Live Appointment | "Five Lectures on Real-time Lake Warehouse Practice" Lecture 2: Real-time Lake Warehouse Functional Architecture Design and Implementation

From data to large model application, on November 25th, Hangzhou Yuanchuang Conference was held to share development tips.

Nowadays, large-scale, high-timeliness, and intelligent data processing are "rigid needs". Enterprises need more powerful data platforms to cope with the challenges of data query, data processing, data mining, data display, and parallel computing models. Hucang An all-in-one solution was born.

"Five Lectures on Real-time Lake Warehouse Practice" is a series of live broadcast events created by Kangaroo Cloud. It will focus on the construction trends and common issues of real-time lake warehouses, invite core product & technology experts who are working on the front line of enterprise digitalization, and combine practical case analysis to share with the audience Discuss the cutting-edge technology in the field of real-time lake warehouse.

The second lecture of "Five Lectures on Real-time Lake Warehouse Practice" - "Real-time Lake Warehouse Functional Architecture Design and Implementation" will be broadcast on October 11 at 15:00-16:00.

Hurry up and book a live broadcast~

Click the link or scan the QR code on the poster to make an appointment: https://www.dtstack.com/bbs/article/10110?src=szsm

"Dtstack Product White Paper": https://www.dtstack.com/resources/1004?src=szsm

"Data Governance Industry Practice White Paper" download address: https://www.dtstack.com/resources/1001?src=szsm Friends who want to know or consult more about Kangaroo Cloud big data products, industry solutions, and customer cases, please browse Kangaroo Cloud official website: https://www.dtstack.com/?src=szkyzg

At the same time, students who are interested in big data open source projects are welcome to join "Kangaroo Cloud Open Source Framework DingTalk Technology qun" to exchange the latest open source technology information, qun number: 30537511, project address: https://github.com/DTStack

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