Deciding on a modern data platform can be a huge decision. It impacts the way you store, process, analyze, govern, and use your data for years to come.
So let’s talk about two platforms that you are going to come across: Databricks and Snowflake. Both provide data engineering, analytics, AI, machine learning, and governance support, as well as large-scale data workloads. However, they take different views on these needs.
So, between Databricks and Snowflake, which would you go for?
There is not a single right or wrong answer for each business. Your choice should be based on your workloads, existing architecture, cloud environment, technical skills, analytics needs, and long-term data strategy.
In this guide, you’ll see where each platform fits and what you should consider before making a decision.
What Is Databricks?
Databricks is a unified data, analytics, and AI platform centered on the lakehouse architecture. It combines data engineering, analytics, machine learning, AI, and governance in a unified space.
Databricks is known for its Apache Spark integration, PySpark, Delta Lake, data engineering, and big data processing capabilities. It involves data that can be structured, semi-structured, and unstructured and can be used for various downstream applications.
It may be beneficial to have these capabilities in the same platform if your team is looking to transform raw data into analytics, machine learning, or AI workflows.
What Is Snowflake?
Snowflake is a cloud-based data platform for data storage, processing, analytics, data engineering, AI, applications, and data collaboration.
One of its fundamental architectural ideas is that of separating storage, compute, and cloud services. This enables organisations to allocate resources as per the workload.
Snowflake has a proven track record in cloud data warehousing and SQL-based analytics, but the company has transformed the capabilities of its flagship products from traditional data warehouses to much more.
Databricks vs Snowflake: Key Differences
At first glance, the 2 platforms might appear identical. This is because over time, each has become more capable. It becomes more evident when you consider the way that you interact with your data.
1. Data Engineering
Databricks is embedded in the world of big data engineering and distributed processing. It integrates seamlessly with Apache Spark, making it ideal for complex transformation workflows, batch processing, streaming applications, and large-scale data engineering.
Additionally, Snowflake’s platform is highly managed, enabling data engineering and continuous data pipelines.
Aside from that, I have seen firsthand that this is something to examine here with your current team and workloads. Using Databricks may be a natural fit if your developers use Spark or PySpark a lot and process complex pipelines.
For teams looking for managed data engineering that’s tightly integrated with an enterprise analytics platform, then Snowflake is another option to consider.
2. Analytics and Data Warehousing
Snowflake is well-versed in data warehousing and SQL analytics. It is architected to provide the capability to scale compute resources as needed and to be used for analytical workloads.
You don’t have to restrict yourself to data engineering or machine learning workloads, as Databricks also provides analytics capabilities with SQL through Databricks SQL.
For organizations that primarily use reporting, dashboards, SQL queries, and business intelligence, consider the compatibility and ability of these platforms in a current analytics process.
3. Data Architecture
Among the key factors of the Databricks vs Snowflake comparison is the architecture.
The lakehouse architecture is adopted by Databricks, which is a blend of data lakes and data warehouses. It can integrate data in cloud object storage while providing processing, analysis, governance, and AI capabilities.
Snowflake is built as a cloud-native system, which uncouples storage, compute, and cloud services. It can accommodate both structured and unstructured data and works across the major cloud providers.
Here the existing infrastructure comes in. There’s no sense in choosing a platform if you’re not sure how it will interface with the existing systems you’re using.
4. AI and Machine Learning
Another aspect that has seen growth in both platforms is AI.
Databricks makes data engineering, data science, analytics, machine learning, and AI more unified. This can come in handy when moving from data preparation to machine learning and AI applications.
Snowflake has also enhanced its next-generation artificial intelligence and machine learning features, including AI applications, machine learning, conversational analytics, and AI-driven workflows.
A platform shouldn’t be decided on the basis of the AI being in its features. Rather, you should specify the data you’re using for your AI workload, your model needs, and how you’ll be developing your teams.
5. Data Governance
As data is spreading continuously across workloads, clouds, applications, and departments, the need for governance grows.
Centralized control of data and AI resources and assets, with access, discovery, and lineage, is available with Unity Catalog in Databricks.
Governance and security features are part of Snowflake’s platform and Snowflake Horizon.
When considering these platforms, keep in mind the requirements such as the following: access control, compliance, auditing, data discovery, and lineage.
Databricks vs. Snowflake: Which One Should You Choose?
Rather than comparing the cons and pros of each platform, start by analyzing your own business needs. If you’re interested in Databricks, you might consider:
- Knows many of the data engineering requirements
- Work heavily with Apache Spark or PySpark
- Looking for a workhorse solution that can handle high-volume operations, either batch or stream
- Have solid data science/machine learning needs
- Want a lakehouse architecture?
- Require linking data engineering, analytics, and AI workloads
Snowflake may be worth considering if you:
- Take full advantage of enterprise analytics and SQL workloads
- A very managed cloud data platform
- Own teams that mainly utilize SQL.
- Looking for flexible analytical workloads across multiple users and teams
- Need strong data sharing and collaboration capabilities
- Need a platform with a suite of warehouse, analytics, engineering, and AI features
Can You Use Databricks and Snowflake Together?
Yes. It is not always necessary to select one.
There are some organizations that pair up Databricks with Snowflake in their data architecture as well. For instance, you can use Databricks to build data pipelines, do more complex data processing, or build machine learning models, and use Snowflake for your specific analytics and data warehousing needs.
When you run 2 platforms, there are other considerations, however.
The following factors will need to be considered, such as integration, data movement, governance, operational complexity, and total cost. Having them all is only useful when the advantages outweigh the need to have multiple systems.
What Should You Consider Before Choosing?
Before choosing Databricks or Snowflake, closely examine your existing setup and plans.
Before deciding, ask these questions:
- What is the total volume of data you use? Take into account daily volume and forecasted volume.
- Which workloads do you execute?
- Which applications do you use for the cloud?
- What are your team’s talents? Think about SQL, Python, Spark, and knowledge of data engineering.
- What governance are you looking for? Look at security, access, compliance, and lineage protocols.
- What needs to integrate? Know your databases, applications, BI tools, and data sources.
- What will it cost? Think about computation, storage, data transfer, and ongoing processes.
- What will your requirements be? Your platform should be able to accommodate future workloads and data growth.
- What is your AI strategy & plan?
Final Thoughts
The only time it should work out to be a feature comparison is if you are looking to swap out your existing environment. It isn’t really about features when you are choosing between Databricks and Snowflake.
The characteristics of your data architecture, workloads, team capabilities, governance, integration, scale requirements, and costs are must-considerations.
Databricks may be a good choice for companies with a data engineering focus, lakehouse architecture, large processing tasks, machine learning, and AI. Snowflake provides a very managed platform that supports AI, collaboration, data engineering, data warehousing, and analytics. It is important to begin with your business requirements.
After realizing your teams are asking for so many things today and so many things in the future, you can be better equipped to make the best platform decision.
In the realm of Databricks consulting firms, Top Data AI Companies can assist you in finding providers based on their Databricks skills, services, industries, and capabilities.
