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Schema Evolution

14 articles · Page 1 of 1
Xike
Xike
Aug 1, 2026 · Big Data

Big Data Series #6: Getting Started with Iceberg Lakehouse – Snapshots, Time Travel, and Writes

This tutorial explains how Apache Iceberg adds snapshot metadata to files on HDFS or object storage, enabling atomic writes, time‑travel queries, and schema evolution, and walks through a Docker‑based setup with a REST catalog, Spark 3.5.3, and hands‑on examples including table creation, data insertion, version queries, and troubleshooting tips.

Apache IcebergData LakeHDFS
0 likes · 16 min read
Big Data Series #6: Getting Started with Iceberg Lakehouse – Snapshots, Time Travel, and Writes
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 18, 2026 · Big Data

How AI-Driven Real-Time Data Lakes Are Ditching ETL: A Kafka‑to‑Iceberg Architecture Simplification

In the AI era, enterprises need a data foundation that supports both low‑latency streaming and long‑term analytics, and the combination of Kafka, Iceberg and object storage is emerging as a preferred solution; by moving ingestion capabilities closer to the message layer and eliminating external ETL jobs, a "zero‑ETL" approach reduces architectural complexity, improves consistency, and streamlines schema evolution and small‑file management.

CDCData LakeIceberg
0 likes · 27 min read
How AI-Driven Real-Time Data Lakes Are Ditching ETL: A Kafka‑to‑Iceberg Architecture Simplification
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 29, 2025 · Big Data

How MaxCompute Streaming Insert Revolutionized Real‑Time Data Migration from BigQuery

This article details how a leading Southeast Asian tech group migrated its real‑time write workloads from Google BigQuery to MaxCompute using MaxCompute Streaming Insert, covering architecture, core features, migration challenges, optimization strategies, business impact, and future enhancements.

Big DataBigQuery MigrationMaxCompute
0 likes · 9 min read
How MaxCompute Streaming Insert Revolutionized Real‑Time Data Migration from BigQuery
Java Baker
Java Baker
Jul 7, 2025 · Databases

Choosing the Right Database Schema for Dynamic Business Field Expansion

This article compares five common database extension strategies—from simple MySQL column additions to a hybrid MySQL‑HBase solution—detailing their implementation, advantages, drawbacks, and ideal scenarios, helping architects select the most scalable and maintainable design for evolving business data requirements.

Database DesignDynamic FieldsHBase
0 likes · 8 min read
Choosing the Right Database Schema for Dynamic Business Field Expansion
Big Data Technology & Architecture
Big Data Technology & Architecture
Aug 20, 2024 · Big Data

Practical Insights on Using Apache Paimon for Real-World Data Lake Scenarios

This article shares a personal, experience‑driven overview of Apache Paimon, highlighting its design simplicity, key capabilities such as schema evolution, stream‑batch unified processing, primary‑key support, and closed‑loop data handling, while discussing when its features are appropriate for production environments.

Apache PaimonBatch ProcessingBig Data
0 likes · 5 min read
Practical Insights on Using Apache Paimon for Real-World Data Lake Scenarios
StarRocks
StarRocks
May 22, 2024 · Big Data

Unlocking Data Lake Power: Iceberg Architecture & StarRocks Acceleration

Apache Iceberg offers a modern, ACID‑compliant table format for data lakes with features like hidden partitions and schema evolution, while StarRocks provides high‑performance query acceleration, metadata caching, and distributed planning to address Iceberg’s latency challenges, enabling seamless lake‑warehouse integration and real‑time analytics.

Apache IcebergData LakeMetadata Caching
0 likes · 19 min read
Unlocking Data Lake Power: Iceberg Architecture & StarRocks Acceleration
DataFunSummit
DataFunSummit
Oct 1, 2023 · Big Data

Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans

This presentation introduces Iceberg's core capabilities, details Xiaomi's practical applications—including log ingestion, near‑real‑time warehousing, offline challenges, column‑level encryption, and Hive migration—and outlines future development directions such as materialized views and cloud migration, providing a comprehensive view of modern data‑lake engineering.

Big DataData LakeFlink
0 likes · 22 min read
Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans
DataFunTalk
DataFunTalk
Jun 26, 2023 · Big Data

Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans

This presentation details Iceberg's core capabilities—transactional writes, schema evolution, implicit partitioning, and row‑level updates—while showcasing Xiaomi's real‑world applications such as log ingestion redesign, near‑real‑time warehousing, offline optimizations, column‑level encryption, Hive migration strategies, and outlining upcoming enhancements like materialized views and cloud migration.

Big DataColumn EncryptionData Lake
0 likes · 20 min read
Iceberg Data Lake: Core Features, Xiaomi Use Cases, and Future Plans
DataFunTalk
DataFunTalk
May 11, 2023 · Big Data

Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap

This article describes how ByteDance tackled petabyte‑scale feature storage by adopting Apache Iceberg, detailing the problem background, design choices, implementation of COW and MOR back‑fill strategies, performance optimizations, and future plans such as lake‑cold‑layering and materialized views.

Apache IcebergBig DataData Lake
0 likes · 16 min read
Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap
JavaEdge
JavaEdge
Jun 26, 2022 · Backend Development

Ensuring Forward and Backward Compatibility in Distributed Systems

This article explains why forward and backward compatibility are crucial for evolving systems, covering database encoding, schema evolution, REST and RPC communication, message brokers, and actor frameworks, and provides practical guidance for designing compatible data flows across services.

Message BrokerRESTRPC
0 likes · 22 min read
Ensuring Forward and Backward Compatibility in Distributed Systems
dbaplus Community
dbaplus Community
Jan 15, 2020 · Databases

How Didi Built Fusion-NewSQL: A High‑Throughput, Low‑Latency NewSQL on Distributed KV

Fusion-NewSQL is Didi’s internally‑developed NewSQL system built atop the Fusion distributed KV store, offering MySQL compatibility, high throughput, low latency, flexible schema changes, secondary indexes, and integration with ElasticSearch and Hive, with detailed architecture, data flow, and future roadmap.

MySQL CompatibilityNewSQLSchema Evolution
0 likes · 16 min read
How Didi Built Fusion-NewSQL: A High‑Throughput, Low‑Latency NewSQL on Distributed KV
Didi Tech
Didi Tech
Oct 8, 2019 · Databases

Design and Implementation of Fusion-NewSQL: A NewSQL System Built on Distributed NoSQL Storage

Fusion‑NewSQL is a NewSQL layer built atop Didi’s distributed KV store Fusion, translating MySQL queries into Redis‑style hashmaps, asynchronously maintaining secondary indexes, supporting fast Hive‑to‑Fusion loads and Elasticsearch integration, thereby delivering over 2 million QPS, 600 TB storage and flexible schema evolution for dozens of services.

IndexingMySQL CompatibilityNewSQL
0 likes · 15 min read
Design and Implementation of Fusion-NewSQL: A NewSQL System Built on Distributed NoSQL Storage