In this ever-evolving digital age, many companies have a need to manage large amounts of data that help them analyze and interpret data so they can make better decisions for their business. Dealing with these large amounts of raw data can be a challenge, even on dedicated servers, so many companies look for more powerful solutions that help them perform their business intelligence tasks daily.
This is where cloud computing comes in to save the day, through platforms designed specifically to handle these large amounts of data. The data they receive from these platforms allows them to present their insights in user-friendly reports, charts, and graphs that make decision-making simpler when it’s driven by the company’s raw data.
Two of these cloud platforms—Databricks and Snowflake—offer companies an array of benefits:
- Access to data in real-time 24/7
- Data protected from unauthorized access or manipulation
- Lower costs because they’re not required to pay for maintenance or upgrades
- Reliable access to data
- Easy data interpretation
Solutions for working with big data can often get complicated, but you’ve come to the right place if you want to gain a deeper understanding of how these two business intelligence platforms work with it. Let’s dive in!
Databricks vs. Snowflake: Side-by-Side Comparison
|Developed by||Ali Ghodsi, Matei Zaharia, Reynold Xin, Ion Stoica||Benoît Dageville, Thierry Cruanes, Marcin Żukowski|
|Easy to use?||Advanced tech knowledge required||User-friendly|
|Data storage||Maintains original format||Internal structured format|
|Architecture||Data lake||Data warehouse|
|Pay structure||Open source (non-cloud solution) or subscription (cloud solution)||Pay for what you use|
Databricks vs. Snowflake: What’s the Difference?
Built on top of Apache Spark, Databricks is a data lake that provides a big data engine that allows you to process large amounts of data that will ultimately help you make decisions based on a real-time flow of information. One of the ways it does this is by giving you visual tools to interpret the story the data tells about your company’s customers, operations, and other data assets.
One excellent use case for Databricks is using your customer data to make predictions based on past sales, product returns, and support requests. Having access to this data allows you to make better decisions for company growth and operations.
In a similar vein, but not exactly the same thing, you have Snowflake. Snowflake is a data warehouse built on top of AWS, Google Cloud, or Microsoft Azure, which are platforms that help you manage the hosting and delivery of a variety of software services on the cloud. Snowflake allows you to store, process, and access large amounts of data.
One of the key differences between Databricks and Snowflake lies in the architecture they use to store and retrieve data. Databricks is built upon the concept of data lakes, which host large amounts of data in a raw and unstructured format. There are no files or folders like you typically see on your hard drive. Data lakes find the information they need through unique identifiers and metadata, often making it easier to locate the precise piece of data needed for an operation.
On the other hand, Snowflake uses a data warehouse architecture, which is a much more structured data format. It takes the raw data and uses a predefined structure to store and process for streamlined analytics. Unlike data lakes, the data warehouse uses these schemas to organize data in a specific way through a three-tier architecture:
- Bottom Tier – This is where the data warehouse server lives and collects, cleanses, and transforms data that come from different origins.
- Middle Tier – Querying the data is handled on the middle tier, where the online analytical processing (OLAP) server lives. This is what allows for fast querying speeds in a data warehouse.
- Top Tier – This is where the user interacts with the data and can access their analytics, allowing them to make business decisions driven by the data stored in the data warehouse.
Databricks provides multiple security features that work together to protect your sensitive data:
- Customer-Managed Keys (CMKs)
These CMKs encrypt your code, data, models, and credentials through AWS Key Management Service or Microsoft Azure Key Vault.
- Private Link
Databricks secures your data through private networking on cloud platforms. On AWS and Azure, this feature is called PrivateLink. On Google, this is called Private Service Connect. It allows you to set up end-to-end private networking that routes your network traffic between your users and your data and vice versa.
- Enhanced Security and Compliance
Databricks uses enhanced hardened images, behavior-based malware monitoring, and reporting of image vulnerability through Enhanced Security Monitoring. Added on top of ESM is the Compliance Security Profile that gives you additional encryption and cluster update enforcement.
