Elementary
Elementary DataReviews from AWS customer
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Data Observability made simple
What do you like best about the product?
- Seamless integration with dbt, via the open source package which is super easy to set up
- Webinars to present the tool are very effective
- Documentation site is great
- Cool features of the Cloud paid offering: example column level lineage and integration with other tooling you might have (BI Tool, Data Catalog); possibility to bulk set tests
- Possibility to tailor and customise alerts; warnings in dbt do not become silent, but are still tracked and monitored
- Elementary Data Report gives you a pulse on your data quality status in your dbt project keeping track of the history and making it accessible outside of the data org, to the whole business
- Anomaly detection package is an amazing way to integrate stats (and AI in the cloud offering) to time series monitoring
- Webinars to present the tool are very effective
- Documentation site is great
- Cool features of the Cloud paid offering: example column level lineage and integration with other tooling you might have (BI Tool, Data Catalog); possibility to bulk set tests
- Possibility to tailor and customise alerts; warnings in dbt do not become silent, but are still tracked and monitored
- Elementary Data Report gives you a pulse on your data quality status in your dbt project keeping track of the history and making it accessible outside of the data org, to the whole business
- Anomaly detection package is an amazing way to integrate stats (and AI in the cloud offering) to time series monitoring
What do you dislike about the product?
- Only integrates with dbt, so not available for other Data Trasformation tools (yet)
What problems is the product solving and how is that benefiting you?
- Auditing dbt projects data quality over time
- Auditing dbt models runtime over time
- Overview of data quality in your dbt project at a glance with the Elementary Data Report
All of this enable more visibility on data quality and track bottlenecks and issues in your data pipelines.
The dbt package is open source and gives already lots of benefits.
- Auditing dbt models runtime over time
- Overview of data quality in your dbt project at a glance with the Elementary Data Report
All of this enable more visibility on data quality and track bottlenecks and issues in your data pipelines.
The dbt package is open source and gives already lots of benefits.
Advanced Our Data Quality by a Big Step
What do you like best about the product?
1. Made data quality measurable and transparent through its dashboard.
2. Significantly reduced the effort required to build and maintain a robust data monitoring and observability system.
3. Amplified the impact of data validation in DBT by tracking validation history and seamlessly integrating with our alerting system.
4. Fast response to any issue and inqueries.
2. Significantly reduced the effort required to build and maintain a robust data monitoring and observability system.
3. Amplified the impact of data validation in DBT by tracking validation history and seamlessly integrating with our alerting system.
4. Fast response to any issue and inqueries.
What do you dislike about the product?
It would be helpful to include information on the underlying mechanisms, such as how data freshnes anamoly were checked.
What problems is the product solving and how is that benefiting you?
Built a new data platform under a highly constrained timeline, ensuring scalability, reliability, and efficient data processing while meeting critical business needs.
Growing Data Governance tools
What do you like best about the product?
- Simplicity
- Ease of use
- Dashboards Analytics
- Ease of use
- Dashboards Analytics
What do you dislike about the product?
- No integration with Metabase
- Not Ai tool to docs
- Not Ai tool to docs
What problems is the product solving and how is that benefiting you?
Understanding relationships between data and ensuring good data quality
A solid platform to add observability on your dbt workloads
What do you like best about the product?
If you're using dbt, Elementary data is great addon to your data stack to monitor your queries, test coverage and report failures on Slack. The cloud version adds quality of life improvements to the OSS version.
It's fairly easy to plug in your dbt project and the different options to be added to the alerts are helpful to do some triage. The community and team is helpful and provide some support on the company's Slack.
It's fairly easy to plug in your dbt project and the different options to be added to the alerts are helpful to do some triage. The community and team is helpful and provide some support on the company's Slack.
What do you dislike about the product?
The platform is adding metadata through pre-hooks and post-hooks in dbt which slows down a bit your workflow.
The Slack alert template has little customization support and though it's OK, I think it could a bit more readable.
The Slack alert template has little customization support and though it's OK, I think it could a bit more readable.
What problems is the product solving and how is that benefiting you?
Elementary Data helps to cover dbt test reporting on Slack, dbt test coverage reporting and dbt model lineage.
Effortless Data Quality Monitoring Made Simple
What do you like best about the product?
Elementary Data excels in simplifying the process of data quality monitoring. Its intuitive interface and seamless integration with modern data stack make it a standout tool. I particularly appreciate the anomaly detection and detailed insights it provides, which help identify and resolve data issues before they escalate. Additionally, the visual reporting dashboards are both user-friendly and highly informative, enabling stakeholders to quickly grasp key metrics without needing deep technical expertise.
What do you dislike about the product?
I would say that some sections of the UI are not clear enough and I hope Metrics feature and their automatic anomaly detection could improve soon, but so far I am very confortable with the tool.
What problems is the product solving and how is that benefiting you?
Anomaly detection for models, visualization and management of incidents with data quality, including alerting, etc.
Easy and pleasant to use
What do you like best about the product?
* Improved Data Validation & Anomaly Detection.
* Seamless Integration with dbt.
* Automated Data Quality Monitoring.
* Intuitive Dashboard & Reporting.
