More

    Snowflake vs. Databricks: Big Data Platform Comparison

    The extraction of meaningful information from Big Data is a key driver of business growth.

    For example, the analysis of current and past product and customer data can help organizations anticipate customer demand for new products and services and spot opportunities they might otherwise miss.

    As a result, the market for Big Data tools is ever-growing. In a report last month, MarketsandMarkets predicted that the Big Data market will grow from $162.6 billion in 2021 to $273.4 billion in 2026, a compound annual growth rate (CAGR) of 11%.

    A variety of purpose-built software and hardware tools for Big Data analysis are available on the market today. To make sense of all that data, the first step is acquiring a robust Big Data platform, such as Snowflake or Databricks.

    Current Big Data analytics requirements have forced a major shift in Big Data warehouse and storage architecture, from the conventional block- and file-based storage architecture and relational database management systems (RDBMS) to more scalable architectures like scale-out network-attached storage (NAS), object-based storage, data lakes, and data warehouses.

    Databricks and Snowflake are at the forefront of those changing data architectures. In some ways, they perform similar functions—Databricks and Snowflake both made our lists of the Top DataOps Tools and the Top Big Data Storage Products, while Snowflake also made our list of the Top Data Warehouse Tools—but there are very important differences and use cases that IT buyers need to be aware of, which we’ll focus on here.

    What is Snowflake?

    Snowflake logo

    Snowflake for Data Lake Analytics is a cross-cloud platform that enables a modern data lake strategy. The platform improves data performance and provides secure, quick, and reliable access to data.

    Snowflake’s data warehouse and data lake technology consolidates structured, semi-structured, and unstructured data onto a single platform, provides fast and scalable analytics, is simple and cost-effective, and permits safe collaboration.

    Key differentiators

    • Store data in Snowflake-managed smart storage with automatic micro-partitioning, encryption at rest and in transit, and efficient compression.
    • Support multiple workloads on structured, semi-structured, and unstructured data with Java, Python, or Scala.
    • Access data from existing cloud object storage instances without having to move data.
    • Seamlessly query, process, and load data without sacrificing reliability or speed.
    • Build powerful and efficient pipelines with Snowflake’s elastic processing engine for cost savings, reliable performance, and near-zero maintenance.
    • Streamline pipeline development using SQL, Java, Python, or Scala with no additional services, clusters, or copies of data to manage.
    • Gain insights into who is accessing what data with a built-in view, Access History.
    • Automatically identify classified data with Classification, and protect it while retaining analytical value with External Tokenization and Dynamic Data Masking.

    Pricing: Enjoy a 30-day free trial, including $400 worth of free usage. Contact the Snowflake sales team for product pricing details.

    What is Databricks?

    Databricks logo

    The Databricks Lakehouse Platform unifies your data warehousing and artificial intelligence (AI) use cases onto a single platform. The Big Data platform combines the best features of data lakes and data warehouses to eliminate traditional data silos and simplify the modern data stack.

    Key differentiators

    • Databricks Lakehouse Platform delivers the strong governance, reliability, and performance of data warehouses along with the flexibility, openness, and machine learning (ML) support of data lakes.
    • The unified approach eliminates the traditional data silos separating analytics, data science, ML, and business intelligence (BI).
    • The Big Data platform is developed by the original creators of Apache Spark, MLflow, Koalas, and Delta Lake.
    • Databricks Lakehouse Platform is being developed on open standards and open source to maximize flexibility.
    • The multicloud platform’s common approach to security, data management, and governance helps you function more efficiently and innovate seamlessly.
    • Users can easily share data, build modern data stacks, and avoid walled gardens, with unrestricted access to more than 450 partners across the data landscape.
    • Partners include Qlik, RStudio, Tableau, MongoDB, Sparkflows, HashiCorp, Rearc Data, and TickSmith.
    • Databricks Lakehouse Platform provides a collaborative development environment for data teams.

    Pricing: There’s a 14-day full trial in your cloud or a lightweight trial hosted by Databricks. Reach out to Databricks for pricing information.

    Snowflake vs. Databricks: What Are the Differences?

    Here, in our analysis, is how the Big Data platforms compare:

    FeaturesSnowflakeDatabricks
    Scalability
    Integration
    Customization
    Ease of Deployment
    Ease of Administration and Maintenance
    Pricing Flexibility
    Ability to Understand Needs
    Quality of End-User Training
    Ease of Integration Using Standard Application Programming Interfaces (APIs) and Tools
    Availability of Third-Party Resources
    Data Lake
    Data Warehouse
    Service and Support
    Willingness to Recommend
    Overall Capability Score

    Choosing a Big Data Platform

    Organizations need resilient and reliable Big Data management, analysis and storage tools to reliably extract meaningful insights from Big Data. In this guide, we explored two of the best tools in the data lake and data warehouse categories.

    There are a number of other options for Big Data analytics platforms, and you should find the one that best meets your business needs. Explore other tools such as Apache Hadoop, Apache HBase, NetApp Scale-out NAS and others before making a purchase decision.

    Further reading:

    Surajdeep Singh
    Surajdeep Singh
    Surajdeep Singh has been working as an IT and blockchain journalist since 2018. He is a contributor to publications including IT Business Edge, Enterprise Networking Planet & Smart Billions and works as a consultant at Drofa Communications Agency.

    Latest Articles