Scalable Metadata Management in Data Lakes: The Role of Apache Iceberg
Year: 2024
Type: article
Abstract: As organizations accumulate vast volumes of diverse, rapidly changing data, data lakes have emerged as flexible storage solutions enabling scalable analytics and machine learning. Yet, as data lakes grow to petabyte scales, efficiently managing and querying metadata information about data location, schema, versioning, and lineage becomes a critical challenge. Traditional approaches often rely on directory structures and external catalogs that degrade in performance over time. Apache Iceberg, a high-performance table format designed for data lakes, fundamentally rethinks how metadata is stored, indexed, and evolved. By using immutable snapshots, partition evolution, and efficient metadata caching, Iceberg enables fast table operations, incremental ingestion, schema evolution, and time travel queries at scale.
Author: Pradeep Bhosale
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949972009
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