Equalum Reviews

Equalum is used to take legacy data, siloed data and information in the enterprise, and integrate it into a consolidated database that is then used, for the most part, for data transaction, whether it's an actual transaction within the enterprise itself, or a BI dashboard. BI dashboards and analytics are a big area, but overall the main use case is for large data integration across disparate data sources.

The way it's deployed is a combination of on-premises and cloud. It's mainly on-prem, but in Japan and Korea, the adoption of cloud is definitely nowhere near the same as in North America or even Europe. So most of it is deployed on-prem, but there is some hybrid connectivity, in terms of targets being in the cloud and legacy sources being on-prem. Next-gen infrastructure is hosted in the cloud, either by a global partner like AWS, or by their own data infrastructure, in more of a hosted scenario. 

The other model that we're looking at with a couple of partners is a managed service model, whereby Equalum is hosted in the partner's cloud and they provide multi-tenancy for their mainly SME customers.

The advantage of having a no-code UI, with Kafka and Spark fully managed in the platform engine, comes down to human resources. The number of engineers and that it takes to both develop something of this nature yourself, and then maintain it, is significant. It's not easy. Even if you do have a boatload of engineers and a homegrown-type of capability for looking at open source Spark or Kafka, trying to integrate them and trying to integrate multiple other open source technologies in one platform is a major challenge. Purely from the point of view of getting up to speed, out-of-the-box, within 30 minutes you can literally spin up an instance of Equalum. Anybody who tries to deal with Kafka as well as Spark and then tries to use the technologies, is quite blown away by how quick and easy it is to get moving. You can realize ROI much faster with Equalum. 

Equalum allows a lot of staff to focus on what they know and what they can do, versus having to learn something from scratch, and that lowers the overall risk in a project. Moving the  risk profile is one of the key benefits as well. 

Another benefit is that it frees staff to focus on the consultation aspects, the data workflow and mapping out of an organizations' data, and understanding what one can do with that data. It enables them to focus on the real value, instead of getting into the nitty-gritty of the technology. The added value is immediate for companies deploying this tool versus having to develop their own and maintain their own.

It gives you the ability to handle those data transformations and migrations on-the-fly. It would take a huge effort if you had to do that manually or develop your own tool. The overall legacy situation of databases in most organizations does not allow for real-time crunching of information. Without Equalum, it would be impossible to implement those kinds of data-related processes, unless you have a tool that can really be performant at that level and that speed. That's what this solution is going on: data transformation inside of an enterprise and getting it to go from legacy databases to future database capabilities, from batch to real-time.

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. We've looked at a number of other products but Equalum, from a technology perspective, comes out head and shoulders above the rest. We tend to focus mainly on trying to move the legacy businesses here in Japan, which are very slow in moving, and in Korea, from batch and micro-batch to real-time. The combination of those technologies that I just mentioned is really powerful.

It also stands out, very much so, in terms of its ease of use. It's super-simple to use. It has its own Excel-type language, so as long as you know how to use Excel, in terms of data transformation, you can use this tool. And we're talking about being able to do massive data integration and transformation. And that's not referring to the drag-and-drop capabilities, which are for people who have zero skills. Even for them it's that easy. But if somebody does want to do some customization, it has a CLI that's based on the solution's own CLI code transformations, which are as easy as an Excel-type of command. And they've got all the documentation for that.

For consultants, it's a dream tool. A large consultancy practice providing services to large enterprises can make a boatload of money from consultants spending hours and hours developing workflows and actually implementing them right away. And they could then copy those workflows across organizations, or inside the same organization. So you create a drag-and-drop scenario once, or a CLI once, and you could use that in multiple situations. It's very much a powerful tool from a number of angles, but ease of use is definitely one of them.

In addition, it's a single platform for core architectural use cases: CDC replication, streaming ETL, and batch ETL. It also has micro-batch. 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.

The speed of data delivery is super-fast. In some cases, when you look at the timestamp of data in the target versus the source, it's down to the hundredths of a second and it's exactly the same number. That's at the highest level, but it's super-fast. It's lightning fast in terms of its ability to handle data.

There are areas they can do better in, like most software companies that are still relatively young. 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.

If I had the wherewithal to create a tool that could allow for all that connectivity, it would be massive, out-of-the-box. There are all the updates every month. An open source changes constantly, so compatibility for these sources or targets is not easy. And a lot of targets are proprietary and they actually don't want you to connect with them in real time. They want to keep that connectivity for their own competitive tool.

What happens is that a customer will say, "Okay, I've got Oracle, and I've got MariaDB, and I've got SQL Server over here, and I've got something else over there. And I want to aggregate that, and put it into Google Cloud Platform." Having connectors to all of those is extremely difficult, as is maintaining them.

So there are major challenges to keeping connectivity to those data sources, especially at a CDC level, because you've got to maintain your connectors. And every change that's made with a new version that comes out means they've got to upgrade their version of the connector. It's a real challenge in the industry. But one good thing about Equalum is that they're up for the challenge. If there's a customer opportunity, they will develop and make sure that they update a connector to meet the needs of the customer. They'll also look at custom development of connectors, based on the customer opportunity. It's a work in progress. Everybody in the space is in the same boat. And it's not just ETL tools. It's everybody in the Big Data space. It's a challenge.

The other area for improvement, for Equalum, is their documentation of the product. But that comes with being a certain size and having a marketing team of 30 or 40 people and growing as an organization. They're getting there and I believe they know what the deficiencies are. Maintaining and driving a channel business, like Equalum is doing, is really quite a different business model than the direct-sales model. It requires a tremendous amount of documentation, marketing information, and educational information. It's not easy.

We've been involved with Equalum for about 18 months.

We're partners with Equalum. We resell it into Japan and Korea. The model in North Asia is a reseller/channel model. The majority of our activity is signing resellers and managing the channels for Equalum into markets. We're like an extension of their business, providing them with market entry into North Asia.

We've been reselling every version from that day, 18 months ago, up until now. We've been up to version 2.23, which is the most recent. We're reselling the latest version and installing it on channel partners' technology infrastructure. We tend to use the most recent version, as long as there are no major bugs. So far, that's been the case. We've been installing the most recent product at that moment, without any issues, and the reseller is then using that for internal PoCs or other ones.

