Where Does Your Data Live Pt. 2

In our first post we discussed the pros and cons of each of the big three data players and what it would look like implementing a solution with each.

Now let’s discuss exactly what a hybrid solution would look like.

Integration Architecture:

graph TD
    A[Snowflake Data Warehouse] --> B[Databricks]
    A --> C[Amazon Bedrock]
    B --> C
    B[Databricks] --> D[ML Models & Features]
    C[Amazon Bedrock] --> E[Foundation Models]
    D --> F[Applications]
    E --> F

In the above example the data flow is such that:

1. Raw data lands in Snowflake

2. Databricks reads from Snowflake for feature engineering

3. Features stored back in Snowflake

4. Models trained in Databricks

5. Bedrock handles foundation model inference

6. Results stored back in Snowflake

This is the best approach to utilize each platforms core strengths in three key areas:

Data Layer & Processing: Use Snowflake as the core data warehouse for all your structured data. Have it handle your financial, customer, and operational data. It provides the strongest governance and security, will act as the single source of truth for business data and it is the best when it comes to managing data sharing and access controls.

ML Development & Training: Use Databricks for feature engineering and data preparation, training traditional ML models and managing ML experiments through MLflow. Let your data science team basically live here for collaboration. It’s especially strong for processing unstructured data (text, images, etc.) so it can be used for training and fine-tuning smaller custom models.

Foundation Model Deployment: Use Amazon Bedrock for accessing and deploying foundation models. It’s strong at API-based model serving and of course if you’re heavily invested in AWS it’s got its strong integration with AWS services. Utilize production inference endpoints and you can have a cost-effective scaling of model serving.

Key Integration Points:

1. Data Connectivity:

# Eample of DataBricks reading from Snowflake
options = {
"sfURL": "your-account.snowflakecomputing.com",
"sfUser": "username",
"sfPassword": "password",
"sfDatabase": "database",
"sfSchema": "schema",
"sfWarehouse": "warehouse"
}
df = spark.read \\
.format("snowflake") \\
.options(**options) \\
.option("query", "SELECT * FROM my_table") \\
.load()

2. Model Integration:

# Example of combining custom ML model with Bedrock
from databricks.models import load_model
import boto3
# Load custom model from Databricks
custom_model = load_model("models:/my_model/production")
# Initialize Bedrock
bedrock = boto3.client('bedrock-runtime')
def hybrid_inference(input_data):
# Get custom model prediction
custom_prediction = custom_model.predict(input_data)
# Enhance with foundation model
foundation_response = bedrock.invoke_model(
modelId='theinfinite.machine',
body=json.dumps({
"prompt": f"Enhance this prediction: {custom_prediction}",
"max_tokens": 500
})
)
return combine_predictions(custom_prediction, foundation_response)

3. Monitoring & Governance:

# Example of unified monitoring setup
def log_model_metrics(model_name, metrics):
# Log to Databricks MLflow
with mlflow.start_run():
mlflow.log_metrics(metrics)
# Store in Snowflake for governance
snowflake_conn.execute(f"""
INSERT INTO model_metrics
VALUES ('{model_name}', '{json.dumps(metrics)}')
""")

Cost Optimization Strategy

In the Land of Narnia (or if you’re bankrolled by Microsoft) where resources grow on trees and I assume CPU time is infinite and cheap, you could get away with hilarious and ludicrous throwing of compute and storage power at ML. But for those of us not named OpenAI, we have to worry about budgets and not setting $100 bills on fire. You and I need to have cost optimization in mind when designing solutions, especially if you’re going to be selling this kind of implementation to a customer. We’ve all heard of the dreaded AWS surprise bill for a Lambda run amok.

Use Snowflake for data storage and basic analytics. It excels in data lake and data warehousing and has good tools to ensure your data sets don’t sprawl out of control. Set Databricks compute to on-demand and use only when needed for ML workloads. Use Bedrock on-demand for foundation model inference. If you set proper limits in place and are mindful of your usage, you should be able to keep finance from trying to take an ax to the fiber lines.

Best Practices

I may not work at Salesforce anymore, but the words still ring true, “Trust Is Our #1 Value”. It’s of vital importance in the age of everything and anything being hacked that we are all security champions and put the integrity and security of our data, be it sensitive or not as a prime priority when developing these solutions. It’s important to:

1. Implement clear data lineage tracking across platforms

2. Use unified authentication (e.g., through AWS IAM)

3. Maintain consistent naming conventions across platforms

4. Implement robust error handling between systems

5. Set up comprehensive monitoring across all platforms

Potential Challenges

Even still there are serious challenges to this kind of approach. There’s the complexity of managing multiple vendor relationships, along with potentially complex pricing structures. With the data taking multiple hops between platforms there is potential latency between systems and you need an engineering team with expertise across platforms. Finally, it’s critical that you have synchronization of security policies and constantly fire drill data integrity and security tests. Your security is only as strong as your weakest policy.

Next time, we’ll get into the nitty gritty of developing a hybrid approach.