Coalesce

My main use case for Coalesce.io involves data transformation for the finance company that I have been working with.

A specific example of how I use Coalesce.io for data transformation in my finance company is that we start with the raw layer and engage in all sorts of data transformations, creating different nodes, layer by layer on top of each other, moving from the raw layer to the curated, report-specific views, which helps us adopt a data-driven approach to find the best suitable results.

Regarding my main use case, it is a basic one where we perform all the transformations such as deleting duplicate records, cleaning up transformations, and handling some complex queries, including breaking out the CTEs into separate nodes, which makes it easier to track and debug.

Coalesce.io's best features include its drag-and-drop approach, which is convenient for someone who is not very well-versed with coding. Although it can be hectic for people who have been coding for a while, overall the drag-and-drop functionality makes it easier as it eliminates the need to specify data types repeatedly as it traces back from where it began up to the curated layer, which is handled end-to-end.

Additionally, Coalesce.io is intelligent enough to understand the requirements and work on itself. I have also been using Coalesce.io AI, which is handy when creating new nodes, helping to save nodes, particularly for someone who prefers fewer clicks, where I think AI significantly aids in this sphere.

Coalesce.io AI helps me create new nodes or saves me time by allowing me to interface with it when working for a finance database. For instance, when many changes came in as the client wanted something different than what we planned, I would give the code to the AI and ask Coalesce.io to backtrack and fix it. While it works well for most node creation, when it comes to editing, it sometimes makes mistakes, but I find it really helpful for creating new tables and backing up data.

Coalesce.io has positively impacted my organization by improving workflow, productivity, and business outcomes. For example, the company has been doing well with any data transformations since this is a pretty new use case for them, where they were not very report-based before, but now they actively make use of the reports we provide. Coalesce.io allows some users to track bugs directly when developers are unavailable, making support activities easier for end users with basic SQL knowledge.

A specific outcome I can share is that for a report we created for the marketing team, they used to spend almost three days fixing data and creating their transformations using basic Excel. However, now it is a daily run report curated by data transformation within Coalesce.io, making it easy for them to track and update daily and saving almost two days of work hours for employees.

One improvement I would suggest for Coalesce.io is that it can be pretty slow at times. For example, when I make a change within Coalesce.io and click the debug button, the changes do not always reflect quickly, requiring me to click again to see the updates, which can be frustrating as a developer.

Additionally, while the AI can be helpful in creating nodes, it can sometimes place them unexpectedly, and undoing those changes does not always work as intended, which I consider a negative aspect of using Coalesce.io. I would emphasize that the speed could definitely be improved, and sometimes the drag-and-drop functionality is hectic, particularly when dragging a column rather than a table. It can go null or not pop up at all, and although Coalesce.io does identify if you want to join one table to another based on naming convention, occasionally it fails to recognize this, which is frustrating. Overall the features look good and only need these specific improvements.

I have been using Coalesce.io for almost one and a half years.

Regarding the accuracy and reliability of Coalesce.io's output, I am not entirely sure it is reliable. However, for a developer familiar with their data end-to-end, it is effective, but it is not very reliable for someone without a basic understanding of coding.

My advice for others looking into using Coalesce.io is that I would definitely recommend it for its user-friendly setup, which is manageable for business analysts who may not be very tech-savvy, making it easy for them to navigate. It is an excellent choice for companies needing a baseline setup and where users can take it from there, thus I see Coalesce.io as a strong option. I would rate this product a 7 out of 10.

My main use case for Coalesce.io is data transformation.

A specific example of how I use Coalesce.io for data transformation is that we have data coming from multiple sources, which resides in Snowflake. We read it, perform some transformations, merge data from multiple sources, and add it into sync tables.

Regarding my main use case and how I use Coalesce.io day-to-day, we use data vault architecture from Coalesce.io, where we have hubs, links, satellites, and other stage tables before moving the data to the final layer of tables.

Coalesce.io has positively impacted my organization by making development faster and keeping the logic clearer, so we do not have to see 1000-2000 lines of code every time. We can go to a specific table and see its logic.

