Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.
The cost of this solution is less than competitors such as Amazon or Google Cloud.
We pay approximately $500,000 a year. It's approximately $10,000 a year per license.
The cost of this solution is less than competitors such as Amazon or Google Cloud.
We pay approximately $500,000 a year. It's approximately $10,000 a year per license.
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.
Spark is an open-source solution, so there are no licensing costs.
Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.
Spark is an open-source solution, so there are no licensing costs.
AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.
AWS Batch's pricing is good.
The pricing is very fair.
AWS Batch's pricing is good.
The pricing is very fair.
The price of the solution depends on many factors, such as how they pay for tools in the company and its size.
Google Cloud is slightly cheaper than AWS.
The price of the solution depends on many factors, such as how they pay for tools in the company and its size.
Google Cloud is slightly cheaper than AWS.
A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.
I rate the price of AWS Fargate a four out of five.
I rate the price of AWS Fargate a four out of five.
It's an open-source solution.
We use the free version of Apache NiFi.
It's an open-source solution.
We use the free version of Apache NiFi.
Amazon EC2 Auto Scaling helps you maintain application availability and allows you to automatically add or remove EC2 instances according to conditions you define. ... Dynamic scaling responds to changing demand and predictive scaling automatically schedules the right number of EC2 instances based on predicted demand.
Pricing could be a little bit more competitive.
The pricing is not fixed and it is based on usage.
Pricing could be a little bit more competitive.
The pricing is not fixed and it is based on usage.
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.
Pricing appears to be cheap, however, it is extremely difficult in calculating what something will cost.
It has helped to reduce costs with infrastructure.
Pricing appears to be cheap, however, it is extremely difficult in calculating what something will cost.
It has helped to reduce costs with infrastructure.
Oracle Compute Cloud Service is an infrastructure as a service (IaaS) offering that provides flexible and scalable computing, block storage, and networking services on Oracle Cloud. You can now set up and manage your computing and storage workloads in the cloud, on demand, using a self-service portal. You'll significantly reduce your computing costs and increase your business efficiency and agility. Use Oracle Compute Cloud Service to migrate your on-premises workloads to the cloud and reap the many cloud benefits.
Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances or Amazon ECS tasks to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.
In most deep learning applications, making predictions using a trained model—a process called inference—can drive as much as 90% of the compute costs of the application due to two factors. First, standalone GPU instances are designed for model training and are typically oversized for inference. While training jobs batch process hundreds of data samples in parallel, most inference happens on a single input in real time that consumes only a small amount of GPU compute. Even at peak load, a GPU's compute capacity may not be fully utilized, which is wasteful and costly. Second, different models need different amounts of GPU, CPU, and memory resources. Selecting a GPU instance type that is big enough to satisfy the requirements of the most demanding resource often results in under-utilization of the other resources and high costs.
Amazon Elastic Inference solves these problems by allowing you to attach just the right amount of GPU-powered inference acceleration to any EC2 or SageMaker instance type or ECS task with no code changes. With Amazon Elastic Inference, you can now choose the instance type that is best suited to the overall CPU and memory needs of your application, and then separately configure the amount of inference acceleration that you need to use resources efficiently and to reduce the cost of running inference.
StackStorm is a powerful open-source automation platform that wires together all of your apps, services and workflows. It’s extendable, flexible, and built with love for DevOps and ChatOps.
AWS Compute Optimizer recommends optimal AWS Compute resources for your workloads to reduce costs and improve performance by using machine learning to analyze historical utilization metrics. Over-provisioning compute can lead to unnecessary infrastructure cost and under-provisioning compute can lead to poor application performance. Compute Optimizer helps you choose the optimal Amazon EC2 instance types, including those that are part of an Amazon EC2 Auto Scaling group, based on your utilization data.
I find the solution's pricing reasonable. You need to pay extra for IP and other miscellaneous costs.
I find the solution's pricing reasonable. You need to pay extra for IP and other miscellaneous costs.
