Question 1

How to compare data between PostgreSQL and BigQuery using DVT?

Accepted Answer

Set up connections for PostgreSQL and BigQuery using the connections documentation, then use commands like 'validate column' with --source-conn and --target-conn flags to specify tables or custom queries. Results can be output to stdout or stored in BigQuery for analysis.

Question 2

DVT vs Great Expectations for data validation?

Accepted Answer

DVT excels at cross-database comparisons during migrations with its extensive connector support and scalability for large datasets, while Great Expectations is better for data quality testing within a single environment with richer assertion libraries and profiling. DVT is more migration-focused.

Question 3

How to handle out of memory errors when validating large tables in DVT?

Accepted Answer

Use the 'generate-table-partitions' command to split the validation into smaller chunks based on primary keys, then run validations in parallel using Kubernetes or Cloud Run Jobs. This is documented in the scaling section to avoid MemoryError.

Question 4

Can DVT validate real-time data streams?

Accepted Answer

No, DVT is designed for batch validation of static datasets, not real-time streams. It requires full table scans or queries, so it's not suitable for streaming pipelines without intermediate batch storage.

Question 5

How to set up DVT for on-prem databases without GCP access?

Accepted Answer

Create connections for on-prem databases like Oracle or MySQL, but note that features like BigQuery result storage or GCS config files won't work directly. You may need to use local storage alternatives and configure network endpoints as per the on-prem documentation.

Question 6

What are the limitations of row hash validation in DVT?

Accepted Answer

Row hash validation is not supported for FileSystem connections, and SHA256 isn't available on Teradata without a custom UDF. It also requires primary keys and can be memory-intensive, necessitating partitioning for large tables.

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