The stability of Equalum is very good. We deal with double-byte character sets in Japan, so there are little things here and there to deal with when new versions come, but there are basically no issues at all. It's very solid. It's running in multi-billion dollar organizations around the world. I haven't heard any complaints at all. The stability factor seems to be very good.

The scalability is the beauty of this product. It scales both vertically and horizontally. It provides ease of scalability. You've got the ability to add a CPU horizontally, in terms of its hardware servers, and you've got the ability to add additional nodes in a cluster, as well. It's really good for continuing to build larger and larger clusters to handle larger and larger datasets. It does require manual intervention to scale horizontally. Vertically it is literally just a matter of adding hardware to the rack, but horizontal scaling does take some professional services. It's not like pressing a button and, presto. It's not a cloud-based, AWS-type of environment at this time. But that's fine because sometimes, with this kind of data and these types of customer environments, you definitely want to be able to understand what you're dealing with. You've got hundreds, if not thousands, of workflows. It's not something as simple as just clicking a button.

The scalability is a really interesting component and is a reason we are very interested in working with Equalum. It's easy to expand. Obviously, we're in the business to generate revenue. So if we can get adoption of the tool inside an organization, and they continue to add more and more data to the overall infrastructure's architecture, all we need to do is expand the scalability of the solution and we generate additional revenue.

Their technical support is fantastic. They have support in Houston and Tel Aviv. We mainly deal with Tel Aviv because it's a much better time zone for us here in Japan and Korea. We use Slack with them. And I was just talking with them this morning about a new support portal they've just released, that we're going to have full access to. 

We carry Equalum business cards and Equalum email addresses. We really are like an extension of the business in Asia, even though we're a separate entity. So when we communicate with the channel partners, we use Equalum email addresses and in doing so we also then answer technical support requests. Our own technical team is going to be integrated into the support portal at some point very soon so that our teams in Korea and Japan can handle questions coming from the customers in the language of the country. We'll be a second line of support to our resellers and, if necessary, of course, it's easy to escalate to the third line support in the U.S. or in Tel Aviv, 24/7.

We looked at a few, but we haven't actually worked with another ETL vendor. We had good relations with a number, but we never actually took on an ETL tool in the past.

We dealt in other solution offerings and BI, real-time business intelligence and database infrastructure products, and we did have some interaction with them and we weren't really impressed. There wasn't anything that could really keep up with the kind of data speeds we were trying to process. I came across Equalum at a Big Data event back in 2019. I walked up to their booth and started chatting with Moti who is the head of global sales. At the time, we were looking for something along these lines to combine with our current offering.

This is the first time we have taken on a tool of this nature and it has become the core of our sales approach. We lead with Equalum because you first need to get access to the data. When you transform the data, you can land the data in a database, so we sell another database product. And after we've landed the data in the database target, we then connect it with business analytics and AI tools or platforms, which we also sell. It has become the core, the starting point, of all of our sales activities.

The difficulty level of the initial setup depends on the skills of the engineer who's involved. It now takes our guys a maximum of 30 minutes to deploy it, but the first time they did it, it took a little bit of time to go through it and to understand how it's done. Now, it's really easy, but that first time requires a little bit of hand holding with Equalum's engineers. It's relatively easy once you go through it the first time.

We don't do implementations at the customer location. That's what our partners do. But we support that and we help them with that. Getting the software up and running has been pretty easy. The challenges are around connecting to the database of the customers and getting through VPNs and the like. That's the challenge of getting into any enterprise infrastructure.

In terms of savings based on insights enabled through Equalum's streaming ETL, we're not a customer, so we haven't seen the savings. And gathering ROI on these kinds of topics is always difficult, even if you are talking to a customer. But it all comes down to the cost of the technology and the cost of human resources to develop it, maintain it, and manage it, and the project it's being used for. But those savings are certainly the reason our resellers and their customers are looking at acquiring the tool.

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. It's a really easy model for us and, so far, it's worked well. 

The actual pricing is relative. You talk to some people and they say, "Oh, it's too expensive." And then you talk to other people and they say, "Whoa, that's cheap." It's a case-by-case issue.

For the most part, when we go up against local CDC products, we're always priced higher, but when we go up against big, global ETL vendors, we're priced lower. It comes down to what the customer needs. Is what we have overkill for them? Do they really just need something smaller and less expensive? If pricing is a problem, then we're probably in the wrong game or talking to the wrong people.

Overall, we feel that the pricing is reasonable. We just hope that they don't increase the price too much going forward.

We had looked at the likes of Talend, and Informatica, of course. There are so many local Japanese and Korean CDC products. We didn't go really heavily in-depth, looking into that sector because as soon as we saw and understood what Equalum had to offer, and we liked the people that we were dealing with and they liked our approach, they were also interested. Frankly, we didn't see anybody that had the same combination of technologies and that they weren't North Asia.

There are a lot of functionalities that are similar to other products, such as drag-and-drop capabilities and workflows, when you get into the nitty-gritty, but it's the overall package in one tool that is very powerful.

And in terms of consolidating and reducing the number of tools you use, if you look at Informatica, you need four products from Informatica to do what the one product from Equalum does. There's serious consolidation. There are definitely a lot of ETL products that don't do CDC, so you have to buy two products. The whole concept of having one tool handle most of the requirements is a strong selling point of Equalum, and it's good for customer adoption. The side note to that is that if customers have already spent millions of dollars with their current tool, it becomes difficult for them to adopt Equalum. 

It's a great tool to use in any data transformation opportunity, especially focusing on real-time. The word "batch" should never be used in an organization, going forward. I know it's useful and it has its use cases and there are situations where it's helpful. Batch is a form of history and it will probably always be there until that legacy finally disappears. But, overall, if anybody wants to look at migrating and transforming their overall data into a real-time enterprise, there's not a better tool in the market today, in terms of its performance, price, usability, and support. Those four things are the reasons we're selling it.

The biggest thing I have learned from working with Equalum is how difficult it is to actually manage your own Spark and Kafka clusters, and to process data at speed. It's difficult to have the infrastructure and all of the other software to combine everything. In some organizations the effort takes hundreds of people, depending on the size of the enterprise, how much data is involved, and the overall system architecture. What opened my eyes was the fact that, with this tool, you have the ability to alleviate all of the potential headaches associated with developing or maintaining your own clusters of these open source products. 

