Amazon AWS
Google Cloud Platform
Microsoft Azure
DigitalOcean
Linode
Heroku
Vultr
CloudFlare
Google Cloud Dataflow
Amazon EMR
Google BigQuery
Qubole
Snowflake
Databricks
Apache Beam
Amazon Kinesis
Amazon AWS Google Cloud DataflowYou could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.
Based on our record, Amazon AWS seems to be a lot more popular than Google Cloud Dataflow. While we know about 486 links to Amazon AWS, we've tracked only 14 mentions of Google Cloud Dataflow. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In conclusion, Cloudflare Workers and AWS Lambda are two popular edge computing solutions that offer a range of benefits, including reduced latency, improved performance, and enhanced security. By understanding the key differences between these solutions, businesses can make informed decisions about which one to use. Whether you're building a real-time analytics application or a serverless API, Cloudflare Workers... - Source: dev.to / 21 days ago
> but it's still a singleton instance, so where do you run it? Most hardware doesn't give you enough uptime for what you need here, because what you actually needed was a re-architecture for distribution / failover / whatever, and while you could ask your LLM to do that you aren't going to run your bank on the result. If only we had a way to solve these issues with tools capable of running Rust programs in that... - Source: Hacker News / about 1 month ago
Not because infrastructure isn't important. It is. Not because Amazon Web Services (AWS) is a bad platform. It isn't. - Source: dev.to / 2 months ago
The AWS S3 documentation covers all of these in detail. The configuration takes about an hour to get right the first time and rarely needs changes after. - Source: dev.to / 3 months ago
The first pattern is direct-to-storage. The client uploads chunks directly to an object storage service like Amazon S3 using pre-signed URLs. The application server creates the upload session and grants permission but never sees the file bytes. This pattern scales well because the application servers do not handle the upload bandwidth. - Source: dev.to / 3 months ago
Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 3 years ago
This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: almost 4 years ago
I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: almost 4 years ago
You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: almost 4 years ago
It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / over 4 years ago
Google Cloud Platform - Google Cloud provides flexible infrastructure, end-to-security, modern productivity, and intelligent insights engineered to help your business thrive.
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
