This article aims to solve the performance problems of offline data warehouses (daily and hourly) during production and usage.
This article is an overview of the best practices for big data processing in Spark taken from a lecture.
This article introduces the optimization and evolution of Flink Hudi's original mini-batch-based incremental computing model through stream computing.
This article explains the background of Delta Lake along with practices, problems, and solutions.
This article briefly discusses the metadata service and multi-engine support capabilities of the Alibaba Cloud Data Lake Formation (DLF) service.
This article explains the benefits, architecture, and implementation challenges of data lake metadata services.
This article introduces the major changes and new features of Flink 1.11
This article introduces the enhanced capabilities of Flink 1.11 to support SQL to process batch and streaming data
One of the release managers of Flink 1.11.0 shares his deep insights into the long-awaited features and explains them from different perspectives.
Read on to see exactly what happened to Flink in 2019, in particular how Alibaba has contributed to Flink.
In this blog, we'll discuss the evolution of Cainiao's Flink implementation solution and supply chain data in terms of real-time data technology architecture.
This article covers the evolution of the OPPO real-time data warehouse and development of Flink SQL.
This article briefly introduces Netflix's data platform team and its key product, Keystone.
In this article, Lu Hao of Meituan-Dianping shares the company's practices using the Flink-based real-time data warehouse platform.
This article introduces the architecture and practices of the Bilibili's Saber real-time computing platform by considering the pain points of real-time computing.
This article introduces the technical evolution of Apache Flink during its application in Kuaishou and Kuaishou's future plans regarding Apache Flink.
This blog shares how Lyft built a large-scale near real-time data analytics platform based on Apache Flink.
This article describes the new features, improvements, and important changes of Flink 1.11 and Flink's future development plans.
Jason addresses the bugs and compatibility issues with Flink-Hive by operating on a Hive database using Flink SQL to demonstrate some of the features provided.
Jason introduces the architecture of Hive integration in Flink, discusses problems, and how to solve them.
