Pepperdata Integrations

Managed Service for Apache Spark is a Google Cloud solution that simplifies running Apache Spark workloads with either serverless execution or fully managed clusters. It allows users to process large-scale data without needing to manage infrastructure, reducing operational complexity. The platform features Lightning Engine, which accelerates Spark performance by up to 4.9 times compared to open-source Spark. It supports data engineering, data science, and machine learning workflows at scale. Integration with Gemini enables AI-powered development, including automated code generation and troubleshooting. The service works seamlessly with open data formats like Apache Iceberg and integrates with tools like BigQuery and Knowledge Catalog. It offers flexible deployment options to suit different workloads and use cases. Overall, it provides a faster, smarter, and more efficient way to run Spark workloads in the cloud.

AWS Marketplace is a curated digital catalog that enables customers to discover, purchase, deploy, and manage third-party software, data products, AI agents, and services directly within the AWS ecosystem. It provides access to thousands of listings across categories like security, machine learning, business applications, and DevOps tools. With flexible pricing models such as pay-as-you-go, annual subscriptions, and free trials, AWS Marketplace simplifies procurement and billing by integrating costs into a single AWS invoice. It also supports rapid deployment with pre-configured software that can be launched on AWS infrastructure. This streamlined approach allows businesses to accelerate innovation, reduce time-to-market, and maintain better control over software usage and costs.

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed Kubernetes service. Customers such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS to run their most sensitive and mission-critical applications because of its security, reliability, and scalability. EKS is the best place to run Kubernetes for several reasons. First, you can choose to run your EKS clusters using AWS Fargate, which is serverless compute for containers. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Second, EKS is deeply integrated with services such as Amazon CloudWatch, Auto Scaling Groups, AWS Identity and Access Management (IAM), and Amazon Virtual Private Cloud (VPC), providing you a seamless experience to monitor, scale, and load-balance your applications.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. With EMR you can run Petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. For short-running jobs, you can spin up and spin down clusters and pay per second for the instances used. For long-running workloads, you can create highly available clusters that automatically scale to meet demand. If you have existing on-premises deployments of open-source tools such as Apache Spark and Apache Hive, you can also run EMR clusters on AWS Outposts. Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting.