Comparing Apache Iceberg and Databricks in building data lakes and mesh architectures
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P105Keywords:
Apache Iceberg, Databricks, data lakes, data mesh, big data, scalability, interoperability, data engineering, data governance, cloud-native, analytics, metadata management, open table format, data pipelines, real-time data, performance optimization, distributed data, cost efficiency, ecosystem integration, schema evolution, ACID compliance, lakehouse, transactional consistency, query optimization, partitioning, streaming data, batch processing, machine learning, business intelligence, multi-cloud, data democratization, data lineage, storage optimization, open-source tools, unified data analyticsAbstract
Data lakes and mesh architectures have completely changed the way organizations handle and make good use of their data, providing scalable and flexible solutions for storage, processing, and analysis of huge datasets. Apache Iceberg and Databricks are two of the most important technologies, amongst others, driving these changes. They are the most outstanding by their different capabilities and approaches. Apache Iceberg is an open table format that is intended to solve the problem of managing big datasets over features like schema evolution, time travel, and multi-engine compatibility. Due to its modular design and the ability to optimize queries, enterprises receive a great tool for creating interoperable, high-performance data lakes. Iceberg, through its focus on data consistency & scalability, is particularly good for those organizations that are picturing flexibility and long-term resilience in their minds. Databricks is an all-in-one platform that links data engineering, analytics, and machine learning into a collaborative environment for building unified data pipelines. The seamless integration of different workflows plus support for domain ownership corresponds well to the principles of data mesh, which makes it an attractive offer for those organizations that are all about decentralizing the data management. Databricks is more concerned with operational efficiency, and it gives strong tools that teams can use to cooperate and come up with innovations in different data domains
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