Category: Software

Given the breadth of use cases, data lakes need to be a complete analytical environment with a variety of analytical tools, engines, and languages supporting a variety of workloads. These include traditional analytics, business intelligence, streaming event and Internet of Things (IoT) processing, advanced machine learning, and artificial intelligence processing. Learn how Cazena builds and deploys a production ready data lake in minutes for customers.

Organizations need a comprehensive data management solution that includes data quality, cleansing, de-duplication, and curation capabilities. After consolidating trusted golden records, they need to enforce governance requirements and track changes over time. Semarchy’s xDM platform is an innovation in multi-vector Main Data Management (MDM) that leverages smart algorithms and material design to simplify data stewardship, governance, and integration.

A seamless employee experience requires the workflow of user onboarding and offboarding requires integration between HR systems, ITSM tools, cloud resources, identity and access control, and intelligent automation. To address these common needs, Relevance Lab worked closely with AWS to create an intelligent automation solution using RLCatalyst, ServiceNow, and Jira Service Desk. In this post, walk through the workflow and learn about this solution for automated user onboarding and offboarding.

Learn how image scanning implemented at various points of the container and Kubernetes lifecycle can provide you with critical insights to ensure security and compliance, without impacting the flexibility you need to build and run your applications. Built on open source, the Sysdig Secure DevOps Platform enables cloud teams to secure build pipelines, detect runtime threats, validate compliance, and monitor performance.

With Amazon Keyspaces, AWS has enabled SaaS providers to run their Apache Cassandra workloads using a fully managed, serverless offering. This option allows you to leverage the strengths of Cassandra while getting all of the scale, cost, reliability, and operational efficiency that comes with a managed model. Learn how Amazon Keyspaces can be used to store data in a multi-tenant architecture and review common models for partitioning each tenant’s data.

As a SaaS provider, it’s essential to have a clear picture of how tenants are exercising your system. Being able to model the functional and operational profile of tenants and tenant tiers is critical to evolving the business and technical strategies of a SaaS organization. Learn about the role metrics play within a SaaS-based application, and dive deep into a solution that provides all of the infrastructure that will support the ingestion, aggregation, and analysis of SaaS metric data.

In addition to delivering value-adding products and services, next-gen MSPs are increasingly expected to provide validation that they can meet all of your cloud needs. For AWS Managed Service Providers, learn how using CloudHealth by VMware can help you meet many of the requirements to become an AWS MSP Partner. CloudHealth makes it possible for enterprises to control and analyze the costs, compliance, and performance of their computing environments.

The AWS SaaS Factory team had the chance to work with Ridgeline, a privately funded fintech startup determined to modernize investment management software, and help build their purpose-built enterprise platform on AWS. We speak with Chris Petrunik, Director of Engineering at Ridgeline, to learn how their platform works and what advice they have for other businesses building SaaS solutions on AWS.

Managing backups, archiving, and disaster recovery traditionally required piecing together multiple software solutions across physical locations and network topologies. Trying to balance these disparate systems can result in expensive solutions, operational inefficiencies, and fragmented data. Learn how Cohesity worked with the AWS SaaS Factory team to released Data Management as a Service (DMaaS) as a solution on AWS that will radically simplify data management.

Sumo Logic ingests data across your entire application stack to provide observability and, therefore, reliability of mission-critical apps. It eases the burden placed on engineers, simplifying data collection, unifying visibility, and leveraging machine learning to speed troubleshooting. Sumo Logic’s Observability solution is designed specifically for AWS and seamlessly detects anomalies and events of interest correlated with application incidents.