Goal Similarly to the previous tutorial Switflow Pipeline / Part 1 – Dataflow To BigQuery (nested format), the goal here is to create and execute a real-time pipeline tailored for exploratory analytics on SWIFT MT using Pub/Sub, Dataflow, Swiftflow and BigQuery. The difference is the format of the output: a tabular format (flatenned and exploded) while we produced […]
Read MoreGoal Create and execute a real-time pipeline tailored for exploratory analytics on SWIFT MT using Pub/Sub, Dataflow, Swiftflow and BigQuery, and delivering data in a nested format, persisted as JSON strings. At the end of this tutorial, a streaming data pipeline will be continuously writing data into BigQuery that is ready for downstream explorative analytics, […]
Read MoreBack-up and restore This is not applicable as the current Swiftflow version is a fully stateless API. Update images Edit the Swiftflow statefulset by running the below command: kubectl edit sts RELEASE_NAME-swiftflow Modify the Swiftflow pod image version to the targeted version.Save and quit.Warning : the pod will be restarted once you changes have been […]
Read MoreInstallation using Helm Prerequisites You will need the below tools installed in order to deploy Swiftflow on premise : helm kubectl docker Request Helm package and Docker images You can contact us on [email protected] in order to request the necessary archives (Helm package and Docker images) plus a dedicated license to deploy Swiftflow on premise. […]
Read MoreBanking The banking industry is evolving in an uncertain global context. Some major change drivers – digitalization, sustainability, regulation, competition – are challenging its ability to optimally satisfy its customers, responsibly act on the markets, comply to stringent regulations and efficiently deliver operations while having to absorb an increasing volume of digital transactions. Technology Data […]
Read MorePricing and support You will start your journey with a 30 days free trial period that can be extended. Upon the realization of a successful proof-of-concept and in case you are operating in GCP environment, a usage-based billing model will apply that is proportional to the number of requests made on the application. For non-GCP […]
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Read MoreEnd-User License Agreement (EULA) of Swiftflow This End-User License Agreement (“EULA”) is a legal agreement between you and Alpina Analytics LLC. Our EULA was created by EULA Template for Swiftflow. This EULA agreement governs your acquisition and use of our Swiftflow software (“Software”) directly from Alpina Analytics LLC or indirectly through a Alpina Analytics LLC […]
Read MoreConfiguration Some runtime parameters, like the API port, the filename of the location database etc. are configurable. To determine the value of an option, the following logic is applied: If a System property with the name swiftflow.<configkey> (e.g. swiftflow.serverPort) exists, this value is selected. Example: java -Dswiftflow.serverPort=8080 -jar /…/swiftflow-api.jar Otherwise, the setting is looked for […]
Read MoreOverview The Swiftflow package described here is containerized as a Docker/Kubernetes web server application. It can be deployed on any infrastructure that accepts Kubernetes, which includes multi-cloud or hybrid cloud contexts. Google Cloud Platform (GCP) In addition of the environment-agnostic Kubernetes application, specific requirements have to be satisfied to integrate to GCP. In particular, the […]
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