Data Mesh for AI Agents: How to Prepare the Data Layer
Data Engineering for AI agents: how data mesh, semantic models, and MCP tools help balance precision, security, and cost in enterprise systems.
Data Engineering
Data Engineering on ThecoreGrid focuses on building scalable, reliable, and efficient data platforms for modern highload systems.
We cover architecture and operation of data pipelines, batch and stream processing, data modeling, and storage systems designed for performance and consistency. Topics include distributed processing frameworks, real-time ingestion, ETL/ELT patterns, schema evolution, and data quality management in production environments. We analyze trade-offs between latency, throughput, and cost, as well as failure handling, observability, and governance in large-scale data systems. Content is based on real-world BigTech practices, including incident post-mortems, platform design decisions, and lessons from operating data infrastructure at scale. Instead of introductory tutorials, we provide deep technical insights into building and maintaining data platforms that support critical business workloads. The tag is aimed at data engineers, platform teams, backend engineers, and architects responsible for robust and scalable data ecosystems.
Signature Search in tree networks without unnecessary magic
Signature Search in tree networks: probabilistic analysis of five strategies, exact and approximate time estimation, occupancy and synchronization overhead.
FHIR and Kafka for Wearable Analytics
FHIR and Kafka for wearable analytics: analysis of cloud-native architecture, FHIR normalization, low-latency ingestion, and clinical workloads
GPU LZ77 Decode: Where Serialization is Hidden
GPU LZ77 decoding on the H100: where serialization is hidden, why parsing matters more than copying, and the trade-offs involved in data addressability
RAP in Spotify: an index for point queries
RAP in Spotify demonstrates how an external index on top of Parquet accelerates point queries in the data lake without copying data to serving databases
Agentic Nesting Changes System Integration
How Agentic Nesting Transforms Enterprise Application Integration via Multi-Agent Architecture and Semantic System Orchestration
Satellite inference on continents without degradation
Satellite inference on terabytes of data: how the OlmoEarth platform works and the architectural solutions that eliminate I/O and scaling bottlenecks
AI accelerators for scientific computing limits
AI accelerators for scientific computing: bridging the precision, memory, and execution gap when migrating HPC workloads to NPUs –>
Data provenance without over-deletion in AI pipelines
Accurate data provenance at the record and token level bridges the gap in AI unlearning: how to find the forget set without mass data deletion. The problem arises at the moment of consent withdrawal by the author. Unlearning algorithms (e.g., NPO or RMU) expect a ready forget set, but in real pipelines, it does not … Read more
Distributed Model Counting without Bottlenecks
Distributed model counting with work-stealing: how gDMC reduces overhead and solves the load balancing problem in #SAT systems