The Real-World Guide to Migrating Snowflake to Databricks: Lessons from the Trenches
I remember the first time a client—a major player in the retail space—came to me with the question: "We're thinking of moving from Snowflake to Databricks. What does that really take?" They weren't just kicking tires. They had a mandate to unify their analytics and AI initiatives on a single platform, break free from what they perceived as a proprietary data format, and gain more granular control over their cloud costs.
Since that first project, I've led and executed several of these large-scale migrations. Each one was a unique beast, but the patterns of success and failure have become strikingly clear. This isn't a theoretical exercise or a marketing pitch. This is a field guide, forged from late-night debugging sessions, tough architectural decisions, and the hard-won satisfaction of seeing a complex system go live on a new platform.
Migrating from a world-class cloud data warehouse like Snowflake to a data lakehouse platform like Databricks is not a simple "lift-and-shift." It's a modernization effort. You’re trading the managed simplicity of Snowflake's virtual warehouses for the flexible, open power of Spark, Delta Lake, and a unified governance model in Unity Catalog. Get it right, and you unlock incredible potential for everything from ETL to real-time analytics and generative AI. Get it wrong, and you're left with a tangled mess of half-migrated pipelines, spiraling costs, and frustrated users.
Let’s walk through what it actually takes, from the initial discovery to navigating the inevitable technical hurdles that you won't read about in the official documentation.
Before You Move a Single Byte: The Discovery and Assessment Imperative
The single biggest mistake I see teams make is underestimating the complexity of their existing Snowflake environment. They think they know what they have, but the reality is often a sprawling ecosystem of interconnected objects, hidden dependencies, and shadow IT. You cannot migrate what you don't understand. A thorough, automated discovery phase is non-negotiable.
Building a Complete Inventory of Snowflake Objects
First, we need a complete, machine-readable inventory of every object in Snowflake. This means every database, schema, table, view, stored procedure, UDF, stage, pipe, task, and stream. Manually clicking through the Snowflake UI is a recipe for disaster. We automate this with Python.
Here’s a script my team uses as a starting point. It connects to Snowflake and systematically catalogues the core objects, dumping them into a structured format (like JSON or CSV) for later analysis.
import snowflake.connector
import pandas as pd
import os
# --- Configuration ---
SNOWFLAKE_USER = os.getenv("SNOWFLAKE_USER")
SNOWFLAKE_PASSWORD = os.getenv("SNOWFLAKE_PASSWORD")
SNOWFLAKE_ACCOUNT = os.getenv("SNOWFLAKE_ACCOUNT")
SNOWFLAKE_WAREHOUSE = "COMPUTE_WH" # Use a dedicated WH for discovery
SNOWFLAKE_ROLE = "ACCOUNTADMIN" # Requires high privileges to see everything
def get_snowflake_connection():
"""Establishes a connection to Snowflake."""
try:
conn = snowflake.connector.connect(
user=SNOWFLAKE_USER,
password=SNOWFLAKE_PASSWORD,
account=SNOWFLAKE_ACCOUNT,
warehouse=SNOWFLAKE_WAREHOUSE,
role=SNOWFLAKE_ROLE,
autocommit=True
)
return conn
except Exception as e:
print(f"Error connecting to Snowflake: {e}")
return None
def fetch_all_objects(conn):
"""Fetches a comprehensive list of all objects from Snowflake."""
cursor = conn.cursor()
inventory = []
try:
# 1. Get all databases
cursor.execute("SHOW DATABASES;")
databases = [row[1] for row in cursor.fetchall()]
for db in databases:
# Avoid system databases
if db.upper() in ['SNOWFLAKE', 'SNOWFLAKE_SAMPLE_DATA']:
continue
print(f"--- Processing Database: {db} ---")
cursor.execute(f'USE DATABASE "{db}";')
# 2. Get all schemas in the current database
cursor.execute("SHOW SCHEMAS;")
schemas = [row[1] for row in cursor.fetchall()]
for schema in schemas:
if schema.upper() in ['INFORMATION_SCHEMA', 'PUBLIC']:
continue
print(f" -- Processing Schema: {db}.{schema} --")
current_schema = f'"{db}"."{schema}"'
# 3. Get Tables, Views, Stages, Pipes, Tasks, Procedures, Functions
object_types = {
"TABLES": "table_name", "VIEWS": "name", "STAGES": "name",
"PIPES": "name", "TASKS": "name", "PROCEDURES": "name",
"FUNCTIONS": "name"
}
for obj_type, name_col in object_types.items():
try:
query = f"SHOW {obj_type} IN SCHEMA {current_schema};"
cursor.execute(query)
results = cursor.fetchall()
for row in results:
