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Training and Certification

Empower Your Partnership with Alibaba Cloud Professional Cloud Computing 2-Day Training

Explore the cloud computing training with Alibaba Cloud.

Alibaba Cloud x MongoDB Bootcamp: Getting Started with ApsaraDB for MongoDB

Explore this article to learn MongoDB's popular scenarios and practice your skills with ApsaraDB for MongoDB.

Alibaba Cloud Academy Student Central: The Invaluable Portal for Students

This short article introduces Alibaba Cloud Student, explaining its benefits and how to join the program.

Learning about Distributed Systems – Part 24: Massive Parallel Processing (MPP)

Part 24 of this series introduces massive parallel processing (MPP) and how it relates to the exploration of system extensibility

Learning about Distributed Systems - Part 23: Distributed Data Warehouse

Part 23 of this series explains why Offline data warehouses based on Hive and real-time data warehouses based on Kafka + Flink make it easy to distribute data warehouses.

Unlocking Potential through the Alibaba Digipreneur Program

The Alibaba Digipreneur Program is a groundbreaking training initiative designed to impart crucial skills to entrepreneurs and young individuals, enabling them to thrive in the digital age.

Learning about Distributed Systems - Part 22: Adaptive Optimization

Part 22 of this series discusses whether there is a more flexible method to optimize SQL query performance than CBO.

Learning about Distributed Systems - Part 21: Cost-based Optimization

Part 21 of this series focuses on why there is a CBO and how it is implemented.

Learning about Distributed Systems - Part 20: Rule-Based Optimization (RBO)

Part 20 of this series discusses another important SQL optimization method: rule-based optimization (RBO).

Learning about Distributed Systems – Part 19: Performance-Impacting Operations in SQL

Part 19 of this series discusses SQL performance optimization.

Learning about Distributed Systems – Part 18: Run AND Write Fast

Part 18 of this series explains how to improve application development efficiency on distributed systems.

Learning about Distributed Systems - Part 17: Shuffle

Part 17 of this series introduces several possible Shuffle methods and their adoption in MapReduce and Spark.

Learning about Distributed Systems – Part 16: Solve the Performance Problem of Worker

Part 16 of this series discusses problems with slaves' performance and MapReduce and whether there is room for improvement.

Learning about Distributed Systems - Part 3: Solving Short Storage

As the data grows rapidly and exponentially, cloud servers often run out of space to store them. Luckily, with distributed file systems like HDFS, we are now cracking the problem of low memory.

Learning about Distributed Systems - Part 14: Causes of Inconsistency

Inconsistency is so protruding, and we have tried every means to solve it. We want high availability under scalability.

Learning about Distributed Systems - Part 15: The Last Obstacle to Scalability

Part 15 of this series shows that distributed systems are not completely distributed, typical solutions to centralization problems, and the performance problems of masters.

Learning about Distributed Systems - Part 2: The Interaction Between Open Source and Business

This is the second blog of the distributed systems series. Today we look at the intriguing history of how academia and industry, open-source and business get along with each other.

Learning about Distributed Systems - Part 1: Why Using Distributed Systems?

This is the first part of a carefully conceived series of 20-30 articles on distributed systems, I hope to take the journey with you to understand the ins and outs of the distributed systems.

Learning about Distributed Systems - Part 5: Cracking Slow Calculation

How does data calculation work? Check out how MapReduce help divides the problem into smaller tasks and execute the task!

Learning about Distributed Systems - Part 4: Smart Ways to Store Data

Last time we talked about WHERE to store massive data, and this time, HOW. Massive data brings massive costs.