Pepperdata Integrations

Dataproc makes it easy to process open source data and analytic processing in the cloud. Faster build custom OSS clusters for custom machines Dataproc can speed up your data and analytics processing, whether you need more memory for Presto or GPUs to run Apache Spark machine learning. It spins up a cluster in less than 90 seconds. Cluster management is easy and affordable Dataproc offers autoscaling, idle cluster deletion and per-second pricing. This allows you to focus your time and resources on other areas. Security built in by default Encryption by default ensures that no data is left unprotected. Component Gateway and JobsAPI allow you to define permissions for Cloud IAM clusters without the need to set up gateway or networking nodes.

Amazon Elastic Kubernetes Service is a fully managed Kubernetes services. EKS is trusted by customers such as Intel, Snap and Intuit. It also supports GoDaddy and Autodesk's mission-critical applications. EKS is reliable, secure, and scaleable. EKS is the best place for Kubernetes because of several reasons. AWS Fargate is serverless compute for containers that you can use to run your EKS clusters. Fargate eliminates the need for provisioning and managing servers. It allows you to specify and pay per application for resources and improves security by application isolation by design. EKS is also integrated with AWS Identity and Access Management, AWS CloudWatch, Auto Scaling Groups and AWS Identity and Access Management, IAM, and Amazon Virtual Private Cloud (VPC), allowing you to seamlessly monitor, scale, and load balance your applications.

AWS Marketplace is an online catalog that allows customers to discover, buy, deploy and manage third-party products, data and services within the AWS ecosystem. It offers thousands of listings in categories such as security, machine-learning, business applications, DevOps, and more. AWS Marketplace offers flexible pricing models, such as pay-as you-go, annual subscriptions and free trials. This simplifies billing and procurement by integrating costs in a single AWS bill. It also supports rapid implementation with pre-configured applications that can be launched using AWS infrastructure. This streamlined approach allows companies to accelerate innovation, reduce the time-to market, and maintain better controls over software usage and cost.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Amazon EMR is the market-leading cloud big data platform. It processes large amounts of data with open source tools like Apache Spark, Apache Hive and Apache HBase. EMR allows you to run petabyte-scale analysis at a fraction of the cost of traditional on premises solutions. It is also 3x faster than standard Apache Spark. You can spin up and down clusters for short-running jobs and only pay per second for the instances. You can also create highly available clusters that scale automatically to meet the demand for long-running workloads. You can also run EMR clusters from AWS Outposts if you have on-premises open source tools like Apache Spark or Apache Hive.