Informatica to Databricks migration: challenges, patterns, and AI automation

Informatica has been the default enterprise ETL platform for two decades, but it comes in two very different flavors: PowerCenter (on-prem) and Informatica Intelligent Cloud Services, or IICS (cloud-native). If you’re reading this, you’re probably running one or both and planning an Informatica to Databricks migration. The migration path differs depending on which product you’re leaving behind.

PowerCenter stores transformation logic in proprietary XML mappings built through a Windows-based GUI. There is no SQL to copy, no Spark code to export. Every mapping, every workflow, every session variable must be reverse-engineered and rebuilt as Spark SQL, notebook code, Lakeflow Declarative Pipelines pipeline definitions, or dbt models running on Databricks SQL. IICS stores similar logic in a JSON-based format accessible via REST API. The transformation types overlap with PowerCenter (Expression, Lookup, Joiner, Aggregator), but the metadata structure is different enough that you need separate parsing logic for each.

That’s what makes this migration different from database-to-database moves like Oracle to Databricks or Teradata to Databricks. Those migrations are mostly about SQL dialect differences. An Informatica to Databricks migration is a paradigm shift: from GUI-based ETL to code-first data engineering on a distributed compute platform. The good news is that AI-powered migration tools now automate the bulk of this work, turning what used to be a year-long manual project into something that can be done in weeks.

Teams choose Databricks as the migration target for several reasons:

1. Unified platform for data engineering, SQL analytics, and ML on a single runtime
2. Delta Lake brings ACID transactions and time travel to open-source storage (Parquet on S3, ADLS, or GCS)
3. The Photon engine makes SQL workloads competitive with dedicated warehouses while keeping Python and Scala flexibility

The driver behind most of these projects is Informatica license cost. The company generates $1.6 billion in annual revenue from customers who often feel locked in.

If you’re evaluating this migration path, get a migration estimate from Datafold to understand the scope before you start.

PowerCenter vs IICS: what changes for migration

Before getting into the Databricks migration itself, it’s worth understanding how PowerCenter and IICS differ, because the migration effort is not the same.

Aspect	PowerCenter	IICS (CDI)
Deployment	On-prem servers	Cloud-hosted, Secure Agents run locally
Mapping format	XML stored in repository database	JSON-based, accessed via REST API
Designer	Windows desktop client (PowerCenter Designer)	Browser-based Mapping Designer
Execution engine	Integration Service (row-pipeline with grid partitioning)	Secure Agent with optional elastic Spark engine (CDI-E)
Connectors	Native adapters bundled with PowerCenter	Cloud-native connectors, marketplace add-ons
Orchestration	Workflow Manager (sessions, timers, events)	Taskflows, Schedules, and Monitor service
Export method	pmrep CLI or Repository Manager export	REST API export, or IICS Migration Utility

The practical impact on your Databricks migration:

If you’re migrating from PowerCenter, you export repository objects as XML using pmrep, parse the XML to extract transformation logic, and rewrite everything as Spark SQL, notebook code, or Lakeflow Declarative Pipelines. This is the harder path because the XML format is complex and proprietary. Tools like Datafold’s Migration Agent parse this XML automatically.

If you’re migrating from IICS CDI, the mappings are JSON-based and accessible through Informatica’s REST API. The transformation logic is conceptually similar to PowerCenter, but the format is easier to parse programmatically. An interesting nuance: IICS CDI-Elastic (CDI-E) actually uses a Spark engine under the hood. If you’re coming from CDI-E, some of your mapping logic is already running on Spark, which can make the transition to Databricks conceptually easier. The mapping definitions still need to be rewritten, but the execution model is familiar.

If you’re migrating from both (common in enterprises that partially moved from PowerCenter to IICS before deciding to exit Informatica entirely), you’ll have two separate export processes and potentially different transformation patterns for the same data domains. Consolidating these into a single Databricks lakehouse is the right move, but plan for the additional complexity of reconciling duplicate logic.

One thing that’s the same regardless of source: neither PowerCenter nor IICS stores transformations as SQL. Both use visual, GUI-designed mappings with proprietary internal representations. The core challenge of reverse-engineering business logic from a visual ETL tool applies to both.

