Big data and machine learning platforms give data and business intelligence teams resources to manage large volumes of data and derive accurate insights from them. These platforms allow enterprises to set consistent policies and access controls to protect their company’s valuable resources.
Databricks and Snowflake, two of the top data platforms for enterprises, offer data analytics, machine learning, and security features to businesses that want to better process and utilize their information. Both solutions are delivered as a service and can be hosted on Amazon Web Services, Microsoft Azure, and Google Cloud Platform. To decide which data platform is a better fit for your company, consider the business’s specific requirements for data analytics, machine learning, and access management.
What is Databricks?
Databricks is a data lake platform built on the Apache Spark platform. Its data lake can process raw data. Databricks offers data warehousing, engineering, streaming, and science capabilities, as well as machine learning features. All of these operations have Databricks’ data lake, known as the Delta Lake, as an underlying foundation.
Databricks fosters collaboration among dev teams with notebooks, which allow developers to work together using multiple languages—R, Scala, Python, and SQL. Notebooks permit real-time coauthoring, showing the comment history, and also automatically track changes and record each version of the notebook. Through notebooks, dev teams can share code and automatically run machine learning and data pipelines. Databricks’ collaboration features reduce information silos within teams as they prepare data to be analyzed.
What is Snowflake?
Snowflake is a relational database management system (RDBMS) and data warehouse for storing and analyzing structured and semi-structured big data volumes. Snowflake separates storage and compute resources, so those can scale differently depending on organizational needs. Snowflake can be utilized as a storage solution for structured data; it doesn’t require a hardware investment from enterprises.
Snowflake is a solution for teams that heavily rely on a database format for their analytics. It offers advanced database features. For example, zero-copy cloning allows users to clone an entire database, after which changes to both databases are independent of each other. If users clone a database, all the schemas and tables within the database are also cloned. Users can clone individual schemas and tables too.
Databricks vs. Snowflake: Machine learning features
In Databricks, teams can store and share machine learning models through the Model Registry repository. In the registry, users can view different versions of ML models and receive email notifications for particular model events. For example, if another team member creates a new version of a model, users receive a notification about it. When users comment on a model or request a transition of a model’s stage, they’re automatically subscribed to future notifications for that model.
MLflow, Databricks’ open-source platform for managing machine learning lifecycles, allows developers to share machine learning models with each other and collaborate to test and experiment on them before they’re run.
Snowflake allows data science teams to bring many data types into their machine learning, including semi-structured data (XML, JSON, Parquet) and even unstructured data. Snowflake has a partner ecosystem for integrations with machine learning tools, so teams can make data within Snowflake available to those.
Snowflake integrates with tools like Spark, Qubole, Alteryx, and Databricks itself. It also has partnerships with machine learning platforms like DataRobot and Amazon SageMaker, which help businesses train and deploy machine learning models.
Bottom line: Consider Databricks if your team plans to collaborate frequently on developing new ML models, since it offers sharing, notification, and testing features. If you plan to use multiple third-party BI and ML tools, look at Snowflake’s many integration choices.
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Databricks vs. Snowflake: Analytics
Databricks offers SQL analytics on the Databricks SQL, a serverless warehouse for BI applications and SQL. In Databricks SQL, part of the Databricks Lakehouse Platform, businesses can run their SQL and business intelligence applications. Although the analytics happens in the warehouse, the data lake is still the underlying foundation. Databricks SQL can run BI tools like Looker, Tableau, and Power BI.
Databricks SQL allows data to be ingested from locations like cloud storage solutions and CRM software. The compute and storage within the warehouse are separate, meaning that enterprises can automatically scale each depending on their needs.
Snowflake offers partnerships with Tableau, Looker, Talend, and other BI tools so teams can analyze their big data within a tool they already use. Snowflake fully supports JSON for semi-structured data processing, too. If your dev team uses JSON file formats regularly, and you know they’ll want to process that data within a big data tool, consider Snowflake.
Administrators can set and run extract, transform, and load (ETL) operations, which allow them to pull data from multiple storage sources and consolidate it into a Snowflake warehouse. This can reduce data silos within the business—data is no longer tied to one storage location and is available for analysis within Snowflake.
