A collection of 54 posts from Brenna Buuck
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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AWS recently unveiled Amazon S3 Tables, claiming to optimize Iceberg analytics on S3. Yet, these "special buckets" mainly fix AWS's own limits—like request caps—not universal object storage issues. With AIStor, you get unmatched performance, no vendor lock-in, and no extra costs for table maintance.
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AIStor's Prompt API transforms healthcare data—analyze MRI scans, uncover trends in medical records, and accelerate research with natural language prompts. From automating image analysis to streamlining patient care, it empowers better outcomes for providers, researchers, and patients
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How does Exness handle massive data volumes and demanding AI/ML workloads? By moving to an on-prem infrastructure powered by MinIO. From scaling their data lake to managing traffic peaks of 200 Gbps, MinIO supports their AI workflows, disaster recovery, and more.
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An educational services leader transformed its IT with MinIO, achieving 40% faster performance and 30% lower costs. Powered by Kubernetes, MinIO supports AI, ML, and scalable data lakes with active-active replication. Discover how they modernized for innovation and resilience.
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As AI workloads drive cloud costs through the roof, many companies are rethinking their approach. Moving select AI tasks back on-prem offers a path to predictable costs, improved performance, and stronger data control.
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Interoperability is the key to building a flexible, future-ready AI data stack. As proprietary systems lock down innovation and drive up costs, open tools like S3-compatible storage and multi-format table systems offer the freedom to scale and adapt.
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Unlock real-time analytics, scalability, and future-proof your data with a lakehouse. Hadoop can't keep up with AI, but a lakehouse blends the best of data lakes and warehouses. Get actionable tips to smoothly phase your migration with minimal disruption.
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To ensure AI success, start by hiring a data engineer, not an AI/ML expert. Learn from our experience and find out why a strong data foundation—focused on object storage, data lakehouses, and optimized pipelines—is critical for scalable, efficient AI/ML workloads.
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Tesla just open-sourced its Tesla Transport Protocol over Ethernet (TTPoE). Like Tesla’s earlier move to share EV patents, this opens the door to faster innovation through open-source contributions, proving once again that transparency drives progress.
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Enhance your AI workflows by combining MinIO’s scalable AIStor with Polars, a lightning-fast DataFrame library. Learn how this powerful duo accelerates data pipelines, handles massive datasets, and offers powerful performance and scale.
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Faced with skyrocketing compute costs, MinIO data scientist Archana Vaidyanathan leveraged the power of the data lakehouse, which allows for flexible compute choices without overhauling storage. AIStor enhances this model, delivering speed, scalability, and cost savings.
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Simplifying your data streaming architecture with WarpStream, a cloud-native, Kafka-compatible platform that cuts costs and complexity, recently acquired by Confluent. Paired with MinIO's high-performance object storage, it's a powerful alternative to Kafka for scalable, cost-effective streaming.
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Confluent's WarpStream acquisition highlights the future of data streaming built on object storage. WarpStream’s cloud-native design cuts costs by 85% over traditional Kafka. Believe the hype: object storage drives low-cost, scalable performance.
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MinIO introduced its conditional write feature long before AWS S3’s recent announcement. This powerful tool offers greater control in high-concurrency environments, ensuring data consistency and reliability, especially in AI and ML workflows.
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Databrick's CEO Ali Ghodsi Decouple storage and compute for more control, lower costs, and scalability. Modern datalakes, built on high-performance object storage like MinIO, empower you to handle AI/ML workloads with flexibility and performance—without relying on proprietary platforms.
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The rise of lakehouse functionality is reshaping data management. ParadeDB's pg_lakehouse extension lets PostgreSQL integrate with object storage, enabling scalable, secure analytics. This makes the modernization of data infrastructure possible without extensive overhauls. Welcome to the future!
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Amid the AI frenzy, one silent hero powers it all: modern object storage. It may not be glamorous, but it's the backbone of today's data lakes, enabling vast, efficient data management. Discover how AIStor elevates your data infrastructure.
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AutoMQ enhances Kafka's architecture by using MinIO's object storage, cutting costs, and boosting elasticity while keeping Kafka API compatibility. This combo offers scalable, secure, and efficient data streaming, ideal for diverse cloud environments.
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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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