User Journey: Working with the Iceberg-Based Data Platform

Modern Enterprise Data Platform with Apache Iceberg

Storage

Cloud-native object storage with Iceberg tables providing ACID transactions and time travel

Processing

Spark for ETL, Kafka+Flink for streaming, DuckDB for interactive analytics

Access

Unified data access through BI tools, APIs, and centralized security

UI

Modern interfaces for data discovery, querying, visualization, and self-service

Based on the architecture above, here's how different users would interact with the system after the infrastructure team deploys the Iceberg technology stack:

Data Engineer Journey

1. Initial Access:

   • Login to the self-service portal using SSO credentials
   • Navigate to the Data Catalog section to view available datasets and infrastructure

2. Setting Up Data Sources:

   • Use the UI to register a new data source:
     • Define connection parameters for source systems
     • Set up Kafka topics for streaming data
     • Configure batch ingestion schedules

3. Creating Iceberg Tables:

   CREATE TABLE customer_transactions (
     transaction_id STRING,
     customer_id STRING,
     amount DECIMAL(10,2),
     transaction_time TIMESTAMP
   ) USING iceberg
   PARTITIONED BY (days(transaction_time))
   LOCATION 's3://data-lake/gold/transactions';

4. Data Pipeline Creation:

   • Configure Spark jobs through the UI:
     • Select source and target tables
     • Define transformations using SQL or visual tools
     • Set up quality checks and monitoring

Data Analyst Journey

1. Data Discovery:

   • Browse the Data Catalog to find relevant datasets
   • View table schemas, partitioning strategy, and data freshness
   • Check documentation and sample queries

2. Analysis with DuckDB:

   SELECT 
     date_trunc('month', transaction_time) as month,
     count(distinct customer_id) as unique_customers,
     sum(amount) as total_sales
   FROM customer_transactions
   WHERE transaction_time >= current_date - interval '90 days'
   GROUP BY 1
   ORDER BY 1;

3. Visualization Creation:

   • Use the query results to build dashboards in the Data Visualization module
   • Create charts showing trends, distributions, or comparisons
   • Save and share visualizations with teammates

Data Scientist Journey

1. Exploratory Analysis:

   • Access historical data through DuckDB interface
   • Analyze time-travel versions of data using Iceberg capabilities:

   SELECT * FROM customer_transactions FOR TIMESTAMP AS OF '2023-01-01 00:00:00';

2. Feature Engineering:

   • Create features using Spark through the UI
   • Store feature tables in Iceberg format
   • Version and track feature changes

3. Model Deployment:

   • Register models in the catalog
   • Connect models to streaming data via Kafka+Flink
   • Monitor performance through the visualization interface

Business User Journey

1. Dashboard Access:

   • Login to the Self-service Portal
   • View pre-built dashboards with key metrics
   • Apply filters for specific business questions

2. Self-service Analytics:

   • Use natural language interface to query data
   • Export results to spreadsheets
   • Schedule regular reports via the UI

System Administration

1. Access Control:

   • Manage user permissions through the centralized security interface
   • Apply table, row, and column-level access policies
   • Review audit logs of data access

2. Performance Monitoring:

   • View system health dashboard
   • Monitor query performance across components
   • Optimize partitioning and compaction strategies for Iceberg

The architecture delivers a seamless experience where the complexity of the underlying Iceberg implementation is abstracted away, allowing users to focus on deriving value from data rather than managing infrastructure.

Getting Started with the Platform

To begin using the platform, follow these steps:

1. Request access from your system administrator
2. Complete the onboarding tutorial available in the self-service portal
3. Connect your preferred tools using the provided connection strings
4. Start exploring the data catalog to discover available datasets

Best Practices

• Keep partitions reasonably sized (100MB-1GB)
• Use time-based partitioning for time-series data
• Leverage Iceberg's schema evolution for field additions
• Run periodic compaction jobs for optimal query performance
• Use time travel capabilities for audit and compliance needs

Code Sample

Iceberg Tutorial