Battle-Tested Architectures: Databricks vs Snowflake for Scalable Enterprise AI

Databricks vs. Snowflake: A Technical Comparison for Enterprise AI and Big Data

As organizations continue to navigate the complexities of multi-cloud environments, data ecosystems are evolving at an unprecedented pace. In this article, we'll delve into the architecture mapping between Databricks and Snowflake, focusing on practical implementation details and real-world applications.

Terminology: A Barrier to Enterprise Solutions

When architecting enterprise data solutions, terminology can often be a barrier to understanding core concepts. This gap in knowledge can lead to inefficient workarounds or, worse, vendor lock-in. To bridge this gap, let's explore the key design patterns and structures that underlie both Databricks and Snowflake.

Data Governance: A Shared Concern

Both Databricks and Snowflake prioritize data governance, ensuring that enterprise data is secure, compliant, and accessible across platforms. This shared concern translates to several critical aspects:

• Metadata management: Both platforms offer robust metadata management capabilities, enabling users to track data lineage, schema evolution, and access controls.
• Security and access control: Databricks and Snowflake provide fine-grained security features, including encryption, access control lists (ACLs), and role-based access control (RBAC).
• Data quality and integrity: Both platforms offer tools for data profiling, data validation, and data cleansing to ensure high-quality data.

Example: Data Governance in Databricks

from pyspark.sql import SparkSession

# Create a SparkSession with the necessary configurations
spark = SparkSession.builder \n    .appName("Data Governance") \n    .config("hive.metastore.uris", "thrift://metastore:9083") \n    .enableHiveSupport() \n    .getOrCreate()

# Define metadata for the dataset
dataset_metadata = spark.createDataFrame([
    ("customer_id", "integer"),
    ("order_date", "date")
], schema="column_name data_type")

# Register the dataset as a Hive table
dataset_metadata.write.saveAsTable("customer_orders")

Example: Data Governance in Snowflake

-- Create a table with metadata
CREATE TABLE customer_orders (
  customer_id INTEGER,
  order_date DATE
);

-- Define access controls and permissions
GRANT SELECT ON TABLE customer_orders TO ROLE analytics;
GRANT INSERT ON TABLE customer_orders TO ROLE marketing;

Domain-Driven Design: A Common Language

Domain-driven design (DDD) is an approach that emphasizes the importance of domain language and modeling in software development. Both Databricks and Snowflake support DDD principles, ensuring that domain models are well-represented across platforms.

Example: Domain Model in Databricks

from pyspark.sql import SparkSession

# Define a Python class to represent the Customer entity
class Customer:
    def __init__(self, customer_id, name):
        self.customer_id = customer_id
        self.name = name

# Create a DataFrame with data from the customer_orders table
customer_df = spark.createDataFrame([
    (1, "John Doe"),
    (2, "Jane Smith")
], schema="id string, name string")

# Apply business logic using the Customer class
def calculate_revenue(customer_id):
    # Query the database for revenue data
    revenue_df = spark.sql(f"SELECT SUM(amount) FROM orders WHERE customer_id = {customer_id}")

    return revenue_df.collect()[0][0]

revenue = calculate_revenue(1)
print(revenue)

Example: Domain Model in Snowflake

-- Define a table to represent the Customer entity
CREATE TABLE customers (
  id INTEGER,
  name VARCHAR(255)
);

-- Insert data into the table
INSERT INTO customers (id, name) VALUES (1, "John Doe"), (2, "Jane Smith");

-- Apply business logic using SQL
SELECT SUM(amount) FROM orders WHERE customer_id = 1;

Data Products: A Common Goal

Both Databricks and Snowflake prioritize data products as the ultimate goal of enterprise data solutions. This shared focus translates to several key aspects:

• Data integration: Both platforms offer robust data integration capabilities, enabling users to combine datasets from various sources.
• Data transformation: Databricks and Snowflake provide tools for data transformation, including ETL (extract, transform, load) processes and data processing workflows.

Example: Data Product in Databricks

from pyspark.sql import SparkSession

# Create a SparkSession with the necessary configurations
spark = SparkSession.builder \n    .appName("Data Product") \n    .config("hive.metastore.uris", "thrift://metastore:9083") \n    .enableHiveSupport() \n    .getOrCreate()

# Define data integration and transformation logic
orders_df = spark.createDataFrame([
    (1, 100.00),
    (2, 200.00)
], schema="id integer, amount decimal")

customer_orders_df = orders_df.join(
    customers_df,
    on=spark.sql("SELECT id FROM orders"),
    how="inner"
)

# Register the data product as a Hive table
customer_orders_df.write.saveAsTable("customer_orders_product")

Example: Data Product in Snowflake

-- Create a view to represent the customer orders dataset
CREATE VIEW customer_orders_product AS
SELECT c.id, o.amount, c.name
FROM customers c
JOIN orders o ON c.id = o.customer_id;

-- Apply data transformation logic using SQL
SELECT SUM(amount) FROM customer_orders_product;

Conclusion

In conclusion, Databricks and Snowflake share a common architecture mapping for enterprise AI and big data solutions. By understanding the key design patterns and structures that underlie both platforms, organizations can build resilient and governed architectures across platforms without vendor lock-in.

By focusing on practical implementation details and real-world applications, this article has provided a comprehensive guide to bridging the gap between Databricks and Snowflake. Whether you're a data architect migrating workloads or a leader fostering cross-team collaboration, this blueprint for hybrid operations supports multi-platform models and fosters data-driven decision-making across your organization.

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By Malik Abualzait