Software Alternatives, Accelerators & Startups

Materialize VS Qrvey

Compare Materialize VS Qrvey and see what are their differences

Materialize logo Materialize

A Streaming Database for Real-Time Applications

Qrvey logo Qrvey

Embedded Analytics built exclusively for SaaS applications.
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  • Materialize Landing page
    Landing page //
    2023-08-27
  • Qrvey Landing page
    Landing page //
    2023-11-21
  • Qrvey
    Image date //
    2024-05-20
  • Qrvey
    Image date //
    2024-05-20

Qrvey is the only purpose-built solution for embedded analytics

Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application.

Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less.

Qrvey’s multi-tenant data lake includes:

  • Elasticsearch as the analytics engine
  • A unified data pipeline for ingestion and transformation
  • A complete semantic layer for simple user and data security integration

Qrvey’s embedded visualizations support everything from: - Standard dashboards and templates - Self-service reporting - User-level personalization - Individual dataset creation - Data-driven workflow automation

Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users.

The result: Less time and money on analytics.

Materialize features and specs

No features have been listed yet.

Qrvey features and specs

  • Embedded Dashboards: Yes
  • Embedded Dashboard Builders: Yes
  • Embedded Single Charts/Metrics: Yes
  • Embedded Single Chart/Metric Builder: Yes
  • Data Warehouse: Yes
  • ETL: Yes
  • Alerts and Automation: Yes
  • Embedded Pixel-Perfect Reports: Yes
  • Native Multi-Tenant Data Security: Yes
  • Tenant Specific Content Deployment: Yes
  • Prebuilt Data Connectors (Redshift, PostgreSQL, Snowflake, etc): Yes

Materialize videos

Bootstrap Vs. Materialize - Which One Should You Choose?

More videos:

  • Review - Materialize Review | Does it compete with Substance Painter?
  • Review - Why We Don't Need Bootstrap, Tailwind or Materialize

Qrvey videos

Qrvey Embedded Analytics Demo

More videos:

  • Demo - Qrvey Intro Video

Category Popularity

0-100% (relative to Materialize and Qrvey)
Databases
100 100%
0% 0
Data Dashboard
0 0%
100% 100
Database Tools
100 100%
0% 0
Business Intelligence
0 0%
100% 100

Questions and Answers

As answered by people managing Materialize and Qrvey.

How would you describe your primary audience?

Qrvey's answer:

Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies

What makes your product unique?

Qrvey's answer:

Qrvey takes a different approach to embedded analytics. Instead of focusing almost completely on the front end, we know that any analytics function starts with data.

Qrvey includes a full-featured data lake powered by Elasticsearch, not a basic relational caching layer. Furthermore, by including a data lake, the cost to scale out is much less than traditional data warehouses.

For the user-facing components of the platform, Qrvey offers more embedded components and APIs to personalize the experience beyond static dashboards. Qrvey offers:

  • Everything is a JS embed, no iFrames
  • Dashboards and builders
  • Dataset creation for individual users
  • No-code workflow automation
  • Full white-labeling and CSS support

All of this is backed by a semantic layer that makes integrating Qrvey into the security model of SaaS applications simple.

Why should a person choose your product over its competitors?

Qrvey's answer:

Customers choose Qrvey for the following reasons:

  • In-house engineering teams spend less time developing analytics features
  • Infrastructure costs are significantly less
  • Engineering teams get JS embeds and a richer API suite than anyone else
  • Fully customizable to blend in seamlessly with the parent SaaS application

User comments

Share your experience with using Materialize and Qrvey. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Materialize and Qrvey

Materialize Reviews

We have no reviews of Materialize yet.
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Qrvey Reviews

10 Best Big Data Analytics Tools For Reporting In 2022
Qrvey is an embedded analytics platform used for SaaS data, analytics, and automation technologies. You can deploy it right into your pre-existing AWS account in order to visualize your entire data pipeline. Their start-ups package includes specialized support for pre-launch or early-launch companies, like quick installation and launch, serverless analytics scalability,...
Source: theqalead.com
Top 5 Embedded Analytics Tools for Amazon Redshift (Plus 1 Bonus Option)
Qrvey is an embedded analytics and automation tool designed specifically for SaaS applications. It connects directly to AWS and offers an all-in-one platform that includes data collections, analysis, visualizations, automation, and more.
Source: yurbi.com

Social recommendations and mentions

Based on our record, Materialize seems to be a lot more popular than Qrvey. While we know about 65 links to Materialize, we've tracked only 1 mention of Qrvey. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Materialize mentions (65)

  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize,... - Source: dev.to / 4 months ago
  • We Built a Streaming SQL Engine
    Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views. https://github.com/timelydataflow/differential-dataflow. - Source: Hacker News / 8 months ago
  • Ask HN: Who is hiring? (October 2023)
    Materialize | Full-Time | NYC Office or Remote | https://materialize.com Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that... - Source: Hacker News / 9 months ago
  • Ask HN: Who is hiring? (June 2023)
    Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/ You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL... - Source: Hacker News / about 1 year ago
  • Ask HN: Who is hiring? (April 2023)
    Materialize | NY, NY | https://materialize.com/ The Cloud Database for Fast-Changing Data. We put a streaming engine in a database, so your team can build real-time data products without the cost, complexity, and development time of stream processing. Cloud team openings: https://grnh.se/0ad6ab6b4us Senior PM openings: https://grnh.se/415c267f4us. - Source: Hacker News / about 1 year ago
View more

Qrvey mentions (1)

  • Looking for an embedded report builder solution that doesn't require giving a third-party access to the data
    Since you're on AWS already, check out https://qrvey.com. Source: 7 months ago

What are some alternatives?

When comparing Materialize and Qrvey, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.