseit2410612412.

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Data lakes with BI allows organizations to effectively navigate the advantages of unstructured, semi unstructured and structured data. This paper therefore focuses on BI technologies in data lakes, with especial consideration to the challenges, integration techniques, and the technologies that enable appropriate interoperation. Successful BI in data lakes: Hadoop and Spark as distributed computing frameworks; cloud platforms; and data integration tools. However, the need to develop suitable solutions for integrating such enterprise applications is still a work in progress because of issues lik

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Giebler, Corinna, Christoph Gröger, Eva Hoos, Rebecca Eichler, Holger Schwarz, and Bernhard Mitschang. "Data Lakes auf den Grund gegangen." Datenbank-Spektrum 20, no. 1 (2020): 57–69. http://dx.doi.org/10.1007/s13222-020-00332-0.

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Krtalić, A., A. Kuveždić Divjak, and A. Miletić. "TOWARD DATA LAKES FOR CRISIS MANAGEMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 539–46. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-539-2023.

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Abstract. The content of the data lake comes (is filled) from different sources, and different users (experts in various fields) of the same data can download and analyse the same data for their (different) needs and analysis. Big Data about the human environment and the effect of natural and human-caused disasters (in this case: heat islands, earthquakes and lava flows, and landmine contamination) on that environment have been available to many people for years and are the subject of discussions, but there are still numerous research challenges in the form of structuring and storing data and

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Satyam, Chauhan. "Enterprise Data Lakes for Financial Services." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 5, no. 5 (2019): 1–17. https://doi.org/10.5281/zenodo.14384176.

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Enterprise Data Lakes (EDLs) have emerged as transformative solutions for financial institutions seeking to address challenges associated with fragmented data systems, inefficient workflows, and stringent regulatory compliance. This paper presents an AWS-centric framework for building resilient and scalable EDLs tailored to the needs of financial services. Key aspects such as controlled change management, robust failover protocols, workflow orchestration, and secure data sharing are analyzed in detail. The study explores the design and implementation of data pipelines, leveraging AWS tools lik

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Parate, Vrushali. "Hadoop To Bigquery: Migrating Automotive Data Lakes Without Downtime." American Journal of Interdisciplinary Innovations and Research 07, no. 07 (2025): 16–27. https://doi.org/10.37547/tajiir/volume07issue07-03.

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The automotive industry is undergoing a tremendous increase in data generation, mostly driven by advancements in vehicle technology, connectivity, and autonomous driving features. The Apache Hadoop data lake was adopted by companies to store and analyze the huge volume, velocity, and variety of automotive data. However, with technological advancement and the need for real-time analytics, operational complexity, scalability, and cost efficiency, Apache Hadoop-based data lakes started presenting challenges. Google BigQuery, on the other hand, is a fully managed, serverless data warehouse and ana

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Somasundaram, Prakash. "Hybrid Data Management Systems: Integrating Data Lakes and Data Warehouses." Journal of Artificial Intelligence, Machine Learning and Data Science 1, no. 1 (2023): 318–21. http://dx.doi.org/10.51219/jaimld/prakash-somasundaram/103.

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Khilchevskyi, Valentyn K., Liudmyla V. Plichko, Myroslava R. Zabokrytska, and Nataliia P. Sherstyuk. "Assessment of the dynamics of the surface area of the Shatsk Lakes over a long-term period based on remote sensing data in connection with fluctuations in their level (1985–2023)." Journal of Geology, Geography and Geoecology 34, no. 1 (2025): 126–35. https://doi.org/10.15421/112512.

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The purpose of the article was to assess the long-term dynamics of the area of the Shatsk Lakes (Volyn region, Ukraine) and the spread of water blooms on their surface using remote sensing data in connection with fluctuations in the level of these water bodies for the period 1985-2023. Satellite images from the American Landsat-5,7,8 mission and the European Sentinel-2 L2A mission were used. Long-term data from state monitoring were also used to monitor: a) water level at the hydrological station of Svitiaz Lake; b) the amount of precipitation at the Svitiaz weather station. It was established

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Sarıbaş, Dilara, Nehir Kaymak, Özgül Yahyaoğlu, and Battal Çıplak. "Invasive Coptodon (Perciformes: Cichlidae) in southwest Turkey: Species identification using sequence data." Ege Journal of Fisheries and Aquatic Sciences 39, no. 2 (2022): 135–44. http://dx.doi.org/10.12714/egejfas.39.2.07.

