Apache Spark vs. Azure Data Factory

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Azure Data Factory
Score 8.5 out of 10
N/A
Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.N/A
Pricing
Apache SparkAzure Data Factory
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkAzure Data Factory
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkAzure Data Factory
Considered Both Products
Apache Spark
Chose Apache Spark
  • Apache Spark works in distributed mode using cluster
  • Informatica and Datastage cannot scale horizontally
  • We can write custom code in spark, whereas in Datastage and Informatica we can only choose the different features proivided already.
Azure Data Factory

No answer on this topic

Top Pros
Top Cons
Features
Apache SparkAzure Data Factory
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
Azure Data Factory
9.1
8 Ratings
9% above category average
Connect to traditional data sources00 Ratings9.38 Ratings
Connecto to Big Data and NoSQL00 Ratings9.08 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
Azure Data Factory
8.4
8 Ratings
0% below category average
Simple transformations00 Ratings9.38 Ratings
Complex transformations00 Ratings7.58 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
Azure Data Factory
8.0
8 Ratings
2% below category average
Data model creation00 Ratings8.56 Ratings
Metadata management00 Ratings7.87 Ratings
Business rules and workflow00 Ratings7.88 Ratings
Collaboration00 Ratings7.67 Ratings
Testing and debugging00 Ratings7.88 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
Azure Data Factory
7.8
8 Ratings
6% below category average
Integration with data quality tools00 Ratings7.68 Ratings
Integration with MDM tools00 Ratings8.08 Ratings
Best Alternatives
Apache SparkAzure Data Factory
Small Businesses

No answers on this topic

Skyvia
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Score 9.5 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
InfoSphere
InfoSphere
Score 8.4 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
InfoSphere
InfoSphere
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkAzure Data Factory
Likelihood to Recommend
9.8
(24 ratings)
9.4
(8 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
7.0
(1 ratings)
User Testimonials
Apache SparkAzure Data Factory
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Microsoft
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Microsoft
  • Once a sales funnel is defined and configured, it can be interconnected to another, so that we can create a complete network where it is possible to monitor the different executions of each funnel linked to our data from one place.
  • Azure Data Factory promotes excellent data management strategies, which we have been able to leverage during the workday, and all thanks to the help provided by their support team, which from the beginning of our interactions kept us properly informed about solutions to every issue that arose.
  • An advantage of Data Factory is that data structures can be stored in several warehouses at the same time, and these can be moved from one warehouse to another by configuring a trigger that is automatically executed when certain predefined parameters are met, such as the generation of a blob within the platform.
  • Azure Data Factory has helped us to carry out data assignments with much more integrity and comfort, and we will continue to use it, given its excellent ease of execution of administrative operations and its incredible approach to business intelligence management.
  • Creating data infrastructures without prior design is entirely possible with Azure, as the platform properly defines all the processes to be followed to create a solid foundation for information and data flows.
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Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Microsoft
  • For complex JSON when it comes to mapping nested attribute it's not easy to flatten out
  • Data Factory V1 does not have a good implementation experience as compared to V2
  • Work with on premise solutions sometimes is not too friendly because you will need to set a VPN
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Microsoft
No answers on this topic
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Microsoft
No answers on this topic
Support Rating
Apache
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.
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Microsoft
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Microsoft
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
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Return on Investment
Apache
  • 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.
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Microsoft
  • It is very useful and make things easier
  • Debugging can improve
  • Its better suited than other products with the same objective
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ScreenShots