Transform patterns

Prerequisites · Completed Destination & load patterns.
End state · A correct transform block in each dataflow that needs data shaping.

The transform block is optional. When present it controls DataCoolie's built-in transformer pipeline, which runs between read and write in this order:

Order	Transformer	Triggered by
5	ColumnValueTransformer	transform.value_rules
10	SchemaConverter	transform.schema_hints
18	HashColumnAdder	transform.hash_columns
20	Deduplicator	transform.deduplicate_columns
30	ColumnAdder	transform.additional_columns
35	RowFilter	transform.filter_expression
60	SCD2ColumnAdder	destination.load_type = "scd2"
70	SystemColumnAdder	Always (adds __created_at, __updated_at, __updated_by)
80	PartitionHandler	destination.partition_columns
84	DataMasker	transform.masking_rules
85	ColumnProjector	select_columns, drop_columns, or rename_columns
90	ColumnNameSanitizer	Always (lower by default; configurable as snake)

System columns are always added

__created_at, __updated_at, and __updated_by are added to every dataflow output automatically. You do not configure them — just expect them in the destination table.

Transform at a glance

"transform": {
  "schema_hints": [
    { "column_name": "order_id", "data_type": "long" }
  ],
  "deduplicate_columns": ["order_id"],
  "latest_data_columns": ["updated_at"],
  "additional_columns": [
    { "column": "order_year", "expression": "EXTRACT(YEAR FROM order_date)" }
  ],
  "select_columns": ["order_id", "email", "order_year"],
  "rename_columns": {"email": "contact_email"},
  "value_rules": [
    {"operation": "trim", "columns": ["email"], "order": 10},
    {"operation": "case", "columns": ["email"], "mode": "lower", "order": 20}
  ],
  "masking_rules": [
    {"method": "partial", "columns": ["email"], "keep_start": 1, "keep_end": 3}
  ],
  "hash_columns": [
    {"target_column": "order_hash", "columns": ["order_id"], "algorithm": "sha256"}
  ],
  "configure": {
    "convert_timestamp_ntz": true,
    "deduplicate_by_rank": false
  }
}

Field	Purpose
schema_hints	Cast weakly typed columns into the types you want
deduplicate_columns	Define the grouping key for deduplication
latest_data_columns	Define the ordering columns for deduplication
additional_columns	Add SQL-derived columns
filter_expression	Discard rows by a SQL WHERE-style predicate after computed columns are available
value_rules	Normalize source values with native engine expressions before schema casting
masking_rules	Irreversibly mask structured scalar columns late in the pipeline
hash_columns	Add stable SHA-256 business hashes from explicitly ordered source columns
select_columns / drop_columns	Keep or remove business columns; the two fields are mutually exclusive
rename_columns	Atomically rename columns with an old-name to new-name object
configure	Control transformer behavior such as convert_timestamp_ntz and deduplicate_by_rank

partition_columns stays on the destination, not in transform.

Normalize, mask, and project columns

"transform": {
  "value_rules": [
    { "operation": "trim", "columns": ["email"], "order": 10 },
    { "operation": "case", "columns": ["email"], "mode": "lower", "order": 20 },
    { "operation": "map", "columns": ["status"], "mapping": {"A": "active", "I": "inactive"} }
  ],
  "masking_rules": [
    { "method": "partial", "columns": ["phone"], "keep_end": 4, "mask_char": "*" },
    { "method": "numeric_bucket", "columns": ["age"], "bucket_size": 10 },
    { "method": "date_truncate", "columns": ["birth_date"], "unit": "year" }
  ],
  "drop_columns": ["raw_payload"],
  "rename_columns": {"phone": "masked_phone"},
  "configure": {"missing_column_policy": "error"}
}

hash_columns is separate from masking and never becomes a merge or dedup key implicitly:

"hash_columns": [
  {
    "target_column": "customer_hash",
    "columns": ["country_code", "customer_id"],
    "algorithm": "sha256"
  }
]

Hash inputs currently support string, integer, boolean, and date columns. The declared column order is significant. DataCoolie builds a shared canonical payload with type tags, null markers, and UTF-8 byte lengths, so Spark and Polars produce identical lowercase SHA-256 output and distinguish null from an empty string. This immutable format is named dc_hash_v1; changing the format requires a new serialization name and target column. Polars loads polars-hash only when this feature runs; install it with pip install 'datacoolie[polars-hash]'.

Plain SHA-256 is suitable for deterministic business identifiers, but not for protecting low-entropy PII such as phone numbers or national identifiers. Use the explicit masking methods above; keyed pseudonymization remains outside the current contract.

