DeltaStreamer with incremental ETL and Broadcast Joins for Faster ETL

DeltaStreamer with incremental ETL and Broadcast Joins for Faster ETL
Try out labs
github.com/soumilshah1995?tab...
Blog www.linkedin.com/pulse/increm...
A broadcast join is a type of join operation used in distributed data processing systems, such as Apache Spark. In a broadcast join, one of the datasets is small enough to be distributed (broadcasted) to all worker nodes in the cluster. This small dataset is then joined with a larger dataset that is partitioned across the worker nodes.
How Broadcast Join Works
Broadcasting the Small Dataset: The small dataset is copied to every worker node in the cluster. Joining with the Larger Dataset: Each worker node then performs the join operation locally using its partition of the larger dataset and the entire small dataset. Advantages of Broadcast Join
Improved Performance: Since the small dataset is available locally on each worker node, the need for expensive shuffling of the larger dataset across the network is eliminated. This significantly reduces the data transfer overhead and speeds up the join operation.
Reduced Network I/O: By avoiding the shuffling of large datasets, broadcast joins reduce the amount of data that needs to be transferred over the network. This leads to lower network latency and bandwidth usage.
Scalability: Broadcast joins are particularly effective when one dataset is significantly smaller than the other. They allow for efficient scaling of join operations in distributed environments by leveraging local processing.
Simpler Execution Plan: The execution plan for a broadcast join is typically simpler compared to other join strategies, such as shuffle joins. This can result in better resource utilization and easier optimization.
Effective Use of Memory: Modern distributed processing frameworks are designed to handle in-memory operations efficiently. Broadcasting a small dataset leverages this capability, making the join operation faster compared to disk-based methods.
When to Use Broadcast Join
Size Disparity: When one dataset is much smaller than the other and can fit into the memory of each worker node.
Minimizing Network Traffic: When the goal is to minimize network traffic and reduce data shuffling.
Read-Heavy Workloads: In scenarios where the read operations are frequent, and the small dataset is relatively static and doesn’t change often.

[@zikomo8913]

Hey please keep making these backend and Data engineering related videos!
Just found your channel and it is one of those deeply hidden goldmines.
Big thanks to you!

[@SoumilShah]

Thank you sir