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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. Though basic and easy to use, traditional table storage formats struggle to keep up. Track data files within the table along with their column statistics.

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A Flexible and Efficient Storage System for Diverse Workloads

Cloudera

Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases. There are also newer AI/ML applications that need data storage, optimized for unstructured data using developer friendly paradigms like Python Boto API. Diversity of workloads.

Systems 87
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Building Meta’s GenAI Infrastructure

Engineering at Meta

We focused on building end-to-end AI systems with a major emphasis on researcher and developer experience and productivity. Grand Teton builds on the many generations of AI systems that integrate power, control, compute, and fabric interfaces into a single chassis for better overall performance, signal integrity, and thermal performance.

Building 145
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Turbocharging Atlas: How we reduced server initialization time to less than 2 minutes

ThoughtSpot

ThoughtSpot prioritizes the high availability and minimal downtime of our systems to ensure a seamless user experience. In the realm of modern analytics platforms, where rapid and efficient processing of large datasets is essential, swift metadata access and management are critical for optimal system performance. What is metadata?

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Inside Agoda’s Private Cloud - Exclusive

The Pragmatic Engineer

In a previous two-part series , we dived into Uber’s multi-year project to move onto the cloud , away from operating its own data centers. But there’s no “one size fits all” strategy when it comes to deciding the right balance between utilizing the cloud and operating your infrastructure on-premises.

Cloud 232
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Data Engineering Weekly #206

Data Engineering Weekly

DeepSeek development involves a unique training recipe that generates a large dataset of long chain-of-thought reasoning examples, utilizes an interim high-quality reasoning model, and employs large-scale reinforcement learning (RL). It employs a two-tower model approach to learn query and item embeddings from user engagement data.

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How To Future-Proof Your Data Pipelines

Ascend.io

This elasticity allows data pipelines to scale up or down as needed, optimizing resource utilization and cost efficiency. Ensure the provider supports the infrastructure necessary for your data needs, such as managed databases, storage, and data pipeline services.