Self-hosted console for Kafka, Redis, RabbitMQ and Apache Artemis: one permission model, production guardrails and a tamper-evident audit trail.
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Which is more popular?
Based on our record, Redash
seems to be more popular. It has been mentioned
19 times
since March 2021.
social mentions
19 vs 0
Data Dashboard popularity
100% vs 0%
Base details
Website, pricing, platforms and company facts side by side.
Open Source Redash is an open-source tool, allowing users to customize and extend its functionalities to suit their specific needs.
Cost As an open-source product, Redash can be used for free, making it cost-effective for organizations with limited budgets.
Data Source Integration Redash supports a wide range of data sources, including SQL databases, NoSQL databases, and cloud services, making it versatile for different data needs.
Query Editor Redash comes with a powerful query editor that supports SQL, which makes it easy for data analysts to write and execute queries.
Visualization Options Redash provides multiple visualization options such as bar charts, line charts, and pie charts to help users interpret data effectively.
Collaboration Redash allows multiple users to collaborate on queries and dashboards, fostering teamwork within organizations.
Alerting Users can set up alerts to notify them when certain data conditions are met, enabling proactive decision-making.
Possible disadvantages
User Interface The user interface of Redash can be less intuitive, especially for new users who are not familiar with data analytics tools.
Scalability Redash might face performance issues when dealing with very large datasets or a high number of simultaneous queries.
Community Support Being an open-source product, Redash relies heavily on community support, which can be inconsistent and slower compared to commercial products with dedicated support teams.
Advanced Features Compared to more established BI tools, Redash may lack some advanced features and functionalities like detailed user access controls and more complex data transformations.
Documentation The documentation for Redash can be lacking or outdated, making it challenging for users to find the information they need.
Deployment Complexity Setting up and maintaining a Redash instance can be complex and require a good understanding of infrastructure management.
Efficient Data Curation Lightly uses active learning and self-supervised learning techniques to help teams select the most valuable and diverse subset of data for labeling, reducing the amount of data needed for training while maintaining model performance.
Reduces Labeling Costs By intelligently selecting which images or videos to label, Lightly can significantly cut down on the manual annotation costs and time required for building computer vision datasets.
Self-Supervised Learning Integration The platform leverages self-supervised learning methods to extract meaningful embeddings from unlabeled data, enabling better understanding of dataset structure and redundancy before labeling.
Scalable for Large Datasets Lightly is designed to handle large-scale image and video datasets, making it suitable for enterprises dealing with massive amounts of visual data in computer vision projects.
Integration with ML Workflows Lightly offers APIs and integrations that allow it to fit into existing machine learning pipelines, working alongside popular annotation tools and cloud storage solutions.
Possible disadvantages
Learning Curve Teams new to active learning and self-supervised learning concepts may face a learning curve in understanding how to best utilize Lightly's data curation capabilities.
Narrow Use Case Focus Lightly is primarily focused on computer vision data curation, which may limit its usefulness for teams working with other data types such as text, audio, or tabular data.
Dependency on Data Quality The effectiveness of Lightly's curation algorithms depends heavily on the quality and representativeness of the initial dataset, which can limit results if the source data is poor.
Pricing for Smaller Teams Depending on the pricing tier, smaller teams or individual developers might find the cost of enterprise-level data curation features less accessible compared to open-source alternatives.
Requires Technical Expertise Effectively leveraging Lightly's advanced features like self-supervised learning models may require a certain level of machine learning expertise, which could be a barrier for less technical teams.
Analysis
An editorial look at what each product does well and who it suits.
RedashLightly
Overall verdict
Yes, Redash is considered good for users who need a straightforward, yet powerful, tool for data visualization and exploration. Its ease of use, combined with the capabilities to support various data sources, makes it a solid choice for companies and data teams.
Why this product is good
Redash is well-regarded for its simplicity and powerful visualization capabilities. It is an open-source platform that allows users to connect to a wide range of data sources, create dashboards, and share insights easily. It provides users with the flexibility to write SQL queries to fetch data and then visualize it in an interactive and intuitive manner. Redash's support for multiple data source connections, along with its collaborative features, makes it a great tool for teams looking to leverage data efficiently.
Recommended for
Data Analysts
Business Intelligence Teams
Organizations looking for an open-source data visualization tool
Teams needing collaboration features for data-driven decision making
Users with SQL knowledge needing flexible query capabilities
Overall verdict
Lightly is a solid, well-regarded platform for data curation and active learning in computer vision, offering strong tooling for selecting the most valuable images/frames to label, which helps teams reduce annotation costs and improve model performance efficiently.
Why this product is good
Focuses on smart data selection and curation, helping avoid wasted labeling budget on redundant data
Offers both open-source (Lightly SSL) and commercial (Lightly Worker/Platform) options for flexibility
Uses self-supervised learning techniques to find diverse, representative, or edge-case samples
Integrates with common MLOps and labeling tools, fitting into existing computer vision pipelines
Scales to large unlabeled datasets, making it useful for teams with massive raw data but limited labeling resources
Provides visualization and embedding tools to explore dataset distribution and detect biases or gaps
Recommended for
Computer vision teams looking to reduce labeling costs through smarter data selection
ML engineers building active learning pipelines
Companies with large volumes of unlabeled image or video data
Teams wanting to improve model performance by curating more diverse or representative training sets
Organizations needing to detect dataset bias, redundancy, or edge cases before annotation
Redash is a lightweight, open-source business intelligence tool designed for easy data exploration using SQL queries and interactive dashboards. It helps teams visualize, share, and collaborate on insights quickly....
Accessibility: Though it also requires support from your data team, Looker is more targeted to non-tech users than Redash, since Redash requires SQL expertise.
So all-in-all, Redash is meant for users who have the technical knowledge and depend a lot on KPIs, and Datapad is for users and businesses who just want an overview of KPI performance but quickly.
Recommendations tracked on public social media and blogs since March 2021.
Redash19 mentionsLightly0 mentions
Tool or service for querying and exposing database through API
I am looking for service or tool similiar to Metabase or Redash that allows me to add data source - for example Postgres connection, and create raw SQL queries that can be shared or exposed through API. So instead of keeping raw SQL code...
Source:
over 3 years ago
Did anyone try Openblocks for multi-tenant client reporting?
I have tried Metabase, Redash beore (both self hosted open source versions), from my experience I find Metabase a bit easy to work with.
Source:
over 3 years ago
Regarding visualization tools, sqliteviz has proven to be the best I've found so far. Their web app runs locally but has some trackers, so I run it locally via a simple, static HTTP server. Falcon and Redash seem like overkill for my needs.
Source:
over 3 years ago
Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
