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Databricks vs Domino: What are the differences?

Introduction: Databricks and Domino are both data science platforms that provide collaborative environments for data scientists and analysts. However, they differ in several key aspects, as outlined below.

  1. Deployment Options: Databricks offers a cloud-based SaaS platform, where users can directly access their data and perform analysis using Apache Spark. On the other hand, Domino provides a flexible deployment model, allowing users to choose between cloud-based, on-premises, or hosted options, providing more control over data security and compliance.

  2. Collaboration and Communication: Databricks provides a collaborative workspace where teams can work together on data projects, sharing notebooks, data, and insights. It also supports real-time collaboration, enabling users to work simultaneously on the same notebook or code. Domino, on the other hand, focuses on facilitating efficient communication among team members by integrating with tools like Slack and Jira, enabling seamless collaboration and knowledge sharing.

  3. Infrastructure Management: Databricks handles the underlying infrastructure management, allowing users to focus solely on data analysis and model building. It automatically scales resources based on workload demands, providing a hassle-free experience. In contrast, Domino allows users to manage their own infrastructure, giving them more control over resource allocation and security configurations.

  4. Reproducibility and Versioning: Domino emphasizes the importance of reproducibility by automatically capturing and versioning the entire data science workflow, including code, data, and environment. This enables users to easily reproduce and audit results. Databricks, although it supports version control for notebooks, does not offer the same level of granular control over reproducibility, making it less suitable for regulated industries and rigorous research.

  5. Model Deployment: Databricks provides seamless integration with MLflow, allowing users to deploy and manage machine learning models at scale. It also supports easy integration with popular cloud-based services like Azure ML and AWS SageMaker. In contrast, Domino provides its own model deployment and management solution, providing end-to-end support for deploying models in different environments, including cloud, edge, and on-premises.

  6. Pricing Model: Databricks follows a subscription-based pricing model, with different tiers based on the features and resources required. It offers both free and premium plans, suitable for teams of all sizes. Domino offers a more customized pricing model, tailored to the specific needs and usage patterns of the organization. This provides more flexibility in terms of budgeting and scaling.

In Summary, Databricks and Domino differ in their deployment options, collaboration tools, infrastructure management, reproducibility and versioning capabilities, model deployment options, and pricing models. These differences make them suitable for different use cases and organizations based on their specific requirements.

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Pros of Databricks
Pros of Domino
  • 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
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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 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.

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    What are some alternatives to Databricks and Domino?
    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.
    Azure Databricks
    Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.
    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