A collection of 19 posts tagged with "Apache Iceberg"
Apache Iceberg 1.9.0 closes the gap with Delta Lake, adding row-level ops with lineage, JSON-friendly variant type, geospatial support, REST catalog upgrades, and easier Delta migration. Dropping Hadoop 2 and Spark 3.3 signals a modern focus, driving open table format convergence.
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The data lake was once heralded as the future, an infinitely scalable reservoir for all our raw data, promising to transform it into actionable insights. This was a logical progression from databases and data warehouses, each step driven by the increasing demand for scalability. Yet, in embracing the data lake's scale and flexibility, we overlooked a critical difference.
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Apache Iceberg is significantly transforming modern data lakes. Its introduction to object storage platforms has been celebrated for delivering ACID transactions, strong schema evolution, and warehouse-like reliability to data lake architectures. The Iceberg Catalog API standard is crucial to this transformation, as it ensures that various tools can consistently discover tables and execute atomic transactions once a compliant catalog service
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In data engineering, open standards are foundational for building interoperable, evolvable, and non-proprietary systems. Apache Iceberg, an open table format, is a prime example. Along with compute, Iceberg brings structure and reliability to data lakes. When coupled with high-performance object storage like MinIO AIStor, Iceberg unlocks new avenues for creating next-generation, high-performance, cost-effective, and scalable architectures. However, this powerful table
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Apache Iceberg has significantly reshaped how organizations manage and interact with massive structured analytical datasets inside object storage. It brings database-like reliability and powerful features such as ACID transactions, schema evolution, and time travel. Although these features are commonly emphasized, the Iceberg Catalog API is what makes these tables accessible. The Iceberg Catalog API is a centralized interface for managing
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Cloud lakehouses break the bank at scale and compromise control. On-prem Iceberg lakehouses deliver speed, savings, and sovereignty. From cancer research to finance, real-world deployments prove it: petabyte-scale performance, full control, and lower TCO are within reach.
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Choosing the right open table format—Apache Iceberg, Delta Lake, or Apache Hudi—can make or break your data lakehouse. This guide breaks down their strengths, how they integrate with object storage, and which one is best for AI, analytics, and real-time workloads.
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Discover the power of Apache Iceberg and AIStor in transforming data lakehouses! From multi-engine compatibility to time travel, schema evolution, and blazing-fast performance, this guide dives deep into how Iceberg unlocks the full potential of modern AI and analytics workloads.
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Pairing the Iceberg table format with AIStor creates a powerful, flexible and extensible lakehouse platform. The Iceberg Table Spec declares a table format that is designed to manage “a large, slow-changing collection” of files or objects stored in a distributed system.
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Iceberg is shifting the market's focus to scalable, cloud-native storage. This shift is leading to the commoditization of query engines, offering users more flexibility, better pricing, and innovation.
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Catalogs are revolutionizing modern datalakes, with industry giants like Databricks and Snowflake adopting Apache Iceberg’s catalog REST API. A commitment to open standards enhances performance, fosters innovation, and transforms data management for AI and ML.
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Databricks' acquisition of Tabular, founded by the creators of Apache Iceberg, underscores the importance of open frameworks in modern data lake design. Open frameworks ensure interoperability, flexibility, and simplicity, benefiting those leveraging data for AI.
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Explore modern data architecture with Iceberg, Tabular, and MinIO. Learn to seamlessly integrate structured and unstructured data, optimize AI/ML workloads, and build a high-performance, cloud-native data lake.
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Discover how Databricks and Apache Iceberg's strides in open table formats influence data portability in the modern data stack. Learn how the shift to a private cloud operating model aligns with this evolution, fostering an adaptable, interoperable data ecosystem.
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Unleash data collaboration and quality with Nessie! Learn to manage branches, commits, and merges effortlessly. This guide walks you through deploying Dremio, MinIO, and Nessie, transforming your data engineering with collaborative precision. Dive in to revolutionize your workflows!
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Unlock the secrets of modern datalakes migration to the private clouds. Embrace S3 compatibility, data control, and the ever-evolving landscape for cost-effective data management. Don't miss the journey to enhanced flexibility, efficiency, and the future-proofing of your data ecosystem
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Introduction In 1997, Clayton Christensen, in his book The Innovator’s Dilemma, identified a pattern of innovation that tracked the capabilities, cost, and adoption by market segment between an incumbent and a new entrant. He labeled this pattern “Disruptive Innovation.” Not every successful product is disruptive - even if it causes well-established businesses to lose market share or even fail
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Make you Kafka topics performant and efficient with Kafka Schema Registry.
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Build your on-prem data lake with Apache Iceberg, Dremio and MinIO
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