# The column index for the name can vary. We find it dynamically.
col_names = [desc[0] for desc in cursor.description]
name_index = col_names.index(name_col.upper()) if name_col.upper() in col_names else col_names.index('name')
inventory.append({
"database": db,
"schema": schema,
"object_type": obj_type.rstrip('S'),
"object_name": row[name_index],
"full_path": f"{db}.{schema}.{row[name_index]}"
})
except Exception as e:
print(f"Could not fetch {obj_type} from {current_schema}: {e}")
finally:
cursor.close()
return inventory
if __name__ == "__main__":
connection = get_snowflake_connection()
if connection:
print("Starting Snowflake inventory discovery...")
all_objects = fetch_all_objects(connection)
# Convert to DataFrame for easy analysis and export
df = pd.DataFrame(all_objects)
df.to_csv("snowflake_inventory.csv", index=False)
print("\nInventory Discovery Complete!")
print(f"Found {len(df)} objects.")
print(df['object_type'].value_counts())
connection.close()
This script gives us the raw material. Now we know what exists.
Beyond the Database: Uncovering the Full Ecosystem
A Snowflake instance doesn't live in a vacuum. It’s the center of a data universe connected to countless external tools. We have to find them all.
- ETL/ELT Tools: Are you using dbt, Fivetran, Matillion, Informatica, or Pentaho? On one project, we discovered a department running hundreds of critical Pentaho jobs (
.ktrand.kjbfiles) that were completely undocumented. We had to write scripts to scan file servers and Git repos to find them. - Orchestration: How are jobs scheduled? Airflow DAGs, cron jobs, and Snowflake's native
TASKSare the usual suspects. Each Airflow DAG that calls a Snowflake operator is a dependency. EachTASKis a piece of logic that needs to be migrated to a Databricks Job. - BI & Reporting: Tableau, Power BI, Looker. These tools often contain complex, hand-written SQL queries embedded directly in dashboards. We once found a 500-line "monster query" in a Tableau workbook that was responsible for a critical executive KPI dashboard. It had to be identified, analyzed, and rewritten for Databricks.
- Custom Applications: Don't forget applications that connect via JDBC/ODBC. We use network traffic analysis or simply grep through application codebases for connection strings.
Here’s a simple Python utility to scan a codebase or file system for potential SQL files and other artifacts:
import os
import re
def find_data_artifacts(root_dir, extensions=('.sql', '.ktr', '.kjb')):
"""Scans a directory for files with specific extensions and looks for Snowflake keywords."""
artifact_list = []
snowflake_keywords = re.compile(r'snowflake|snowpipe|copy into|qualify|flatten', re.IGNORECASE)
for subdir, _, files in os.walk(root_dir):
for file in files:
if file.endswith(extensions):
file_path = os.path.join(subdir, file)
try:
with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
content = f.read(1024 * 10) # Read first 10KB
if snowflake_keywords.search(content):
artifact_list.append({
"file_path": file_path,
"type": os.path.splitext(file)[1],
"potential_dependency": True
})
except Exception as e:
print(f"Could not read {file_path}: {e}")
return artifact_list
# Example Usage:
# project_codebase = "/path/to/your/company/git/repos"
# artifacts = find_data_artifacts(project_codebase)
# print(f"Found {len(artifacts)} potential data artifacts.")
Usage and Dependency Analysis: What Actually Matters?
Just because a table exists doesn't mean it's used. Just because a stored procedure is there doesn't mean it's critical. The absolute goldmine for this analysis is Snowflake’s ACCOUNT_USAGE.QUERY_HISTORY view.
By analyzing QUERY_HISTORY, we can answer critical questions:
- Which tables are queried most frequently?
- Which tables are never touched and can potentially be archived?
- Which users or service accounts are running the heaviest queries?
- What are the common join patterns?
- Which stored procedures and UDFs are actually being called?
Here's a query I run to get a high-level overview of table read/write frequency. It’s the starting point for prioritizing our migration waves.
