Data Resources

Building a Migration Readiness Engine for Analytics Workflows

The article describes building a "Migration Readiness Engine" to solve the visibility problem in large-scale analytics platform migrations.

October 5, 2026

by Ravi Krishna Palivela

· 682 Views · 1 Like

How Go Maps Work: From Buckets to Swiss Tables

This article is for Go developers who write `m := make(map[string]int)` every day but have never looked under the hood. We will use a coat-check analogy.

October 1, 2026

by Ilia Ivankin

· 1,188 Views · 1 Like

Jakarta Batch in Practice: Reliable Chunk-Oriented Processing for Enterprise Workloads

Jakarta Batch gives enterprise apps a standard model for long-running data processing with jobs, steps, readers, processors, writers, checkpoints, and tunable execution.

September 29, 2026

by Otavio Santana

CORE

· 1,460 Views · 3 Likes

The Warning That Never Stops the Agent

Deepagents tracks real session cost but only shows a toast. Nothing stops the loop from spending past a limit. A before_model hook that can jump to "end" closes that gap.

September 29, 2026

by Ninaad Rao

CORE

· 954 Views · 1 Like

Beyond Screenshots: Building Replayable Production Diagnostics for Hard-to-Reproduce Bugs

Capture privacy-safe production event timelines to reconstruct failures, correlate backend activity, and diagnose bugs that screenshots cannot explain reliably.

September 28, 2026

by Uthej Mopathi

CORE

· 730 Views · 3 Likes

Prompt Caching Doesn't Save Money on Turn One

Cache writes cost 1.25x the input price, and reads cost 0.1x. So a fresh conversation's first turn is more expensive with caching on.

September 25, 2026

by Ninaad Rao

CORE

· 1,419 Views · 2 Likes

Locking Down the Enterprise: Data Security Patterns for AI Integrations

Learn how to secure enterprise data in AI systems using data classification, access controls, encryption, vendor controls, prompt security, and output validation.

September 25, 2026

by Balaji Venkatasubramaniyar

CORE

· 1,663 Views · 1 Like

How to Prevent Retry Storms With Retry Budgets in Distributed Systems

Retry budgets coordinate retries across service boundaries, preventing transient failures from escalating into system-wide traffic amplification.

September 23, 2026

by Uthej Mopathi

CORE

· 2,139 Views · 3 Likes

Exploring the DBMS_DEVELOPER Package: JSON Metadata for Oracle Objects

Learn how Oracle Database 23ai’s DBMS_DEVELOPER package uses JSON metadata and ETags to simplify database schema documentation, migration, and change tracking.

September 18, 2026

by arvind toorpu

CORE

· 2,358 Views · 1 Like

RAG, Vector Databases, and MCP: Wiring Them Together for Production

This article offers a practical walkthrough for engineers moving past the "RAG demo" stage into systems that hold up in production.

September 18, 2026

by Balaji Venkatasubramaniyar

CORE

· 3,308 Views · 1 Like

Common Pitfalls in RAG Applications: What to Avoid When Using Vector Search and Embeddings

RAG is easy to launch and hard to keep reliable. Here are the most common failure modes, their causes, and the Databricks-specific fixes.

September 17, 2026

by Seshendranath Balla Venkata

· 2,773 Views · 1 Like

Why Real-Time Data Pipelines Are Becoming the Foundation of Industrial AI

Real-time data pipelines enable trustworthy industrial AI through continuous context. They ensure the right data reaches the model at the right time.

September 16, 2026

by Ajay Kumar Govindaram

· 3,165 Views · 2 Likes

Architecting Production AI Across Clouds: Patterns That Decide System Survival

In production, enterprise AI rarely fails at the model. It fails in the architecture around it. Here are the cross-cutting patterns that work.

September 16, 2026

by VenkataSrinivas Kantamneni

· 3,147 Views · 1 Like

Freshness Is the Missing SLO in Production Vector Search

Standard metrics can look healthy while an index still serves stale data, because freshness depends on the entire pipeline, not just query speed.

September 15, 2026

by Guru Hegde

· 2,676 Views · 1 Like

KV Cache vs Prompt Cache: What's the Difference, and How Are They Related?

KV cache avoids recomputing historical token states during generation, while prompt cache reuses identical prompt prefixes across requests to reduce latency and cost.

September 15, 2026

by Jake Tao

· 2,504 Views · 2 Likes

dbt Meets Apache Flink: One Workflow for Data Engineers

dbt meets Apache Flink: one SQL workflow for data engineers across Snowflake, BigQuery, Databricks, and real-time streaming pipelines on Confluent Cloud.

September 15, 2026

by Kai Wähner

CORE

· 2,010 Views · 1 Like

Data Governance for the Agentic Era

This article explores how modern data governance and AI-ready architecture help enterprises manage data quality, risk, compliance, and AI at scale.

September 14, 2026

by Dr Gopala Krishna Behara

CORE

· 2,585 Views · 3 Likes

Building a 2KB Histogram with Sub-0.2% Percentile Error

It uses float-like encoding for O(1) bucket indexing and trapezoid interpolation for accurate percentile estimation — with no floating-point math on the recording path.

September 14, 2026

by bingxi Wu

· 1,178 Views · 2 Likes

Freshness as a First-Class Schema Concern: Modeling Data Staleness Across 83 Data Sources

How I modeled data freshness as a first-class schema concern across 83 sources, so the atlas never lies about how old its numbers are.

September 14, 2026

by Josie Leung

· 1,572 Views · 1 Like

Prompt Caching: Overriding Tokenization for Faster and More Cost-Effective AI

Prompt caching allows AI systems to reuse the processing of unchanged token sequences, resulting in faster inference, lower latency, and reduced costs.

September 11, 2026

by Ravi Ranjan Shahi

· 3,747 Views · 2 Likes

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