Concat

Concat merges the current dataset with a table from any connected integration. Three modes: Vertical stacks rows (union), Horizontal aligns columns side by side, Diagonal merges square matrices. Schema compatibility is checked automatically when you select a table. Duplicate handling is configurable before saving.

The source table can come from a different database or integration than the current pipeline source. No need to land both datasets in a shared warehouse first.

When to Use Concat

• Stack regional tables. Vertical concat orders_na and orders_eu into a single orders dataset for global reporting.
• Combine attribute tables. Horizontal concat a transaction table with a metadata table that has the same row count.
• Append new data. Vertical concat last month's orders into the current dataset to extend the time range.
• Merge after sampling. Concat a sampled subset back to a reference dataset for comparison.

Sample Dataset

The examples in this doc use three sample datasets. Download all three to follow along in your own workspace.

Concat Types

How to Apply Concat

Conflict Resolution

Vertical conflicts

When a vertical concat check finds mismatches, a modal opens showing:

Horizontal conflicts

When a horizontal concat check finds common column names in both tables, a modal opens listing the shared columns. You must choose:

• Drop Columns From Left Table - Remove shared columns from the current dataset before merging.
• Drop Columns From Right Table - Remove shared columns from the source table before merging.

Select a side and click Drop Columns to allow the concat.

Before and After

Vertical concat

Concat edilitics_sample_orders (500 rows, 13 columns) with edilitics_sample_orders_h2 (250 rows, 15 columns) on Vertical. H2 has two extra columns (return_flag, delivery_partner) not present in H1 - the compatibility check returns "Concatenation not allowed" (red). Click to open the conflict resolution modal, then click Drop Columns to exclude return_flag and delivery_partner from the source table. Manage Duplicates: Keep All.

Input (current dataset): 500 rows, 13 columns (order_id, customer_name, email, sales_rep, region, product_id, order_date, delivery_date, revenue, units_sold, discount_pct, product_tier, metadata, notes)

Second table: 250 rows, 15 columns (same 13 + return_flag, delivery_partner)

After dropping extra columns from source: 250 rows, 13 columns aligned

Output: 750 rows, 13 columns

Horizontal concat

Concat edilitics_sample_orders (500 rows, 13 columns) with edilitics_sample_products (4 rows, 7 columns) is not valid as a horizontal concat - row counts do not match (500 vs 4). Horizontal concat requires identical row counts. Use Join on product_id to enrich orders with product columns instead.

For a valid horizontal concat example: split edilitics_sample_orders into two derived tables of 500 rows each with no shared column names, then horizontal concat to produce 500 rows with the combined column set.

Code Equivalent

-- Vertical concat (PostgreSQL / BigQuery / Snowflake / DuckDB)
SELECT * FROM orders_q1
UNION ALL
SELECT * FROM orders_q2;

-- Vertical concat with deduplication (keep first)
SELECT DISTINCT ON (order_id) *
FROM (
  SELECT * FROM orders_q1
  UNION ALL
  SELECT * FROM orders_q2
) combined
ORDER BY order_id;

-- Horizontal concat (same row count, no shared columns)
SELECT a.*, b.source, b.channel
FROM orders a
JOIN metadata_table b ON a.rn = b.rn;

import polars as pl

# Vertical concat
df_combined = pl.concat([df_q1, df_q2], how="vertical")

# Vertical concat, keep only common columns
common_cols = [c for c in df_q1.columns if c in df_q2.columns]
df_combined = pl.concat(
    [df_q1.select(common_cols), df_q2.select(common_cols)],
    how="vertical"
)

# Deduplicate after concat
df_combined = df_combined.unique(keep="first")

# Horizontal concat
df_wide = pl.concat([df_orders, df_metadata], how="horizontal")

# Diagonal concat
df_diag = pl.concat([df_a, df_b], how="diagonal")

Operation Reference

Frequently Asked Questions

Next Steps

Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.