VMware Tanzu
AWS Lambda
AWS Elastic Beanstalk
Now Platform
Google App Engine
Plesk
Salesforce Platform
Calisti
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
VMware Tanzu Amazon EMRBased on our record, Amazon EMR should be more popular than VMware Tanzu. It has been mentiond 10 times since March 2021. 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.
Docker swarm still exists, it still works, and some of these other container orchestrators are still hanging on, but for the most part, you’re using Kubernetes if you’re doing this stuff at work. Generally it's well-understood that kubernetes is hard to get right, and so most people use it via a managed provider like Elastic Kubernetes Service from AWS, Azure Kubernetes Service from MSFT, or Google Kubernetes... - Source: dev.to / 4 months ago
Today I found myself looking at the webpage for VMware Tianzu. I have never used Tianzu and I was quite ignorant on what function it serves, so I decided to go straight to VMware to get the juicy details on what this thing actually does. I read through the paragraphs of marketing I found there, and I realized something. Source: about 1 year ago
Tanzu is a Kubernetes platform that emphasizes the developer. It provides custom container images for the most important technology stacks, and several additional abstractions that help with application development and streamlined Kubernetes hosting. As several other stacks as well, its build with strong support of multi-cluster management. - Source: dev.to / over 1 year ago
Moreover, you're likely to be interested in VMware Tanzu - this is effectively their version of Kubernetes: https://tanzu.vmware.com/tanzu. Source: over 2 years ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: over 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: about 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
AWS Lambda - Automatic, event-driven compute service
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
AWS Elastic Beanstalk - Quickly deploy and manage applications in the AWS cloud.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Now Platform - Get native platform intelligence, so you can predict, prioritize, and proactively manage the work that matters most with the NOW Platform from ServiceNow.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost