DataFunCon2024 Beijing: Real‑Time Lakehouse and Big Data Sessions
The DataFunCon2024 Beijing conference on July 5‑6 showcases a series of technical talks about real‑time lakehouse architectures, big‑data analytics, and cloud‑native data warehouses, offering practitioners insights into Apache Paimon, SelectDB, and Doris implementations for faster, more agile data processing.
In today’s fast‑changing digital era, real‑time data insight has become a decisive factor for business success, and the power of the lake‑warehouse architecture is highlighted as a key enabler of speed and accuracy.
On July 5‑6, 2024, the DataFunCon2024 Beijing summit – themed “Big Data • Large Models • Dual‑Core Era” – will be held in Beijing, featuring a sub‑forum titled “Real‑Time Insight, Lake‑Warehouse Power” led by Apache Doris PMC Chair Chen Mingyu.
Talk 1: Real‑Time Lakehouse Architecture with Apache Paimon Speaker: Zhong Yujian, Xiaomi Software R&D Engineer (Master’s from Central South University). Outline: (1) What is Apache Paimon and why adopt it? (2) Building near‑real‑time lake‑warehouse with Paimon. (3) Project summary and future outlook. Audience benefits: understand Paimon’s principles and advantages, its real‑time lake‑warehouse use cases, and tuning techniques for real‑time pipelines.
Talk 2: The New Chapter of Data Warehouses – Cloud‑Native Real‑Time Warehouse SelectDB Speaker: Jiang Guoqiang, Vice President of Product at FeiLun Technology (former lead of Doris storage engine at Baidu and ES/OLAP at Tencent). Outline: (1) SelectDB overview and scenarios. (2) Core pain points and trends of data analysis. (3) Innovations in real‑time, cloud‑native, lake‑warehouse integration. (4) Future roadmap of SelectDB. Audience benefits: learn cutting‑edge data‑warehouse trends, performance advantages of SelectDB, and its cloud‑native innovations.
Talk 3: Apache Paimon Real‑Time Lakehouse Storage Foundation Speaker: Li Jinsong, PMC Chair of Apache Paimon and PMC Member of Apache Flink (Alibaba Cloud). Outline: (1) Real‑time lake‑warehouse for enterprise needs. (2) Building streaming ingestion with Flink. (3) Batch ETL with Spark. (4) High‑speed OLAP queries. Audience benefits: grasp the latest lake‑warehouse integration patterns and the unified storage concept behind Paimon.
Talk 4: Tencent’s Real‑Time Lakehouse Intelligent Optimization Practice Speaker: Chen Liang, Senior Engineer at Tencent. Outline: (a) Real‑time lake‑warehouse architecture under the Tianqiong big‑data system. (b) Intelligent optimization services. (c) Scenario‑based capabilities. (d) AI exploration. Audience benefits: learn how to upgrade traditional data‑warehouse architectures to real‑time lake‑warehouse, solve issues such as small files, query performance, latency, and cost, and apply intelligent management strategies.
Talk 5: Apache Doris Lake‑Warehouse Analytics System in Kuaishou Speaker: Li Zhenwei, Big‑Data Architect at Kuaishou. Outline: (1) Motivation for moving to lake‑warehouse analytics. (2) Caching practices within lake‑warehouse. (3) Automatic materialization techniques. Audience benefits: understand the design of lake‑warehouse analytics to meet performance and business needs, how caching resolves bottlenecks, and how automatic materialization reduces development effort.
The full agenda is available online; QR codes are provided for calendar registration, ticket discounts, and further promotional offers.
DataFunTalk
Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
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