A collection of 20 posts tagged with "Data Lakehouses"
Technical catalogs track table state at the storage layer. Platform catalogs federate technical catalogs within an ecosystem. Business catalogs span the entire organization. Most organizations need all three layers working together.
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On-premises data should participate in cloud AI/ML workflows without copying it anywhere. Delta Sharing should be embedded directly into object storage, eliminating separate servers and infrastructure. Data stays put. Queries travel.
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Executive Summary Nomura’s Risk Advisory division transformed its data infrastructure by modernizing from an aging Hadoop environment to a hybrid object-native data lakehouse powered by MinIO AIStor. This shift delivered measurable gains in performance, stability, and scalability, reducing operational costs and risk, while improving data availability and supporting future AI and analytics workloads. Key benefits included: * Performance: Benchmarking demonstrated
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1. Executive Summary A global financial institution modernized its Data Analytics Platform, shifting from legacy, appliance-based storage to a high-performance, cloud-native MinIO AIStor-powered data lakehouse. This transition cut deployment time by 50%, maximized data insights, boosted AI model efficiency, improved existing analytics workflows, and enabled entirely new AI-driven use cases. 2. The Environment Like many global financial institutions, this organization
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Executive Summary AMD, a global leader in high-performance computing, graphics, and semiconductor technologies, faced challenges with its internal engineering systems. Their systems, including ServiceNow, Jira, GitHub repositories, telemetry pipelines, and infrastructure logs, were developed independently over time, but they were deeply interconnected. This created data silos that obscured the connections across the engineering lifecycle, hindering AMD's agility in
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Our customer, a large electric utility company with millions of users, was faced with challenges common to utility and energy companies around the world: how to manage ever-growing energy demands, while diversifying energy supply to incorporate renewable and sustainable sources. A key element of their strategy is a smart metering initiative - bringing the power of technology and AI to
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1. Executive Summary Our customer, a global telecommunications leader, established a Data Platform team to transform how data improved customer experiences and business operations. Faced with ballooning data growth and legacy storage constraints, they replaced aging legacy data storage systems with a high-performance, cloud-native data lakehouse, built on MinIO’s AIStor. The result: a scalable, cost-efficient foundation ready for AI,
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Delta Sharing + MinIO AIStor enables direct access to on-prem data from Databricks without data duplication. Organizations can now query their hybrid infrastructure through a single interface, eliminating storage costs and synchronization complexity.
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A major financial institution turned their "outdated and escalating" Hadoop costs into savings by combining AIStor + Dremio on existing hardware. The hybrid approach delivered cloud elasticity while reusing their infrastructure, proving you don't need to rip-and-replace everything to modernize
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The "big data king" that enterprises spent billions on is dying. Not from lack of trying, but because cloud-native alternatives now beat it at its own game. AIStor is faster and cheaper than HDFS. The revolution is happening in quarters, not decades.
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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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To support AI and analytics, a data lakehouse must be secure by design. This blog covers best practices for securing storage, metadata, and catalog layers including encryption, fine-grained IAM, audit logging, object locking, and multi-site replication without sacrificing performance.
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Relationships matter, especially in your data. Explore graph analytics without moving data using PuppyGraph, Apache Iceberg, and MinIO AIStor. Quickly set up a cloud-native graph analytics stack that uncovers hidden patterns directly from your data lakehouse.
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Legacy systems drain budgets, slow innovation, and block AI progress. This article shows how phased modernization cuts costs, boosts performance, and builds a future-ready data foundation without disruption. Inaction is the real risk.
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Want real-time analytics and blazing-fast performance? Learn how to build a high-speed, on-prem pipeline with Materialize and MinIO AIStor—faster than S3, high thoughput, and built for AI. Includes a full tutorial to get you up and running locally.
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dbt’s acquisition of SDF Labs reinforces a powerful trend: the modern data stack is open. Learn why this matters for performance, interoperability, and future-proofing your data strategy.
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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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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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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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