The best features Coalesce.io offers include the stage layer of transformations where we have to do step-wise transformation, which is very helpful. Additionally, when we have all the data within Snowflake, it is very helpful as well. It is very easy to make changes once we complete the development.

The UI is what makes it easier to make changes after development, and version control is very helpful as well. We can directly commit the changes to Git.

One additional feature I would like to highlight is that when we have a lot of sources with similar end logic, we can collate all the source data and then apply the end logic at the end. If we want to add any column or new logic, we can directly add it in one place, which will be applied to all the data for all the sources.

Coalesce.io is very good overall.

I believe the editing should be a bit easier, or there should be a bulk edit option where a column can be added in one go in multiple places.

Sometimes a sub-graph or workflow becomes too heavy. For example, we can have 30-40 nodes or tables in one sub-graph, so sometimes that might be a bit slow or a bit much, but it is still acceptable.

I have been using Coalesce.io for two years.

My advice to others looking into using Coalesce.io is that first, the design needs to be thought out well, and many future things have to be taken into consideration before starting any development. It will be very helpful if we know what scaling we have to do further. I would rate this product an 8 overall.

My main use case for Coalesce.io typically involves building pipelines, as I was working in a data migration project where I source data from the source system and move it to the target view using intermediate stages such as stage nodes and dimension nodes.

There was one module for which I had to work, so I created sources for that module, including customer dimensions and raw data for customer sales. Once that data became available from the upstream process, our pipeline started executing, reading data from the raw sources, staging them in stage nodes, and moving it to the dimension node where I created type two dimensions, after which the data moved to the fact node. Once the data was available in the fact node, that data was consumed by the downstream team for reporting purposes.

I have covered my main use case with Coalesce.io.

The best features Coalesce.io offers include the user interface and column-level lineage, which allows me to add an extra column to upstream and downstream stages by clicking on one stage and selecting others to propagate that column, making it easy to manage lineage at the column level. Unlike DBT, which shows only job level or model level lineage, Coalesce.io provides a great column-level lineage feature.

Coalesce.io has positively impacted my organization by making it easy to build and maintain the pipeline, providing traceability, and enabling easy debugging at both the pipeline level and Snowflake level. If I create a node and declare the metadata, I can refer to a specific source, and any issue will be highlighted for fixing there. If there is also a SQL level issue, it is highlighted at the interface, making debugging easy.

Coalesce.io is compatible with Snowflake only, so it must be configured with other platforms such as Databricks and BigQuery, which is a limitation and a drawback.

Coalesce.io should work on the aspects I highlighted, particularly making it compatible with other popular platforms such as Databricks, BigQuery, and Redshift, among other tools.

I have been working in my current field for almost five years.

I chose a rating of seven out of ten because of its limitations with compatibility to platforms such as Databricks and BigQuery, which prevents me from giving it a perfect score. However, it is still a good tool, being easy to implement, maintain, and featuring a good user interface that requires no coding work.

If others are not much interested in writing code or SQL, then they should definitely consider Coalesce.io, which provides a good user interface that is easy to maintain and trace in case of a pipeline failure. With the column level lineage feature, backtracking errors and finding data propagation roots becomes easier, decreasing implementation time significantly. What previously took three months can now be done within four weeks.

Coalesce.io has helped me develop projects faster, as it is easy to implement and maintain, reducing execution time by thirty to forty percent, while also helping me design a modular approach.

I rate Coalesce.io a seven out of ten.

My main use case for Coalesce.io is creating flows, tables, and views that model dependencies from source to target. I created a flow from ADF to Snowflake, and from that Snowflake instance, I created flows for both the stage layer and target layer. I created curated and consumer layers to feed Power BI reports from the stage schema, which was built using the raw data vault model with tables and views.

Coalesce.io's primary value for me is creating the transformation layer. Using this capability, I created stage tables and target tables. This has allowed me to avoid creating stored procedures in Snowflake and managing process timing, which has helped reduce costs and dependencies. I create jobs in Coalesce.io, monitor my flows, and provide outputs to downstream consumers.

The best features Coalesce.io offers in my experience are metadata-driven code generation, column-level lineages, and user-defined node types with Git-native GitOps. I have used all of these features in Coalesce.io.