Architecture comparison: PowerCenter and IICS vs Databricks

PowerCenter’s architecture revolves around a set of tightly coupled server-side components. The Repository Server stores all metadata: mappings, workflows, sessions, connection objects, and scheduler configurations. The Integration Service executes mappings at runtime, reading from sources, applying transformations through an in-memory row pipeline (with optional grid partitioning for parallelism), and writing to targets. The Client tools (Designer, Workflow Manager, Workflow Monitor) provide the GUI where developers build and manage everything.

A mapping in PowerCenter is a directed graph of transformations: Source Qualifier reads from a source, Expression transformations apply calculations, Lookup transformations join against reference tables, Aggregator transformations group data, and a Target transformation writes to the destination. Sessions wrap mappings with runtime configuration (connection strings, commit intervals, buffer sizes). Workflows chain sessions together with scheduling logic, event triggers, and dependency links.

Databricks is a distributed compute platform built on Apache Spark. There is no central designer GUI. You write transformation logic in notebooks (Python, SQL, or Scala), Lakeflow Declarative Pipelines declarations, or dbt SQL models. Compute runs on auto-scaling clusters managed by the Databricks Runtime. Data lives in Delta Lake tables on cloud object storage. Orchestration happens through Databricks Workflows, which chain notebooks, Lakeflow Declarative Pipelines, and SQL tasks into directed acyclic graphs. Databricks’ native capabilities (notebooks, Lakeflow Declarative Pipelines, stored procedures, Workflows) can handle the entire transformation and orchestration layer on their own. Some teams also choose to use dbt on top of Databricks SQL for version-controlled transformations, but dbt is optional, not required.

Informatica concept	PowerCenter	IICS (CDI)	Databricks equivalent
Metadata store	Repository Server (database)	Cloud repository (REST API)	Unity Catalog
Compute engine	Integration Service (grid-capable)	Secure Agent / CDI-E Spark	Spark / Photon (distributed clusters)
Mapping definition	XML in repository	JSON via REST API	Notebook, Lakeflow pipeline, or dbt model
Runtime config	Session + parameter files	Taskflow parameters	Job parameters, notebook widgets (or dbt vars)
Orchestration	Workflow Manager	Taskflows + Schedules	Databricks Workflows
Reusable logic	Mapplets	Shared mappings	Python functions, Lakeflow reusable flows (or dbt macros)
Data loading	Source Qualifier	Source transformation	Auto Loader, COPY INTO, or LakeFlow Connect
Connectors	Bundled native adapters	Cloud marketplace connectors	Auto Loader, LakeFlow Connect, Fivetran, or Airbyte

The key mental shift: both PowerCenter and IICS do everything in one platform. After migration, you may have separate tools for ingestion (Fivetran/Airbyte or Auto Loader), storage and transformation (Databricks with Delta Lake), and optionally orchestration (Databricks Workflows or Airflow). The biggest adjustment for PowerCenter users is moving from row-by-row processing to distributed batch processing. Informatica processes data through a pipeline of transformation stages one row at a time. Spark processes data in distributed batches across cluster nodes. If you try to replicate row-level Informatica logic line for line in Spark, you’ll get code that works but performs terribly. You need to think in DataFrames and set-based SQL.

Migration challenges and how to solve them

Every Informatica-to-Databricks migration involves the same set of translation problems. Modern AI-powered migration tools handle the majority of these automatically. Below is what’s involved and how each challenge gets resolved.

Proprietary mappings have no code to port

This is the single biggest difference between an Informatica migration and a database migration. When you migrate from Oracle to Databricks, you have SQL stored procedures you can read, parse, and translate. Informatica mappings are stored in a proprietary format with no underlying SQL.

For PowerCenter, that format is XML in the repository database. You export using pmrep or Repository Manager, then parse the XML to understand what each mapping does. The Transformation Guide describes each transformation type, but there’s no “export to Spark” button. For IICS CDI, the format is JSON accessible via the platform REST API. The transformation types are similar, but the metadata structure is different from PowerCenter XML.

Done manually, this is months of work for a typical enterprise with 500+ active mappings. With AI-powered migration tools like Datafold’s Migration Agent, the parsing and initial code generation is automated, and engineers focus on reviewing and refining the output rather than writing everything from scratch.

Row-by-row thinking vs distributed processing

Informatica developers are trained to think about data flowing through a pipeline one row at a time. Sorted input on an Aggregator transformation reduces memory usage because the engine only holds one group in memory. In Spark, this pattern is irrelevant. Spark shuffles data across nodes to perform GROUP BY operations, and the optimization levers are completely different: partition strategies, broadcast hints, liquid clustering on Delta tables, and adaptive query execution.