-- This query needs to be run in Snowflake
-- It identifies the most frequently accessed tables
SELECT
f.value:"objectId" AS table_id,
f.value:"objectName" AS table_name,
COUNT(CASE WHEN query_type IN ('SELECT') THEN 1 END) AS read_count,
COUNT(CASE WHEN query_type IN ('INSERT', 'UPDATE', 'DELETE', 'MERGE', 'COPY') THEN 1 END) as write_count,
COUNT(*) AS total_access_count,
MAX(end_time) as last_accessed
FROM
SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY qh,
LATERAL FLATTEN(input => qh.ACCESSED_OBJECTS) f
WHERE
f.value:"objectDomain" = 'Table'
AND start_time >= DATEADD('day', -90, CURRENT_TIMESTAMP()) -- Look at last 90 days
GROUP BY 1, 2
ORDER BY total_access_count DESC
LIMIT 500;
Running this query tells us which tables are the "hot spots" of our data ecosystem. These are the tables we need to focus on migrating first, as they support the most critical use cases. From here, we can build dependency graphs (Table A -> View B -> Procedure C -> Task D) to group objects into logical migration units.
The Migration Readiness Report
The output of this entire discovery phase is a Migration Readiness Report. It’s not just a data dump; it’s an actionable plan. We typically present this as a dashboard.
| Object Full Path | Object Type | Usage Rank | Complexity Score (1-5) | Dependencies | Proposed Action | Migration Wave |
|---|---|---|---|---|---|---|
PROD.SALES.ORDERS |
TABLE | 1 (High) | 1 | 25 | Migrate As-Is | Wave 1 |
PROD.FINANCE.MONTHLY_ROLLUP |
PROCEDURE | 3 (High) | 5 | 12 | Re-architect as Databricks Notebook Job | Wave 1 |
DEV.SANDBOX.TEMP_USER_TABLE |
TABLE | 99 (Low) | 1 | 0 | Archive/Deprecate | N/A |
PROD.MARKETING.CUSTOMER_VIEW |
VIEW | 15 (Med) | 4 | 7 | Rewrite SQL (uses QUALIFY) |
Wave 2 |
PROD.CORE.SNOWPIPE_LOGS |
PIPE | 5 (High) | 2 | 1 | Re-implement with Auto Loader | Wave 1 |
This report becomes our bible for the entire migration project. It allows us to plan sprints, allocate resources, and communicate progress to stakeholders.
The Nitty-Gritty: Common Migration Hurdles and How We Solved Them
Now for the fun part. Here are the battles we fought and the lessons we learned on the ground, broken down by category.
1. Schema & Data Type Mismatches
This is where the first "gotchas" appear. While many ANSI SQL types map cleanly, Snowflake has some unique features that require careful handling.
The Problem: VARIANT, ARRAY, and OBJECT
Snowflake's VARIANT type is incredibly flexible for handling semi-structured JSON data. Databricks doesn't have a direct VARIANT equivalent. On a project ingesting IoT sensor data, we had terabytes of JSON stored in VARIANT columns.
Diagnosis:
When we tried a naive COPY INTO from a Parquet file (exported from Snowflake) into a Delta table, Spark either failed to parse the schema or flattened the JSON into a simple STRING type. Querying this stringified JSON in Databricks was horrifically slow and clumsy.
Remediation and Best Practices:
The correct approach is to map Snowflake's semi-structured types to Databricks' native complex types:
- Snowflake VARIANT (containing JSON) -> Databricks STRUCT or MAP<STRING, STRING>
- Snowflake ARRAY -> Databricks ARRAY<T>
- Snowflake OBJECT -> Databricks STRUCT
We defined an explicit schema for our target Delta table.
Snowflake DDL:
CREATE TABLE raw_events (
event_id STRING,
event_timestamp TIMESTAMP_NTZ,
payload VARIANT
);
Databricks Delta DDL (The Fix):
CREATE TABLE raw_events (
event_id STRING,
event_timestamp TIMESTAMP,
payload STRUCT<
sensorId: STRING,
reading: DOUBLE,
location: STRUCT<lat: DECIMAL(10, 7), lon: DECIMAL(10, 7)>,
tags: ARRAY<STRING>
>
) USING DELTA;
This forces schema-on-write, giving us enormous performance gains and type safety in Databricks. For data export, we used Snowflake’s COPY INTO @... command with TYPE = PARQUET and then used Databricks Auto Loader with schema inference or a predefined schema to ingest it into the structured Delta table.
