Apache Iceberg: A New Table Format for Managing Data Lakes

Authors

  • Naresh Dulam Vice President Sr Lead Software Engineer, JP Morgan Chase, USA Author
  • Venkataramana Gosukonda Senior Software Engineering Manager, Wells Fargo, USA Author
  • Karthik Allam Big Data Infrastructure Engineer, JP Morgan & Chase, USA Author

Keywords:

Apache Iceberg, data lakes, Hive

Abstract

Apache Iceberg is a revolutionary table format designed to address the growing challenges of managing large-scale data lakes. As data lakes have become increasingly popular for storing diverse datasets, they have also revealed scalability, performance, and lack of standardization issues. Traditional data lake management systems often need help with problems such as schema evolution, partitioning inefficiencies, & the need for atomic operations, which can hinder the reliability and performance of analytics workloads. Apache Iceberg was developed to overcome these challenges, offering a robust solution that improves how data is stored, processed, and accessed within a data lake. Its key features include schema evolution, partitioning optimizations, and support for ACID transactions, ensuring data consistency and integrity in multi-tenant environments. This table format allows for easy management of large datasets and enables advanced analytics by ensuring data is stored efficiently and reliably with minimal overhead. Compared to traditional formats like Hive, Iceberg offers improved scalability, performance, and flexibility by providing better support for complex queries, large-scale data processing, & dynamic workloads. Additionally, Iceberg's support for partition evolution and its ability to handle massive datasets without sacrificing performance makes it a game-changer in data lake management. As more organizations turn to data lakes for their big data and analytics needs, Iceberg presents a way to ensure that data lakes remain performant, reliable, and easy to manage, allowing organizations to scale their operations without facing the pitfalls of traditional systems. With its seamless integration into modern significant data ecosystems, Iceberg is poised to become a critical tool in optimizing data lake performance and simplifying the complexities of managing vast amounts of data. By solving essential challenges related to data consistency, schema changes, and performance optimization, Apache Iceberg is setting the stage for more efficient & scalable data lake architectures, making it an essential technology for organizations dealing with ever-expanding datasets and complex data operations.

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References

Ghavami, P. (2016). Big Data Governance: Modern Data Management Principles for Hadoop, NoSQL & Big Data Analytics. Washington, DC.

Shashish, M. (2011). Matching raster and trajectory data using web services (Master's thesis, University of Twente).

Cielen, D., & Meysman, A. (2016). Introducing data science: big data, machine learning, and more, using Python tools. Simon and Schuster.

Mitchell, T. (2005). Web mapping illustrated: using open source GIS toolkits. " O'Reilly Media, Inc.".

Davenport, T. H., & Dyché, J. (2013). Big data in big companies. International Institute for Analytics, 3(1-31).

Brittliff, N. (2014). The'schema-last'Approach: Data Analytics and the Intelligence Life-cycle (Doctoral dissertation, University of Canberra).

Wernecke, J. (2008). The KML handbook: geographic visualization for the Web. Pearson Education.

Xiong, C. (2010). Developing a web-based sea ice information system using GIS (Doctoral dissertation, Toronto Metropolitan University).

Yu, P. (2013). Challenges and solutions for COSL's operation in the Arctic (Master's thesis, University of Stavanger, Norway).

Pope, D. (2017). Big data analytics with SAS: Get actionable insights from your big data using the power of SAS. Packt Publishing Ltd.

Rosenberg, S. (2008). Dreaming in code: Two dozen programmers, three years, 4,732 bugs, and one quest for transcendent software. Crown Currency.

Stuart, D. (2011). Facilitating access to the web of data: A guide for librarians. Facet Publishing.

Eisenstein, D. J., Weinberg, D. H., Agol, E., Aihara, H., Prieto, C. A., Anderson, S. F., ... & Ogando, R. L. (2011). Sdss-iii: Massive spectroscopic surveys of the distant universe, the milky way, and extra-solar planetary systems. The Astronomical Journal, 142(3), 72.

Lake, A. (2000). 6 nightmares: real threats in a dangerous world and how America can meet them. Hachette UK.

Landres, P. B. (2000). National wilderness preservation system database: Key attributes and trends, 1964 through 1999. US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).

Gade, K. R. (2017). Migrations: Challenges and Best Practices for Migrating Legacy Systems to Cloud-Based Platforms. Innovative Computer Sciences Journal, 3(1).

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Published

01-09-2018

How to Cite

[1]
Naresh Dulam, Venkataramana Gosukonda, and Karthik Allam, “Apache Iceberg: A New Table Format for Managing Data Lakes ”, Distrib Learn Broad Appl Sci Res, vol. 4, Sep. 2018, Accessed: Dec. 27, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/226

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