• 09 May 2025

Supported & Unsupported Data Values in Amazon QuickSight for SPICE and JDBC Connections

Amazon QuickSight accommodates several core data types, each with specific rules for formatting, conversion, and limits, particularly when ingesting data into SPICE or connecting through JDBC.

Understanding supported and unsupported data values in Amazon QuickSight is crucial for preventing ingestion errors and maintaining data quality in dashboards and analyses.

Amazon QuickSight's data type constraints impact SPICE ingestion integrity; proper formatting prevents row exclusion.

Overview of Data Values in Amazon QuickSight

Supported Primitive Data Types

Amazon QuickSight currently supports the following primitive types:

supported-and-unsupported-data-values-in-amazon-quicksight
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  • Date
    • Accepts both 12-hour and 24-hour formats.
    • SPICE supports a date range from January 1, 1400, 00:00:00 UTC to February 26, 2364, 23:59:59 UTC.
    • Formats should follow recognized ISO or locale standards to avoid rejection.
  • Decimal
    • Supports up to four decimal places.
    • Values with higher precision are automatically truncated, not rounded.
  • Integer
    • Includes both signed and unsigned whole numbers.
    • Can be derived from Boolean values (e.g., true = 1, false = 0).
  • String
    • Supports UTF-8 encoded text.
    • Improper encoding or the use of non-standard characters may cause row skipping.

Unsupported or Problematic Values

  • Boolean Fields
    • Interpreted as integers: true = 1, false = 0.
  • Unrecognized Values
    • If a data field includes unsupported or malformed values (e.g., non-date strings in a date column), entire rows may be excluded from SPICE ingestion.
  • JDBC Source Anomalies
    • When connecting to external databases via JDBC, values may be replaced with null if the driver cannot interpret a field correctly.
    • This behavior varies based on driver compatibility and database type.

Best Practices for Managing Unsupported Data

  • Pre-validate Data : Before loading into QuickSight, use SQL queries or ETL tools to ensure data types match expected formats.
  • Use Calculated Fields : Create calculated fields in QuickSight to convert or normalize problematic values.
  • Audit SPICE Imports : Regularly check SPICE import logs to identify and investigate dropped rows or null substitutions.
  • Monitor JDBC Compatibility : Review driver documentation and AWS support notices for known limitations.

About DataTerrain

With over 300 clients across the U.S., DataTerrain offers expertise in configuring robust, type-consistent datasets for Amazon QuickSight. Our consultants help identify data compatibility issues and ensure that ingestion pipelines are resilient and accurate, without requiring long-term contracts or fixed-hour engagements.