Another Issue: AUTOINCREMENT / IDENTITY
Snowflake's AUTOINCREMENT guarantees a unique, sequential number, often used for surrogate keys. Databricks/Spark, being a distributed system, doesn't have a direct, performant equivalent that guarantees sequence.
Diagnosis:
A team tried to replicate this by creating a UDF that read a maximum value from a table and added 1. This created a massive bottleneck, as every parallel task had to wait and query this central value, effectively killing the parallelism of Spark.
Remediation and Best Practices:
- If you just need a unique ID (not sequential): Use Spark's built-in monotonically_increasing_id() function. It's fast and guarantees uniqueness within a Spark job run, but not sequence.
- If you need a true surrogate key: Generate it before ingestion or use a combination of other unique business keys to create a composite key. Often, we found that the reliance on surrogate keys was a holdover from traditional data warehousing, and a natural key (e.g., order_id + order_date) was a better fit for the lakehouse model.
- For hash-based keys: Use the sha2 function in Spark SQL on a concatenation of business keys. This is a common and robust pattern. sha2(concat(col1, col2), 256)
2. Code Conversion: The SQL and Stored Procedure Minefield
This is, without a doubt, the most time-consuming part of the migration. You cannot just copy-paste code.
The Problem: Proprietary SQL Functions and Syntax
SnowSQL has fantastic functions that don't exist in Spark SQL. The most common offenders we had to rewrite were:
- QUALIFY: Used for filtering window function results.
- FLATTEN: Used for exploding JSON arrays.
- :: cast syntax (e.g., my_col::string).
- Date/Time functions with slightly different names or arguments.
Diagnosis:
We built a simple static code analysis script in Python that used regular expressions to scan our entire SQL codebase (.sql files, view definitions, procedure bodies) for these specific keywords. This gave us a hit list of every piece of code that needed manual review.
Remediation and Best Practices:
We created a "translation guide" for our developers.
- QUALIFY -> Subquery or CTE with ROW_NUMBER():
* **Snowflake:**
SELECT * FROM my_table
QUALIFY ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) = 1;
* **Databricks Spark SQL:**
SELECT * FROM (
SELECT *, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) as rn
FROM my_table
) tmp WHERE rn = 1;
-
FLATTEN->EXPLODE:- Snowflake:
SELECT raw_data:id, value:name
FROM my_json_table, LATERAL FLATTEN(input => raw_data:items);
* **Databricks Spark SQL:**
SELECT raw_data.id, exploded_item.name
FROM my_json_table
LATERAL VIEW EXPLODE(raw_data.items) AS exploded_item;
::casting ->CAST( ... AS ...): A simple find-and-replace, but it must be done.
The Bigger Problem: Stored Procedures
This is a paradigm shift. Snowflake stored procedures are typically written in JavaScript or Snowflake Scripting (SQL). They run transactionally on the warehouse. Databricks has no direct equivalent.
A Painful Lesson: On an early project, a team tried to use a third-party transpiler to "automatically" convert a 1000-line JavaScript stored procedure into a PySpark script. The result was an unreadable, unmaintainable mess that failed on complex logic and had terrible performance. Do not do this.
Remediation and Best Practices:
Stored procedures must be re-architected, not converted.
- The Target: A Databricks Notebook (Python/Scala).
- The Logic: Business logic from the procedure is re-implemented using Spark DataFrames or Spark SQL.
- The Execution: The notebook is scheduled as a Databricks Job.
Think of it this way: a Snowflake stored procedure is a monolithic block of imperative code. A Databricks job is a series of declarative data transformations on DataFrames.
Example Migration:
- Snowflake Stored Procedure (FINANCE.MONTHLY_ROLLUP):
1. Starts a transaction.
2. Runs a DELETE on a summary table for the current month.
3. Uses a CURSOR to loop through daily transaction tables.
4. Performs complex calculations in JavaScript.
5. INSERTs aggregated results into the summary table.
6. Commits the transaction.
- Re-architected Databricks Notebook Job:
1. Read all relevant daily transaction data for the month into a single Spark DataFrame. spark.read.table("...").where(...)
2. Perform all aggregations and calculations using DataFrame transformations (.groupBy(), .agg(), .withColumn()). This is a single, parallel operation.
3. Write the final aggregated DataFrame to the target Delta table using .write.format("delta").mode("overwrite").saveAsTable(...). Delta Lake's ACID transactions handle the atomicity.