A naive line-by-line translation can produce code that runs 10x slower than it should. Migration tools that understand both Informatica and Spark handle this refactoring as part of the conversion: they generate idiomatic Spark code, not a literal port of row-level logic.

Joiner, Aggregator, and Lookup transformations need Spark rewrites

PowerCenter’s Joiner transformation joins two data streams in memory. The Aggregator transformation performs group-by calculations with optional sorted input optimization. Lookup transformations fetch reference data, often with caching. Each of these maps to standard Spark SQL or DataFrame operations, but the translation isn’t always one-to-one.

A Lookup configured with a static cache and a default value on no match becomes a LEFT JOIN with COALESCE, and for small tables, a broadcast join. A Lookup with dynamic caching for insert-or-update logic becomes a Delta Lake MERGE statement. An Aggregator with sorted input has no direct Spark equivalent because Spark processes entire partitions, not sorted row streams. These translations follow well-known patterns, and migration tools handle most of them automatically.

Session parameters and variables become job configuration

PowerCenter sessions use parameter files (*.par) and session variables ($$) to control runtime behavior: date ranges for incremental loads, file paths, connection strings, commit intervals. Databricks offers multiple options: notebook widgets for interactive parameters, job parameters for scheduled runs in Databricks Workflows, or dbt variables if you use dbt on Databricks. The translation is not hard per mapping, but across hundreds of sessions with different parameter conventions, it becomes a project in itself. Establish a standard pattern early and apply it consistently.

Workflow orchestration requires redesign

PowerCenter Workflows handle scheduling, dependency management, event-based triggers, timers, and error handling. The Workflow Manager GUI makes it easy to chain sessions with on-success/on-failure links.

Databricks Workflows handle most of this natively: you define multi-task jobs with task dependencies, conditions, retries, and scheduling. Event waits and timers become task-level conditions. For cross-system dependencies or complex branching, Airflow is the standard choice. A common discussion on the Databricks Community forums is how to map Informatica’s event-driven patterns to Databricks’ task-based model. The answer is usually simpler than expected: most Informatica workflows are linear chains with error handling, which map directly to Databricks Workflow task dependencies.

Connector diversity must be re-established

Both PowerCenter and IICS have extensive connector libraries, though they differ. PowerCenter bundles native adapters for SAP, mainframes, and legacy databases. IICS provides cloud-native connectors through its marketplace, with stronger coverage for SaaS applications. When you move to Databricks, you need to replace all of them.

Auto Loader handles file-based ingestion from cloud storage. LakeFlow Connect provides managed SaaS connectors. For everything else, you’ll pair Databricks with a dedicated EL tool like Fivetran or Airbyte. If you’re coming from IICS, your sources are probably already cloud-accessible, which makes this transition simpler than a PowerCenter migration where sources may sit behind on-prem firewalls. Inventory every source system your Informatica environment connects to and find a replacement connector for each one.

Code translation examples

The best way to understand the migration work is to see real before-and-after examples. Below are five common Informatica patterns and their Databricks equivalents.

Expression transformation to Spark SQL

Informatica’s Expression transformation uses proprietary functions like IIF(), DECODE(), and TO_DATE().

-- Informatica Expression port
IIF(ISNULL(CUSTOMER_STATUS), 'UNKNOWN',
  IIF(CUSTOMER_STATUS = 'A', 'ACTIVE',
    IIF(CUSTOMER_STATUS = 'I', 'INACTIVE', 'OTHER')))

The Spark SQL equivalent:

-- Spark SQL on Databricks
SELECT
  customer_id,
  CASE
    WHEN customer_status IS NULL THEN 'UNKNOWN'
    WHEN customer_status = 'A' THEN 'ACTIVE'
    WHEN customer_status = 'I' THEN 'INACTIVE'
    ELSE 'OTHER'
  END AS customer_status_label
FROM bronze.customers

The syntax change is minor. Where it gets tricky is Informatica’s IIF() with side effects: if your expression references a variable port that accumulates state across rows, you cannot translate it to a simple CASE statement. You need a window function or a Python UDF with state.

Lookup transformation to Spark broadcast join

A connected Lookup in PowerCenter fetches a value from a reference table, returning a default when no match is found. This is the most common transformation type in most repositories.

