#apache-flink

Apache Flink Agents 0.3.1 Release Announcement

Cộng đồng Apache Flink vừa phát hành bản vá lỗi đầu tiên cho series Flink Agents 0.3, bao gồm 5 bản sửa lỗi, hỗ trợ Flink 2.3 và cải tiến trình cài đặt. Người dùng nên nâng cấp lên phiên bản mới nhất.

Nếu bạn đang phát triển ứng dụng xử lý dữ liệu thực thời với Flink và muốn đảm bảo tính ổn định, hiệu suất tối ưu cùng hỗ trợ mới nhất cho Flink 2.3, thì đây là cơ hội để cập nhật các sửa lỗi và cải tiến mới nhất trong Flink Agents 0.3.1.

Sub-Second Contextualization At Scale: Turning Raw SCADA Data into Analyst-Ready Rows with Flink and Snowflake

Sub-Second Contextualization At Scale: Turning Raw SCADA Data into Analyst-Ready Rows with Flink and Snowflake How to stream industrial sensor data from a historian into Snowflake with full business …

Real-Time Fraud Detection & Financial Compliance

Confluent Cloud provides a real-time fraud detection platform built on Apache Kafka and Apache Flink, addressing the three core requirements for production-grade financial systems: security, resilience, and compliance. Security features include TLS encryption, BYOK, client-side field-level encryption (CSFLE), private networking, RBAC, OAuth 2.0, and SAML SSO. Resilience is backed by a 99.99% uptime SLA, multi-availability-zone deployments, and zero-downtime maintenance. Compliance coverage spans PCI DSS, SOC 2, GDPR, CCPA, DORA, FINRA, and other financial regulations. The post walks through a full production architecture covering transaction ingestion, stream processing, ML model scoring, case management, audit trails, and global fraud intelligence via cluster linking.

Introducing Flink's Native S3 FileSystem: Built for Performance, Designed for Production

Apache Flink 2.3 ra mắt flink-s3-fs-native, một plugin filesystem S3 không phụ thuộc Hadoop, sử dụng AWS SDK v2. Plugin mới này cải thiện hiệu suất với thời gian checkpoint trung bình nhanh hơn 1,85 lần và throughput cao hơn 2,17 lần so với Presto plugin, đồng thời giảm kích thước JAR xuống còn ~13 MB. Người dùng chỉ cần thay thế JAR và cập nhật cấu hình mà không ảnh hưởng đến các checkpoint hiện có.

Nếu bạn đang phát triển ứng dụng xử lý dữ liệu lớn trên S3 với Flink và muốn tối ưu hiệu suất, giảm chi phí bộ nhớ và tránh rủi ro bảo mật từ các phiên bản cũ của AWS SDK, flink-s3-fs-native sẽ là giải pháp tối ưu mới nhất để nâng cấp công nghệ của bạn.

Apache Flink 2.3.0 Release Announcement

Apache Flink 2.3.0 is now available, implementing 15 FLIPs with major improvements across SQL, connectors, and runtime. New SQL operators FROM_CHANGELOG and TO_CHANGELOG bridge append-only and dynamic changelog tables. Materialized tables gain DDL parity with regular tables and fine-grained refresh control via a new START_MODE clause. The SinkUpsertMaterializer is reworked with an explicit ON CONFLICT clause and watermark-based compaction to reduce state size. A new native S3 filesystem plugin built on AWS SDK v2 replaces Hadoop/Presto-based connectors with non-blocking I/O and zero Hadoop dependencies. Runtime improvements include adaptive partition selection for backpressure handling, watermark alignment redesign for faster backlog processing, checkpointing during recovery from unaligned checkpoints, and application-level lifecycle management with a new Web UI Applications tab.

Confluent: Data in motion

Batch customer data platforms can't capture user intent as it forms — by the time a nightly sync completes, the intent moment is gone. A streaming-native architecture built on Apache Kafka and Apache Flink handles the full spectrum of personalization latency windows, from sub-100ms real-time bidding to multi-day email campaigns, using the same four-job pipeline: connect, stream, process, and govern. An AI-native layer (Confluent Intelligence) sits on top, enabling streaming agents with MCP tool-calling, a real-time context engine for LLMs, and built-in ML functions (ML_PREDICT, AI_COMPLETE) for embedding, ranking, and generative copy — all running as Flink jobs with exactly-once semantics and full lineage. The guide covers three production patterns (retail product recommendations, media feed personalization, cross-channel cart abandonment orchestration), a five-capability vendor evaluation framework, and a three-phase rollout roadmap from streaming backbone to autonomous agentic personalization.

How to Eliminate Training-Serving Skew in MLOps (2026)

Training-serving skew — the divergence between features used during model training and those seen at inference time — silently degrades ML accuracy and doubles infrastructure costs. The solution is a unified kappa architecture: compute features once in Apache Flink, dual-write to an offline store (Apache Iceberg or Delta Lake) for training and an online key-value cache for serving. DoorDash measured a 35.7% feature-value mismatch in their dual-pipeline setup; Netflix replaced a $93M/year dual-pipeline backfill with a $2M/year kappa replay. The reference architecture covers Kafka ingestion via Confluent's Kora engine, serverless Flink with event-time watermarks and exactly-once semantics, Tableflow for automated Iceberg/Delta materialization, and Stream Governance for schema enforcement and lineage. A tooling comparison covers Databricks, SageMaker+Kinesis, Tecton, Feast, and Confluent, with a decision framework based on latency requirements, existing stack investment, and pipeline fragmentation. The post is authored by a Confluent employee and promotes the Confluent Data Streaming Platform throughout.