41 docs tagged with "CloudOps Engineer"

This page describes how to deploy a new Qrvey MultiPlatform Environment in Azure.

This document provides a general guide for modifying deployment parameters in Qrvey environments. It includes prerequisites for accessing your Kubernetes cluster on AWS and Azure, and uses the job downscaler as a practical example.

As a cloud operations engineer, you deploy, maintain, and upgrade the Qrvey platform across cloud environments. You ensure the reliability, scalability, and security of Qrvey’s cloud infrastructure, adapting deployment strategies to meet the distinct requirements of platforms such as AWS and Azure. Cloud operations engineers facilitate platform operations that support business continuity and optimal performance for Qrvey's users.

As a cloud operations engineer, you can use Qrvey's logging system to enable monitoring, configure notifications, and set up automatic log cleanup.

Before You Begin

A connection is the link established between Qrvey and your external data source:

By default, the Qrvey MultiPlatform deployment creates an EKS cluster with a public control plane endpoint. This allows the deployment to be executed from your local machine. However, for enhanced security, you can deploy Qrvey with a fully private EKS cluster where the control plane is only accessible from within the VPC.

This page describes the process and requirements for deploying a new Qrvey MultiPlatform Environment in AWS. For v9 installations, contact Customer Support to obtain your Docker registry credentials.

Qrvey supports a process for displaying text in any number of languages.

This article contains frequently asked questions organized by user persona.

As a CloudOps engineer, you can set up Google as the OpenID provider usedd for logging into Qrvey.

The Health Dashboard provides scheduled updates about key health indicators for the current instance of the Qrvey platform.

The following video series shows you how to deploy and maintain a multi-tenant analytics solution, using either a commingled or segregated dataset architecture.

Qrvey supports a process for displaying text in any number of languages.

Qrvey platform supports internationalization and localization for end users in a comprehensive manner. Multi-lingual support is offered for both “static” and “dynamic” text, as well as formatting of numbers and dates based on regions and cultures.

SaaS organizations that want to embed self-service analytics within their core applications to support a multi-tenant deployment should think about the following considerations:

The Qrvey platform is an all-in-one analytics solution that enables SaaS providers to quickly put analytics in the hands of their users.

This page describes how to migrate an existing AWS OpenSearch Service cluster from a public endpoint configuration to a VPC-private deployment. This migration applies to customers upgrading from Qrvey v8 or earlier, where the AWS OpenSearch domain was deployed with a publicly accessible endpoint and all traffic between the EKS cluster and the OpenSearch domain traveled through a NAT Gateway.

Note: Before starting your migration, contact Qrvey Support. The Support team can provide guidance, answer questions, and help ensure a smooth migration from v8 to v9.

This video discusses multi-tenancy architecture of the Qrvey platform for SaaS organizations. Includes a demonstration of a parent application that embeds a Qrvey application in a multi-tenant environment. Topics covered include:

Before Creators can use the OpenID provider to log in, you must set up your OpenID provider account to integrate with Qrvey.

While you can view general information about your Elasticsearch cluster on the Account Info tab, use the Infrastructure page of the Qrvey Admin Center to monitor and manage its configuration.

AWS Deployment

Azure Architecture

This guide explores optimization and configuration of the Qrvey platform, covering integrations with Amazon Redshift, Snowflake, Databricks, and Elasticsearch. It provides best practices for each, addressing both Live Connect datasets (real-time queries against your source) and Managed datasets (data ingested into Qrvey’s internal Elasticsearch engine) to help design optimal solutions. The focus is on conceptual and architectural considerations, performance, and cost trade-offs.

For very large or long-running queries in Postgres, processing can take several minutes to start. To expedite this process, you can enable the "Export Data to S3 to Support Larger Data Loads" option. When selected, the query output is sent to S3, from where Qrvey retrieves the data. This option is available when the Host URL points to an AWS RDS Postgres instance located in the same AWS region as Qrvey.

Whether you're getting started with Qrvey Pro or Qrvey Ultra, use this quickstart guide to go from zero to embedded!

Use the following step-by-step video tutorials to help you get started.

Use the following steps to configure Qrvey to access Redshift clusters for loading data into Qrvey for data analysis. These changes allow Qrvey to access the cluster and enable the cluster to export data to S3 for access by Qrvey.

- If you plan to upgrade to a newer version of Qrvey, review the upgrade and release notes for all previous versions, which can contain platform changes that must be accounted for in the development process.

This document explains the steps required to securely access your AWS RDS instance and load data into the Qrvey Business Analytics platform.

For AWS, Qrvey 9.3 deployments include a Kubernetes controller (Kueue) to manage the number of concurrent Kubernetes Jobs triggered by data sync cronjobs.

For AWS, Qrvey 9.3 deployments include a Kubernetes controller (Kueue) to manage the number of Lake Garbage Collector jobs that can run concurrently.

Dashboard sharing allows users with appropriate permissions to share dashboards with their organization, specific roles, or individual users. Qrvey's dashboard sharing model is available in Composer and Embedded scenarios in v9.3. You can manage sharing options and access levels from the sharing modals in the dashboard list or from the dashboard directly.

Dashboard sharing in Qrvey provides the ability to define custom tenant organizations that enable the sharing of dashboards within individual customer organizations. You can create configurable roles in the Qrvey Admin Center to enable sharing between users within the same tenant and promote more granular control of user access when editing and viewing dashboards.

Follow these steps to configure access to Snowflake clusters for loading data into Qrvey and exporting data to S3.

Snowflake is a compute-based warehouse software built on top of Amazon Web Services or Microsoft Azure infrastructure. Similar to the Qrvey product, it functions more like a SaaS (Software as a Service) platform, without offering Qrvey's unique benefits. This document specifies how to configure and use the Snowflake connector.

This guide provides step-by-step instructions for configuring JWT authentication using RSA key pairs for the Snowflake Connector. JWT-based authentication is a secure and scalable alternative to traditional username/password credentials. It enables programmatic access to Snowflake using a public/private key pair, eliminating the need to store passwords in your application or environment.

To ensure proper communication between Qrvey and Azure Synapse, complete the following configuration tasks after the initial Qrvey deployment. By default, a new deployment creates the Synapse workspace resource.

The Qrvey platform enables end users to display dates and times in their local time zone. The Qrvey platform stores data in UTC/GMT+0 and adjusts the time displayed to the end user based on their preferred settings. The default time zone setting is specified in Qrvey Admin Center, but it can be overridden programmatically. The possible settings are:

This troubleshooting guide is intended for customers who have deployed the Qrvey Platform in their own AWS or Azure cloud accounts. The Qrvey Platform runs as a Kubernetes cluster, providing a scalable and resilient environment for analytics workloads. This document will help you connect to your Qrvey Kubernetes cluster and use basic troubleshooting commands to investigate and resolve common issues.