When using Coalesce.io with Snowflake API and CLS, I created SCD Type 2 mechanisms and implemented column-level lineage. This has been helpful for metadata-driven processing into Snowflake's processing layer. The Git-native driven features have also been beneficial.

Using Coalesce.io, I have reduced my transformation process and eliminated macros that were previously used, creating reusable programs that are helpful for other projects and models. This has reduced code dependencies between teams. I have saved approximately three hundred to four hundred dollars per day in processing costs.

Coalesce.io could be improved from the stage layer perspective. It would be helpful to have the ability to drag code and write tables with dependency lineages displayed, especially to show which objects are involved in downstream dependencies. This would be beneficial for business analyst teams and data scientist teams so they can analyze stakeholders and customers can see the relevant data without needing to involve multiple teams.

I have been using Coalesce.io for four years and onwards.

Coalesce.io is stable and has very useful lineage capabilities.

The scalability of Coalesce.io is based on data volume handling. Data volumes can be huge depending on different countries' data sources. We are handling approximately one to two terabytes of data processing. Sometimes processing takes time, but we consider stateless services, data protection, caching, query response time, and data latency when using Coalesce.io.

Customer support has been very good for us. Whenever we receive any issues, we reach out by submitting tickets, and the Coalesce.io team approaches our requests by gathering updates and resolving issues as needed. I would rate customer support ten out of ten.

Previously, I used Snowflake-based stored procedures, which took a long time and consumed significant costs in terms of storage and processing. I used Coalesce.io to lineage data from different databases and servers for transformation and then transformed that data into Snowflake as the destination. This approach reduced processing time, costs, and allowed for reusability.

I previously used Data Build Tool as well, which was cost-based depending on project requirements from management. After that, I switched to Coalesce.io for new projects. Since making this switch, I have seen improvements in lineages, dependencies, and formulas needed for my work.

Licensing costs are based on the region. We are using the US region, so charges are determined accordingly.

Because I work with code writing, Coalesce.io has helped me reduce code and create reusable components for other projects. I would rate this capability ten out of ten.

Regarding Coalesce.io's governance and security features, these are good offerings. Hiding columns that are not required for user visualization, implementing roles, and applying masking policies are features that can interact with source systems effectively.

Regarding Coalesce.io's accuracy and reliability of output, this is based on service packs and new modules that find the accuracy of the data.

Coalesce.io is deployed on a private cloud environment that we use exclusively for our data at a secure level. We are using a built-in AI data catalog that tracks lineages across databases, applies data quality measures, and implements data platform-specific logic for columns. These capabilities need to be cloud-based and are running in our private environment.

We are using Snowflake for our private cloud deployment. I would give this review an overall rating of ten out of ten.

My main use case for Coalesce.io is ETL transformations. I use Coalesce.io to process my bronze layer data into silver and gold layer tables.

The best features that Coalesce.io offers include a very rigid ETL framework that ensures all developers use the same pattern. Coalesce.io has positively impacted my organization because it solves many of the problems of SSIS.

I cannot tell you more about which specific problems of SSIS Coalesce.io solves for me. I do not know that Coalesce.io can be improved. I have no opinion regarding Coalesce.io's AI capabilities, governance, and security.

I have had mixed success with Coalesce.io's AI features regarding its accuracy and reliability of output; sometimes it performs correctly, and sometimes it does not.

I have been using Coalesce.io for four months.

I do not want to add anything else about the features; I do not find anything particularly helpful or something that stands out yet. Coalesce.io is the type of tool I would provide to business analysts for their ad hoc and prototyping purposes. I think it is well-suited for that audience because it provides a rigid structure that will minimize the tech debt that gets produced.

I would rate Coalesce.io overall a six on a scale of one to ten. I choose a six because it is only good for a very limited set of use cases.

My main use case for Coalesce.io was with a healthcare provider client whose team was not particularly tech savvy, so we wanted something easy to use. I was debating between two options: Coalesce.io and DBT. Coalesce.io had a GUI interface where I could drag and drop objects and build easily, so I was exploring it for building data vault models.

