AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
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SSIS
Score 7.6 out of 10
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Microsoft's SQL Server Integration Services (SSIS) is a data integration solution.
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AWS Lambda
SQL Server Integration Services (SSIS)
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128 MB
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10240 MB
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AWS Lambda
SQL Server Integration Services (SSIS)
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AWS Lambda
SQL Server Integration Services (SSIS)
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Comparison of Access Control and Security features of Product A and Product B
Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.
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.
Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.
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.
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
I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.
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.
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.
Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.
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.
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
AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions
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
Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.
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.