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Databricks vs Snowflake: What are the differences?

Introduction

Here, we will highlight the key differences between Databricks and Snowflake in terms of their functionalities and features. Databricks is a cloud-based analytics and data processing platform, while Snowflake is a cloud-based data warehousing platform.

  1. Scalability: Databricks provides a fully managed, horizontally scalable data platform that is built on Apache Spark. It allows users to automatically scale resources based on demand, enabling the processing of large datasets efficiently. On the other hand, Snowflake offers a cloud-based data warehousing solution that can scale both horizontally and vertically to handle growing data workloads effectively.

  2. Data Warehouse vs. Data Processing: While both Databricks and Snowflake can handle data processing tasks, Databricks primarily focuses on data processing and analytics, offering features like data exploration, machine learning, and collaborative coding. Snowflake, on the other hand, specializes in data warehousing, providing robust capabilities for storing and analyzing structured and semi-structured data.

  3. Data Sharing: Databricks provides built-in functionalities for collaborating and sharing data with other users, enabling seamless collaboration on data projects within the platform’s workspace. Snowflake, on the other hand, offers secure data sharing capabilities where users can easily share data with other Snowflake accounts, allowing for simplified data exchange between organizations.

  4. Compute Model: Databricks follows a serverless compute model, where users do not have to manage or provision compute resources separately. This allows for efficient resource allocation and cost optimization based on workload demands. Snowflake, on the other hand, offers a virtual warehouse concept, allowing users to allocate compute resources separately for data processing tasks. This provides more flexibility in terms of resource allocation and performance tuning.

  5. Ecosystem Integration: Databricks integrates seamlessly with various cloud services and ecosystems, such as AWS and Azure. It offers built-in connectors and APIs to interact with other cloud-based services, simplifying data integration workflows. Snowflake also provides integrations with popular cloud platforms and services; however, its focus is primarily on data warehousing and analytics rather than a broader ecosystem integration.

  6. Pricing Model: Databricks follows a consumption-based pricing model, where users pay for the resources used in terms of compute and storage. This allows for flexible pricing based on actual usage. Snowflake, on the other hand, offers a usage-based pricing model, where users pay for the resources consumed, including compute, storage, and data transfer. It provides different pricing tiers based on usage volumes and performance requirements.

In summary, Databricks is a cloud-based analytics and data processing platform with a focus on collaborative coding and scalable data processing, while Snowflake specializes in cloud-based data warehousing and offers advanced features for structured and semi-structured data analysis.

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Pros of Databricks
Pros of Snowflake
  • 1
    Best Performances on large datasets
  • 1
    True lakehouse architecture
  • 1
    Scalability
  • 1
    Databricks doesn't get access to your data
  • 1
    Usage Based Billing
  • 1
    Security
  • 1
    Data stays in your cloud account
  • 1
    Multicloud
  • 7
    Public and Private Data Sharing
  • 4
    Multicloud
  • 4
    Good Performance
  • 4
    User Friendly
  • 3
    Great Documentation
  • 2
    Serverless
  • 1
    Economical
  • 1
    Usage based billing
  • 1
    Innovative

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What is Databricks?

Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.

What is Snowflake?

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

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Jul 2 2019 at 9:34PM

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What are some alternatives to Databricks and Snowflake?
Azure Databricks
Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
Domino
Use our cloud-hosted infrastructure to securely run your code on powerful hardware with a single command — without any changes to your code. If you have your own infrastructure, our Enterprise offering provides powerful, easy-to-use cluster management functionality behind your firewall.
Confluent
It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Azure HDInsight
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
See all alternatives