Large, well-known Asian companies literally have over 1,000 engineers dedicated to managing open source clusters. Those companies are wasting so much money, effort, and brain power by having their engineers focused on managing these really basic things, when they could be deploying a third-party tool like Equalum. They could be letting their engineers drive larger revenue opportunities with more value added around things like what to do with the data, and how to manage the data and the data flow. They could create real value from integrating data from disparate data sources, instead of focusing on the minutia, such as maintaining clusters. The mindset of some of these Japanese and Korean companies is back in 1995. That's the reality. It's a challenge because getting them to change their older business approach and ideology is always difficult. But that is what opened my eyes, the fact that this tool can literally alleviate thousands of people doing a job that they don't need to be doing.

As for the data quality that results from using the tool, it's dependent upon the person who's using the tool. If you are able to transform the data and take the information you want out of it, then it can help your data quality. You can clean up the data and land it in whatever type of structure you would like. If you know what you're doing, you can create really high-quality data that is specific to the needs of the organization. If you don't know what you're doing, and you don't know how to use the tool, you can create more problems. But for the most part, it does allow for data cleansing and the ability to create higher-quality data.

When it comes to Oracle Binary Log Parser as opposed to LogMiner, my understanding is that it has higher performance capabilities in terms of transacting data. It allows for the performance of data being migrated and/or replicated, as well as the transaction processing that takes place in the database, to happen in a much more performant way. The ability to handle those types of binary log requirements is really important to get the performance necessary. Equalum has a partnership with Oracle. Oracle seems to be very open and very positive about the partnership. Although we are replacing a lot of the Oracle GoldenGate legacy products, they don't seem to be too worried because we're connecting to their databases still, and we're landing data, in some cases, into their cloud-based data infrastructure as well. There's definitely a lot of power in the relationship between Equalum and Oracle. It's a very strong benefit for both the product and the company.

From a business point of view, as a partner, we are continuing to educate and to bring resellers up to speed about the new kid on the block. It's always difficult being a new product in these markets because these markets are very risk-aversive and they don't really like small companies. They prefer to deal with large companies, even though the large companies' technologies are kind of outdated. It's a challenge for us to try to educate and to make them aware that their risk is going to be low. That's what's important at the end of the day: It's about lowering risk for partners. If adopting a new technology increases their risk, even if the performance of the technology is better, they won't go along with it, which is a very different mindset to North America, in my opinion.

Overall, we sell a combination of multiple products in the real-time data and Big Data AI space, and Equalum is the core of our offering. It literally does provide the plumbing to the house. And once you get the plumbing installed, it's generally going to be there for a long time. As long as it fits performance and as long as it continues to evolve and adapt, most companies are really happy to keep it.

We were looking for some ETL tools.

We were writing data from one source to target using 5,000 websites. No matter how genetic you make it, it is never genetic. Also, some things will change requirements-wise. A tool should be easy for a support group to support. They do not need to have access to Linux or wherever Python scripts are running to figure out how to do the logging. Now, I am just opening a box to them, and saying, "This is what you need to do in Equalum." They have to just point and click, which is operational efficiency. It increased my efficiency by 80 percent by wrapping things around it, e.g., the callable API from my application. It really helps with building, support, and monitoring. It is all in the UI.

It has improved our data accuracy. It tells you where things are not matching. For example, bad dates were coming in and the target database would not accept this format. So, Equalum will tell me if there is a problem over there. For error logging and error messaging, it is very efficient. It tells you what the problem is, e.g., your data type is not long enough on the targets. The logging is efficient, very detailed, and will also tell you where the problem is. You can fix the data, transform it, or change the target to accept that type of data. The accuracy is 100 percent. I have not seen any data anomalies.

I do batch loads and write them to temporary/work/staging tables. From there, I want to write them to the real table. When I go over the network with a million letters, if I'm writing to a table by deleting the data and writing it again, that might take two to three minutes. My data will disappear for that time. However, if I am writing from a staging table to another table in the database, like MemSQL, it takes only a few seconds for me to write within MemSQL itself. So, my data disappearing is minimized. For that, I am required to follow a procedure to move the data from the staging table to the final table, and they added that functionality for us by integrating with other database technology and functions. This is one example of integrating other things into the tool. 

Their performance monitoring (how things are flowing) is very visual, if something is failing, you can see it in there at the higher level, e.g., your sources are down, your agent is down, or your flow is not running. All those kinds of things are very visual. You just log into the tool and can see what is happening. 

The alerting system is getting better with every release.

It takes me an hour to transition the solution's knowledge to somebody else. It is really efficient that way. I haven't seen any complications.

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.

It is a self-managed, self-healing system. For example, I have been getting alerts on CPU usage. They say, "CPU usage is high." Then, it sends me a warning or critical alert within seconds so I can see that it has been resolved. For example, if the database goes down, then it stops at that point, keeps on trying until the database comes up, and begins to heal itself. So, it is self-recovering and self-healing. It is the same for the target. If the target goes down, it sends you an alert saying, "The target has gone down." I don't worry about it. I can ignore the alert, because when Target comes back, it starts all over again. I really like self-recovering and self-healing because I don't need to take many actions.

Initially, I wanted the incremental data loss too and batch. So, they put those together very fast for our security. Initially, it was very basic, then they enhanced it to the level that we wanted it. So, they came back with a solution quickly. Alerting is another feature that they put together, but we did not ask for that.

In multi-tenant architecture, if I am in finance and another department is on the operations' side, then we don't have to go into each other's area. We can have our own separation of products, which is pretty cool.

When we bought the tool, some of the features were missing, but we knew the power of the ETL was very good. 

Initially, I wanted scheduling within the tool itself, which is not there. However, I am using another open source software scheduling tool, Rundeck, which calls the Equalum APIs and runs them on the Equalum server. That was a workable solution for me to schedule the data loads. I wanted something with a UI interface where I could schedule within the tool, which is an improvement point for them. Right now, I have a workaround. 

For any application that you start, if it doesn't have a feature but is integratable with other applications, then it is a good tool. We are working with DB2, and there are some roadblocks there for us, but we are working through those. 