-- Informatica Lookup: match product_id, return product_name and category
-- Condition: ORDERS.PRODUCT_ID = LKP_PRODUCT.PRODUCT_ID
-- Default value on no match: 'Unknown'
-- Output ports: PRODUCT_NAME, CATEGORY

In PySpark with a broadcast join for small lookup tables:

from pyspark.sql import functions as F

orders = spark.table("bronze.orders")

product_lookup = spark.table("silver.products").select(
    "product_id", "product_name", "category"
)

# Broadcast join replaces Informatica Lookup cache
enriched_orders = orders.join(
    F.broadcast(product_lookup),
    on="product_id",
    how="left"
).withColumn(
    "product_name", F.coalesce(F.col("product_name"), F.lit("Unknown"))
)

enriched_orders.write.format("delta").mode("overwrite").saveAsTable(
    "silver.enriched_orders"
)

The broadcast() hint tells Spark to send the entire lookup table to every worker node, which mimics the Informatica Lookup cache behavior. For large lookup tables (over ~100MB), drop the broadcast and let Spark handle the join with a shuffle. If the original Lookup used dynamic caching for insert/update detection, you need a Delta MERGE statement instead.

Router transformation to CASE with multiple outputs

PowerCenter’s Router transformation sends rows to different output groups based on conditions. It’s commonly used to split a data stream into insert, update, and delete paths.

-- Informatica Router groups:
-- Group 1 (NEW):     LKP_EXISTING_ID IS NULL
-- Group 2 (CHANGED): LKP_EXISTING_ID IS NOT NULL AND LKP_CHECKSUM != SRC_CHECKSUM
-- Group 3 (DEFAULT): all remaining rows

In Spark SQL:

-- Spark SQL on Databricks
WITH classified AS (
  SELECT
    s.*,
    e.id AS existing_id,
    e.checksum AS existing_checksum,
    CASE
      WHEN e.id IS NULL THEN 'NEW'
      WHEN e.checksum != s.checksum THEN 'CHANGED'
      ELSE 'UNCHANGED'
    END AS row_action
  FROM staging s
  LEFT JOIN existing_records e ON s.business_key = e.business_key
)
-- Use in downstream INSERT/MERGE operations
SELECT * FROM classified WHERE row_action = 'NEW'

You can write the new and changed rows to separate Delta tables, or use the classification column in a downstream MERGE. If you’re using dbt, splitting this into separate models (one for inserts, one for updates) is usually cleaner. With native Databricks notebooks, you’d run separate queries for each action type.

SCD Type 2 to Delta Lake MERGE

Informatica’s SCD Type 2 pattern uses a Lookup (to detect existing records), an Expression (to compare checksums and set flags), and an Update Strategy transformation (to route rows to DD_INSERT or DD_UPDATE). It’s a multi-transformation pipeline that’s tricky to build and harder to debug.

In Databricks, you use Delta Lake’s MERGE statement:

-- Step 1: Expire changed records
MERGE INTO gold.dim_customer AS target
USING staging.customer_updates AS source
ON target.customer_id = source.customer_id AND target.is_current = true
WHEN MATCHED AND (
  target.name <> source.name OR target.email <> source.email OR target.region <> source.region
) THEN UPDATE SET
  target.is_current = false,
  target.end_date = current_timestamp();

-- Step 2: Insert new current rows for changed and net-new records
INSERT INTO gold.dim_customer (customer_id, name, email, region, is_current, start_date, end_date)
SELECT
  source.customer_id, source.name, source.email, source.region,
  true, current_timestamp(), NULL
FROM staging.customer_updates AS source
LEFT JOIN gold.dim_customer AS target
  ON target.customer_id = source.customer_id AND target.is_current = true
WHERE target.customer_id IS NULL
   OR target.name <> source.name
   OR target.email <> source.email
   OR target.region <> source.region;

A Delta MERGE cannot simultaneously UPDATE a matched row and INSERT a new row for the same source record, so SCD Type 2 requires two statements. These two SQL statements replace what was a five-transformation pipeline in PowerCenter. An alternative is Lakeflow Declarative Pipelines’ AUTO CDC API (formerly APPLY CHANGES), which handles SCD2 declaratively. If you’re using dbt, snapshots reduce it further to a config block and a SELECT statement. All three approaches work; pick the one that fits your team’s workflow.

Parameterized session to Databricks notebook widgets

PowerCenter parameter files control runtime behavior. In Databricks, notebook widgets serve the same purpose for interactive development, and job parameters take over for scheduled runs.