The result is code that is more scalable, more testable, and idiomatic to the Databricks platform.
3. Performance & Scalability
This is where teams often get a rude awakening on cost and performance. The models are fundamentally different.
The Problem: Warehouse Sizing vs. Cluster Configuration
In Snowflake, performance tuning is often as simple as ALTER WAREHOUSE my_wh SET WAREHOUSE_SIZE = 'XLARGE'. This simplicity is powerful but abstracts away the underlying infrastructure. In Databricks, you are responsible for configuring the cluster: worker types, number of workers, autoscaling settings, spot instance usage, etc.
Diagnosis:
We saw a team migrate a pipeline and complain that it was "slower and more expensive" on Databricks. We looked at their job configuration: they were using a massive, fixed-size cluster of on-demand instances for a small hourly job. The cluster sat idle most of the time but cost money, and it was oversized for the actual workload.
Remediation and Best Practices:
- Right-size your clusters. Use the Spark UI and Ganglia metrics to see if your CPUs and memory are actually being utilized. Start small and scale up.
- Embrace autoscaling. For most ETL workloads, configure a cluster with a minimum number of workers (e.g., 2) and a reasonable maximum (e.g., 20). Databricks will automatically add and remove workers as needed.
- Use Job Clusters. Don't run production pipelines on all-purpose interactive clusters. Job clusters are provisioned just for the life of the job and then terminate, which is a huge cost-saving mechanism.
- Leverage Spot Instances. For fault-tolerant workloads (most ETL is), using spot instances for a percentage of your workers can cut compute costs by 70% or more.
- OPTIMIZE and Z-ORDER your Delta tables. Snowflake does automatic micro-partition tuning. In Databricks, you need to be more explicit. Regularly run OPTIMIZE my_table to compact small files. For frequently filtered columns, use OPTIMIZE my_table ZORDER BY (col_a). This is the Databricks equivalent of Snowflake’s CLUSTER BY and has a massive impact on query performance.
4. Security & Governance
Migrating your security model is as important as migrating your data.
The Problem: Snowflake Roles vs. Unity Catalog
Snowflake has a mature and well-understood role-based access control (RBAC) model. Databricks' modern answer to this is Unity Catalog (UC). Migrating requires mapping your existing security posture to the UC hierarchy.
Diagnosis:
On one project, the team initially ignored Unity Catalog and used the legacy table ACLs on the Hive metastore. This quickly became an unmanageable mess of permissions at the workspace level, with no central governance or lineage. It was a security and compliance nightmare.
Remediation and Best Practices:
- Start with Unity Catalog from Day 1. Do not even consider a migration without it.
- Map the concepts:
- Snowflake Account -> Databricks Metastore (one per region)
- Snowflake Database -> Databricks Catalog
- Snowflake Schema -> Databricks Schema (or "database")
- Snowflake Roles -> Databricks Groups
- Automate Grant Migration: We used the output from our discovery phase (SHOW GRANTS ON ...) to script the creation of groups and the GRANT statements in Databricks. You can run these GRANT commands directly in a notebook.
- Row-Level Security: In Snowflake, you use Row Access Policies. In Databricks UC, the standard pattern is to create a VIEW on top of the base table that contains the filtering logic. You then grant users access to the view, not the underlying table.
**Databricks Example (Dynamic View for Row-Level Security):**
-- V_ORDERS filters orders based on the user's region, defined in a separate mapping table
CREATE VIEW secure_views.v_orders AS
SELECT * FROM raw_data.orders o
WHERE o.region = (
SELECT region FROM governance.user_region_mapping
WHERE user_email = current_user()
);
-- Grant users access to the view, not the base table
GRANT SELECT ON VIEW secure_views.v_orders TO `sales-team`;
5. Orchestration & Data Loading
The way you schedule jobs and ingest data will change significantly.
The Problem: Snowflake Tasks/Streams vs. Databricks Jobs/Workflows
- Tasks: Snowflake TASKS are scheduled SQL statements. The direct replacement is a Databricks Job that runs a notebook with that SQL.
- Streams: Snowflake STREAMS are a powerful change-data-capture (CDC) mechanism on a table.
Remediation and Best Practices:
- Migrate TASKS to Databricks Jobs. For complex dependencies (TASK B runs after TASK A), use Databricks Workflows to chain multiple job tasks together. This provides a visual DAG and better error handling.
