

AWS Lambda and Apache NiFi compete in serverless computing and data flow management, respectively. AWS Lambda has the upper hand in scalability and ease of integration with AWS services, whereas Apache NiFi excels in data flow management with its visual interface.
Features: AWS Lambda offers scalability, easy integration with AWS services, and a serverless architecture for efficient performance. Developers appreciate its support for multiple languages and the pay-as-you-go model that eliminates the need for infrastructure maintenance. Apache NiFi focuses on visual data flow management, offering extensive processor availability and built-in provenance tracking, enabling effective data orchestration and transformation.
Room for Improvement:AWS Lambda faces challenges with cold start delays, limited non-AWS service integration, and execution time restrictions. It also requires enhancements in monitoring, debugging, and support for additional programming languages. Apache NiFi struggles with complex operations and limited cloud-native features, necessitating better integration with various data formats and improved stability and user-friendly interfaces.
Ease of Deployment and Customer Service:AWS Lambda's deployment is straightforward in public cloud environments, supported by comprehensive documentation and positive customer feedback. However, the need for paid support is sometimes viewed as restrictive. Apache NiFi, typically deployed in on-premises or hybrid environments, presents a steeper learning curve and more complex setup. While support is adequate, configuration and deployment complexities pose challenges.
Pricing and ROI: AWS Lambda provides cost-effective pay-as-you-go pricing, attractive scalability, and no upfront infrastructure costs, yielding high ROI but increasing expenses with frequent invocations. Apache NiFi, an open-source solution, offers a cost advantage for self-managed deployments. Its integration with platforms like Cloudera enhances pricing but delivers strong ROI in large-scale data processing.
Thanks to improvements on both our side in how we run processes and enhancements to Apache NiFi, we have reduced the time commitment to almost not needing to interact with Apache NiFi except for minor queue-clearance tasks, allowing it to run smoothly.
It supports not just ETL but also ELT, allowing us to save significant time.
There may be return on investment based on the technology and easily moving our workloads onto Apache NiFi from our previous system.
The customer support is really good, and they are helpful whenever concerns are posted, responding immediately.
Customer support for Apache NiFi has been excellent, with minimal response times whenever we raise cases that cannot be directly addressed by logs.
I would rate the customer support of Apache NiFi a 10 on a scale of 1 to 10.
When we raise a ticket or have an issue, the support team is responsive.
Depending on the workload we process, it remains stable since at the end of the day, it is just used as an orchestration tool that triggers the job while the heavy lifting is done on Spark servers.
Scaling up is fairly straightforward, provided you manage configurations effectively.
Based on the workload, more nodes can be added to make a bigger cluster, which enhances the cluster whenever needed.
I would rate how scalable AWS Lambda is a nine on a scale from 1 to 10, where 1 would be the lowest and 10 would be the highest level of scalability.
Whenever the number of requests increases, the system automatically scales up to the target we have set and scales down once the requests are resolved.
I have seen Apache NiFi crashing at times, which is one of the issues we have faced in production.
Apache NiFi is stable in most cases.
Apache NiFi should have APIs or connectors that can connect seamlessly to other external entities, whether in the cloud or on-premises, creating a plug-and-play mechanism.
The history of processed files should be more readable so that not only the centralized teams managing Apache NiFi but also application folks who are new to the platform can read how a specific document is traversing through Apache NiFi.
The initial error did not indicate it was related to memory or size limitations but appeared as a parsing error or something similar.
AWS Lambda needs to improve cold start time.
The pricing in Italy is considered a little bit high, but the product is worth it.
Apache NiFi has positively impacted my organization by definitely bridging the gap between the on-premises and cloud interaction until we find a solution to open the firewall for cloud components to directly interact with on-premises services.
Development has improved with a reduction in time spent being the main benefit; before we needed a matter of days to create the ingestion flows, but now it only takes a couple of hours to configure.
The ease of use in Apache NiFi has helped my team because anyone can learn how to use it in a short amount of time, so we were able to get a lot of work done.
Automatic scaling is a valuable feature. When the number of requests increases, the system automatically scales up to the target we have set and scales down once the requests are resolved.
| Product | Market Share (%) |
|---|---|
| AWS Lambda | 13.8% |
| Apache NiFi | 9.5% |
| Other | 76.7% |

| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 1 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 15 |
| Large Enterprise | 43 |
AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).
You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.
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