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. 

Something not in the tool is a CLI interface. The interface is not open to everybody. It is very restricted to admins, a DBA, like me. 

I have been using it for two and a half to three years.

Knock on wood, it doesn't break down that much unless the network goes down, and there is nothing you can do about that. So, I have not seen any problems so far.

They do different loads now:

1. Direct source
2. Others go to Kafka Architecture and keep on getting the data from there. 

Kafka Architecture also provides stability.

We started with a three-node cluster. As we are growing, we are seeing so many flows coming through. We are hoping to extend it, maybe creating another cluster or set of clusters. When it comes to scalability, we haven't done it yet. We have so many projects in there with a lot of data going back and forth. However, we are thinking about it as we grow. It should not be an issue.

On my team, we are only two to three people who do streams and loading for their own projects and systems. I have one DBA who helps me out on the SQL Server side. We have 10 divisions, and in those 10 divisions, I have the same flow names and table names. Everything is the same, except the schema is different. They gave us the specs to copy over, so I have saved time. So, whenever a new division comes in, we just copy over and replicate. This creates the flows and streams from the background on the command line interface.

It is being used for around seven projects: document management system data, financial data, and sending data to other systems or different projects.

Their technical support is amazing. They are on Slack all the time. We check with them and send messages. They have this site where you can see the tickets. For example, if a flow keeps on failing, then I just send it to them, asking, "It is failing. What is the reason?" Most of the time they can come up with the error message regarding the issue, then either we can fix it on our side or they fix it on their side by finding the issue. Their support is 24/7.

They are very technical, even the customer support who is not just taking your requests, and saying, "Let me go back to the technical team, then I'll come back to you." They are the point person who knows everything about the tool technically, in and out. If they can't, then it goes to the technical team. That is the good part.

The way that they are growing is pretty efficient for us. They come and ask, "What else do you want?" Then, we tell them, "This is what we want." Initially, for example, security was not there, but they developed a patch for us, because that was a concern. Also, they develop alerts very fast. 

I keep on sending them issues every now and then. They are a very smart group of people and developers who know what they are doing, which is a good thing. They know if somebody is asking for something that it is a good collaboration for them.

I have extensive experience with ETL tools, starting from Oracle Warehouse Builder to Informatica. So, we were looking for something that can do change data capture (CDC) for us. 

Previously, we did not have an ETL tool. 

The ETL deployments were very straightforward.

The PoC was good. The changes came in right away. Then, we could start using the tool, which was installed on our system.

It is not that bad to set up a flow from a table. If I had to copy 10 tables from a source to a target, then I would have to prepare 10 flows. So, they came up with something called schema Replication Groups. So, I can go in a schema, and say, "I want to copy over that application group for 10 tables." Then, it will create the steps on the target, tables, etc. I can modify those tables, if I want to, and map them again. This makes it more efficient for you to copy data to multiple tables at that time .

I am finding it very efficient for my team. When it comes to device usage, people need to move from the old architecture to new architecture, which is a big effort, and that will take time. We like the solution, but we cannot just stop operations which are happening and move onto this tool. Eventually, that is a direction that we will go.

With a very small footprint, all we had to do is put the hardware together for them. They installed everything for us, which was very convenient for us.

They did the deployment overnight. We didn't do it. We just had to set up the servers, authorizations of the server, and the three-node cluster. After that, they took it over. So, they did that stage first, then they did production after that.

When they came, they did CDC for us, but usually requirements and even more than CDC. For example, if you are in an old lab type of environment, CDC is not the most efficient solution. You want to do some queries based on certain criteria to get incremental loads. That is something that they developed for us pretty quickly, within a month or so. 

Equalum has access to our structure. They upgrade and maintain the solution for us.

Equalum team handles the deployment completely for us. Once they have deployed it to staging, we do some testing, which requires a day or so from us. We want to make sure before we approve it for production that everything works in staging. 

Cost-savings-wise, if I had to do all the Python scripting and using libraries myself, then there would be so much research involved. I might need a few more developers to do it. Now, with Equalum, one person can just keep on creating streams and loading.

It does save you time and money in so many ways at the end of the day. Equalum gave us a solution to copy over and change a few things here and there, instead of creating and developing everything each time a new division joins the company. 

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

If you want to buy it, watch Equalum's YouTube videos first.

We tried a few other ones before we got into Equalum, like Striim and StreamSets. The reason that we were looking at small companies or products, which are not too expensive, was because we adapted some technology called MemSQL. This is a database technology, and we were trying different products with smaller vendors or product owners who were willing to work with us to change their tools to step into our organization. So, we tried that, but the problem was we were getting data from Oracle to MemSQL, and those other tools could not perform on CDC from Oracle. It was super slow because they are not mature yet. So, we tried that, then left it because it was not working for us. 

There were some solutions that we didn't want to touch, like Informatica, which are enormously expensive. You had to really plan, budget, and get that approved. 

We wanted a small solution to just get more data over here and there. I am still writing the scripts left and right. Maintaining tests was another issue. The scripts are open too, where people can access them. If they get broken, you never know who broke it. That manageability is a big thing for me. 

Equalum was another one tool that I found. As soon as we contacted Equalum, they wanted to come and do the free PoC for us, going right to MemSQL from Oracle. So, they came for a week, staying online. They worked with our system, proving the solution will work for us.

Equalum's performance and UI were good at the time of evaluation. Other products did not have such an efficient, good UI. There were quite a few things which impressed our management and directors of operations. Everybody thought this was the tool for us to try.

Other tools were like a black box for me, which was not the case with Equalum.

Go for it. It is a good tool. They are growing. Hop in now and take advantage of their pricing. The tool is worth it

It is a very intuitive tool. You don't have to do much in it, just map the things and it works.

I can use Equalum's API in other tools. 

They have implemented MongoDB.

Initially, they had the Oracle CDC using Oracle LogMiner. Then, they came out with Oracle Binary Log Parser, which was super expensive: Same as Informatica. They were charging for everything, even the PoC. At that time, we were saying, "Equalum, you should have binary reads too." However, when you have a scope of a project for a growing product, you have to prioritize things. 