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Nonnative cichlids (Coptodon zillii) have established populations in the Köyceğiz and Koca Lakes, located on the west coasts of Mediterranean Turkey. Conflicting species names in these lakes have been reported for many years. We studied samples from current populations of Coptodon in these lakes and the Pecenek canal concerning existing GenBank data. We estimated the possible ancestral population using sequence data in the mitochondrial D-loop segment. Inter and intra-population morphological variations of Coptodon were examined using 25 morphological and six meristic characters. Haplotype ana

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Meparishvili, Badri, Gulnara Janelidze, and Giorgi Muradov. "Evolutionary Machine Learning in Data Lake Services." Works of Georgian Technical University, no. 2(536) (May 16, 2025): 133–41. https://doi.org/10.36073/1512-0996-2025-2-133-141.

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In big data analytics, the storage, processing, and analysis of large volumes of various types of data, including unstructured data, in their natural format is of great relevance. The article discusses aspects of the use of machine learning in the context of big data lake services. Modern organizations are increasingly using data lakes to store and manage large volumes of unstructured and structured data from various types of external data sources. Unlike traditional data warehouses, which require pre-processing and organization of data before storage, data lakes allow us to store big data in

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Picazo, Antonio, Juan Antonio Villaescusa, Carlos Rochera, Javier Miralles-Lorenzo, Antonio Quesada, and Antonio Camacho. "Functional Metabolic Diversity of Bacterioplankton in Maritime Antarctic Lakes." Microorganisms 9, no. 10 (2021): 2077. http://dx.doi.org/10.3390/microorganisms9102077.

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A summer survey was conducted on the bacterioplankton communities of seven lakes from Byers Peninsula (Maritime Antarctica), differing in trophic and morphological characteristics. Predictions of the metabolic capabilities of these communities were performed with FAPROTAX using 16S rRNA sequencing data. The versatility for metabolizing carbon sources was also assessed in three of the lakes using Biolog Ecoplates. Relevant differences among lakes and within lake depths were observed. A total of 23 metabolic activities associated to the main biogeochemical cycles were foreseen, namely, carbon (1

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Abhijit, Joshi. "Architectural Paradigms in Data Management: Evaluating Data Lakes and Data Warehouses for Enterprise Data Ecosystems." Journal of Scientific and Engineering Research 6, no. 4 (2019): 221–28. https://doi.org/10.5281/zenodo.11820647.

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In the digital era, the exponential growth of data has necessitated the evolution of robust architectures for efficient data management, storage, and analysis. Data lakes and data warehouses represent two fundamentally different approaches to data storage and utilization. This whitepaper delves into the technical nuances of each architecture, assessing their structural, operational, and functional distinctions. By comparing the two in terms of data integration, scalability, flexibility, and performance, the document aims to furnish businesses with a clear understanding of how each architecture

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Tang, Xiu, Wenhao Liu, Sai Wu, et al. "QueryArtisan: Generating Data Manipulation Codes for Ad-hoc Analysis in Data Lakes." Proceedings of the VLDB Endowment 18, no. 2 (2024): 108–16. https://doi.org/10.14778/3705829.3705832.

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Query processing over data lakes is a challenging task, often requiring extensive data pre-processing activities such as data cleaning, transformation, and loading. However, the advent of Large Language Models (LLMs) has illuminated a new pathway to address these complexities by offering a unified approach to understanding the diverse datasets submerged in data lakes. In this paper, we introduce QueryArtisan, a novel LLM-powered analytic tool specifically designed for data lakes. QueryArtisan transcends traditional ETL (Extract, Transform, Load) processes by generating just-intime code for dat

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Jiao, Yibo, Zifan Lu, and Mengmeng Wang. "Satellite Data Revealed That the Expansion of China’s Lakes Is Accompanied by Rising Temperatures and Wider Temperature Differences." Remote Sensing 17, no. 9 (2025): 1546. https://doi.org/10.3390/rs17091546.

