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When we raise a ticket or have an issue, the support team is responsive.
Overall, it is good, but there is some room for improvement when it comes to response time and overall competence.
if we send an email, we can get a response within twenty-four hours.
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.
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.
When it comes to the increased needs of my customers trying to grow, AWS Lambda is not an issue to grow with them.
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.
On a scale from one to ten, I would rate the scalability of AWS Lambda as eight because you can write as many limiters as you want, and they can trigger whenever that particular time zone event happens.
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.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
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.
AWS Lambda needs to improve cold start time.
Regarding scaling, we can add up to 1,000 execution environments for every 10 seconds per function, per region.
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.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
AWS Lambda pricing is high, similar to the servers for S3 buckets.
It is part of a package received from Google, and they are not charging us too high.
As it is serverless, AWS Lambda has more scope for building scalable architectures.
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.
What I appreciate most about AWS Lambda is that you do not need to purchase the entire service; it only charges you whenever you call that API, which is excellent.
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.
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
| 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 | 36 |
| Midsize Enterprise | 15 |
| Large Enterprise | 45 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 2 |
| Large Enterprise | 12 |
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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