Supported value operations are trim, case, regex_replace, empty_to_null, fill_null, and map. Rules default to order 100; ties retain metadata declaration order. String operations require string columns. trim removes ASCII U+0020 spaces only; tabs, newlines, and non-breaking spaces remain.

regex_replace uses DataCoolie portable regex v1 rather than the complete Java or Rust dialect. Use literals, explicit character classes/ranges, ., anchors, grouping, alternation, and ordinary quantifiers. Lookaround, backreferences, named groups, inline flags, \d/\w/\s/\b, possessive quantifiers, and quantified nested groups are rejected while loading metadata. Patterns are limited to 4,096 characters. Replacement text is always literal, so $ and backslash are emitted as written rather than expanding capture groups.

fill_null and masking redact literals are validated against the actual column type before a native expression is added. Invalid values fail the same way on Spark and Polars and do not depend on Spark ANSI settings.

Supported masking methods are redact, nullify, partial, numeric_bucket, and date_truncate. Partial masking collapses the hidden middle segment to one mask_char, while retaining the configured prefix and suffix. Empty strings remain empty; a non-empty value whose length is less than or equal to keep_start + keep_end becomes exactly one mask_char instead of passing through raw. Masking merge keys, partition columns, and framework-reserved columns is rejected. This is column-level PII masking, not dataset anonymization.

Projection uses pre-rename names. It preserves framework trailing columns and rejects removal or renaming of merge and partition keys. Missing configured columns in typed rules and projection fail by default; set missing_column_policy to ignore to skip them. Missing schema-hint columns are skipped and reported together in one warning because catalog hints may be a superset of the runtime schema. Deduplication remains strict: once configured, a missing partition or order column always fails. Keep the default error policy for PII-sensitive pipelines so schema drift cannot silently bypass a configured masking rule.

---

Pattern 1 — Cast column types (schema_hints)

When to use: your source data has weak types (CSV strings, JSON mixed types) and you need specific types in the destination.

Add schema_hints inside transform:

"transform": {
  "schema_hints": [
    { "column_name": "order_id",    "data_type": "int" },
    { "column_name": "customer_id", "data_type": "int" },
    { "column_name": "amount",      "data_type": "decimal", "precision": 18, "scale": 2 },
    { "column_name": "order_date",  "data_type": "date" },
    { "column_name": "created_at",  "data_type": "timestamp" },
    { "column_name": "is_active",   "data_type": "boolean" }
  ]
}

Supported data_type values

data_type	Notes
int / integer	32-bit integer
long / bigint	64-bit integer
float	32-bit float
double	64-bit float
decimal	Requires precision and scale
string / str	UTF-8 string
boolean / bool	True/False
date	Calendar date (no time)
timestamp	Date + time with microsecond precision
binary	Raw bytes

decimal example

{ "column_name": "price", "data_type": "decimal", "precision": 18, "scale": 4 }

precision = total significant digits; scale = digits after the decimal point.

Less-common schema-hint fields

The schema-hint model supports more than just column_name and data_type:

Field	Use it when
format	The engine needs a format hint while casting or parsing
default_value	You want a documented default for custom logic or downstream tooling
ordinal_position	You want to preserve external column-order metadata
is_active	You want to keep a hint row in metadata but disable it temporarily

How schema hints are applied

• Matching is case-insensitive
• Missing columns are skipped, not fatal
• Hints only apply when source.connection.use_schema_hint is truthy
• timestamp_ntz conversion happens after hint-based casting when enabled

Only list the columns you want to cast

You do not need a hint for every column. Columns not listed keep their inferred type from the source reader.

---

Pattern 2 — Deduplicate rows

When to use: your source can deliver duplicate rows for the same key (common with CDC feeds, API pagination overlaps, or file re-deliveries).

"transform": {
  "deduplicate_columns": ["order_id"],
  "latest_data_columns": ["updated_at"]
}

Field	Meaning
deduplicate_columns	The column(s) that define a unique record — usually your natural key
latest_data_columns	Which column to use to pick the "winner" when duplicates exist — usually a timestamp

Deduplicator groups by deduplicate_columns, orders by latest_data_columns descending, and keeps the first row per group.