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Lake surface water area (LSWA) and lake surface water temperature (LSWT) are critical indicators of climate change, responding rapidly to global warming. However, studies on the synergistic variations of LSWA and LSWT are scarce, and the coupling relationships among lakes with different environmental characteristics remain unclear. In this study, the relative growth rate of LSWA (RKLSWA); the absolute growth rates of annual maximum, mean, and minimum LSWTs (i.e., KLSWT_max, KLSWT_mean, KLSWT_min); and the absolute growth rates of the difference between maximum and minimum LSWT (LSWT_mmd) (KLSW

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Gronewold, Andrew D., Vincent Fortin, Robert Caldwell, and James Noel. "Resolving Hydrometeorological Data Discontinuities along an International Border." Bulletin of the American Meteorological Society 99, no. 5 (2018): 899–910. http://dx.doi.org/10.1175/bams-d-16-0060.1.

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AbstractMonitoring, understanding, and forecasting the hydrologic cycle of large freshwater basins often requires a broad suite of data and models. Many of these datasets and models, however, are susceptible to variations in monitoring infrastructure and data dissemination protocols when watershed, political, and jurisdictional boundaries do not align. Reconciling hydrometeorological monitoring gaps and inconsistencies across the international Laurentian Great Lakes–St. Lawrence River basin is particularly challenging because of its size and because the basin’s dominant hydrologic feature is t

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Zolghadr-Asli, Babak, Mojtaba Naghdyzadegan Jahromi, Xi Wan, et al. "Uncovering the Depletion Patterns of Inland Water Bodies via Remote Sensing, Data Mining, and Statistical Analysis." Water 15, no. 8 (2023): 1508. http://dx.doi.org/10.3390/w15081508.

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Addressing the issue of shrinking saline lakes around the globe has turned into one of the most pressing issues for sustainable water resource management. While it has been established that natural climate variability, human interference, climate change, or a combination of these factors can lead to the depletion of saline lakes, it is crucial to investigate each case and diagnose the potential causes of this devastating phenomenon. On that note, this study aims to promote a comprehensive analytical framework that can reveal any significant depletion patterns in lakes while analyzing the poten

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Lisboa, Filipe, Vanda Brotas, Filipe Duarte Santos, Sakari Kuikka, Laura Kaikkonen, and Eduardo Eiji Maeda. "Spatial Variability and Detection Levels for Chlorophyll-a Estimates in High Latitude Lakes Using Landsat Imagery." Remote Sensing 12, no. 18 (2020): 2898. http://dx.doi.org/10.3390/rs12182898.

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Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. While satellite imagery can monitor phytoplankton biomass using chlorophyll a (Chl) as a proxy over large areas, detection of Chl in small lakes is hindered by the low spatial resolution of conventional ocean color satellites. The short time-series of the newest generation of space-borne sensors (e.g., Sentinel-2) is a b

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Soomets, Tuuli, Kristi Uudeberg, Kersti Kangro, et al. "Spatio-Temporal Variability of Phytoplankton Primary Production in Baltic Lakes Using Sentinel-3 OLCI Data." Remote Sensing 12, no. 15 (2020): 2415. http://dx.doi.org/10.3390/rs12152415.

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Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuati

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El-Bouhali, A., M. Amyay, and Kh El Ouazani Ech-Chahdi. "Recent variations of water area in the Tabular Middle Atlas lakes, Morocco." IOP Conference Series: Earth and Environmental Science 1398, no. 1 (2024): 012012. http://dx.doi.org/10.1088/1755-1315/1398/1/012012.

