Build a High-Performance Quant Research Platform with Apache Iceberg

In the world of quantitative finance, data management is foundational. Quant researchers spend a significant portion of their time on data-related tasks, often diverting attention from high-impact strategic analysis. This article explores how Apache Iceberg, when used in conjunction with Amazon S3 and AWS Glue, offers substantial performance, cost, and productivity benefits over traditional approaches like vanilla Parquet.

Why Apache Iceberg?

Apache Iceberg is an open table format designed to handle petabyte-scale analytic datasets. It supports ACID operations, time travel, and schema evolution, which are indispensable in quant research. Compared to accessing Parquet files directly in Amazon S3, Iceberg introduces a metadata layer that significantly optimises performance and scalability.

Productivity Boost for Quant Teams

Quant teams often ingest, validate, and update large datasets. Unlike vanilla Parquet, Iceberg supports insert, update, and delete operations natively, reducing the need for complex custom code and eliminating risks of inconsistent reads. Furthermore, Iceberg prevents lookahead bias through built-in time travel features.

Seamless Corrections and Historical Data Management

Iceberg simplifies tasks such as:

• Correcting erroneous data entries
• Filling in missing data points
• Performing backdated updates (e.g. stock splits, dividends)

Its metadata and manifest files make these operations scalable and efficient, even on massive datasets.

Time Travel and Snapshotting

Iceberg supports time travel via snapshots, enabling researchers to:

• Backtest strategies on historical states
• Debug data inconsistencies
• Maintain audit trails for compliance

Integration with Existing Tools

Iceberg integrates with:

• SQL-based engines (Athena, Spark, Trino, Hive)
• PyIceberg and DataFrame APIs for programmatic access

This ensures teams can adopt Iceberg without disrupting existing workflows.

Optimised Data Access and Partitioning

Vanilla Parquet often faces performance bottlenecks due to S3 API quotas and partitioning mismatches. Iceberg mitigates this by:

• Abstracting physical file layout
• Supporting intelligent partitioning strategies
• Offering salting to avoid S3 throttling

Performance Benchmark: Iceberg vs. Vanilla Parquet

Query Comparison

# Simple count query
spark.read.parquet("s3://bucket/data").count()  # Vanilla Parquet
spark.read.table("table_name").count()          # Iceberg

# Metadata-optimised Iceberg count
spark.read.format("iceberg").load(f"{table_name}.files")\
    .select(sum("record_count")).show(truncate=False)

Grouped Count by Exchange and Instrument

spark.read.parquet("s3://bucket/data")\
    .groupBy("exchange_code", "instrument")\
    .count().orderBy("count", ascending=False).show()

spark.read.table("table_name")\
    .groupBy("exchange_code", "instrument")\
    .count().orderBy("count", ascending=False).show()

Time-Based Distinct Date Query

spark.read.table("table_name")\
    .select(f.year("adapterTimestamp_ts_utc").alias("year"),
            f.month("adapterTimestamp_ts_utc").alias("month"),
            f.dayofmonth("adapterTimestamp_ts_utc").alias("day"))\
    .distinct().count().show()

Date-Filtered Grouping

spark.read.table("table_name")\
    .filter((f.col("adapterTimestamp_ts_utc") >= "2023-04-17") &
            (f.col("adapterTimestamp_ts_utc") <= "2023-04-18"))\
    .groupBy("exchange_code", "instrument")\
    .count().orderBy("count", ascending=False).show()

AWS Glue Write Job Comparison

AWS Glue Read Job Performance

Conclusion

Apache Iceberg delivers measurable gains across the board:

• Performance: Up to 52% faster reads
• Cost: Up to 32.4% reduction in DPU hours and 10–16% in storage
• Scalability: Handles petabyte-scale datasets with ease
• Productivity: Fewer failures, simplified updates, and time travel support

For quant research teams aiming to reduce engineering burden and focus on financial strategy, Iceberg presents a robust, future-ready data architecture.