Multi-Tenant Data Ingestion with Apache Iceberg Views: A Spark-Powered Single Table Design
In this tutorial, I’ll walk you through a comprehensive system for multi-tenant data ingestion using Apache Spark and Apache Iceberg. We’ll implement a single-table design that partitions data by tenant ID and creates specialized Iceberg views to expose each tenant’s data separately.
Video Guides
Architecture Overview
This solution uses:
The workflow:
Step 1: Set Up the Environment
First, let’s set up our Docker-based environment with the following docker-compose.yml:
This setup provides:
Step 2: Data Ingestion Process
Let’s examine the data ingestion script that reads multi-tenant data and merges it into our partitioned Iceberg table:
Key aspects of this ingestion process:
Step 3: Creating Iceberg Views for Each Tenant
After ingesting data into our partitioned Iceberg table, we need to create tenant-specific views:
his script:
Understanding the Multi-Tenant Architecture
Let’s break down the key components of this solution:
Single Table Design
Data Flow
Tenant-Specific Views
Benefits of Iceberg Views for Multi-Tenant Data
Running the Solution
Start the environment:
Create the tenant views:
Github :
Resources for Learning More
Video Tutorials
By using this approach, you can create a highly scalable, efficient, and secure multi-tenant data platform powered by Apache Iceberg and Spark.
Data Engineer | AWS, GCP, Snowflake, DBT, Iceberg , DataBricks, LLMs, RAG | Scalable Data Pipelines & GenAI Solutions
4moThanks for sharing, Soumil