Big Data Fundamentals: data ingestion tutorial

Data Ingestion with Apache Iceberg: A Production Deep Dive

1. Introduction

The relentless growth of data volume and velocity presents a constant challenge for modern data platforms. We recently faced a critical issue at scale: rebuilding materialized views for ad-hoc analytics was taking upwards of 12 hours, impacting query latency for our data science teams. The root cause wasn’t compute, but the underlying data lake’s inability to efficiently manage large-scale updates and concurrent reads/writes. Traditional Hive-style partitioning and file formats were proving insufficient. This led us to adopt Apache Iceberg as a core component of our data ingestion pipeline, and this post details our experience, focusing on architectural considerations, performance tuning, and operational best practices. We operate a multi-petabyte data lake on AWS, utilizing Spark for ETL, Presto for querying, and a hybrid batch/streaming ingestion model. Cost-efficiency and sub-second query latency are paramount.

2. What is Data Ingestion with Apache Iceberg in Big Data Systems?

Iceberg isn’t a data ingestion tool per se, but a table format that fundamentally changes how we approach data ingestion and management in object storage (S3, GCS, Azure Blob Storage). It provides ACID transactions, schema evolution, time travel, and efficient query planning on data lakes. Unlike Hive tables which rely on directory listings and file metadata, Iceberg uses metadata files (manifest lists and manifest files) to track data files. These metadata files are versioned, enabling consistent snapshots and atomic operations.

From a protocol perspective, Iceberg leverages Parquet (or ORC) for data storage, but adds a layer of metadata management on top. Ingestion involves writing data to Parquet files, creating manifest files listing these files, and then committing a new snapshot to the Iceberg metadata layer. This commit operation is atomic, ensuring data consistency even during concurrent writes. The key benefit is decoupling storage from metadata, allowing for optimized query planning and efficient updates.

3. Real-World Use Cases

• Change Data Capture (CDC) Ingestion: We ingest near real-time changes from transactional databases (PostgreSQL, MySQL) using Debezium and Kafka Connect. Iceberg allows us to efficiently append these changes to our data lake without requiring full table rewrites.
• Streaming ETL: Processing clickstream data from web applications requires continuous updates to aggregated metrics. Iceberg’s snapshot isolation and efficient updates minimize the impact of concurrent reads and writes.
• Large-Scale Joins: Joining large fact and dimension tables is a common analytical workload. Iceberg’s metadata filtering capabilities significantly reduce the amount of data scanned during joins, improving query performance.
• Schema Validation & Evolution: As data sources evolve, schemas change. Iceberg’s schema evolution features allow us to add, drop, or rename columns without breaking existing queries.
• ML Feature Pipelines: Generating features for machine learning models often involves complex transformations and aggregations. Iceberg’s time travel capabilities allow us to reproduce past feature sets for model retraining and auditing.

4. System Design & Architecture

graph LR
    A[Data Sources (DBs, APIs, Streams)] --> B(Kafka);
    B --> C{Spark Streaming/Batch};
    C --> D[Iceberg Table (S3)];
    D --> E[Presto/Trino];
    E --> F[Data Science Tools];
    C --> G[Hive Metastore];
    G --> D;
    subgraph AWS
        D
        G
    end

This diagram illustrates our end-to-end pipeline. Data originates from various sources, lands in Kafka, and is processed by Spark (both streaming and batch jobs). Spark writes data to Iceberg tables stored in S3. Presto/Trino queries these tables for analytical workloads. The Hive Metastore serves as the metadata catalog for Iceberg tables.

For cloud-native setups, we leverage AWS EMR with Spark and Presto. We’ve also experimented with GCP Dataflow for streaming ingestion, which integrates well with Iceberg through the iceberg-spark connector. Partitioning is crucial. We partition by date and a hash of a key identifier (e.g., user_id) to distribute data evenly across partitions.

5. Performance Tuning & Resource Management

Iceberg’s performance is heavily influenced by Spark configuration. Key tuning parameters include:

• spark.sql.shuffle.partitions: Controls the number of partitions during shuffle operations. We’ve found a value of 200-400 to be optimal for our cluster size.
• fs.s3a.connection.maximum: Limits the number of concurrent connections to S3. Increasing this value (e.g., 1000) can improve throughput.
• spark.sql.files.maxPartitionBytes: Controls the maximum size of a partition when reading files. Adjusting this value can impact the number of tasks launched.
• iceberg.spark.compaction.enabled: Enables automatic compaction of small files. Essential for maintaining query performance.
• iceberg.spark.compaction.target-file-size: Sets the target size for compacted files (e.g., 128MB).