* Faster Issue Detection & Resolution.
* Customization & Flexibility.
* Seamless Integration with dbt.
* Automated Data Quality Monitoring.
* Intuitive Dashboard & Reporting.
* Faster Issue Detection & Resolution.
* Customization & Flexibility.
What do you dislike about the product?
* The documentation is a bit unclear, making it difficult to fully understand how to configure and optimize the tool. Many features are not well-documented, leading to trial-and-error implementation rather than a structured onboarding experience.
* The anomaly detection feature needs improvement. Currently, when an anomaly occurs, the upper and lower bounds expand, making it difficult to easily disregard the anomaly.
* The anomaly detection feature needs improvement. Currently, when an anomaly occurs, the upper and lower bounds expand, making it difficult to easily disregard the anomaly.
What problems is the product solving and how is that benefiting you?
* Data Quality Monitoring at Scale
* Anomaly Detection & Data Drift Identification
* Faster Issue Resolution & Debugging
* Anomaly Detection & Data Drift Identification
* Faster Issue Resolution & Debugging
Great and flexible tool for Data Observability
What do you like best about the product?
As a data consultant, I work with several customers who demand high data quality for their data platforms. Elementary is a critical part of features that I can provide to meet those expectations. The combination of compiling dbt dag logs and powerful anomaly detection based in statistics makes it a perfect choice for data teams of all sizes
What do you dislike about the product?
Since Elementary demands integration with only dbt, it may not always be the best fit for teams with lower data maturity levels or teams that don't use dbt as a main data transformation tool. In such cases, foundational issues need to be addressed before leveraging a tool like dbt and Elementary.
What problems is the product solving and how is that benefiting you?
Elementary combine the logs of dbt and serve them better to get insights from the dbt jobs. Not only that but all the anomaly tests and alerts improve the developer experience and the product reliability in the long term.
Going beyond simple dbt testing with ease
What do you like best about the product?
This project combines a startup mindset with an open-source approach, thriving on feedback and continuously improving. Setting up a robust base for data observability is fast and efficient, thanks to their intuitive batch configuration system and dbt-native approach.
The UI is clean and user-friendly, but there’s room for improvement in making it easier to filter down and take action on alerts.
Also, I like the team's incredible spirit and commitment to improving the tool. They actively listen to feedback.
The UI is clean and user-friendly, but there’s room for improvement in making it easier to filter down and take action on alerts.
Also, I like the team's incredible spirit and commitment to improving the tool. They actively listen to feedback.
What do you dislike about the product?
The limited integrations with additional BI platforms and ticketing systems reduce the tool's flexibility and its ability to fit seamlessly into diverse workflows. Expanding these integrations would significantly enhance its versatility. Additionally, the UI filtering options could be more robust, enabling users to quickly identify and act on critical issues. Improving these areas would make the tool more efficient, adaptable, and user-friendly for a wider range of teams.
What problems is the product solving and how is that benefiting you?
The problem of achieving quick and effective data observability by providing seamless integration with dbt and an intuitive batch configuration system. It ensures end-to-end data lineage, including BI tools, which helps us trace data flows and identify issues efficiently.
A lean data observability platform
What do you like best about the product?
Flexibility of integration, especially with dbt models and relevant data warehouse adapters; a free CLI version that is sufficient to start with and allows for custom setup; Data Health score - a recent release, measuring data validity across 6 dimensions; column-lineage.
What do you dislike about the product?
The catalog solution is quite basic; I would still recommend investing in a separate, more developed data catalog solution. Some integrations (such as those with BI tools or GitHub) rely on private keys rather than newer, more public authentication methods (or at least I couldn't find any).
Adjust alerting behavior to prevent alert fatigue, as the default settings may not suit the use case.
Adjust alerting behavior to prevent alert fatigue, as the default settings may not suit the use case.
What problems is the product solving and how is that benefiting you?
Now having a single metric that serves as a proxy for data quality. I faced challenges in measuring the overall data quality posture, and it was an incident and time-based measurement that was cumbersome. Additionally, it displays the execution time of models, which is very helpful for historical analysis and optimization efforts. Column lineage is an excellent way to observe the impact of changed data, even if the fields were renamed; however, the search function per field or model is not intuitive.
A promising solution to enhance data quality
What do you like best about the product?
Elementary Cloud excels in ease of use and provides automated tests covering key aspects like volume, freshness, and schema. Configuring tests through the user interface simplifies collaboration and reduces technical complexity. The field-level data lineage is particularly valuable for assessing impacts and resolving incidents efficiently. Slack alerts are well-designed and configurable, making it easier to notify stakeholders and improve data reliability. Additionally, the integration with Looker adds significant value to data analysis workflows
What do you dislike about the product?
While it’s a robust tool, some processes, such as modifying tests and generating pull requests, could be more streamlined. Improved integration with documentation and a centralized view of defined tests would also be helpful. However, these minor drawbacks don’t detract much from an overall very positive experience
What problems is the product solving and how is that benefiting you?
Elementary Data solves data quality issues by automating tests, providing field-level lineage, and enabling easy test configuration via UI. It improves efficiency, collaboration, and trust in data through real-time alerts and better governance.
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