Azure Databricks vs Azure Functions

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Azure Databricks

+ 1
Azure Functions

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Pros of Azure Databricks
Pros of Azure Functions
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    • 12
      Pay only when invoked
    • 8
      Great developer experience for C#
    • 6
      Multiple languages supported
    • 5
      Great debugging support
    • 2
      Poor developer experience for C#
    • 2
      Easy scalability
    • 2
      Can be used as lightweight https service
    • 1
    • 1
      Event driven
    • 1
      Azure component events for Storage, services etc

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    Cons of Azure Databricks
    Cons of Azure Functions
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      • 1
        No persistent (writable) file system available
      • 1
        Poor support for Linux environments
      • 1
        Sporadic server & language runtime issues
      • 1
        Not suited for long-running applications

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      What is Azure Databricks?

      Accelerate big data analytics and artificial intelligence (AI) solutions with Azure Databricks, a fast, easy and collaborative Apache Spark–based analytics service.

      What is Azure Functions?

      Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

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      Jobs that mention Azure Databricks and Azure Functions as a desired skillset
      What companies use Azure Databricks?
      What companies use Azure Functions?
      See which teams inside your own company are using Azure Databricks or Azure Functions.
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      What tools integrate with Azure Databricks?
      What tools integrate with Azure Functions?

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      What are some alternatives to Azure Databricks and Azure Functions?
      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.
      Azure Machine Learning
      Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to 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.
      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.
      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.
      See all alternatives