Fallback behavior you should know

If you omit some dedup fields, DataCoolie still has a few convenience fallbacks:

Missing input	Fallback
deduplicate_columns	Falls back to destination merge_keys
latest_data_columns	Falls back to source.watermark_columns
Both missing	Deduplication becomes a no-op

Full example with merge_upsert:

{
  "name":  "orders_cdc_to_bronze",
  "stage": "ingest",
  "source": {
    "connection_name":   "cdc_source",
    "table":             "orders_changes",
    "watermark_columns": ["updated_at"]
  },
  "destination": {
    "connection_name": "bronze",
    "schema_name":     "sales",
    "table":           "orders",
    "load_type":       "merge_upsert",
    "merge_keys":      ["order_id"]
  },
  "transform": {
    "deduplicate_columns": ["order_id"],
    "latest_data_columns": ["updated_at"],
    "schema_hints": [
      { "column_name": "order_id",   "data_type": "long" },
      { "column_name": "updated_at", "data_type": "timestamp" }
    ]
  }
}

Relationship to merge_keys

deduplicate_columns is usually the same value as merge_keys but it lives in transform, not destination. They serve different pipeline stages: deduplication happens before the merge.

Keep ties with rank instead of row-number

Normally DataCoolie keeps a single winner per key. If you want rank-style deduplication instead, enable it in transform.configure:

"transform": {
  "deduplicate_columns": ["order_id"],
  "latest_data_columns": ["updated_at"],
  "configure": {
    "deduplicate_by_rank": true
  }
}

merge_overwrite also uses rank-based dedup automatically when merge keys are available and explicit deduplicate_columns are not set.

---

Pattern 3 — Add computed columns

When to use: you need a new column whose value is calculated from existing columns (derived date parts, string concatenation, status labels, etc.).

"transform": {
  "additional_columns": [
    { "column": "order_year",   "expression": "EXTRACT(YEAR FROM order_date)" },
    { "column": "order_month",  "expression": "EXTRACT(MONTH FROM order_date)" },
    { "column": "full_name",    "expression": "first_name || ' ' || last_name" },
    { "column": "is_large",     "expression": "CASE WHEN amount > 1000 THEN true ELSE false END" }
  ]
}

Expressions are SQL evaluated against the DataFrame after schema casting. Use standard SQL scalar functions — the Polars and Spark engines both support EXTRACT, CASE WHEN, string functions, and arithmetic.

Polars SQL limitations

Polars does not support current_timestamp() or NOW(). Use EXTRACT(YEAR FROM col) instead of year(col). Use CAST(col AS DATE) instead of date(col).

Do not reference system columns here

additional_columns runs at transformer order 30. System columns are only added later at order 70, so expressions here cannot rely on __created_at, __updated_at, or __updated_by. Let the framework add those columns for you and use them after the transform stage, not inside it.

---

Pattern 4 — Filter rows (transform.filter_expression)

When to use: you need to drop rows before writing to the destination. The predicate runs at order 35, after ColumnAdder (30), so it can reference columns you created in additional_columns.

"transform": {
  "filter_expression": "status = 'active' AND amount > 0"
}

filter_expression is a SQL predicate — everything you would normally place after WHERE. Both Polars and Spark engines evaluate it against the DataFrame.

Reference a computed column

Because RowFilter runs after ColumnAdder, you can filter on columns added by additional_columns:

"transform": {
  "additional_columns": [
    { "column": "order_year", "expression": "EXTRACT(YEAR FROM order_date)" }
  ],
  "filter_expression": "order_year >= 2023"
}

Combine multiple conditions

"transform": {
  "filter_expression": "region = 'US' AND status NOT IN ('cancelled', 'draft') AND amount > 0"
}

vs source.filter_expression

There are two distinct filter hooks:

Field	Stage	Scope
source.filter_expression	Read time (before watermark)	Raw source columns only
transform.filter_expression	Order 35 (post-ColumnAdder)	Source columns + computed columns

Use source.filter_expression when you want the filter pushed as close to the source as possible. Use transform.filter_expression when you need to filter on a column that is added by additional_columns.

---

Pattern 5 — Partition the output

When to use: your destination table will be large and you want query engines to skip irrelevant data via partition pruning.

Partition columns go on the destination block, not inside transform:

"destination": {
  "connection_name":   "silver",
  "schema_name":       "sales",
  "table":             "orders",
  "load_type":         "overwrite",
  "partition_columns": [
    { "column": "order_date", "expression": "CAST(created_at AS DATE)" }
  ]
}

If the partition column already exists in the DataFrame (no derivation needed), omit expression:

"partition_columns": [
  { "column": "region" }
]

Multi-level partitioning:

"partition_columns": [
  { "column": "order_year",  "expression": "EXTRACT(YEAR FROM order_date)" },
  { "column": "order_month", "expression": "EXTRACT(MONTH FROM order_date)" }
]

Partition expressions run late in the pipeline, after schema hints, deduplication, computed columns, SCD2 columns, and system columns. That means they can rely on columns created earlier in the pipeline.