- Migrate STREAMS to Delta Lake Change Data Feed (CDF) or Structured Streaming.
- For simple CDC use cases, enable CDF on your Delta table (TBLPROPERTIES (delta.enableChangeDataFeed = true)). You can then read the changes in a batch manner using table_changes('my_table', 'version_as_of'). This is great for downstream replication.
- For real-time streaming use cases, re-architect the pipeline using Structured Streaming to read from the source (e.g., Kafka) directly.
The Problem: Snowpipe vs. Auto Loader
This is one of the easier and more satisfying parts of the migration. Snowpipe automatically ingests files from a stage into a table. Databricks Auto Loader does the same but with more features.
Remediation and Best Practices:
- Replace every Snowpipe with an Auto Loader stream. Auto Loader is superior in almost every way.
- Key Benefits of Auto Loader:
- Schema Inference and Evolution: It can automatically detect the schema of your incoming files (JSON, CSV, Parquet) and handle schema drift over time without failing the pipeline. This was a massive operational win for us.
- Scalability: It runs on a Spark cluster, so it can handle billions of files.
- Cost-Effectiveness: It uses a highly optimized file discovery mode to avoid listing huge directories, saving on cloud storage API costs.
Auto Loader Implementation (PySpark):
# A continuous streaming job using Auto Loader
(spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("cloudFiles.schemaLocation", "/path/to/schema/location") # For schema tracking
.load("/path/to/s3/landing/zone/")
.writeStream
.option("checkpointLocation", "/path/to/checkpoint/location")
.trigger(availableNow=True) # Or .trigger(processingTime='5 minutes')
.toTable("my_target_delta_table")
)
My Production-Hardened Migration Playbook
After several of these projects, my team has distilled our process into a set of core principles.
- Automate Discovery Relentlessly. Your initial inventory and analysis scripts are the most valuable IP you will create for the project. Trust the data, not people's memories.
- Migrate by Use Case, Not by Database. Don't try to migrate an entire Snowflake database at once. Pick a business use case (e.g., "Sales Funnel Reporting"). Identify all the tables, views, procedures, and external dependencies for that single use case. Migrate them as a cohesive unit. This delivers business value faster and reduces risk.
- Establish Governance in Unity Catalog from Day Zero. Retrofitting security and governance is 10x harder. Define your catalog structure, naming conventions, and group/role strategy before you migrate the first table.
- Don't "Lift and Shift" Procedural Code. Rewrite stored procedures and complex UDFs to be Spark-native. Use DataFrames and Spark SQL. Embrace the declarative, distributed paradigm. Resisting this is the #1 cause of performance and maintainability issues.
- Profile and Benchmark Everything. Use the
QUERY_HISTORYto benchmark the P95 and P50 query performance in Snowflake. After migrating, use the Databricks Query Profile to analyze and tune the new queries. Prove that the new system is meeting or exceeding performance SLAs. - Train Your People Early and Often. Your team's biggest asset is their knowledge. Snowflake developers need to learn to think in terms of Spark, DataFrames, and distributed computing. BI analysts need to learn how to connect to and query Databricks. Invest in training; it will pay for itself a hundred times over.
- Treat Cost Management as a Feature. The flexibility of Databricks comes with the responsibility of management. Implement cluster policies to enforce tagging and limit sizes. Set up budget alerts. Teach every developer how to read the cost analysis dashboards and understand the DBU (Databricks Unit) model.
The Finish Line: Was It Worth It?
Every client who completed this journey has concluded it was worth the effort, but only because they approached it as a strategic modernization, not a simple platform swap. The primary driver is rarely just about saving money on Snowflake credits. The real value comes from unlocking new capabilities.
By moving to Databricks and its open Delta Lake format, they positioned themselves for the future. Their data was no longer confined to a proprietary ecosystem. Their analytics and AI teams could now work on the exact same, up-to-date data without slow and costly ETL processes between systems. They could build real-time streaming applications, fine-tune LLMs on their own private data, and leverage the entire open-source Spark ecosystem.
The road from Snowflake to Databricks is challenging. It requires meticulous planning, deep technical expertise, and a shift in mindset. But by understanding the landscape, anticipating the common hurdles, and following a disciplined, use-case-driven approach, you can successfully navigate the migration and build a data platform that is truly ready for the next decade of data and AI.