Google Cloud Dataflow vs. SQL Server Integration Services (SSIS)

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google Cloud Dataflow
Score 8.7 out of 10
N/A
Google offers Cloud Dataflow, a managed streaming analytics platform for real-time data insights, fraud detection, and other purposes.N/A
SSIS
Score 7.6 out of 10
N/A
Microsoft's SQL Server Integration Services (SSIS) is a data integration solution.N/A
Pricing
Google Cloud DataflowSQL Server Integration Services (SSIS)
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Google Cloud DataflowSSIS
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
Google Cloud DataflowSQL Server Integration Services (SSIS)
Features
Google Cloud DataflowSQL Server Integration Services (SSIS)
Streaming Analytics
Comparison of Streaming Analytics features of Product A and Product B
Google Cloud Dataflow
7.3
2 Ratings
9% below category average
SQL Server Integration Services (SSIS)
-
Ratings
Real-Time Data Analysis8.02 Ratings00 Ratings
Visualization Dashboards5.01 Ratings00 Ratings
Data Ingestion from Multiple Data Sources9.02 Ratings00 Ratings
Low Latency9.02 Ratings00 Ratings
Integrated Development Tools6.01 Ratings00 Ratings
Data wrangling and preparation7.01 Ratings00 Ratings
Linear Scale-Out8.02 Ratings00 Ratings
Machine Learning Automation6.02 Ratings00 Ratings
Data Enrichment8.02 Ratings00 Ratings
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Google Cloud Dataflow
-
Ratings
SQL Server Integration Services (SSIS)
7.0
56 Ratings
16% below category average
Connect to traditional data sources00 Ratings9.056 Ratings
Connecto to Big Data and NoSQL00 Ratings5.043 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Google Cloud Dataflow
-
Ratings
SQL Server Integration Services (SSIS)
6.8
56 Ratings
16% below category average
Simple transformations00 Ratings9.056 Ratings
Complex transformations00 Ratings4.755 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Google Cloud Dataflow
-
Ratings
SQL Server Integration Services (SSIS)
7.5
54 Ratings
4% below category average
Data model creation00 Ratings9.028 Ratings
Metadata management00 Ratings6.035 Ratings
Business rules and workflow00 Ratings7.045 Ratings
Collaboration00 Ratings9.040 Ratings
Testing and debugging00 Ratings6.351 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Google Cloud Dataflow
-
Ratings
SQL Server Integration Services (SSIS)
5.3
43 Ratings
39% below category average
Integration with data quality tools00 Ratings6.038 Ratings
Integration with MDM tools00 Ratings4.538 Ratings
Best Alternatives
Google Cloud DataflowSQL Server Integration Services (SSIS)
Small Businesses
IBM Streams (discontinued)
IBM Streams (discontinued)
Score 9.0 out of 10
Skyvia
Skyvia
Score 10.0 out of 10
Medium-sized Companies
Confluent
Confluent
Score 9.2 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
Enterprises
Spotfire Streaming
Spotfire Streaming
Score 5.2 out of 10
IBM InfoSphere Information Server
IBM InfoSphere Information Server
Score 8.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Google Cloud DataflowSQL Server Integration Services (SSIS)
Likelihood to Recommend
8.0
(1 ratings)
8.0
(54 ratings)
Likelihood to Renew
-
(0 ratings)
9.0
(4 ratings)
Usability
-
(0 ratings)
8.0
(9 ratings)
Performance
-
(0 ratings)
8.8
(6 ratings)
Support Rating
-
(0 ratings)
8.0
(8 ratings)
Implementation Rating
-
(0 ratings)
10.0
(2 ratings)
User Testimonials
Google Cloud DataflowSQL Server Integration Services (SSIS)
Likelihood to Recommend
Google
It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.
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Microsoft
As I mentioned earlier SQL Server Integration Services is suitable if you want to manage data from different applications. It really helps in fetching the data and generating reports. Its automation make it very easy and time efficient. It works well with large database as well. But it doesn't work well with real time data, it will take some time to gather the real time data. I would not recommend using it in a real time/fast-paced environment.
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Pros
Google
  • Streaming, Real time work load
  • Batch processing
  • Auto scaling
  • flexible pricing
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Microsoft
  • Standard ETL use cases for daily loads
  • Loading incoming data from Vendors which is placed on FTP and adding them to the SQL Warehouse
  • Creating outgoing data files and writing them to Vendor FTPs
  • Easy Active Directory integration for seamless connections to SQL Server
  • CI/CD by hosting the code on visualstudio.com
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Cons
Google
  • More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
  • I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
  • Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.
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Microsoft
  • Connection managers for online data sources can be tricky to configure.
  • Performance tuning is an art form and trialing different data flow task options can be cumbersome. SSIS can do a better job of providing performance data including historical for monitoring.
  • Mapping destination using OLE DB command is difficult as destination columns are unnamed.
  • Excel or flat file connections are limited by version and type.
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Likelihood to Renew
Google
No answers on this topic
Microsoft
Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
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Usability
Google
It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.
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Microsoft
SSIS is a great tool for most ETL needs. It has the 90% (or more) use cases covered and even in many of the use cases where it is not ideal SSIS can be extended via a .NET language to do the job well in a supportable way for almost any performance workload.
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Performance
Google
No answers on this topic
Microsoft
SQL Server Integration Services performance is dependent directly upon the resources provided to the system. In our environment, we allocated 6 nodes of 4 CPUs, 64GB each, running in parallel. Unfortunately, we had to ramp-up to such a robust environment to get the performance to where we needed it. Most of the reports are completed in a reasonable timeframe. However, in the case of slow running reports, it is often difficult if not impossible to cancel the report without killing the report instance or stopping the service.
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Support Rating
Google
No answers on this topic
Microsoft
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
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Implementation Rating
Google
No answers on this topic
Microsoft
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
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Alternatives Considered
Google
Google Cloud Dataproc Cloud Datafusion
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Microsoft
I think SQL Server Integration Services is better suited for on-premises data movement and ADF is more suited for the cloud. Though ADF has more connectors, SQL Server Integration Services is more robust and has better functionality just because it has been around much longer
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Return on Investment
Google
  • cost saving from managing our own data center for ETL servers
  • consumption based pricing
  • with auto scaling feature, we were able to expand components to support work load
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Microsoft
  • Without this, we would have to manually update a spreadsheet of our SQL Server inventory
  • We would also have poor alerting; if an instance was down we wouldn't know until it was reported by a user
  • We only have one other person who uses SQL Server Integration Services , he's the expert. It would fall to me without him and I would not enjoy being responsible for it.
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ScreenShots