Designing and Building Scalable Data Solutions with Snowflake and Databricks

Whether you’re building a modern Data Lake or a robust Data Warehouse, selecting the right platform is key. Two industry leaders—Snowflake and Databricks—offer powerful, scalable, cloud-native architectures that enable organizations to harness the full potential of their data. In this post, we’ll be doing a bit of a quadruple feature, covering both data lakes and warehouses as well as how you can use Snowflake and Databricks to your advantage!

Snowflake: The Modern Data WarehouseWhat is Snowflake?

Snowflake – whom we haven’t touched yet in our data series – is a fully managed, multi-cloud data warehouse platform that supports structured and semi-structured data (think JSON, Avro, Parquet). It separates the compute from storage, allowing for fantastic scalability!

Key Features:Automatic scaling and optimization. The main benefit of using a cloud-based data provider: they worry about getting you storage, you just have to worry about setting up the connections.
Support for SQL and semi-structured data. As I mentioned, Snowflake supports all your favorite data types and query languages!
Secure data sharing across accounts. Having the data up in the cloud makes it very easy to give access to the data to other people on your team. Think about how you might set up a Google Drive, just imagine 1,000x the data
Built-in data governance and compliance. Snowflake deals with thousands of companies across the globe, meaning that they have to have best-in-class governance just to stay running. This should help you sleep a little better at night!
How to Use Snowflake for a Data Warehouse

First, let’s recap what a data warehouse is. In its simplest form, a data warehouse is a place for structured data. In other words, it’s data that’s made easy to access and use by pretty much anyone at the company. This makes it a great place to store knowledge that could be used for an off-the-cuff BI analysis or dashboard.

Data warehouses just so happen to be Snowflake’s bread and butter. Let’s dive in on how it’s done:

Design Your Schema
Snowflake supports traditional star and snowflake schema designs (hence the name!). These schemas are a bit out of this post’s range, so check this article out if you want to learn more. You can also start with either normalized or denormalized tables depending on performance needs!
Ingest Data
Use Snowflake’s Snowpipe for real-time ingestion, or bulk load files using COPY INTO from cloud storage (AWS S3, Azure Blob, GCP, etc.).
Transform with SQL or dbt
Snowflake’s compute clusters, called virtual warehouses, can handle transformation logic using pure SQL or through tools like dbt.
Enable BI and Reporting
Connect BI tools like Tableau, Looker, or Power BI via Snowflake’s JDBC/ODBC drivers to give the rest of your team reporting and dashboarding powers!
Manage Cost and Performance
Monitor usage with built-in analytics, auto-suspend idle warehouses, and scale resources up/down with ease using Snowflake’s native tools!
When to Use Snowflake

Use Snowflake when you need:

A scalable SQL-based warehouse with near-zero ongoing maintenance.
Fast and easy data sharing between teams or external partners.
Strong support for BI and reporting workloads

In other words, Snowflake is great when used for data warehouses. But what if you need something less structured? What if you prefer flexibility, customizability, and want data that can be analysed by power users such as data scientists? Well, that leads us into…

Databricks: The Unified Data LakeWhat is Databricks?

Databricks is a cloud-based platform built on Apache Spark. It is ideal for big data analytics, machine learning, and data engineering pipelines. It provides Delta Lake—an open-source storage layer that brings ACID transactions to your Data Lake!

Key Features:Delta Lake for reliable Data Lakes – Built-in ACID means that your data lake can have top-tier data integrity standards while still allowing almost all of your data to be pipelined through. This is a great benefit given how messy data lakes can be!
Unified notebook experience (Python, SQL, Scala, R) – Whether you’re using Jupyter or Knit, Databricks allows you to use any notebook package for your data analysis.
Highly scalable data processing – Just like Snowflake, since Databricks is built in the cloud it can scale up your storage and compute whenever it’s necessary.
How to Use Databricks for a Data Lake

If a data warehouse is for structured data, data lakes are for unstructured content. Let’s see how Databricks can help you build a data lake:

Ingest Raw Data into a Data Lake
Use Auto Loader or Apache Spark to stream in data from files, Kafka, or cloud storage into the Bronze-level (AKA raw) Delta tables.
Build A Medallion Architecture
Databricks structuring involves converting your data into Bronze (raw), Silver (cleaned), and Gold (curated) layers for scalable, layered processing.
Transform with Notebooks
You can perform complex transformations using PySpark, R, Scala, and more. Use the Delta Lake to ensure ACID compliance!.
ML & AI Workloads
We touched on Databricks’ ML features a bit during our dedicated article. For utilizing your data lake for ML content, you can use MLflow to manage models, pipelines, and experiments. Databricks also supports scalable training and inferencing!
When to Use Databricks

Use Databricks when you need:

Scalable batch or real-time data processing.
Data science and ML pipelines alongside ETL.
An open-source friendly data lake architecture.
Snowflake vs. Databricks: Which One to Use?

Now, it’s worth noting that both Snowflake and Databricks have data warehouse and data lake capabilities. They even have the ability to combine both, into what’s known as a “lakehouse” (because of course it is). Still, each company has their own dedicated focus. Here’s a breakdown:

FeatureSnowflakeDatabricksBest ForBI, analytics, data warehousingData engineering, ML, big dataInterfaceSQL-centricNotebook-centric (SQL, PySpark)Real-time IngestionSnowpipeStructured Streaming, Auto LoaderMachine LearningExternal integration (e.g., SageMaker)Built-in MLflow, scalable SparkStorageManagedBring your own cloud storageFile Format SupportStructured, semi-structuredAll file formats + Delta Lake Combining Snowflake and Databricks

Now, you don’t have to choose. Many modern enterprise architectures actually use both platforms together:

Ingest and transform data in Databricks, using Spark and Delta Lake for heavy ETL.
Load curated data into Snowflake for business intelligence, dashboards, and ad-hoc queries.
Use the Unity Catalog (Databricks) and Snowflake’s governance tools to ensure secure, compliant access across both tools.

Using both tools might be a bit overkill for a smaller company, but if you’re running a large organization (or just have a big focus on data, such as an AI startup) then combining the best uses of both Snowflake and Databricks can be a huge advantage!

Final Thoughts

Both Snowflake and Databricks are powerful platforms—but their strengths lie in different areas. Snowflake excels in the realm of the data warehouse, while Databricks shines in big data processing, machine learning, and streaming workloads – the data lake.

By understanding your use cases—whether it’s building a robust data lake for ML or a fast data warehouse for reporting—you can choose the right platform (or combination of both) to build scalable, future-proof data solutions.

Have questions or want help designing your data platform? Drop a comment or reach out—we’d love to hear how you’re using Snowflake, Databricks, or both!

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