With the sheer volume of data that companies accumulate and use, the tasks of storing, managing, and analyzing data for business insights have become increasingly complex, necessitating platforms that can assist via automation, easy search, data visualization, and more.
Databricks and Snowflake are two data management and analytics platforms on the market that unify data infrastructure, facilitate collaboration, automate data pipeline tasks, among many other functionalities. Here, we’ll compare how they approach the shared features of automation, collaboration, the data lake, machine learning, and SQL.
What is Databricks?
Databricks strives to “make big data simple” for companies with its open-source lakehouse platform that brings together data, data analytics, and AI all in one place. Databricks has more than 450 partners to secure global functionality of its Lakehouse platform, and it’s compatible with major cloud providers, such as Alibaba Cloud, AWS, Azure, and Google Cloud.
What is Snowflake?
With Snowflake’s data management platform, users can manage multiple data workloads, automate data pipeline tasks, manage data application builds, and collaborate with others across Snowflake’s data cloud or data lake. Snowflake’s Data Cloud seamlessly integrates multiple cloud environments and provides a centralized solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing. Snowflake’s platform is compatible with AWS, Azure, and Google Cloud.
Databricks vs. Snowflake for Automation
Automation is a shared feature between these two platforms and is an important functionality for data management because it means fewer tasks that data administrators need to tend to. Plus, with the high volume of data that these platforms handle, automation makes data management possible for today’s data needs.
Databricks features automation for complex data pipeline and data engineering tasks. The data pipeline begins with the stream of incoming data that will inform business intelligence tools and feed machine learning models. On the Databricks platform, incoming data gets ingested for automatic processing so that it’s ready for analysis. Data automatically undergoes quality control to weed out errors before it’s ready to be culled for valuable business insights and shared among users. This simplified extract-transform-load (ETL) process ultimately allows data engineers to work more efficiently. It allows them to focus on tasks that require human intelligence and saves them time when it comes to handling data errors and recovery.
Snowflake’s cloud-based data warehouse uses its “Snowpipe” tool to automatically manage incoming data in near-real time. It applies auto-schema to incoming data, identifies sensitive information, and classifies it as such, addressing a common data classification challenge that companies face in security posture management. Whether sensitive or not, Snowflake automatically encrypts data at rest and in transit to ensure security in data exchange.
Read also: The Best ETL Tools for Managing Your BI Data
Databricks vs. Snowflake for Collaboration
Databricks facilitates collaboration among data engineers, scientists, and analysts through Delta Sharing. Delta Sharing is an open protocol data sharing tool that allows for platform-agnostic data exchange and controlled data access. So if a company shares information with a supplier, for example, that supplier doesn’t necessarily need to operate from the same system. Databricks can share information to several major data platforms, such as Tableau, Power BI, and many more. Databricks also features Unity Catalog, a native integration that allows users to centrally manage and audit data sharing across organizations.
Snowflake’s Data Cloud enables secure cross-departmental, cross-cloud, and cross-region collaboration among internal and external stakeholders. All authorized users get access to one live data set to ensure that everyone is on the same page, but there is also an option for personalized access to limit a user’s view/access. Snowflake also allows administrators to govern who gets access, monitor usage and access, and regulate the publishing workflow on top of Snowflake’s built-in security features.
Databricks vs. Snowflake for the Data Lake
Databricks’ Delta Lake is an open-format storage layer that unifies structured, semi-structured, unstructured, and streaming data formats. Powered by Apache Spark, Databricks’ Delta Lake allows for scalability and speed. Delta Lake makes it easier for data engineers to build the data lakehouse foundation through its Delta Live Tables that manage data pipelines and keep them flush with new data.
Snowflake offers both Data Cloud and Data Lake architectures which makes it different from Databricks. Data Cloud operates on a modernized, cloud-based data warehouse that offers low-cost storage with 2-3x data compression, zero-copy cloning to ensure that all users access the most up-to-date information, secure and governed data sharing, and supports various data types, including semi-structured data and native geospatial analysis. Snowflake also enables data lake architecture that automatically ingests, classifies, and protects data while retaining its analytical value with Dynamic Data Masking and External Tokenization. It gathers structured, semi-structured, and unstructured data of any format across clouds and regions. Snowflake’s elastic processing engine facilitates quick and reliable data processing as well as query, so that variously structured data is readily available for business insights. Snowflake’s Snowpark feature allows for streamlined pipeline development using SQL, Scala, Python, or other preferred language.
Databricks vs. Snowflake for Machine Learning (ML)
In comparison to Snowflake, Databricks has more built-out ML capability to manage the ML cycle from start to finish. It assists those working in data science roles to prepare and process data for learning models, while facilitating collaboration among team members. ML on Databricks automatically tracks experiments and applies version control so that data scientists can more easily find the best model to upload to the Model Registry. From the Model Registry, users can manage the selected models and their movement through the stages from experimentation to archival. Finally, with Databricks’ AutoML functionality, more of the model-building tasks are automated. Databricks’ AutoML feature not only allows for faster deployment, but its low-code approach makes it easier for non-technical users to jumpstart a machine learning project.
With Snowflake, users can connect their ML tool of choice to the Dative Cloud. Snowflake has native connectors and supports integration from a broad ecosystem of partner tools, such as AWS, Alteryx, DataRobot, and more. Once a selected ML tool is connected to Snowflake’s Data Cloud, a user can run scalable and secure models and conduct test runs. Snowflake makes it easy to share model results with other users and applications in order to act on ML-driven insights.
Databricks vs. Snowflake for SQL
With SQL on Databricks, data analysts can quickly cull insights through SQL queries pulled directly from the data lake on Databricks. Databricks features the Photon query engine for fastier queries at a lower cost and without code changing. SQL on Databricks allows users to leverage data insights through their BI tool of choice without latency through Databricks’ SQL endpoints. Finally, analysts can save queries and share dashboards that they create to facilitate collaboration, save time, and cut down on redundancy.
Snowflake’s SQL query function enables seamless data analytics, supporting any size workload. Its provision computer clusters match demand needed for a workload, and users can choose multi-cluster computing resources for nearly unlimited concurrency. Snowflake supports ANSI SQL and semi-structured data to power insights that users get through their BI tool of choice. Snowflake’s SQL capability can connect directly to popular BI/analytics tools. Insights from SQL queries are also shareable via Snowsight which is Snowflake’s built-in visualization tool.
Databricks vs. Snowflake: Choosing the Right Platform
Databricks and Snowflake are similar on many fronts. One key difference is that Snowflake offers a cloud-based data warehouse (Data Cloud), while Databricks does not. However, data lakes are overall more cost-effective than data warehouses. The vendors’ data lake architecture offerings are comparable and did not yield significant differences in user ratings. Databricks has more to offer in terms of ML, SQL, and collaboration, and users appreciate its feature updates. However, users generally find Snowflake easier to implement, use, and administer. So if your company is looking for robust and varied functionality, check out Databricks first, but if your company prioritizes ease-of-use, start by researching Snowflake more. In any case, TechnologyAdvice advisors can point you in the right direction with free BI software recommendations.