---

title: 'Delta Lake Data Connector Deployment Guide' sidebar_label: 'Deployment Guide' description: 'Operating guide for the Delta Lake data connector in production: object store auth, metadata caching, metrics, and observability.' sidebar_position: 10 pagination_prev: null pagination_next: null tags:

• data-connectors
• delta-lake
• observability

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Production operating guide for the Delta Lake data connector covering object-store authentication, metadata handling, and operational tuning.

Authentication & Secrets

The Delta Lake connector reads Delta tables directly from an underlying object store (S3, ABFS/Azure, GCS, or the local filesystem). Authentication parameters depend on the object store:

Object store	Auth parameters
S3 / S3-compatible	delta_lake_aws_region, delta_lake_aws_access_key_id, delta_lake_aws_secret_access_key, delta_lake_aws_session_token, delta_lake_aws_endpoint, delta_lake_aws_allow_http. Defaults to the AWS credential chain when unset.
Azure ADLS	delta_lake_azure_storage_account_name, delta_lake_azure_storage_account_key, delta_lake_azure_storage_client_id, delta_lake_azure_storage_client_secret, delta_lake_azure_storage_sas_key, delta_lake_azure_storage_endpoint, delta_lake_azure_storage_tenant_id.
Google Cloud Storage	delta_lake_google_service_account.
Local filesystem	No auth. Ensure the Spice process has read permission on the Delta table directory.

Credentials must be sourced from a secret store in production. For AWS deployments, prefer instance-profile or IRSA-based auth (leave aws_access_key_id unset).

Databricks Unity Catalog

To access Delta tables registered in Unity Catalog, use the Databricks connector in mode: delta_lake. It handles UC metadata resolution automatically — see the Databricks Deployment Guide for details.

Resilience Controls

Object Store Retry

Object-store I/O uses the AWS/Azure/GCS SDK default retry strategies (adaptive backoff on throttling and transient 5xx responses). Per-operation retry parameters are not exposed at the Spice layer.

Transaction Log Caching

The Delta Lake connector reads the _delta_log transaction log on each query plan. For high-query-rate workloads, accelerate the dataset (see Acceleration) to avoid repeatedly scanning the log on the hot path.

Capacity & Sizing

• Metadata cost: Reading a Delta table requires reading its _delta_log, which grows with every commit. Checkpointing (handled by the writer) bounds this cost. Ensure the writer is issuing checkpoints every 10-100 commits for large, high-churn tables.
• Partition pruning: The connector prunes partitions using Delta's partition metadata during query planning. Partition datasets by the dominant filter columns (date, tenant) for best performance.
• Column pruning and predicate pushdown: Filters and column projections are pushed down into the underlying Parquet readers. Queries over a narrow column set over a large table are efficient without requiring acceleration.
• Acceleration sizing: When materializing into DuckDB/SQLite/Postgres, size the target engine to hold the refreshed snapshot plus WAL overhead (typically 1.3–2× the raw Parquet size for row-oriented accelerators).

Metrics

Object-store I/O metrics are collected via the shared runtime-object-store layer and exposed through Spice's runtime metrics. See Component Metrics for configuration.

The Delta Lake connector does not currently register connector-specific dataset-level instruments. Monitor Delta operations via:

• Query execution metrics (query_duration_ms, query_processed_rows) from runtime.metrics.
• Upstream cloud metrics (S3 request count, Azure blob throughput).
• Acceleration refresh metrics when the dataset is accelerated.

Task History

Delta Lake reads participate in Spice task history through DataFusion's execution-plan spans. Individual object reads are attributed to their enclosing sql_query or accelerated_table_refresh task.

Known Limitations

• Read-only: The Delta Lake connector cannot write or update Delta tables.
• Time travel: Querying by version or timestamp is not exposed through the connector; only the latest snapshot is read.
• Drop table features: Some Delta features (deletion vectors, column mapping, row tracking) may require specific writer protocol versions. See Databricks docs on dropping Delta table features when compatibility issues arise.
• Change Data Feed (CDF): CDF is not exposed through the Delta Lake connector. For CDC, use the Debezium connector against the source database, or use Databricks mode to federate a CDF-enabled view.

Troubleshooting

Symptom	Likely cause	Resolution
Access Denied on _delta_log/ GET	Role lacks read on the _delta_log/ prefix.	Grant s3:GetObject / equivalent on the table root and _delta_log/ prefix.
Query returns an empty result after a new commit	Stale transaction-log cache.	Trigger a dataset refresh (acceleration) or re-plan the query.
Protocol version unsupported	Writer committed a newer Delta protocol version than the reader supports.	Upgrade Spice to a version with the required Delta protocol reader, or drop the writer feature.
Slow queries on very high-commit tables	Transaction log grown very large without checkpoints.	Ensure the writer is issuing checkpoints; consider acceleration.
No such file or directory for _delta_log/...	Table is uninitialized or path is incorrect.	Confirm the from: path points at the Delta table root, not a data subdirectory.