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Abstract The shrinkage of the lake’s water area is considered an indicator of change in climatic parameters and anthropogenic impact on landscapes through changes in land use practices. The present study focuses on utilizing remote sensing data to track the evolution of the water area in three lakes (Aoua, Afourgagh, and Ifrah) located in the Tabular Middle Atlas. The processing of Landsat satellite images between August 1984 and August 2022 reveals a significant shrinkage of the lakes, with drying periods in recent years. The concerning situation of the lakes is attributed to the increased ra

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Chandrakanth, Lekkala. "Building Resilient Big Data Pipelines with Delta Lake for Improved Data Governance." European Journal of Advances in Engineering and Technology 7, no. 12 (2020): 101–6. https://doi.org/10.5281/zenodo.12755136.

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The rapid development of data, thereby real-time dealing with analytics, has drawn the attention of enterprises in building better, scalable big data pipelines. Nevertheless, the big data architectures of the old school, like the data lake, which is based on Apache Hadoop or the cloud object store, often technologically suffer from inconsistency, quality and governance bottlenecks. The Delta Lake is an open-source storage layer, which allows one to ACID transactions and schema enforcement and gives an easy way to batch and stream data processing with data lakes. This paper examines how Delta L

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Ganachari, Girish. "IMPACT OF DATA MESH ARCHITECTURE FOR ENTERPRISE DATA LAKES." Journal of Artificial Intelligence, Machine Learning and Data Science 2, no. 2 (2024): 954–57. http://dx.doi.org/10.51219/jaimld/girish-ganachari/227.

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Avinash Reddy Thimma Reddy. "Demystifying data lakes and data warehouses: A technical perspective." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 2056–69. https://doi.org/10.30574/wjaets.2025.15.3.1121.

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This article examines the fundamental concepts, architectural distinctions, and strategic implications of data warehouses and data lakes in contemporary enterprise data management. As organizations face exponential growth in data volume and diversity, traditional siloed approaches prove increasingly insufficient to address the full spectrum of analytical requirements. The article provides a comprehensive technical analysis of data warehouse structures—characterized by subject-orientation, integration, time-variance, and non-volatility—alongside the defining features of data lakes, including sc

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Sun, Fangdi, Ronghua Ma, Bin He, et al. "Changing Patterns of Lakes on The Southern Tibetan Plateau Based on Multi-Source Satellite Data." Remote Sensing 12, no. 20 (2020): 3450. http://dx.doi.org/10.3390/rs12203450.

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More than 1100 lakes covering an area greater than 4500 km2 are located on the Tibetan Plateau, and these lakes are important regulators of several large and famous rivers in Asia. The determination of hydrological changes that have occurred in these lakes can reflect climate change and supply scientific data to plateau environmental research. Data from high frequency (moderate-resolution imaging spectro-radiometer) MODIS images, altimetry, and the Hydroweb database collected during 2000–2015 were integrated in this study to delineate the detailed hydrological changes of 15 lakes in three basi

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Abhijit, Joshi. "Scalable Data Integration Frameworks: Enhancing Data Cohesion in Complex Systems." Journal of Scientific and Engineering Research 9, no. 10 (2022): 83–94. https://doi.org/10.5281/zenodo.12772820.

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Data integration in large-scale environments is crucial for organizations to leverage diverse data sources for advanced analytics and decision-making. This paper delves into the latest frameworks and methodologies designed to enhance data cohesion in complex systems. We explore the challenges associated with integrating heterogeneous data sources and present scalable solutions to achieve seamless data integration. The study highlights advanced techniques and tools, including ETL processes, data lakes, and modern data integration platforms. Through detailed methodologies, pseudocode, and illust

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Kropáček, J., F. Maussion, F. Chen, S. Hoerz, and V. Hochschild. "Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data." Cryosphere 7, no. 1 (2013): 287–301. http://dx.doi.org/10.5194/tc-7-287-2013.

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Abstract. The Tibetan Plateau includes a large system of endorheic (closed basin) lakes. Lake ice phenology, i.e. the timing of freeze-up and break-up and the duration of the ice cover may provide valuable information about climate variations in this region. The ice phenology of 59 large lakes on the Tibetan Plateau was derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite data for the period from 2001 to 2010. Ice cover duration appears to have a high variability in the studied region due to both climatic and local factors. Mean values for the duration of ice cove

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Yaseen, Aiman k., Maath I. Mahmood, Ghasaq k. Yaseen, Afraa A. Ali, Mahir Mahmod, and Araz H. Mustafa. "Area Change Monitoring of Dokan & Darbandikhan Iraqi Lakes Using Satellite Data." Sustainable Resources Management Journal 3, no. 2 (2018): 25–41. https://doi.org/10.5281/zenodo.1284844.

