Category: AWS Marketplace

In spite of the rich set of machine learning tools AWS provides, coordinating and monitoring workflows across an ML pipeline remains a complex task. Control-M by BMC Software that simplifies complex application, data, and file transfer workflows, whether on-premises, on the AWS Cloud, or across a hybrid cloud model. Walk through the architecture of a predictive maintenance system we developed to simplify the complex orchestration steps in a machine learning pipeline used to reduce downtime and costs for a trucking company.

In order to deliver serverless applications, customers often turn to DevOps principles to efficiently build, deploy, operate, and iterate on features and changes. CI/CD is one of the major components of DevOps that helps deliver code faster and more reliably to production. GitLab’s continuous integration offering provides a rich set of features for automating how new code is incorporated into your software and how new versions of your software get built and deployed.

Running Hadoop, Spark, and related technologies in the cloud provides the flexibility required by these distributed systems. Cazena provides a production-ready, continuously optimized and secured Data Lake as a Service with multiple features that enables migration of Hadoop and Spark analytics workloads to AWS without the need for specialized skills. Learn how Cazena makes it easy to migrate to AWS while ensuring your data is as secure on the cloud as it is on-premises.

One of the biggest obstacles for brands today is improving the performance of their digital properties. While much of today’s marketing technologies leverage modern advances in cloud computing, many of the technologies powering digital properties (web and mobile sites) still use the prior generation of server-based technologies. Learn how to modernize your digital technology stack with Crownpeak to optimize customer experiences for speed, and ultimately improve performance for your brand.

To fully leverage the versatility and scalability of containers, you need a monitoring solution capable of providing clarity into a highly dynamic environment comprising thousands (or even tens of thousands) of ephemeral containers. Learn how Datadog provides visibility into dynamic, ephemeral container workloads running on Amazon EKS, a service that makes it easy for you to run Kubernetes on AWS without needing to install and operate your own Kubernetes clusters.

As application complexity increases, the debugging process in production environments gets more complicated as well. AWS understands this challenge and includes tracing tools in its cloud services. For instance, AWS X-Ray helps developers analyze and debug distributed applications, such as those built using a microservices architecture. Epsagon specializes in automated tracing for cloud microservices, providing automated end-to-end tracing across not distributed AWS services.

Attribution models allow companies to guide marketing, sales, and support efforts using data, and then custom tailor every customer’s experience for maximum effect. Combined together, cloud-based data pipeline tools like Fivetran and data warehouses like Amazon Redshift form the infrastructure for integrating and centralizing data from across a company’s operations and activities, enabling business intelligence and analytics activities.

Red Hat Insights helps you manage Red Hat Enterprise Linux (RHEL) Systems on AWS by analyzing physical, virtual, container, and hybrid private and public cloud environments, comparing them to more than 1,000 rules. These rules identify potential threats that could lead to business disruptions. Insights scans your environments daily, summarizes any identified risks in its dashboard, and provides remediation steps for those risks.

Organizations these days are inundated with data. Learn how engineers and analysts can handle the critical challenges of gaining insights from large and complex data sources while also democratizing data for improved adoption across the organization. The Sisense platform simplifies end-to-end data and analytics, reducing time-to-insights by empowering data and IT teams to build advanced data models and perform advanced analysis for their needs.

Enterprises often choose to mask, remove, or encrypt sensitive data in the ETL step to minimize the risk of sensitive data becoming stored, logged, accessible, or breached from their data lake or data warehouse. Xplenty’s ETL and ELT platform allows customers to quickly and easily prepare their data for analytics using a simple-to-use data integration cloud service. Xplenty’s global service uses AWS KMS to create and control the keys used to encrypt or digitally sign your data.