For that specific use case, I was able to accomplish what I was trying to do during the demo, but we encountered certain scenarios such as post-hooks, update statements, edge cases, or particular SQL query modifications and custom cases that could not be implemented through the GUI interface. Additionally, jobs that were interdependent on each other and orchestration features were not available at that time, which made us reconsider our options and ultimately go with DBT since it was open source and DBT Cloud had all the features we wanted.

I used Coalesce.io to build a data vault model by reaching out to the Coalesce.io team when the initial account provided to us did not have access to packages used for data vault modeling. They enabled a package specific for data vault modeling within the account. In that specific scenario, we wanted to replicate or build a small demo for the client to show them how we could leverage Coalesce.io to build data vault models. In data vault, we have a use case of needing to build multiple objects for creating dimensions and facts with an intermediate layer of hubs, links, and satellites. We used the package provided by the Coalesce.io team, which streamlined the process of creating these objects and provided a template that had all these columns in a particular format. We only needed to enter the column names and drag and drop to create our final model, which was very convenient. However, there were some problems and nuances due to which we went with DBT.

Some of the best features that Coalesce.io offers include the easy graphical interface where I can see the data flow, lineage graph, and how the data is moving from the source to the end target table. It has an easy drag and drop interface so I do not need to write SQL queries. Especially for my use case of data vault modeling, which required a lot of coding, using Coalesce.io would reduce the development process and I could quickly develop models.

Coalesce.io has positively impacted my organization by being a tool that can be leveraged by a non-technical team, especially business analysts who are working on bringing data from different sources. This is particularly beneficial for those who do not prefer coding or want to build their data models as quickly as possible. That is what I would consider to be the best use case for any organization.

Coalesce.io can be improved by making it handle more edge cases and providing better documentation. When I was initially working and trying to find information, there was not much available on their official website. Documentation with screenshots and steps on how to leverage this tool should be developed. An online community was not present either, which would help users deal with any issues or errors they are facing or ask developmental questions related to edge cases that are not generally used. I believe those are some of the things that can help the community of developers using Coalesce.io.

I have been using Coalesce.io for two months for a demo in my previous company.

I cannot think of anything else to add about the features since I used it only for two months, and it has been a while since I used it.

I saw measurable benefits during my demo since I found that the general impression was that I was developing much faster, and the development time was saved significantly. Work that could take me a day could be done in an hour or so, so I believe time saving is the most important factor.

I did not explore Coalesce.io's AI capabilities much, so I cannot provide an opinion on its governance and security.

While I was using Coalesce.io, it was a very new tool and I did not come across its AI capabilities, because it was mostly an open account I was using and it did not have those capabilities at that point in time, which was around last year, probably in 2024. So it did not have many capabilities at that point in time.

I would rate this product a six out of ten.

I have used Coalesce.io for one year and twelve months, setting up a data pipeline for the student survey system at the University of Sydney.

The main use case for Coalesce.io is the developed data pipeline that was used by Coalesce.

We had an Oracle database that I connected, and for orchestration, we used Control-M. For the pipeline development in terms of setting up the workflows, we used Coalesce.io.

The best features Coalesce.io offers include lineage and ease of use, allowing us to easily create workflows in both the dev, test, and prod environments, and we were able to replicate those fairly quickly and create those pipelines much more easily.

The lineage feature helped our team significantly because once we completed the project and it was given off to the data engineering team, they were able to follow the workflows, which was a very big plus. We did not have to do an extensive amount of documentation, and all of that was embedded in the workflows and very easy to provide information to the data engineering team for business as usual.

Coalesce.io has positively impacted my organization by allowing us rapid build of data pipelines so that workflows and deploying the solution much more quickly increased the velocity of implementation.

Some of the improvements for Coalesce.io could be related to the artificial intelligence feature, perhaps some sort of a wrapper around it.

If the AI-driven feature is embedded, it can guide workflows and make the pipeline development even much faster, acting as an agent that assists people to develop in hours rather than in months. That would be a great improvement.

I have used Coalesce.io for one year and twelve months, setting up a data pipeline for the student survey system at the University of Sydney.

My advice for others looking into using Coalesce.io is to give it a go and compare it against other pipeline tools such as DBT, Fabric, or Databricks to see how it stacks up. My review rating for Coalesce.io is eight out of ten.