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Iraq is one of the richest countries, especially in the Middle East and generally in the world, in natural resources such as water due to existing of Tigris and Euphrates rivers, tributaries branches, marshlands, and lakes which are already affected by climate change. Thus, Dokan and Darbandikhan lakes (in the north of Iraq) have been monitored and studied throughout the past eighteen years (1999-2016) in term of area and average monthly rainfall (AMR) of feeding basin to Figure out the effect of historical climate change. Landsat images satellite (5, 7, and 8) types were used to collect 36 sa

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Ashraf, Syed Ziaurrahman. "Building a Data Lake on AWS: From Data Migration to AI-Driven Insights." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem19620.

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As organizations generate and process increasing amounts of data, building data lakes on cloud platforms like AWS has become crucial to managing large datasets efficiently. This paper outlines the key steps in constructing a scalable data lake on AWS, starting from data migration to leveraging AI for insights. It explores how AWS services like S3, Glue, and SageMaker work together to facilitate data storage, transformation, and machine learning. In addition, it highlights the importance of orchestrating data pipelines with automation tools like AWS Lambda and Apache Airflow to ensure smooth, s

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PURBA, GANDI Y. S., EKO HARYONO, SUNARTO SUNARTO, et al. "Jellyfish Lakes at Misool Islands, Raja Ampat, West Papua, Indonesia." Biodiversitas Journal of Biological Diversity 19, no. 1 (2018): 172–82. http://dx.doi.org/10.13057/biodiv/d190124.

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Purba GYS, Haryono E, Sunarto, Manan J, Rumenta L, Purwanto, Becking LE. 2018. Jellyfish Lakes at Misool Islands, Raja Ampat, West Papua, Indonesia. Biodiversitas 19: 172-182. Misool Islands, located in southern Raja Ampat in West Papua, has dozens of anchihaline lakes (marine lakes). Three of these lakes, Lenmakana, Karawapop, and Keramat, house populations of jellyfish. This study mapped and described the characteristics of the three â€˜jellyfish lakesâ€™ during field surveys in October 2015 and May 2016. The lakes ranged in area from 0.5âˆ’3.2 hectares. All three lakes harbored Mastigias pa

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Goutham, Bilakanti. "Cloud-based Data Lakes in Healthcare: Challenges and Opportunities." International Journal of Leading Research Publication 4, no. 8 (2023): 1–12. https://doi.org/10.5281/zenodo.15196922.

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The swift digital revolution of healthcare has produced enormous amounts of data that require effective storage, handling, and analysis. Cloud-based data lakes have become a key technology to manage large-scale healthcare data sets, supporting interoperability, real-time analytics, and AI-informed decision-making. Cloud-based data lakes like AWS S3, Azure Data Lake, and Google BigQuery offer elastic storage, high-security platforms, and sophisticated computing power that allow data integration and accessibility. This article illustrates how cloud-based data lakes simplify healthcare data manag

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Cortés-Guzmán, Daniela, and Javier Alcocer. "Turnover Drives High Benthic Macroinvertebrates’ Beta Diversity in a Tropical Karstic Lake District." Diversity 14, no. 4 (2022): 259. http://dx.doi.org/10.3390/d14040259.

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Beta diversity is useful to explain community assembly across landscapes with spatial variation. Its turnover and nestedness components help explain how beta diversity is structured across environmental and spatial gradients. Assessing beta diversity in freshwater ecosystems is essential to conservation, as it reveals the mechanisms that maintain regional diversity. Nonetheless, so far, no studies have examined the beta diversity patterns of benthic macroinvertebrates in tropical lakes. We aimed to examine the beta diversity patterns and components of the deep benthic macroinvertebrate communi

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