1. Window Functions (Rank, Row Number, Running Totals)

Magic Domo ETL now supports window functions previously only available in SQL.

What you can do:

• Rank products by sales
• Calculate running totals
• Build cumulative metrics
• Create cohort analyses

Example Use Case:

“Top 10 SKUs by revenue per store – updated daily.”

Why It Matters:

You avoid materialising intermediate tables or writing complex SQL queries.

2. Recursive Lookups (Self-Joins Without SQL)

Magic Domo ETL can join a dataset to itself-perfect for:

• Hierarchies
• Parent-child relationships
• Multi-level grouping
• Time-series mapping

Example:

Linking a product category to its parent category to build full hierarchy trees.

Pitfall to Avoid:

Always reduce dataset size before recursive joins to prevent slow Domo ETL runs.

3. Multi-Row Formula Tile (The Most Underused Feature in Domo)

This tile lets you write formulas referencing previous or next rows.

You can create:

• Rolling averages
• YOY/WOY comparisons
• Trend detection flags
• Inventory coverage logic

Example:

Flagging “inventory dip days” when stock falls faster than forecast.

4. Case-Level Logic Using Condition Tiles

Many teams overuse Beast Modes for logic that should live in ETL.

Use Condition tiles for:

• Business logic
• Classification
• Bucketization
• Mapping rules

Example:

Segment customers by lifetime value ranges before dashboard load → faster performance.

5. Group By With Custom Aggregations (Hidden Power User Feature)

Most users use SUM and COUNT only, but Group By supports:

• MEDIAN
• MODE
• Standard deviation
• Variance
• Percentiles (P90, P95, etc.)

Real-World Example:

Calculating P90 delivery time across warehouses.

Expert Tip:

Pre-aggregate in Domo ETL to reduce dashboard load time by 40–70%.

6. Using the “Select Columns” Tile to Improve Performance

Removing unused columns is one of the fastest ways to speed up Domo ETL.

Why It Works:

• Reduces memory footprint
• Reduces credit consumption
• Speeds up downstream pipelines

You only need the columns used in dashboards or calculations.

7. Advanced Joins (Left + Inner + Full + Anti-Join Patterns)

Magic ETL supports ALL join types-even uncommon ones.

Anti-join example use cases:

• Find missing SKUs between ERP and Shopify
• Identify orders missing shipping data
• Detect inactive customers

8. Pivot & Unpivot Tiles (Reshape Your Entire Dataset)

Most teams misuse Excel shaping before loading data.

Magic Domo ETL allows:

• Melt/unpivot
• Reshape columns into rows
• Create time-series formats
• Rebuild wide datasets
• Convert non-analytics-friendly tables into dimensional models

Example:

Turning monthly sales columns (Jan, Feb, Mar…) into a single date-value structure.

9. Dataset Views (Virtual ETL Without Materializing Data)

Dataset Views allow transformations without reprocessing data.

Use Cases:

• Quick changes without rebuilding ETL
• Rapid prototyping
• Reducing ETL duplication
• Maintaining multiple business logic versions

Dataset Views are essential for scaling without credit explosion.

10. Reusable ETL Subflows (Enterprise-Grade Governance)

Instead of copying ETL logic across flows, use subflows to:

• Standardize business logic
• Manage KPIs centrally
• Reduce maintenance
• Avoid duplication errors
• Speed up development

Example:

A unified “Date Dimension Subflow” used across 20+ dashboards.

✔ 1. Minimize the number of output datasets

Fewer datasets = lower maintenance and clearer lineage.

✔ 2. Move all heavy logic into ETL (not Beast Modes)

Dashboards load 50–80% faster.

✔ 3. Use incremental ETL writing wherever possible

Avoid reprocessing millions of rows every time.

✔ 4. Document every ETL flow (naming conventions = authority)

This increases trust with both teams and algorithms.

✔ 5. Archive unused datasets

Keeps your Domo instance clean and efficient.