Fabric Spark Reviews
(Rated by 12 users)
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Overall Rating
4.4
Base on 12 Reviews
Ratings by Feature
Ratings by Feature
- Shipping & Delivery4.5
- Return Policy4.4
- Good Value4.7
- Price & Quality4.8
- Customer Service4.3
Recent Customer Reviews (12)
Juanita Price
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Antje Kohl
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Isabella Dennis
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Anke Rothstein
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Chelsea Stephenson
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Nicolas Goguen
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Keira Byrne
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Fabric Spark Pros & Cons
Pros
1
Microsoft Fabric is a unified analytics platform that integrates multiple engines, including Data Factory, Data Warehouse, and Data Science, to provide a comprehensive data analytics solution.
2
Fabric Spark uses Apache Spark as the parallel processing engine for data analytics, which is fully managed and abstracted, reducing the complexity of setting up a Spark instance.
3
Starter Pool: Provides pre-defined configurations for node size, autoscale, and dynamic allocation, offering fast session start times (within 5-10 seconds).
4
Custom Pool: Allows users to customize node size, scale properties, and other configurations based on workload needs.
5
Users can add a native Spark runtime engine to both Starter and Custom Pools, providing more control over runtime configurations.
6
Optimized Write: Enabled by default to improve performance for certain downstream workloads
7
Fabric Spark is specifically designed for data engineering and data science workloads, supporting interactive exploratory data analysis and real-time intelligence.
8
Microsoft Fabric offers a pay-as-you-go model, allowing organizations to scale resources based on dynamic requirements, reducing storage and implementation costs.
9
MSSparkUtils (NotebookUtils): A built-in package for working with file systems, environment variables, chaining notebooks, and managing secrets, ensuring continued support and access to new features.
10
Fabric is built on a secure and compliant platform, ensuring data security and protection across the environment with features like resiliency, conditional access, and service tags.
CONS
1
Optimized Write ... can have a negative impact on Spark unless partitioning Delta tables.
2
V-Order: Not recommended for Fabric Spark as it can result in slower writes and larger Parquet files, impacting performance.
Fabric Spark Features and Benefits
Features
Unified Analytics Platform
Integrates multiple engines, including Data Factory, Data Warehouse, and Data Science, to provide a comprehensive data analytics solution.
Managed Spark Compute
Uses Apache Spark as the parallel processing engine for data analytics, which is fully managed and abstracted, reducing the complexity of setting up a Spark instance.
Starter Pool
Provides pre-defined configurations for node size, autoscale, and dynamic allocation, offering fast session start times (within 5-10 seconds).
Custom Pool
Allows users to customize node size, scale properties, and other configurations based on workload needs.
Native Spark Runtime Engine
Users can add a native Spark runtime engine to both Starter and Custom Pools, providing more control over runtime configurations.
Optimized Write
Enabled by default to improve performance for certain downstream workloads.
Data Engineering and Science
Specifically designed for data engineering and data science workloads, supporting interactive exploratory data analysis and real-time intelligence.
Cost-Effective and Scalable
Offers a pay-as-you-go model, allowing organizations to scale resources based on dynamic requirements, reducing storage and implementation costs.
Integration with Other Tools
MSSparkUtils (NotebookUtils) is a built-in package for working with file systems, environment variables, chaining notebooks, and managing secrets, ensuring continued support and access to new features.
Security and Compliance
Built on a secure and compliant platform, ensuring data security and protection across the environment with features like resiliency, conditional access, and service tags.