-- Informatica parameter file
[session_name]
$$LOAD_DATE=2026-03-19
$$SOURCE_SCHEMA=PROD_DB
$$TARGET_TABLE=CUSTOMER_FACT

# Databricks notebook with widgets
dbutils.widgets.text("load_date", "2026-03-19")
dbutils.widgets.text("source_schema", "prod_db")
dbutils.widgets.text("target_table", "customer_fact")

load_date = dbutils.widgets.get("load_date")
source_schema = dbutils.widgets.get("source_schema")
target_table = dbutils.widgets.get("target_table")

# Use parameterized queries to avoid SQL injection
df = spark.sql(
    "SELECT * FROM IDENTIFIER(:schema || '.raw_customers') WHERE load_date = :load_date",
    args={"schema": source_schema, "load_date": load_date}
)

df.write.format("delta").mode("append").saveAsTable(
    f"silver.{target_table}"
)

When these notebooks run as scheduled jobs in Databricks Workflows, you pass the parameters from the job configuration instead of a parameter file.

Feature mapping reference

This table maps Informatica PowerCenter concepts to their Databricks-stack equivalents. “Direct translation” means the mapping is mechanical. “Redesign required” means you need to rethink the approach.

PowerCenter feature	Databricks-stack equivalent	Translation difficulty
Expression transformation	Spark SQL CASE, COALESCE, built-in functions	Direct translation
Filter transformation	Spark SQL WHERE clause	Direct translation
Lookup transformation (static)	Spark SQL JOIN with broadcast hint (or dbt ref)	Direct translation
Lookup transformation (dynamic cache)	Delta Lake MERGE statement	Moderate redesign
Joiner transformation	Spark SQL JOIN (INNER, LEFT, FULL)	Direct translation
Aggregator transformation	Spark SQL GROUP BY + aggregate functions	Direct translation
Router transformation	Spark SQL CASE + CTE (or separate dbt models)	Moderate redesign
Sorter transformation	Spark SQL ORDER BY (usually unnecessary in Spark)	Drop it; Spark sorts as needed
Update Strategy transformation	Delta MERGE (or Lakeflow APPLY CHANGES, or dbt snapshots)	Moderate redesign
Sequence Generator	Delta IDENTITY column (gap-free) or monotonically_increasing_id() (non-consecutive)	Direct translation
Stored Procedure transformation	Databricks notebook or SQL stored procedure	Moderate redesign
Normalizer transformation	Spark SQL EXPLODE / LATERAL VIEW	Direct translation
XML Generator/Parser	Spark from_xml() / to_xml() functions	Moderate redesign
Mapplet (reusable)	Python function, Lakeflow reusable flow (or dbt macro)	Redesign required
Workflow (scheduling)	Databricks Workflow or Airflow DAG	Redesign required
Session parameter file	Notebook widgets, job parameters (or dbt vars)	Redesign required
PowerCenter connector	Auto Loader, LakeFlow Connect, Fivetran, or Airbyte	Redesign required

One early decision shapes your entire migration: do you target notebooks, Lakeflow Declarative Pipelines, or dbt on Databricks? Notebooks give you maximum flexibility and are the most natural fit if your team already writes Python. Lakeflow Declarative Pipelines is the best fit if you want declarative, self-healing pipelines with built-in data quality expectations. dbt works well if your transformations are primarily SQL and you want the dbt ecosystem (testing, documentation, lineage). Many teams use a combination: Auto Loader + Lakeflow Declarative Pipelines for ingestion, notebooks or dbt for transformation, and Databricks Workflows for orchestration.

Data type mapping: Informatica to Databricks

Informatica PowerCenter has its own internal type system used during transformations. When moving to Databricks, these internal types need to map to Spark SQL / Delta Lake types. Most are direct, but a few deserve attention.