They are now coming out with the latest version Oracle Binary Log Parser, which they installed for us. The next version will be even better. It has very good collaboration with their clients. It is not just Oracle Database features that they are putting in. I think pretty much every other client gives them requests, then they put them in the priority list and they keep on growing with them. 

They just introduced Oracle Binary Log Parser. Two to three months back, we tested it. It is faster than LogMiner by 30 to 40 percent, which is an improvement time-wise. We have not implemented it yet. I would like to implement it for any new project. I have to find time to do that. I haven't worked on changing the existing one from LogMiner to Binary Log Parser. I have to work with Equalum on how redo all of them or how we can switch over to Binary Log Parser. It is not the highest priority, but if tomorrow I have to do a new project, then I would go with Oracle Binary Log Parser.

There is a lot of promise in the future, which is something to think about. We do plan to increase usage. We have a lot of projects coming up. I want to experiment with it in more ways, like with Kafka as a source and as a target where we can distribute data to multiple applications. There are quite a few things in our pipeline. It is just finding time to figure out how we can do them. We have not fully explored this tool yet. It has a lot of potential, especially the transformation and creating workflows, which has very simple data replication to the target.

Overall, I would rate Equalum as a nine out of 10. 

We use it for replication. We have databases and SQL Server. There is some data that needs to go to Oracle for the application team because the application is connected to Oracle Databases, but the back-end application is connected to SQL Server. Then, we create workflows, where SQL Server is the source, Oracle is the target, and all the tables in SQL Server replicate to Oracle. We have 59 flows for five Databases. These go into production, development, and staging multiplied by three. This is how many flows that we have.

There are applications which have stopped supporting Oracle. Now, the entire application is being migrated to SQL Servers. The entire application data comes into SQL servers, but because the other applications are still linked to Oracle data, they still need Oracle. So, initially when we didn't have Equalum, we used to write Python scripts to pull data from a SQL Server and put it in Oracle, but the Python script requires a lot of maintenance and development. Also, if there were any problem, you needed to have the development knowledge to go and change the Python script. 

Since getting Equalum, the data has been flowing very fast. I don't have any knowledge of Python scripts, but still I can create flows. The data streams very well. The data is in synchronization as well. The notification system is good. So, if there are any problems in SQL Server or Oracle, Equalum notifies us that there is a problem, then we can go and check the problem on our end. If the problem is on their end, we have the ticketing system, which is very good. You can open the tickets. If it is a critical production issue, then you open a ticket and they respond very quickly. 

Initially, there was a big development team running the Python scripts. Now, we don't need to hire anyone extra. As an SQL Server DBA, I take care of it. We also have a help desk team who takes care of it. With all the people who are using Equalum, it does not need any extra support, hires, or resources.

Overall, Equalam has resulted in a lot of system performance improvements in our organization. It has helped us out by keeping the source and target synchronized at all times.

It has good features. It has a replication feature that is wonderful because the data is streaming live and we can change the pulling rates. Initially, this took 50 seconds. However, whatever changes happened from SQL Server to Oracle, they now happen within 30 seconds when it is pulled via Equalum. 

The Equalum tool is a good development tool and user-friendly as well. The front-end is user-friendly because it has a nice, easy methodology. It takes hardly a day to teach someone who can then create the workflow. Once the workflow is set, you don't have to do anything. The data constantly flows from SQL Server to Oracle, i.e., the source to the target. 

It has a strong command line feature. So, if it is front-end, like in SSIS, then I have to create each flow manually. However, in Equalum, we can write a command line program and deploy 50 to 100 flows together at once through the command line. 

Equalum provides a single platform for the following core architectural use cases: CDC replication, streaming ETL, and batch ETL. The CDC is important for me as an SQL Server DBA. So, if there is no CDC, then all my data has to be pulled directly from my tables, which then have to already be linked to the application. So, there will be a performance hit. Now, because there is CDC, the change data captured goes into the CDC table and Equalum pulls from that CDC table. Therefore, there is no user impact on my DB servers. 

They have something called binary logs for Oracle. If you have these logs in place, then you can pull the data through the logs. That is convenient because you can pull the big data in through batch processing, which I have not personally used myself. Though I have seen, in my organization, people using batches because they can schedule them. While my data is live streaming and keeps on streaming every three minutes, some data doesn't require live streaming. So, every day in the morning, after I pull the data from source to target, then they can use batch processing, which is good.

It is important to me that the solution provides a no-code UI, with Kafka and Spark fully-managed in the platform engine, because then I don't have to take care of anything. There are no backup problems. For the flows that I create, I don't have to make a backup, restore or maintain them. I just need to create the workflow from my end. I need a user in my source and a user in target from the database perspective. Then, the front-end is taken care by Equalum to Kafka, which makes it very user-friendly.

When we are taking the data from the source to target, we can add fields, like timestamp. So, data accuracy is very prompt and 100 percent. Whatever data you have in the source, that is the exact data reflected in the target. For the many months that I have been using it for all my projects, I haven't found any data discrepancies, etc. There has not been a time when the source of data is different from the target data, which is very good.

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. They are working on this improvement because we told them about it. There are the small changes that we keep on asking from them, and they do them for us. If you want features or to modify it, they help us with that. So, the team is on it at all times.

I have been using it for six months. 

The company has been using the solution for six to seven years.

It is robust. The stability is good. Long-term, it is a nice, strong tool.

We have multiple nodes. For failover, the data fails over to another node, then it is distributed. Initially, when we started Equalum, it was only one project with 59 flows. Now, we have 400 to 500 flows. It is easily scalable. We didn't have to do much on our side for scalability purposes. 

If my number of loads were 50 initially, but now I am running 500 flows, then we are bringing in more applications to SQL Server as Oracle support is stopped. The more data that comes into SQL Server, the more streaming we have to do to use Equalum. We are talking about huge scalability. For the users, we don't have to do much. Instead of seeing 50 flows on the screen, I see 500 flows on the screen. However, from behind the scenes, I think Equalum has to give us more resources.

If there is anything that we want to change, we go to the Equalum team. The support is wonderful. They came back to us, giving us a demo on how to use it. They were very nice in that way. They respond very quickly. Their support is very good.

They keep giving us more training on how to use Equalum. The Equalum team comes in and tells us about new features. We have a meeting where they talk with us every week.