One of the great things about Snowflake is that their platform was built upon a foundation of security features. This business intelligence platform offers the following protection for your data so you can focus on what you do best—analyzing your business data:
- Comprehensive Data Security
Weaved within the foundation of the Snowflake platform are multiple security features that are constantly working to protect the data you host in the cloud. This is achieved through automated controls for all functions you perform, constant monitoring of activity on your data, and detection of malicious threats to your data that are quickly handled by the platform.
- Government and Industry Data Security Compliance
Snowflake has a portfolio of over ten security and compliance reports available to all its customers (and prospective customers who have signed an NDA). Additionally, they are adding to this portfolio, and any customers can request a report not currently being offered by Snowflake.
- Infrastructure Security and Resiliency
While Snowflake uses the standard security features found in all cloud platforms, it also goes above and beyond with its built-in data protection. One feature that helps you achieve this is their Time Travel feature, which allows you to recover data going back up to ninety days. And, because of Snowflake’s three-tier architecture, you also have built-in protection against node failures without any additional impact on performance.
Ease of Use
One of the disadvantages of using Databricks is that it has a much steeper learning curve than Snowflake. One of the reasons for this is that it requires access to data science engineers, machine learning engineers, and other tech experts, depending on your use case.
If you have large amounts of data, it could take anywhere from several hours to get it set up—or several days—because you have to do a lot of things manually in the beginning. In fact, one of the many complaints about Databricks is that its difficulty prohibits use by non-programmers because of its lack of visualization tools and drag-and-drop capabilities. Even some users who have programming knowledge find it difficult to implement in their data stack.
On the other hand, Snowflake is widely known for its ease of use. The primary reason for this is that much of the computing you’ll need to do requires understanding how to code SQL queries. (This stands for a structured query language, and it is the coding language used widely with relational databases.) So, even if you have only an intermediate knowledge of how to run SQL queries with MySQL, PostgreSQL, and other SQL servers, the Snowflake business intelligence platform will be much more accessible to you than Databricks.
Here’s the good news. Regardless of your technical knowledge, there are plenty of free courses and tutorials out there focused on both platforms. Both Databricks and Snowflake also have lengthy documentation on their websites to help deepen your knowledge of what you’ll need to set them up and use them regularly with success. Additionally, they both have great online communities that are happy to help you when you run into any challenges.
Databricks vs. Snowflake: 8 Must-Know Facts
- More than 75% of companies believe business intelligence platforms like Databricks and Snowflake are a must-have to grow their businesses using data science.
- Databricks allows you to transform your raw data into formats ready for machine learning and artificial intelligence use cases.
- Snowflake performs queries on virtual warehouses that have no computing effect on the other virtual warehouses.
- Because of its data lake architecture, Databricks typically allows for a wider variety of analytics to be performed on your data.
- The Snowflake platform combines traditional shared-disk and shared-nothing database architectures, allowing for access to data from a central repository.
- The data lake architecture used by Databricks allows users of varying skill levels to perform analytics on data all at the same time.
- Snowflake allows for multiple types of connections to its servers, including web-based interfaces, command lines, ODBC and JDBC drivers, native connectors (through software development), and third-party connectors.
- If you know how to code SQL queries, you will more than likely have an easy time learning how to use Snowflake. Additionally, this knowledge will also help you a bit with Databricks because both platforms use SQL.
Databricks vs. Snowflake: Which One is Better? Which One Should You Use?
There is no definitive answer to the Databricks vs. Snowflake debate simply because they are not the same, and it really depends on your organization’s specific needs and use cases. Databricks excels in handling massive volumes of data and provides advanced data processing capabilities, such as real-time analytics, machine learning, and artificial intelligence. Its data lake architecture allows for flexibility in handling diverse data types and faster processing. However, Databricks has a steeper learning curve and requires advanced technical knowledge.
On the other hand, Snowflake has a much more user-friendly interface and ease of implementation, making it more accessible for teams with a good understanding of SQL. It is better for businesses that want to focus on structured data storage and processing but may not be as efficient for real-time data processing. Snowflake operates on a pay-as-you-go model, potentially offering cost-effective solutions for organizations with fluctuating data processing needs.