Kafka Project: Building Reliable, Scalable Data Pipelines with Apache Iceberg

Introduction

The increasing demand for real-time analytics and data-driven decision-making presents a significant engineering challenge: reliably ingesting, transforming, and serving massive datasets with low latency. Traditional batch processing pipelines often struggle to meet these requirements, especially when dealing with rapidly changing data schemas and the need for ACID transactions on data lakes. We recently faced this issue while building a fraud detection system for a large e-commerce platform. Ingesting 100+ GB/day of clickstream data, enriched with user profiles and transaction history, required a system capable of handling high velocity, schema evolution, and complex analytical queries with sub-second latency. This led us to deeply invest in a “kafka project” centered around Apache Iceberg as our table format, built on top of a Kafka-based ingestion layer and a Spark-based processing engine. The project aimed to deliver a unified view of customer behavior, enabling real-time fraud scoring and proactive risk mitigation.

What is "kafka project" in Big Data Systems?

In our context, “kafka project” refers to a comprehensive data management strategy built around Apache Kafka as the central nervous system for data ingestion and distribution, coupled with Apache Iceberg as the table format for our data lake. It’s not simply using Kafka; it’s architecting the entire pipeline to leverage Kafka’s strengths – high throughput, fault tolerance, and scalability – while addressing the limitations of traditional data lake approaches (lack of ACID transactions, schema evolution challenges).

Iceberg provides a crucial layer on top of object storage (S3, GCS, Azure Blob Storage) by introducing a metadata layer that enables ACID transactions, schema evolution, time travel, and efficient query planning. Data is typically ingested into Kafka in formats like Avro or Protobuf, then consumed by Spark streaming jobs that write data to Iceberg tables in Parquet format. The protocol-level behavior is critical: Kafka’s consumer groups ensure parallel consumption, and Iceberg’s metadata management allows for concurrent writes and reads without data corruption. We also leverage Kafka Connect for CDC (Change Data Capture) from relational databases, further enriching our data lake.

Real-World Use Cases

1. Clickstream Analytics: Ingesting user clickstream data via Kafka, transforming it with Spark, and storing it in Iceberg allows for real-time dashboards and personalized recommendations.
2. Fraud Detection: Combining clickstream data with transaction history (CDC from databases via Kafka Connect) in Iceberg enables complex feature engineering and real-time fraud scoring using machine learning models.
3. Supply Chain Optimization: Tracking inventory levels, shipment statuses, and demand forecasts through Kafka streams, and storing aggregated data in Iceberg, provides a holistic view of the supply chain.
4. Log Analytics: Aggregating application logs via Kafka, enriching them with metadata, and storing them in Iceberg facilitates troubleshooting, performance monitoring, and security auditing.
5. Personalized Marketing: Building user profiles based on behavioral data ingested through Kafka and stored in Iceberg allows for targeted marketing campaigns and improved customer engagement.

System Design & Architecture

graph LR A[Data Sources (Databases, APIs, IoT)] --> B(Kafka Topics); B --> C{Spark Streaming Jobs}; C --> D[Iceberg Tables (Parquet on S3/GCS/Azure Blob)]; D --> E[Query Engines (Spark SQL, Trino, Presto)]; E --> F[BI Tools & Applications]; subgraph CDC G[Relational Databases] --> H(Kafka Connect); H --> B; end subgraph Metadata Management D --> I[Hive Metastore/Glue Catalog]; end 

This diagram illustrates the core components. Data originates from various sources, lands in Kafka topics, is processed by Spark Streaming jobs, and is stored in Iceberg tables. Query engines then access the data for analytical purposes. Kafka Connect handles CDC from relational databases. A metadata catalog (Hive Metastore or AWS Glue) manages Iceberg table metadata.

For cloud-native deployments, we utilize AWS EMR with Spark and Kafka, leveraging S3 as the underlying storage. We’ve also experimented with GCP Dataflow for stream processing and Google Cloud Storage for storage, achieving similar results. Partitioning strategy is crucial: we partition Iceberg tables by event time and user ID to optimize query performance for time-series analysis and user-specific reporting.

