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For Apache DolphinScheduler cluster deployment, the author has summarized a document that can be followed directly from start to finish, facilitating subsequent operations such as deployment, upgrade, adding nodes, and reducing nodes.

Since our data warehouse data source is Kafka, the offline data warehouse needs to use Flume to collect data from Kafka to HDFS.

In actual projects, it is not feasible to always start Flume tasks in Xshell. Firstly, because there are many Flume tasks in the project, and secondly, once the Xshell page is closed, the Flume tasks will stop, which is very inconvenient. Therefore, it is necessary to start Flume tasks in the background.

Apache DolphinScheduler is an open-source distributed task scheduling system designed to help users automate the scheduling and management of complex tasks. DolphinScheduler supports multiple task types and can be run in both single-node and cluster environments. Below, we will introduce how to achieve automated packaging and single-node/cluster deployment of DolphinScheduler.

Apache DolphinScheduler is a modern data workflow orchestration platform with powerful visualization capabilities. DolphinScheduler aims to make it simple and easy for data engineers, analysts, data scientists, and other data professionals to build various data workflows, making data processing processes simpler and more reliable.

When talking about data processing, people often abbreviate it as “ETL.” However, if we look closely, data processing has undergone several iterations from ETL, ELT, XX ETL (such as Reverse ETL, Zero-ETL) to the currently popular EtLT architecture. While the Hadoop era mainly relied on ELT (Extract, Load, Transform) methods, the rise of real-time data warehouses and data lakes has rendered ELT obsolete. EtLT has emerged as the standard architecture for real-time data loading into data lakes and real-time data warehouses.

Some users originally used Airflow as their scheduling system. However, Airflow only allows workflow definition through code and lacks resource and project granularity which fails to meet customer needs in scenarios requiring strong permission control. Consequently, some users need to migrate their scheduling system from Airflow to Apache Dolphinscheduler.

Some users originally used Airflow as their scheduling system. However, Airflow only allows workflow definition through code and lacks resource and project granularity, which fails to meet customer needs in scenarios requiring strong permission control. Consequently, some users need to migrate their scheduling system from Airflow to Apache Dolphinscheduler.

There is a very interesting topic, do you agree?

why EtLT is gradually replacing ETL and ELT as the global mainstream data processing architecture?

By using Apache SeaTunnel, Milvus, and OpenAI, we can achieve more accurate book title similarity searches through large language models, bringing significant technological breakthroughs to the field of book search, while also providing valuable references for semantic understanding. I hope this can provide some inspiration to everyone.

With the cost of a cup of Starbucks and two hours of your time, you can own your own trained open-source large-scale model. The model can be fine-tuned according to different training data directions to enhance various skills, such as medical, programming, stock trading, and love advice, making your large-scale model more “understanding” of you. Let’s try training an open-source large-scale model empowered by the open-source DolphinScheduler!

It only takes 2 steps to finish.

On February 15, 2023, Teradata officially withdrew from China after 26 years.

I also record a tutorial on YouTube: https://youtu.be/pXBINJR2l5Y

I’m one of the zealous fans who crave to see which team will bring the cup home this year. I take action right away and make a Machine Learning prediction model by DolphinScheduler to forecast the champion of FIFA World Cup 2022 and get a betting strategy every day, in a few hours.

This is not a complex job, it takes only 3 steps to finish the predictions actually, and I share on GitHub how to do the prediction: https://github.com/jieguangzhou/FIFA-World-Cup-2022/tree/mas...