30 docs tagged with "Data Engineer"

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.

Overview

This video provides an in-depth overview of Column-Level Security and how to configure it. It demonstrates how to create a Role in Qrvey Admin Center, how to use the Get Dataset, Update Dataset, and Apply Dataset Changes APIs to enable Column-Level Security for a dataset, and how to include the Role in a JSON Web Token (JWT).

A Connection is a link established between Qrvey and an external data source.

By customizing the appearance of a Qrvey widget in your application, you can provide a more native presentation within your product.

Well-designed dataset transformations prevent fragmented insights and keep the data clean in your dashboards. By applying the following techniques, you can ensure that your charts reflect reality, not accidental string differences.

As a data engineer, you use Qrvey to design, implement, and optimize data solutions tailored to your organization’s business objectives. Data engineers design scalable data pipelines that can integrate different data sources. When deployed, the Qrvey platform helps your teams to manage data efficiently and create advanced analytics.

After you've installed the Qrvey Platform, you can design your data pipeline. This article highlights best practices regarding how your data gets imported into Qrvey, optimized for visualizations, and refreshed over time.

Introduction

To encrypt and protect sensitive embed configuration and render a Qrvey dashboard in a client environment, generate and deploy a secure token.

As an administrator, use the Admin Center's Entity Management tab to manage datasets and data loads.

This article contains frequently asked questions organized by user persona.

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.

Introduction

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 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:

The first step with any self-service embedded analytics implementation is to design, develop and then populate the datasets that will become the sources for all charts, metrics and visualizations created in the platform. SaaS organizations building datasets for multi-tenant deployments of embedded analytics must think about the following topics with respect to the data architecture:

Qrvey’s approach to multi-tenant security architecture is to use the assert model for both authentication and authorization. The assert model effectively allows you to maintain your existing user accounts, roles/groups and permissions, and then declare a user’s identity and permissions to the Qrvey platform dynamically at runtime. The ability to assert/declare a user’s identity and access permissions is favorable when embedding third party platforms in your core application for a multi-tenancy deployment, because it means that you will not need to redefine and replicate your existing security schema.

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.

- 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.

The Row-Level Security feature restricts access to individual rows (record) in your datasets. With RLS, each user only sees the data they are authorized to view, even when all records are stored in a single dataset.

This video provides an in-depth overview of Row-Level Security, which is especially important for multi-tenant environments. It demonstrates how to enable Row-Level Security for the columns in a dataset, and how to configure a widget in code to enforce Row-Level Security. It also describes how to enable Row-Level Security in the Qrvey Admin Center.

Use row-level security to protect and filter rows of data so users only see the records they are allowed to see. This control can help you to manage multi-tenant or role-sensitive applications.

This video provides an overview of the Security features in the Qrvey platform. Topics discussed include authentication versus authorization, configuring user roles and groups, and configuring role-level security and column-level security.

Append and Update Data Sync automatically pulls new and updated records from your sources, keeping your base and managed datasets current without unnecessary full reloads.

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:

Embedding Qrvey widgets speeds up delivery and keeps your product focused on core values while offering rich analytics, reporting, and automation capabilities. With a small HTML element, a JSON configuration, and a launcher script, you can add dashboards, analysis tools, scheduled exports and more with configurable security and UI controls tailored to your users.