Informatica type	Databricks / Spark SQL type	Notes
String	STRING	No length limit in Delta. Informatica precision can be dropped.
Integer	INT	32-bit signed. Direct mapping.
Bigint	BIGINT	64-bit signed. Direct mapping.
Small Integer	SMALLINT
Decimal(p,s)	DECIMAL(p,s)	Preserve precision and scale. 28-digit max applies only with PowerCenter high precision mode; without it, Decimal promotes to Double (15 digits). Spark supports up to 38.
Double	DOUBLE	64-bit IEEE 754.
Float	FLOAT	32-bit IEEE 754.
Date/Time	TIMESTAMP_NTZ	Includes both date and time. Map to TIMESTAMP_NTZ, not DATE, or you lose the time component.
Date	DATE	Date-only type (no time component).
Timestamp	TIMESTAMP_NTZ	High-precision timestamp.
Binary	BINARY	Raw byte data.
Text	STRING	CLOB equivalent. Databricks STRING has no length limit.
Nstring	STRING	Unicode string. Databricks STRING is Unicode by default.
Ntext	STRING	Unicode CLOB equivalent.
Raw	BINARY	Raw binary data.
Number(p,s)	DECIMAL(p,s)	Preserve precision and scale.
Number (no precision)	DOUBLE	Acts as floating point.
Boolean	BOOLEAN	Direct mapping.

Delta Lake also supports complex types that Informatica cannot represent: STRUCT, ARRAY, and MAP. If your target schema uses nested data (common in lakehouse architectures), you’ll need to restructure flat Informatica outputs into nested Delta tables during migration. This is a design decision, not just a type mapping exercise.

The most common type-related bug: Informatica DECIMAL fields with precision 28 that get silently rounded when loaded into a Spark DECIMAL(28,s) column due to differences in how the two engines handle intermediate calculation precision. Validate decimal columns explicitly during testing.

Best practices for Informatica to Databricks migration

Catalog your active mappings before you migrate anything. Most PowerCenter repositories contain hundreds or thousands of mappings accumulated over a decade. Run a full inventory of your repository and cross-reference with actual session execution logs from the Workflow Monitor. In our experience, 40-60% of mappings in a typical enterprise repository are no longer active. Migrating dead code is a waste of time and budget. If a mapping has not run in six months, flag it for retirement instead of migration.

Pick your target architecture before writing code. Deciding between notebooks, Lakeflow Declarative Pipelines, and dbt after you’ve already converted 50 mappings is expensive. Make the architectural decision up front. Consider your team’s skills (Python-heavy vs SQL-heavy), your operational requirements (streaming vs batch), and your governance needs (Unity Catalog integration). Document the decision and create templates so every migrated pipeline follows the same pattern.

Use Auto Loader to replace file-based Informatica sources. If your Informatica workflows read flat files from SFTP servers or shared drives, Auto Loader is the direct replacement. It monitors a cloud storage directory, automatically detects new files, infers schema, and loads data into Delta tables. Set up Auto Loader early in the migration so you can test transformed outputs against the original Informatica results while both systems run in parallel.

Validate with value-level data diff, not row counts. Row counts tell you almost nothing. Two tables can have identical row counts and completely different data in every column. You need value-level comparison across every column to confirm your translated logic produces the same results as the original Informatica pipeline. This is especially true for decimal precision, date truncation, and NULL handling, where Informatica and Spark behave differently enough to produce silent data discrepancies.

How Datafold automates Informatica to Databricks migrations

Everything described above, from parsing proprietary mappings to rewriting transformation logic to validating the output, is exactly what Datafold’s Migration Agent was built to do.

The agent reads both PowerCenter XML exports and IICS CDI mapping definitions, then generates Spark SQL, PySpark, or dbt models. It doesn’t just translate syntax. It refactors procedural ETL patterns into set-based operations, collapsing multi-transformation pipelines into clean queries. Expression transformations, Lookups, Aggregators, Routers, and Update Strategy transformations are all handled automatically. The 90%+ of mappings that follow standard patterns are converted without manual intervention. Engineers focus their time on the remaining edge cases and architectural decisions (notebooks vs Lakeflow Declarative Pipelines vs dbt), not on rewriting hundreds of mappings by hand.

For validation, Datafold’s cross-database data diffing compares the actual output data between your Informatica targets and Databricks Delta tables, column by column, value by value. This catches the silent data mismatches (decimal precision, NULL handling, date truncation) that row-count checks miss entirely. Teams like FanDuel, who migrated to Databricks with Datafold, validated 150+ models and cut months off their timeline.

The result: fixed-price, outcome-based delivery with a guaranteed timeline. You know the cost and completion date before you start, not after months of open-ended consulting. Get a migration estimate to see the scope for your environment.

Related resources

• Get a migration quote - fixed-price, guaranteed-timeline migration delivery from Datafold
• Hadoop to Databricks migration guide - another common Databricks migration path
• The opportunity hidden in legacy ETL migrations - why companies are leaving Informatica, Talend, and SSIS behind