Big Data Resources

Training a Neural Network Model With Java and TensorFlow

Learn how to train a neural network model using the TensorFlow platform with Java and using a pre-trained model in a proper Spring Boot application.

April 17, 2026

by George Pod

· 3,358 Views · 1 Like

You Are Using Claude Wrong (And So Is Everyone You Know)

Claude isn't ChatGPT with a different logo, it's built on different principles that reward a different way of working, and that difference compounds.

April 14, 2026

by Faisal Feroz

· 4,984 Views · 5 Likes

Boost Your Spark Jobs: How Photon Accelerates Apache Spark Performance

Photon is Databricks’ native C++ engine that bypasses JVM bottlenecks by processing data in vectorized, SIMD-accelerated batches instead of row by row.

April 13, 2026

by Seshendranath Balla Venkata

· 3,073 Views · 1 Like

Apache Spark 3 to Apache Spark 4 Migration: What Breaks, What Improves, What's Mandatory

A comprehensive guide to migrating from Apache Spark 3.x to Spark 4.0, covering breaking changes, new features, and mandatory updates for smooth transition.

April 10, 2026

by Rambabu Bandam

· 4,853 Views · 1 Like

Schema Evolution in Delta Lake: Designing Pipelines That Never Break

Delta Lake prevents pipeline failures from schema drift using schema enforcement and schema evolution, allowing Spark pipelines to adapt safely to new columns.

April 10, 2026

by Seshendranath Balla Venkata

· 3,058 Views · 1 Like

Why Queues Don’t Fix Scaling Problems

Queues absorb spikes but not sustained overload. Without backpressure, limits, and monitoring, backlogs grow until systems fail.

April 8, 2026

by David Iyanu Jonathan

· 3,718 Views · 2 Likes

Spark on AmpereOne® M Arm Processors Reference Architecture

Deploy and tune Apache Spark on AmpereOne M, with setup steps, cluster configs, and benchmarks showing gains vs Ampere Altra in performance and efficiency.

April 6, 2026

by RamaKrishna Nishtala

· 3,528 Views · 2 Likes

Hadoop on AmpereOne Reference Architecture

Hadoop on AmpereOne M shows improved throughput, scaling, and efficiency, with setup, tuning, and benchmark insights for optimizing big data workloads.

April 3, 2026

by RamaKrishna Nishtala

· 5,638 Views

End-to-End Streaming Optimization: Kafka to Delta With Exactly-Once Guarantees

Kafka feeds the stream, Spark tracks progress via checkpoints, and Delta's transaction log ensures every event lands exactly once, even across failures and restarts.

April 1, 2026

by Seshendranath Balla Venkata

· 2,731 Views · 2 Likes

Delta Change Data Feed Deep Dive: Building Incremental Pipelines Without Complexity

Delta CDF in Databricks enables pipelines to process only changed rows with commit metadata, simplifying incremental ETL without full scans.

April 1, 2026

by Seshendranath Balla Venkata

· 3,163 Views · 1 Like

Queues Don't Absorb Load — They Delay Bankruptcy

Queues hide overload. Without back-pressure, limits, and scaling, lag just grows until failure. Bound queues, alert on lag, fail fast, and plan capacity.

March 30, 2026

by David Iyanu Jonathan

· 1,796 Views · 2 Likes

Scaling Kafka Consumers: Proxy vs. Client Library for High-Throughput Architectures

Scaling Apache Kafka consumption requires new patterns; proxy layers and client libraries offer practical solutions for high-throughput.

March 30, 2026

by Kai Wähner

CORE

· 1,523 Views · 3 Likes

Stop Leap-Second AI Drift in IoT Streams With PySpark

Leap seconds can corrupt timestamps and trigger AI drift in fintech IoT systems. Learn about drift types and how PySpark streaming fixes them in real time.

March 27, 2026

by Ram Ghadiyaram

CORE

· 2,271 Views · 1 Like

From Stream to Strategy: How TOON Enhances Real-Time Kafka Processing for AI

The TOON data format specifically targets the propagation of structured, validated, and semantically consistent data, thereby reducing ambiguity in real time.

March 27, 2026

by Gautam Goswami

CORE

· 2,090 Views

The Phantom Write Problem: Why Your Idempotency Implementation Is Silently Losing Data

A practical explanation of why idempotent APIs still produce phantom writes in production, and a race-free, transactional pattern to prevent them.

March 24, 2026

by Saumya Tyagi

· 4,203 Views · 2 Likes

From DLT to Lakeflow Declarative Pipelines: A Practical Migration Playbook

Migrating from DLT to Lakeflow is mostly an API refactor, swapping DLT for pipelines, separating streaming and materialized tables, and updating CDC logic.

March 19, 2026

by Seshendranath Balla Venkata

· 4,358 Views · 1 Like