Parquet in Production at Scale

The Real World Pipeline: In a modern data platform, Parquet sits at the heart of the storage layer in a lakehouse architecture. Consider a typical clickstream pipeline at a company processing 100 to 500 thousand events per second. Events arrive through Kafka or Kinesis, get micro batched or hourly batched, then land as Parquet files in cloud object storage like Amazon S3, Google Cloud Storage (GCS), or Azure Blob Storage. A single day of events might represent 5 to 20 TB uncompressed. After Parquet encoding and compression, this compresses down to 1 to 4 TB on disk: a 3x to 10x reduction. Over 30 days, you accumulate 30 to 120 TB of Parquet data representing 150 to 600 TB of logical events. Query engines like Apache Spark, Trino, Presto, or AWS Athena read these files for analytics. Query Performance at Scale: A dashboard query scanning 30 days of data logically touches 150 to 600 TB uncompressed. Because the query selects only 5 to 10 columns out of 200, the engine reads perhaps 5 to 15 percent of bytes: 7.5 to 90 TB from disk. Predicate pushdown on event_time filters further eliminate 50 to 80 percent of row groups. The final read might be just 2 to 8 TB of Parquet data. On a 50 to 200 node cluster, this completes in tens of seconds, with p95 query latencies under 30 seconds for complex aggregations and joins. Without Parquet's columnar layout and statistics, the same query on raw JSON would require reading 100 to 600 TB and take minutes to hours. Integration with Table Formats: Higher level table formats like Delta Lake, Apache Iceberg, and Apache Hudi all use Parquet as the physical storage layer. These systems add transaction logs, schema evolution rules, and partitioning schemes on top of Parquet. When an analyst runs a query through a Business Intelligence (BI) tool, the heavy lifting at the storage level is still Parquet: encoding, compression, and vectorized columnar scans. This decoupling is powerful. You design partitioning, compaction, and indexing at the table format layer. Meanwhile, Parquet handles efficient encoding, compression, and statistics that enable fast scans. Delta Lake might maintain a transaction log with file level metadata, but when Spark actually reads the data, it's reading Parquet files and leveraging row group statistics. Cost and Latency Wins: Parquet's efficiency translates directly to cost savings. Storing 30 to 120 TB instead of 150 to 600 TB means lower S3 or GCS bills. Reading 2 to 8 TB instead of 100 to 600 TB means fewer compute hours and lower data transfer costs. For organizations running thousands of queries per day, these savings compound into millions of dollars annually. Latency improvements are equally dramatic. Complex reports that took 5 to 10 minutes on JSON complete in 10 to 30 seconds on Parquet. This transforms user experience: dashboards become interactive, analysts can iterate faster, and ad hoc exploration becomes feasible at petabyte scale.