Azure Data Factory vs Equalum comparison

"We have been using drivers to connect to various data sets and consume data."

"We have found the bulk load feature very valuable."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"The valuable feature of Azure Data Factory is its integration capability, as it goes well with other components of Microsoft Azure."

"The flexibility that Azure Data Factory offers is great."

"The user interface is very good. It makes me feel very comfortable when I am using the tool."

"It's a really powerful platform in terms of the combination of technologies they've developed and integrated together, out-of-the-box. The combination of Kafka and Spark is, we believe, quite unique, combined with CDC capabilities. And then, of course, there are the performance aspects. As an overall package, it's a very powerful data integration, migration, and replication tool."

"I found two features in Equalum that I consider the most valuable. One is that Equalum is a no-code tool. You can do your activities on its graphical interface, which doesn't require complex knowledge of extracting, changing, or loading data. Another feature of Equalum that I like the most is that it monitors the data transfers and tells you if there's any issue so that you can quickly check and correct it. Equalum also tells you where the problem lies, for example, if it's a hardware or communication issue."

"Equalum has resulted in system performance improvements in our organization. Now, I am ingressing data off of multiple S3 sources, doing data processing, and formatting a schema. This would usually take me a couple of days, but now it takes me hours."

"It's got it all, from end-to-end. It's the glue. There are a lot of other products out there, good products, but there's always a little bit of something missing from the other products. Equalum did its research well and understood the requirements of large enterprise and governments in terms of one tool to rule them all, from a data migration integration perspective."

"Equalum is real-time. If you are moving from an overnight process to a real-time process, there is always a difference in what reports and analytics show compared to what our operational system shows. Some of our organizations, especially finance, don't want those differences to be shown. Therefore, going to a real-time environment makes the data in one place match the data in another place. Data accuracy is almost instantaneous with this tool."

"The main impact for Oracle LogMiner is the performance. Performance is drastically reduced if you use the solution’s Oracle Binary Log Parser. So, if we have 60 million records, initially it used to take a minute. Now, it takes a second to do synchronization from the source and target tables."

"All our architectural use cases are on a single platform, not multiple platforms. You don't have to dump into different modules because it is the same module everywhere."

"Equalum provides a single platform for core architectural use cases, including CDC replication, streaming ETL, and batch ETL. That is important to our clients because there is no other single-focus product that covers these areas in that much detail, and with this many features on the platform. The fact that they are single-minded and focused on CDC and ETL makes this such a rich solution. Other solutions cover these things a little bit in their multi-function products, but they don't go as deep."

"For some of the data, there were some issues with data mapping. Some of the error messages were a little bit foggy. There could be more of a quick start guide or some inline examples. The documentation could be better."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

"Azure Data Factory's pricing in terms of utilization could be improved."

"The pricing scheme is very complex and difficult to understand."

"The pricing model should be more transparent and available online."

"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."

"Their UI could use some work. Also, they could make it just a little faster to get around their user interface. It could be a bit more intuitive with things like keyboard shortcuts."

"I should be able to see only my project versus somebody else's garbage. That is something that would be good in future. Right now, the security is by tenants, but I would like to have it by project, e.g., this project has this source and flows in these streams, and I have access to this on this site."

"There is not enough proven integration with other vendors. That is what needs to be worked on. Equalum hasn't tested anything between vendors, which worries our clients. We need more proven vendor integration. It is an expensive product and it needs to support a multi-vendor approach."

"Right now, they have a good notification system, but it is in bulk. For example, if I have five projects running and I put a notification, the notification comes back to me for all five projects. I would like the notification to come back only for one project."

"If you need to use the basic features of Equalum, for example, you don't even need data integration, then many competitors in the market can give you basic features. For instance, if you need batch ETL, you can pick among solutions in the market that have been around longer than Equalum. What needs improvement in Equalum is replication, as it could be faster. Equalum also needs better integration with specific databases such as Oracle and Microsoft SQL Server."

"The deployment of their flows needs improvement. It doesn't work with a typical Git branching and CI/CD deployment strategy."

"They need to expand their capabilities in some of the targets, as well as source connectors, and native connectors for a number of large data sources and databases. That's a huge challenge for every company in this area, not just Equalum."

"The solution's pricing is competitive."

"ADF is cheaper compared to AWS."

"The pricing model is based on usage and is not cheap."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"I would not say that this product is overly expensive."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

"Equalum licensing costs vary, but I won't be able to give information on its fees."

"They have a very simple approach to licensing. They don't get tied up with different types of connectivity to different databases. If you need more connectors or if you need more CPU, you just add on. It's component-based pricing."

"Equalum is rather expensive compared to its competitors. So, you have to make up that cost in time savings, and we usually do that. If we are saving money, it is because we are reducing our development time."

"Equalum was reasonably priced. It is not like those million dollar tools, such as Informatica."

"As soon as you have more than six users, Equalum is lower in cost [than Talend] and if the group gets bigger, it's quite a big delta. If more users want to use it, you don't end up with an increase in licensing costs, so that makes it very easy. And if you need more licenses or more sources, it's a very simple upgrade methodology."