Performance Tuning & Resource Management

Performance tuning is critical for maintaining low latency and high throughput. Key strategies include:

• Spark Configuration: spark.sql.shuffle.partitions is set to 200 to maximize parallelism during joins and aggregations. spark.driver.memory and spark.executor.memory are tuned based on data volume and complexity.
• Kafka Configuration: num.partitions for Kafka topics is set to a multiple of the number of Spark executors to ensure even distribution of data. fetch.min.bytes and fetch.max.wait.ms are adjusted to optimize Kafka consumer throughput.
• S3 Configuration: fs.s3a.connection.maximum is set to 100 to handle concurrent S3 requests. fs.s3a.block.size is tuned to optimize I/O performance.
• Iceberg Compaction: Regular compaction of small Parquet files is essential to improve query performance. We use Iceberg’s built-in compaction features, scheduled via Airflow.
• File Size Optimization: Aim for Parquet file sizes between 128MB and 1GB for optimal read performance.

These configurations significantly impact throughput, latency, and infrastructure cost. Improper tuning can lead to data skew, out-of-memory errors, and increased S3 costs.

Failure Modes & Debugging

Common failure modes include:

• Data Skew: Uneven distribution of data across Spark partitions, leading to performance bottlenecks. Diagnosed using Spark UI and mitigated by salting keys or using broadcast joins.
• Out-of-Memory Errors: Insufficient memory allocated to Spark executors or drivers. Diagnosed using Spark UI and mitigated by increasing memory allocation or optimizing data transformations.
• Job Retries: Transient errors during Kafka consumption or S3 writes. Monitored using Datadog alerts and mitigated by implementing robust retry mechanisms.
• DAG Crashes: Errors in Spark DAG execution. Diagnosed using Spark UI and logs.

Monitoring metrics like Kafka consumer lag, Spark executor memory usage, and S3 request latency are crucial for proactive identification of issues. We use Datadog for comprehensive monitoring and alerting.

Data Governance & Schema Management

We leverage the AWS Glue Catalog as our central metadata repository. Iceberg’s schema evolution capabilities are essential for handling changing data schemas. We use schema registries (Confluent Schema Registry) to enforce schema compatibility and prevent data corruption. Data quality checks are implemented using Great Expectations, ensuring data accuracy and completeness. Backward compatibility is maintained by adding new columns with default values and avoiding breaking changes to existing columns.

Security and Access Control

Data encryption is enabled at rest (S3 encryption) and in transit (TLS for Kafka and S3). We use AWS Lake Formation to manage fine-grained access control to Iceberg tables, granting permissions based on user roles and data sensitivity. Audit logging is enabled to track data access and modifications.

Testing & CI/CD Integration

We use Great Expectations for data validation, DBT for data transformation testing, and Apache Nifi unit tests for Kafka Connect pipelines. Pipeline linting is performed using terraform validate and spark-submit --validate. We have staging environments for testing new features and configurations before deploying to production. Automated regression tests are run after each deployment to ensure data quality and pipeline stability.

Common Pitfalls & Operational Misconceptions

1. Ignoring Schema Evolution: Leads to data corruption and pipeline failures. Mitigation: Implement a schema registry and enforce schema compatibility.
2. Insufficient Kafka Partitioning: Limits parallelism and throughput. Mitigation: Increase the number of Kafka partitions based on the number of Spark executors.
3. Lack of Compaction: Results in slow query performance due to a large number of small files. Mitigation: Schedule regular compaction jobs.
4. Incorrect S3 Configuration: Leads to I/O bottlenecks and increased costs. Mitigation: Tune fs.s3a.connection.maximum and fs.s3a.block.size.
5. Ignoring Data Skew: Causes performance bottlenecks and uneven resource utilization. Mitigation: Salt keys or use broadcast joins.

Enterprise Patterns & Best Practices

• Data Lakehouse vs. Warehouse: We’ve adopted a data lakehouse architecture, combining the flexibility of a data lake with the reliability and performance of a data warehouse.
• Batch vs. Micro-batch vs. Streaming: We use micro-batching for most use cases, balancing latency and throughput. True streaming is reserved for critical real-time applications.
• File Format Decisions: Parquet is our preferred file format due to its efficient compression and columnar storage.
• Storage Tiering: We use S3 Glacier for archiving historical data, reducing storage costs.
• Workflow Orchestration: Airflow is used for orchestrating data pipelines and scheduling compaction jobs.

Conclusion

The “kafka project,” centered around Kafka and Iceberg, has proven to be a robust and scalable solution for building reliable data pipelines. It addresses the challenges of high velocity, schema evolution, and ACID transactions in our data lake. Next steps include benchmarking new configurations, introducing schema enforcement using a schema registry, and migrating to a more efficient compression codec for Parquet files. Continuous monitoring, performance tuning, and adherence to best practices are essential for maintaining a healthy and scalable data platform.