Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service.
When we raise a ticket or have an issue, the support team is responsive.
Google's support team is good at resolving issues, especially with large data.
The fact that no interaction is needed shows their great support since I don't face issues.
Whenever we have issues, we can consult with Google.
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.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
The job we built has not failed once over six to seven months.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The automatic scaling feature helps maintain stability.
AWS Lambda needs to improve cold start time.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
Dealing with a huge volume of data causes failure due to array size.
It is part of a package received from Google, and they are not charging us too high.
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.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
The integration within Google Cloud Platform is very good.
| Product | Market Share (%) |
|---|---|
| AWS Lambda | 15.2% |
| AWS Batch | 14.1% |
| Apache Spark | 11.4% |
| Other | 59.300000000000004% |
| Product | Market Share (%) |
|---|---|
| Google Cloud Dataflow | 4.6% |
| Apache Flink | 13.4% |
| Databricks | 10.8% |
| Other | 71.2% |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 15 |
| Large Enterprise | 43 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 10 |
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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