When I used to stream the flow, from the source to target, if something changed or stopped working, then I would bring my entire source to the target as brand new. This is called restreaming. When I used to restream, it would take a lot of time. Now, they have done new upgrades. In those upgrades, the restreaming is very fast. Also, previously they didn't have this restreaming feature on the front-end. Wherever restreaming had to be done, it had to be done from the command line. Now, they have brought the feature of restream to the front-end. These are the two very good features that they have done for us recently.

We used Python scripts previously. Heavy development on the Python side was needed. Also, it needs a developer experienced in writing Python scripts. They must have that understanding. Plus, maintenance also needs to be done through a developer. Because Equalum is a UI tool, you can do so many things. It is a good tool to use too. It's like a tool versus a script. Obviously, you will prefer the tool,

I like the overall ease of use of the solution’s user interface very much because I was a heavy user of SSIS before, which was the only ETL tool that I have used before for data warehousing. When I came to this company six months back, I got introduced to Equalum. I find Equalum very good because it has multiple sources and targets. There are quite a bit of very good options, like SQL Server to Oracle, then SQL. As long as the source and the target have Java Database Connectivity (JDBC), they can be replicated. The tool is very simple to use. The command line takes time for you to understand, but once you understand it, then it is easy-going. The front-end is very user-friendly, so there aren't any issues.

The initial setup was straightforward. There is nothing complex. Obviously, there were commands that I didn't know to write first. They helped me to understand the commands. Once you understand the commands, using the command line and front-end, then it is all straightforward. There are no hidden complexities. 

They have good documentation. Yesterday, I was asking the Equalum about something, so they sent me the documentation for that. The documentation is well-detailed. They have videos supporting it. If there is a new feature coming out, or any new training you want to do, then they have videos in place. The videos are very good. So, you can review the code and follow the video, then do your work.

If it is a SQL Server, then as a DBA, I have to enable CDC and make sure there is a user with proper privileges. Then, if I have Oracle, I need a user over there with proper privileges, based on what they have given us in the documentation. Once all this is ready on my end, then it is a straightforward deployment.

Deployment does not take much time. If you do a brand new deployment, it is like half an hour or an hour maximum. When bringing all the tables from source to data for the first time, it takes some time, around five hours maximum, for all the data from the source to the target to stream. Once it is streamed, then it is very quick. If there are very few tables, I have seen deployment finishing in half an hour.

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.

If we were not using Equalum, then we would need to use Python scripts, C#, etc., which need heavy development and more time. Timing is okay, because you only need to write the script one time, then you can use it. However, the maintenance is very difficult. If you don't have someone with the knowledge of Python and C#, then you cannot go and modify the scripts. Whereas, in Equalum, we work with an Equalum support team, and our Flex team also takes care of Equalum. If there is an issue or if they want a flow to be created, they do it themselves. We don't even have to have any scripting or programming knowledge.

Equalum has improved the speed of data delivery more than 50 percent. Python script used to take time to run, then you had to schedule it and take care of the scheduler. Sometimes, for some reason, the scheduler did not work, then your job fails. With this solution, it does not have that issue. This can do live streaming, if you want. Or, if you want batch processing, then you can schedule batches, and it runs.

Our team did PoCs and selected Equalum.

We don't use it much for its transformation part. We didn't initially know about the transformation part of it. For example, if I have a new number column in the source and I want to round up the figures or do some string transformation, find, or replace, then I can directly do that from the transformation operators. We obviously used it for replication before. Now, we are using it for transformation as well.

If you want strong replication between any source and target with JDBC, go for Equalum. It's simple, easy to use, and requires less maintenance and tasks to be done. The tool takes care of all your requirements. So, you don't need to do daily backup and restore tasks. It is a straightforward tool. So, if you're using ETL, try Equalum. It is the best bet.

I would rate the solution as 10 out of 10. I have no issues so far.

We are Equalum consultants for our clients. We sell it as a service and we're selling it as a product.

We are using the SaaS version but the predominant request from clients is to have it on-premises. That's more because of political reasons, as the public cloud is not really trusted too much.

Because we've only been using it for about the last four months, it's more in PoCs than in an actual working environment. But so far, we have been able to demonstrate the speed of creating ETL solutions, which is something that was not possible with any other product in the market. In proofs of concept, we have demonstrated the ease of use of the product and the short time it takes to learn.

In the testing, Equalum has definitely resulted in system performance improvements. We will hopefully be in a deployment stage soon. It has been slow because of the whole global situation with COVID, but in the proofs of concept, we have seen quick improvements in performance.

We have also seen improvements in the speed of data delivery. That's something that is visible very quickly. While I don't have exact numbers, we're talking about a significant improvement. It's not just a few percent. It's half the time or more.

The no-code UI, with Kafka and Spark fully managed in the platform engine, is what made us take the product in. This is the big issue in the market where we operate. It's extremely important because, currently, alternative solutions require our clients to bring in a lot of engineering resources. They have to outsource part of their work because those solutions are too complex. We are able to give them a solution which they can manage with in-house expertise, because things are greatly simplified compared to alternative solutions. That's all due to zero coding. You still need to know what you're doing, but the learning curve is quite short.

The effect of the solution on our data architecture is still a process in motion. But for new implementations, we are integrating it in a way that makes for a much more streamlined architecture. We have been able to take some complexities out of the data architecture by introducing Equalum. It is helping us implement a project that would not exist without Equalum. We will see a revenue increase, and our clients will see a cost reduction because they will be able to reduce outsourced staffing, by one-third to two-thirds. And that is why the ROI is so good.

For us, one of the most valuable features is the zero-coding part, which makes it a lot easier to accomplish the building of data pipelines. The interface goes hand-in-hand with the zero coding and provides an immediate response to anything that you code. You have a real-time view of the input and output. That is extremely important because this is the type of work where you work for a week, and then you find out you have to go back to the source. The zero coding and the user interface are the elements that sell the product.

The second one is more to do with how we work with Equalum. It's not necessarily a product feature, but more a company-related feature. It's the flexibility of their company, how they respond to us. That is quite important for a new product and they are definitely very supportive. Whatever needs to be done, they do it, 24-hours-a-day, if necessary. That's what separates it and why we think it's a successful solution.