Bottom line: Consider Databricks for big data analytics if your business needs the data lake as its primary foundation for BI and other applications. Snowflake’s full support for processing JSON files will benefit development teams that heavily rely on JSON structures and want native support in a big data solution.
Databricks vs. Snowflake: Security
Databricks is a GDPR-compliant organization. Using any solution for GDPR-protected data automatically affects your business’s compliance, so choosing platforms that comply with regulatory requirements is a step toward overall organizational compliance.
Other security features include encryption at rest for control plane data and encryption in transit between the control plane and the data plane. When data passes between the two planes, encryption shields the content of the data from eyes that don’t need to view it. Protecting data while both moving and resting is critical for maintaining organizational information security and regulatory compliance. Customer-managed encryption keys add an extra element of data security: Databricks only has so much access to data—customers control that by handling the encryption keys that shield the data.
Databricks also offers workload security. Built-in secret management helps developers and engineers avoid hardcoding sensitive credentials into code. This feature is available for all three cloud environments.
In Snowflake, administrators use network policies to configure site access features such as IP allowlists and blocklists. The SCIM (system for cross-domain and identity management) specification allows admins to set user and groups administration policies for their cloud applications through a REST API. Specific user access policies are critical for protecting data and complying with regulatory standards, which often require businesses to know exactly who is able to access customer data.
Tri-Secret Secure, an optional system for encryption keys, uses a Snowflake-maintained key, a customer-controlled key, and a composite master key that protects all of the customer’s encryption keys. It’s intended to increase data security because if the customer revokes the customer-managed encryption key, Snowflake will not be able to decrypt their data.
Aside from the double encryption key feature, Snowflake offers user authentication, making it a three-fold security system. Tri-Secret Secure is only available in the Business Critical Edition or higher versions of Snowflake.
Bottom line: Both Databricks and Snowflake offer advanced security features. If your dev and engineering teams need additional protection for code development and workload runtime, look at Databricks’ workload security. Snowflake’s detailed user access policies help businesses protect multiple cloud applications from multiple environments.
Databricks vs. Snowflake: Pricing
Databricks’ pricing is pay-as-you-go and charges customers for compute resources used per second. Google Cloud and Azure pricing have a standard and a premium tier, and AWS pricing has standard, premium, and enterprise tiers.
Pricing is based on Databricks Units (DBUs) used. Databricks also offers a 14-day free trial for businesses that need to test the platform before committing to a paid plan. Databricks pricing does not include any required cloud provider resources.
In Snowflake, compute resources can be turned on and off to granularly manage usage—customers won’t be charged for what they can’t use. Compute usage is billed per second, and pricing is determined by both compute usage and the amount of data stored in Snowflake.
Plans include Standard, Enterprise, Business Critical, and Snowflake Virtual Cloud, which offers customer-dedicated virtual servers and metadata stores. Specific pricing is available based on cloud platform and region and is priced by credit, a Snowflake unit that measures consumed resources. Snowflake offers a thirty-day free trial, which gives businesses an entire month to decide whether the tool is right for them.
Bottom line: While both vendors determine prices by cloud platform and geographical region, Databricks users don’t pay specifically for their storage through Databricks’ pricing model, while Snowflake users do. If you want both storage and compute prices covered by your ML and big data tool, consider Snowflake. Snowflake also has a significantly longer free trial if your organization wants more time to decide which ML tool to purchase.
Is Databricks or Snowflake right for your business?
Databricks’ data lake, Delta, supports raw data for enterprises that need to store and analyze unstructured volumes. For businesses that also want the benefits of a warehouse, Databricks SQL provides a more structured source for running BI applications and pulling data from multiple storage solutions. If your data teams will want to analyze data from a variety of locations or use multiple BI tools, Databricks is a flexible solution.
Engineering and dev teams experienced in database management will benefit from Snowflake’s many database features. For businesses that want to store and analyze mostly structured or semi-structured data, Snowflake scales to support large volumes of data. Snowflake also does offer a data lake solution with unstructured data capabilities. However, if your business mostly needs to analyze unstructured data, Snowflake may not give your business the functionality it needs.
Both tools integrate with multiple business intelligence solutions and provide customer-managed encryption keys for data security. Databricks and Snowflake are both useful analytics and ML platforms, but to choose between the two, determine what types of data your team mainly needs to store and analyze.
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