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When we raise a ticket or have an issue, the support team is responsive.
If it is a priority issue, they will give the response quicker, but if it is moderate, they take some time.
The fact that no interaction is needed shows their great support since I don't face issues.
Google's support team is good at resolving issues, especially with large data.
Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company.
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.
When it comes to the increased needs of my customers trying to grow, AWS Lambda is not an issue to grow with them.
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 can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
The job we built has not failed once over six to seven months.
The automatic scaling feature helps maintain stability.
Regarding scaling, we can add up to 1,000 execution environments for every 10 seconds per function, per region.
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 feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that manually.
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.
As it is serverless, AWS Lambda has more scope for building scalable architectures.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.
| Product | Mindshare (%) |
|---|---|
| AWS Lambda | 14.2% |
| Amazon EC2 | 13.6% |
| AWS Fargate | 10.4% |
| Other | 61.800000000000004% |
| Product | Mindshare (%) |
|---|---|
| Google Cloud Dataflow | 3.7% |
| Apache Flink | 8.9% |
| Databricks | 8.1% |
| Other | 79.3% |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 15 |
| Large Enterprise | 44 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 11 |
AWS Lambda offers a serverless architecture that facilitates seamless integration with other AWS services, providing rapid scalability and cost efficiency. It supports event-driven computing and multiple programming languages, allowing for automatic scaling and enhanced performance.
AWS Lambda is favored for its ease of integration with AWS services like S3, API Gateway, and DynamoDB, ensuring efficient application and scaling. It supports rapid deployment with low coding requirements, parallelism, and event-triggered execution, making it suitable for event-driven processes, API services, data processing, and backend functions. While improvements in integration with external services, execution time limits, cold start latency, and support for more programming languages are needed, its price and monitoring tools could be optimized further. Users desire simplified deployments and improved documentation, especially for high-demand applications.
What are AWS Lambda's most valuable features?AWS Lambda is widely used in industries like IoT, finance, and education for its ability to handle image processing, authentication, and real-time notifications. Its flexibility and integration capabilities make it suitable for integrating CI/CD pipelines, automating workloads, and supporting event-driven processes across diverse industry applications.
Google Cloud Dataflow provides scalable batch and streaming data processing with Apache Beam integration, supporting Python and Java. It's designed for efficient data transformations, analytics, and machine learning, featuring cost-effective serverless operations.
Google Cloud Dataflow is a robust tool for handling large-scale data processing tasks with flexibility in processing batch and streaming workloads. It integrates seamlessly with other Google Cloud services like Pub/Sub for real-time messaging and BigQuery for advanced analytics. The platform supports a wide array of data transformation and preparation needs, making it suitable for complex data workflows and machine learning applications. Despite its advantages, users have noted challenges such as incomplete error logs, longer job startup times, and some limitations in the Python SDK.
What are the key features of Google Cloud Dataflow?Industries, especially in retail and eCommerce, implement Google Cloud Dataflow for effective batch job execution, data transformation, and event stream processing. It aids in constructing distributed data pipelines for handling extensive analytics tasks, supporting effective large-scale data-driven decisions.
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