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. At the end of the day, that's why a client pays for the license.

When it comes to the product as it's designed, we haven't really seen any flaws. It's doing what it's supposed to do. However, 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.

A proven integration would be that two vendors, let's say Cloudera and Equalum, agree that this is a product that works seamlessly with the other product. That includes testing the functionality and even looking at the support model. If you have a multi-vendor environment, you want to know who to call if there is an issue. For large investments, these kinds of things are bottlenecks, and they cause clients to stay with their incumbent suppliers, because of ease of use and not because of the technical quality.

This is not something that can be solved in the field. This has to be done strategically by the vendor.

We have been an Equalum partner since December 2020.

Since we don't have it in a full, working production environment, I can't provide detailed feedback on the stability. What have we seen in the testing environment is that there has never been an error. It has never been unstable. But obviously, when you start to throw terabytes of data at it, then you will know if it's doing everything it's supposed to.

Scaling it is very easy because of the unlimited number of users. In that respect, it's better than the competitive products. 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. Obviously, you need to have the hardware for it, but that's outside of the Equalum solution. 

We're very happy with the support that we receive. It's almost like we're part of the company.

It's quite easy to set up. The first one took time, but the setups after that have been very fast. We are not seeing any difficulties. We can do it very fast.

The last one took our CTO half a day, but that was because certain folders weren't setting up automatically. But in general, setting it up takes half an hour. That does not include migrating data, as that will always take longer.

Overall, increasing capacity is a very easy, straightforward process. Scaling it is, absolutely, much easier to achieve than with competitive products.

We have looked at Talend, for instance. We have also seen MicroStrategy. We have different vendors for different projects, but Talend was the one that we just competed against. They price per-user, so if you have a very small number of users, Talend is cheaper. But this is not a common reality. Most companies will have more than a few users. As soon as you have more than six users, Equalum is lower in cost, and if the group gets bigger, it's quite a big delta.

On the other hand, Talend and some others have an enormous number of features already embedded in their platforms. They have something like 300 features, and if you use them then it's perfect. That is the pro for these other products: they have richer feature sets. There are also more people who are able to use them because they have been in the market longer. You'll more easily find engineers to help with implementation. But the beauty of Equalam is that it's actually quite a slimmed-down product, which has just two line-items to order. And if you want to expand it, you just hook up more users. That's the absolute benefit of Equalum.

The user interface of all of them is good. The user interface of the competitive products is at a very high level. It's more a matter of preference. Some people like a more singular type of user interface, but all the products are at a very high level. The standards of these products on the design side are really high now. The advantage for Equalum is its real-time user interface, which is embedded in the product. Others don't seem to have that, so there are more steps to go through before you know if your coding has actually been effective.

As for the complexity of transformations Equalum is able to perform compared to the other solutions, we have not really done that kind of function with a lot of competitive products. From our testing, Equalum has a faster setup time for transformations, and we can create the output much faster as well, compared to other products that our clients are considering.

I would encourage clients to start testing it. There is a very good way to get pilots set up, and they very quickly show the benefits of Equalum, and this is an area that is very strategic to most large clients' operations. If you're now in batch processing and you need to move to real-time, this is the product that does it, and it does it very fast. Clients that take this area seriously need to test Equalum. Whether they decide in the end to choose it or not, that's a different story, but it cannot be avoided as a real solution that also creates quite good cost savings through its efficiency. Technically, there are others, but the fact that this can be used with much less knowledge of coding makes a huge difference in the marketplace.

In terms of Equalum improving data quality and accuracy, we haven't used it for that in a working environment. But in a demo and in proofs of concept we can show data quality. That's definitely one of the reasons to use it.

We are happy with the product.

We use it for micro-batching of Kafka topics, which is like small, little bits of clickstream data. For almost all our use cases, the target of the data goes into our data warehousing solution, Snowflake. We also take large XML files from multiple parties, transform them, and put them into our Snowflake.

I am part of the data engineering/data science group and governance. I am a senior software engineer on the team. We ingest data from dozens of systems. We ingest that data through Equalum and orchestrate it. We then deploy it to our data warehousing solutions. Then, we work with the data science team to come up with more metrics/measurements.

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 has redefined how we architect our data system. Before, once a day, we would go and grab data. Now, a vast majority of the time, we are actively scanning for new data to come in. As much as possible, we try not to wait for a person to tell us the data is there. We actually actively go out and get it whenever we can. That is a big change for us. So, if somebody says that the file was supposed to be there at 8:00 PM, and it doesn't show up, that kind of mucks up your data flow. Now, if the data shows up at 8:01, you are already actively checking the directory, so you won't miss the file if they are late.

Its most valuable feature is the change data capture (CDC). This is usually a little bit more of a pain if I was using an open source or other tools, but I find their change data capture and data query pretty intuitive.

Equalum provides a single platform for the following core architectural use cases: CDC replication, streaming ETL, and batch ETL. This is core to our company. I would score it as a nine out of 10. It is pretty much how we were moving all our data through their system.

The no-code part is useful. It is like a seven out of 10 for us. We are all software engineers, so it just helps speed up a lot of the data mapping. For example, I just did five documents now. It probably saved me 50 percent of my time.

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. They already know this from me, because I have already complained to them about it.

I have been using it for a year and a half.

The stability has been okay. There have been some rough patches at times. It was on and off in the beginning.

It basically runs itself. We check in on it every day, because we use it all the time, but our team is pretty small.

The scalability has been pretty good. We have had to work with them with memory management, but it is more of a learning curve from us than from the system itself.

There are three or four people who use it. Most people just care about the end results, not necessarily the pipes.

Because it goes into a Snowflake, any of our data analysts, data scientists, and anybody who touches a Tableau dashboard are end users.

More than 90 percent of our data flows through Equalum. It is a core piece of our data platform.

I would rate their technical support as 10 out of 10. They are quite lovely. I can reach them pretty much 24/7. They are very responsive. We have a Slack channel with them. If we post something in it, they will respond within the hour and usually open a ticket. We sit with them once a week and go over the backlog. They are very hands-on and willing to talk about improvements in terms of the system. They take feedback very well. If they can't figure out the problem themselves, they will log onto our production or back-end systems, when we give them permission, helping us resolve problems faster.

