Enhancements:
New Type: ETL
- Load from virtually any source – Move data into SQL Server from databases such as Oracle, SQL Server, MySQL, PostgreSQL, Snowflake, Access, and files including Excel, CSV, JSON, XML, and Parquet.
- Much easier than SSIS – Configure ETL processes through a simple web interface instead of building and maintaining complex SSIS packages.
- Incremental data loads – Load only new or changed records instead of repeatedly reloading entire tables.
- Automatic retry and recovery – Automatically retries intermittent failures to help ensure data is successfully loaded.
- Built-in scheduler – Run ETL jobs hourly, daily, weekly, on specific calendar dates, or on custom schedules.
- Automatic notifications – Notify selected users or roles when an ETL succeeds or fails.
- Complete logging and monitoring – Every process is logged, with detailed status, errors, execution history, performance statistics, and source-to-destination counts.
- Automated Type 2 SCD – Built-in Slowly Changing Dimension Type 2 history automatically preserves previous versions of changed records.
- Parallel processing – Process multiple tables simultaneously for faster data loads.
- SQL Server Change Tracking support – Use native SQL Server Change Tracking for highly efficient incremental loads.
- Built-in data validation – Check primary keys for duplicates and null values before loading data.
- Custom SQL support – Create custom extracts and automatically execute post-load SQL for additional processing and business logic.
New Report Type: DuckDB
- Fast analytics on CSV and Parquet files – Query large files directly using DuckDB’s high-performance columnar, vectorized query engine.
- Use familiar SQL – Analyze data using PostgreSQL-style SQL instead of MDX or DAX.
- Upload files directly – Upload CSV and Parquet files and start querying them immediately.
- Query entire folders – Point to a folder containing CSV or Parquet files and work with the files as analytical data sources.
- Dynamic folder paths – Use the {folder_path} placeholder to automatically reference the folder configured in the connection or report options.
- Built-in SQL editor – Browse available files and columns, write SQL, run queries, and view results directly in the browser.
- Create Parquet from existing data – Use Python reports as an ETL tool to convert existing databases or files into high-performance Parquet datasets.
- No separate analytical database required – Analyze file-based data directly without first importing it into a traditional database.
Data Entry
- Calculated column search
- Calculated column data type: Text, Number, date, Boolean
- Text Area - Markdown
- Min, Max for Date type
Blog and Wiki
- Markdown Editor type
- Sanitize Html
Subscription
- Vector, Python
Vector
- Oracle 23ai (3500 char chunks for TO_VECTOR(TO_CLOB()))
Report Import
- Support for extensions: .r, .html, .py, .md. parquet, csv
DbAdmin
- PostgreSQL and DuckDB
- SSAS Process - support for tabular model
Folder Report Type
- Save file hash option
Markdown
- Line Wapping on
OLAP Report
- XmlaProxyValidate and AddEffectiveUserName (hidden options)
Python
- Filter Support
Rolap
- PostegreSQL and DuckDB support
- SQL server 2016 and up
- ROLAP Admin Icon Change to dice
Admin
Connection
New connection types:
- Oracle native
- Folder DuckDB
Setting
- Let users star menu items
- Password: Two-Factor Authentication (2FA)
- Email, Text (Twilio) or Time-Based One-Time Password (TOTP)
Security
- Sanitize HTML (Wiki and Blog)
Users
- Permissions and Jobs buttons
- Two-Factor Authentication (user level settings)
Window Login - better error handling
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Report Portal
ReportPortal 6, Build 211, Aug 22, 2026
Forum Locked
Posted - 08/24/2026 : 11:31:36
