Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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Microsoft Fabric
Score 8.0 out of 10
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Microsoft Fabric: A Comprehensive Data Management Solution Microsoft Fabric presents a unified, robust platform designed to optimize data management, enhance AI model development, and empower users across an organization. It focuses on integrating data seamlessly, ensuring governance and security, and providing AI capabilities. Microsoft Fabric is presented as an all-encompassing data management solution, providing organizations with tools for efficient data integration,…
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Pricing
Apache Spark
Microsoft Fabric
Editions & Modules
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Pricing Offerings
Apache Spark
Microsoft Fabric
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
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No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Use Microsoft Fabric by purchasing Fabric Capacity, a billing unit that enables each Fabric experience. Pay for every data tool in one transparent, simplified pricing model and save time for other business needs.
Fabric Capacity is priced uniquely across regions.
Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.
I would highly recommend Microsoft Fabric, especially for medium to large enterprises aiming to build a robust, scalable, and secure data analytics platform. It effectively unifies various data workloads, streamlining data integration, engineering, and particularly enhancing our ability to create and share reliable Power BI dashboards. The deep integration with Azure AD for features like Row-Level Security is a significant advantage for data governance.
It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
I've rated Microsoft Fabric's overall usability as a 4, primarily due to its extensive and multifaceted feature set, which can make it challenging to navigate and determine the optimal functionality for a given task.While the breadth of capabilities is a core strength for large enterprises, it often leads to a sense of being "lost" or overwhelmed for teams like ours that do not have highly formalized roles or dedicated specialists for each Fabric "experience" (e.g., Data Engineering, Data Warehousing, Data Science).
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
We used Surprise Kit for one of the other research works. It is more fine-tuned to Recommendation systems and their algorithms. Apache Spark has MLlib for majority of ML problems. Where as software like Surprse Kit - it suitable for a specific task of Recommendations only
Microsoft Fabric integrates data ingestion, engineering, warehousing, and Power BI visualization into one cohesive environment. This "one-stop shop" approach dramatically reduces complexity, minimizes operational overhead, and eliminates the need to integrate disparate tools and manage data across multiple systems. It provides superior scalability for large datasets, supports open data formats, and offers a much broader suite of data engineering and data science capabilities.In essence, Fabric's integrated ecosystem and streamlined operational management were key differentiators, providing a more cohesive, scalable, and efficient solution for our evolving data strategy than combining specialized tools.
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