File size is critical. We aim for Parquet files around 128-256MB. Smaller files lead to increased metadata overhead and slower query performance. Regular compaction is essential to prevent file fragmentation. We monitor the number of files per partition and trigger compaction jobs automatically when the count exceeds a threshold.

6. Failure Modes & Debugging

• Data Skew: Uneven data distribution can lead to tasks taking significantly longer than others. We use Spark’s adaptive query execution (AQE) to mitigate skew. Monitoring task durations in the Spark UI is crucial.
• Out-of-Memory Errors: Large joins or aggregations can exhaust memory. Increasing executor memory and enabling AQE’s skewed join optimization can help.
• Job Retries: Transient network errors or S3 throttling can cause job failures. Configuring appropriate retry policies in Spark is essential.
• DAG Crashes: Complex pipelines can experience DAG crashes due to unexpected errors. Detailed logging and monitoring are critical for identifying the root cause.

We use Datadog for monitoring Spark jobs and S3 metrics. Alerts are configured to notify us of long-running tasks, high memory usage, and job failures. The Spark UI provides valuable insights into task execution and data skew.

7. Data Governance & Schema Management

We use the Hive Metastore as our central metadata catalog for Iceberg tables. This allows us to leverage existing tooling and integrations. We’ve integrated a schema registry (Confluent Schema Registry) to enforce schema validation during ingestion. Schema evolution is managed using Iceberg’s built-in features, ensuring backward compatibility. We version our Iceberg tables using tags, allowing us to easily roll back to previous versions if necessary.

8. Security and Access Control

We leverage AWS IAM roles and policies to control access to S3 buckets and Iceberg tables. We’ve implemented row-level security using Presto’s access control features, allowing us to restrict access to sensitive data. S3 bucket policies are configured to enforce encryption at rest and in transit. Audit logging is enabled on S3 to track data access and modifications.

9. Testing & CI/CD Integration

We use Great Expectations to validate data quality during ingestion. We define expectations for schema, data types, and data ranges. DBT tests are used to validate data transformations. Our CI/CD pipeline includes automated regression tests that verify the correctness of our Iceberg tables. We use Terraform to manage our infrastructure and ensure consistent deployments.

10. Common Pitfalls & Operational Misconceptions

• Ignoring Compaction: Leads to file fragmentation and poor query performance. Mitigation: Implement automated compaction jobs.
• Insufficient Partitioning: Results in large partitions and slow query performance. Mitigation: Carefully choose partitioning keys based on query patterns.
• Over-Partitioning: Creates excessive metadata overhead and slows down metadata operations. Mitigation: Balance partitioning granularity with metadata overhead.
• Not Monitoring File Sizes: Leads to suboptimal performance. Mitigation: Monitor file sizes and trigger compaction when necessary.
• Assuming Iceberg Solves All Problems: Iceberg is a table format, not a magic bullet. Proper data modeling, partitioning, and tuning are still essential.

11. Enterprise Patterns & Best Practices

• Data Lakehouse vs. Warehouse Tradeoffs: Iceberg enables a data lakehouse architecture, combining the flexibility of a data lake with the reliability of a data warehouse.
• Batch vs. Micro-Batch vs. Streaming: Choose the appropriate ingestion pattern based on latency requirements.
• File Format Decisions: Parquet is generally preferred for analytical workloads due to its columnar storage and compression capabilities.
• Storage Tiering: Leverage S3 storage tiers (Standard, Intelligent-Tiering, Glacier) to optimize cost.
• Workflow Orchestration: Use Airflow or Dagster to orchestrate complex data pipelines.

12. Conclusion

Adopting Apache Iceberg has been transformative for our data platform. It has enabled us to overcome the limitations of traditional data lake architectures, improve query performance, and simplify data management. Moving forward, we plan to benchmark different compaction strategies, introduce schema enforcement using a schema registry, and explore migrating to newer file formats like Apache Arrow. The key takeaway is that a robust data ingestion strategy, built on a solid table format like Iceberg, is essential for building a reliable, scalable, and cost-effective Big Data infrastructure.