---

Pattern 6 — SCD2 audit columns

When load_type is scd2, the SCD2ColumnAdder transformer automatically adds three columns. You do not configure them in transform — just set the effective column in the destination configure:

"destination": {
  "connection_name": "gold",
  "schema_name":     "dims",
  "table":           "customer",
  "load_type":       "scd2",
  "merge_keys":      ["customer_id"],
  "configure":       { "scd2_effective_column": "updated_at" }
}

Columns added automatically:

Column	Type	Meaning
__valid_from	timestamp	Copied from scd2_effective_column
__valid_to	timestamp (nullable)	NULL = still current
__is_current	boolean	true for the active version

---

Pattern 7 — System columns (always present)

SystemColumnAdder runs on every dataflow, regardless of configuration. Your destination table will always receive these three columns:

Column	Content
__created_at	Framework timestamp when the row was first written
__updated_at	Framework timestamp of the current write
__updated_by	Job ID of the driver run

You do not configure these. If a merge destination already has __created_at from a previous run, the engine preserves its original value on the matched row and sets __updated_at to the current run timestamp.

Remember the ordering: these columns are always present in the written output, but they are not available to additional_columns because they are added later in the pipeline.

---

Transform configure flags

Three flags in transform.configure change how built-in transformers behave:

Key	Default	Effect
convert_timestamp_ntz	true	Converts timestamp_ntz columns to timestamp after schema hints
deduplicate_by_rank	false	Uses rank-based deduplication instead of row-number semantics
missing_column_policy	error	Controls absent columns in typed value/hash/masking rules and projection; schema hints warn and skip, while dedup remains strict

Example:

"transform": {
  "schema_hints": [
    { "column_name": "created_at", "data_type": "timestamp" }
  ],
  "configure": {
    "convert_timestamp_ntz": false,
    "deduplicate_by_rank": true
  }
}

---

Final step: column-name sanitization

After all configured transforms run, ColumnNameSanitizer applies the driver's column_name_mode: lower by default, or snake when requested. Plan for normalised destination columns when the source uses mixed case, quoted identifiers, or API keys like CustomerID.

---

Full example: multi-pattern transform block

{
  "name":  "orders_to_silver",
  "stage": "bronze2silver",
  "source": {
    "connection_name":   "bronze",
    "schema_name":       "sales",
    "table":             "orders",
    "watermark_columns": ["updated_at"]
  },
  "destination": {
    "connection_name":   "silver",
    "schema_name":       "sales",
    "table":             "orders",
    "load_type":         "merge_upsert",
    "merge_keys":        ["order_id"],
    "partition_columns": [
      { "column": "order_date", "expression": "CAST(created_at AS DATE)" }
    ]
  },
  "transform": {
    "deduplicate_columns": ["order_id"],
    "latest_data_columns": ["updated_at"],
    "additional_columns": [
      { "column": "order_year",  "expression": "EXTRACT(YEAR FROM order_date)" }
    ],
    "schema_hints": [
      { "column_name": "order_id",   "data_type": "long" },
      { "column_name": "amount",     "data_type": "decimal", "precision": 18, "scale": 2 },
      { "column_name": "order_date", "data_type": "date" },
      { "column_name": "updated_at", "data_type": "timestamp" }
    ],
    "configure": {
      "convert_timestamp_ntz": true
    }
  }
}

---

Common mistakes

Symptom	Likely cause	Fix
Duplicate rows after merge	No dedup key or no ordering columns	Add deduplicate_columns and latest_data_columns, or let ordering fall back to source.watermark_columns
Wrong types in destination table	No schema_hints, or use_schema_hint is disabled on the source connection	Add hints and confirm source.connection.use_schema_hint is true
year() function fails on Polars	Polars SQL doesn't support year()	Use EXTRACT(YEAR FROM col)
date() function fails on Polars	Polars SQL doesn't support date(col)	Use CAST(col AS DATE)
Partition column missing	expression references a column that doesn't exist yet	Cast or add the column via schema_hints or additional_columns first
__updated_at not found in additional_columns	System columns are added later in the pipeline	Do not reference system columns in additional_columns
Destination columns are unexpectedly lowercase	ColumnNameSanitizer runs at the end and defaults to lower	Expect lowercase output or run with column_name_mode="snake"

---

Next

→ Validation checklist