analytics in AI

As of 2026, there is a consistent analytics stack being deployed across leading AI model labs that consists of:

1. Apache Kafka for streaming data ingestion (from inferencing sessions?)
2. Apache Flink to automatically transform data streams into tabular data
3. Apache Iceberg (Parquet and object) as the tabular format
4. Apache Spark for data transformation
5. Airflow for data pipeline orchestration
6. Trino for interactive and ad-hoc data exploration

Based on data platform jobs,

Software	Role in stack	Named by OpenAI	Named by Anthropic	Named by xAI	Named by TML	Named by Together
Spark	Large-scale batch ETL, training data pipelines, data lake writes	Yes	Yes	Yes	Yes	Yes
Ray	Distributed Python compute; data processing + ML workloads on same cluster	No	No	No	Yes	No
Beam	Unified batch + streaming pipelines; portable across runners	No	No	No	Yes	No
Kafka	High-throughput event streaming backbone; decouples producers from consumers	Yes	Yes	Yes	Yes	Yes
Flink	Stateful stream processing; transforms event streams into aggregates or tables	Yes	Yes	Yes	No	Yes
Airflow	DAG-based workflow scheduler; sequences pipeline steps, manages dependencies	Yes	Yes	No	No	Yes
Iceberg	Open table format on object storage; schema evolution, time travel, ACID	Yes	No	No	No	No
Delta Lake	Open table format on object storage; similar to Iceberg, strong Spark integration	Yes	No	No	Yes	No
Parquet	Columnar file format; the physical storage layer under Iceberg/Delta	No	Yes	No	No	No
Trino	Interactive SQL over data lakes; ad-hoc queries without moving data	Yes	No	Yes	No	Yes
DuckDB	Embedded OLAP engine; fast columnar queries in-process, no cluster needed	No	Yes	No	No	No
Snowflake	Managed data warehouse; named as comparable alternative at Anthropic	No	Yes	No	No	Yes
ClickHouse	Columnar OLAP DB; sub-second queries on event/telemetry data at high ingest rates	No	No	Yes	No	Yes
dbt	SQL-based transformation layer; turns raw warehouse data into clean analytics models	No	Yes	No	No	Yes
Chronon	Feature platform; computes ML features consistently across training and serving	Yes	No	No	No	No