Stick All Of Your Systems And Data Together With SaaSGlue As Your Workflow Manager

Summary At the core of every data pipeline is an workflow manager (or several). Deploying, managing, and scaling that orchestration can consume a large fraction of a data team’s energy so it is important to pick something that provides the power and flexibility that you need. SaaSGlue is a managed service that lets you connect all of your systems, across clouds and physical infrastructure, and spanning all of your programming languages. In this episode Bart and Rich Wood explain how SaaSGlue is architected to allow for a high degree of flexibility in usage and deployment, their experience building a business with family, and how you can get started using it today. This is a fascinating platform with an endless set of use cases and a great team of people behind it. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Rich and Bart Wood about SaasGlue, a SaaS-based integration, orchestration and automation platform that lets you fill the gaps in your existing automation infrastructure Interview Introduction How did you get involved in the area of data management? Can you describe what SaasGlue is and the story behind it? I understand that you are building this company with your 3 brothers. What have been the pros and cons of working with your family on this project? What are the main use cases that you are focused on enabling? Who are your target users and how has that influenced the features and design of the platform? Orchestration, automation, and workflow management are all areas that have a range of active products and projects. How do you characterize SaaSGlue’s position in the overall ecosystem? What are some of the ways that you see it integrated into a data platform? What are the core elements and concepts of the SaaSGlue platform? How is the SaaSGlue platform architected? How have the goals and design of the platform changed or evolved since you first began working on it? What are some of the assumptions that you had at the beginning of the project which have been challenged or changed as you worked through building it? Can you talk through the workflow of someone building a task graph with SaaSGlue? How do you handle dependency management for custom code in the payloads for agent tasks? How does SaasGlue manage metadata propagation throughout the execution graph? How do you handle the myriad failure modes that you are likely to encounter? (e.g. agent failure, network partitions, individual task failures, etc.) What are some of the tools/platforms/architectural paradigms that you looked to for inspiration while designing and building SaaSGlue? What are the most interesting, innovative, or unexpected ways that you have seen SaasGlue used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on SaasGlue? When is SaaSGlue the wrong choice? What do you have planned for the future of SaaSGlue? Contact Info Rich LinkedIn Bart LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links SaaSGlue Jenkins Cron Airflow Ansible Terraform DSL == Domain Specific Language Clojure Gradle Polymorphism Dagster Podcast Episode Podcast.__init__ Episode Martin Kleppman The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast