Single service comparison

Engine and API boundary differ. Athena exposes a single Trino/Presto-derived SQL dialect on S3 with the AWS Glue Data Catalog; DLI is a multi-engine service bundling Spark SQL, Flink SQL, and an openLooKeng/Trino engine for interactive analysis, batch, and streaming. DLI thus covers broader workload types but its SQL dialects, functions, and connector set are engine-specific and cannot be assumed ANSI-equivalent to Athena's Trino SQL.

Operating and scaling model differ. Athena is purely on-demand, auto-scaled per query with no queue management. DLI runs jobs on queues or elastic resource pools measured in CUs (1 CU = 1 vCPU, 4 GB), where you choose capacity and priority; idle pay-per-use pools release resources after an hour and re-acquisition takes minutes. DLI also supports CUH packages, which is a capacity-planning responsibility Athena users do not carry.

Metadata and integration boundaries differ. Athena depends on AWS Glue Data Catalog and S3 for storage and governance. DLI pairs with OBS for data, maintains its own table storage (compressed, billed by GB) plus external OBS/DWS/RDS datasource connections, and integrates with DataArts Studio for orchestration and IAM for column-level permissions. BI catalog, identity, and governance integrations must be re-mapped rather than assumed equivalent.

Start with assessment and target selection. Catalog Athena workloads, schemas, and connectors, then decide which DLI engine fits each: openLooKeng/Trino for interactive S3/OBS ad hoc queries, Spark SQL for batch ETL, Flink SQL for streaming. Where DLI's interactive engine lacks parity for federated sources or BI governance, plan DWS for warehouse workloads and DataArts Studio for orchestration and lineage. Treat DLI as the core equivalent, not a drop-in replacement.

Migrate data, metadata, and query semantics. Move S3 datasets to OBS via CDM or parallel copy, and rebuild schema definitions as DLI tables or DLI datasource connections over OBS. Reconcile AWS Glue Data Catalog entries against DLI's own metadata, and refactor Trino SQL functions, types, and connector-specific syntax to the target engine (openLooKeng or Spark SQL). Validate row counts, types, and partition handling, since column formats, compression, and decimal/timestamp semantics may differ.

Validate and cutover carefully. Re-run representative Athena queries against DLI and compare result sets, runtime, and costs, accounting for DLI's queue allocation and warm-up. Set up IAM-based access and column-level permissions equivalent to AWS Lake Formation policies, and instrument DataArts Studio for monitoring where Athena used CloudWatch metrics. Cutover per query set, keeping a parallel-read period, because no automated one-click path exists.

Account for cost-model and governance changes. Athena bills by bytes scanned; DLI bills compute by CUH (or scanned-data-volume only on the default queue), plus table storage in GB and OBS storage/transfer separately. Re-estimate TCO using peak concurrency, queue sizing, retention, and cross-region or interconnect traffic, since reserved CUH packages change break-even versus on-demand. Confirm regional DLI availability, quotas, and engine support per target region before production.