They are pretty quick. They patch the system quickly when we find bugs. They will hotfix stuff for us. We are finding less stuff as we go along, but we are pretty picky.

Prior to using this solution, it was quite difficult, especially for real-time streaming. Most solutions that we had used before were batching. Moving over to Equalum, we went to more data streaming.

The initial setup is medium complex. It is semi-hosted. It is not a full-stack platform, so there is still hands-on stuff. For example, we wrote Terraform scripts to help deploy their whole system in our particular way that we wanted to do it.

The deployment didn't take long. Once we got the training wheels going, it was about a week or two. It was mostly just figuring out where and how we were going to run it.

Our original plan was a lot of SQL stuff. Now, we have moved onto mostly Kafka and more smaller micro-batching. So, we had to reconfigure the system as we evolved it. They have worked with us hand-in-hand to do that.

Deploying throughout the entire organization was pretty smooth. We have needed to reconfigure a few things here and there because our use cases have changed.

We had one or two people for the deployment. It was probably a week's worth of coding work to get it the way that we wanted to go operate it.

We worked with them pretty extensively and talked to them probably every week when doing our upgrade.

We have most definitely seen ROI with the ability to onboard data, i.e., the speed of business.

This solution has enabled us to consolidate the use of other tools. We are actively phasing out some other tools. We are probably saving an hour or two every day. We have gotten rid of Stitch Data, Airflow, and Luigi, which we are slowly replacing with Equalum.

We assessed several solutions, but Equalum beat most of them. We could actually see what was going on inside Equalum's systems. They have their low code interface, but we can get access to a vast majority of the back-end. So, it runs like Kafka and Spark, and that is what they are using, which allows us to use open source technology with it rather than be completely closed off from everything.

There are not a lot of other systems out there that I find as intuitive as Equalum, which can do some pretty complex stuff. We haven't used a ton of what we could do. We are converting the whole company over to data strings, so we haven't been able to take advantage of a ton of their more advanced features yet.

Know your use cases, e.g., will you be doing a lot of micro-batching, database work, or pulling data straight off of Kafka topics?

The user interfaces are pretty good for data products. There is nothing amazing about it, but there is nothing that really detracts from it.

We don't do any data testing inside of Equalum. It doesn't mean that we couldn't, but we don't at the moment.

Eventually, when there are new data features coming out using Jupyter Notebooks, we will start incorporating those into our data science.

The biggest lesson learnt: How to operate a Kafka cluster in Spark and do it well.

I would rate it as a nine out of 10. Their customer support is phenomenal. Most companies usually sell it to you, then they disappear. Equalum is very interested in customer feedback.

• Change data capture
• Data streaming

Right now, it is on-premises. We will also be having the solution on the cloud.

CDC replication and data streaming are important to us. At our company, we are moving away from batch in ETL architecture towards a streaming architecture. So, I look at those two things as alternatives. You can stream or you can batch with Equalum. For batch architectures, there are other tools on the market which are superior because they offer more connectors. When it is a popular source and target, like Oracle and SQL Server, the batch works fine. However, the bestreason to use Equalum is for change data capture and data streaming.

We have been able to create features in some of our analytical environments, which probably would have not been possible without Equalum. The ability for Equalum to stream data in real time is very powerful.

Before using Equalum, our batch processes caused operation reports and our analytics environments to be in slight disagreement. Equalum has allowed us to close this gap. Data in different environments now match.

The ability to stream data out of Oracle and SQL Server databases onto Kafka topics. When using alternative technologies, there is usually a lot of software development. With Equalum, it is just configuration.

Equalum provides us with a single platform for the following core architectural use cases:

• CDC replication
• Data Streaming
• Batch ETL (though much less for this).

CDC replication and data streaming are very important to us. Batch ETL is not that important because we have other solutions for that.

The Equalum user interface is extremely easy to use. I would rank it really highly on user-friendliness. The only issue with the user interface is it doesn't supply everything that you need for somebody who has to work with Equalum. For example, when you get deep into development, there are many areas where you have to go to the command line to do things and the Equalum user interface does not have that functionality. 

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

If you have multiple projects, all working in one Equalum environment, separating out their work is something that you have to design into your implementation, as opposed to baked into the product.

I have used it for about a year and a half.

Stability is probably where I would give this solution its weakest marks but it is partially our fault. At our company, we have asked Equalum to create new features. Therefore, we are taking very frequent updates from the vendor. Because of that, we expect stability issues. If you stay with their stable releases, you will not have any issues. It is stable.

It is very scalable. You can scale vertically and horizontally with multiple machines. You can also scale with multiple clusters. Equalum has some third-party products, like OEMs, integrated into their products, e.g., the logging and monitoring that you can remove off the Equalum cluster and stick on its own machine. There are a lot of ways to scale, but environments become more complex when you do that.

I would put people who work with Equalum into two categories: 

1. The group who supports it: We have three people, but they don't support Equalum full-time. That group has a few data products and all three of them support those data products.
2. Working with Equalum, we probably have around 20 to 25 developers who are developing Equalum flows for various products. We expect that to grow.

We have eliminated the use of StreamSets and have greatly reduced the use of Informatica. We have mostly seen time savings. We switched from StreamSets because of superior change data capture with Equalum.

It is extremely important to us that the solution provides a no-code UI, with Kafka and Spark fully managed in the platform engine. It is why we purchased Equalum. We have scenarios where data is changing in one of our source systems, and we want to be alerted of certain kinds of changes. 

Previously, we had the right Kafka producers and Kafka consumers. Even before Kafka, we had these things working on IBM MQ series. These integrations are very complex and difficult to maintain. With Equalum, you are using a graphical UI and connecting to the source. In a few minutes, you are configuring the tool, then live streaming data out of the source and into your target.

Our initial plan was just deploying Equalum on one server because we wanted Equalum to prove itself. So, we bought a very modest license for Equalum. After about six months with a lot of success using Equalum, we planned a larger deployment. Now, we have several instances of three-node clusters with Equalum.

Our first deployment was more of an evaluation. The vendor came in and helped us with the deployment. The vendor was very good initially with support, then throughout our process. They were very reactive and willing to help. The people who supported us were extremely knowledgeable.

It saves time in development. An integration with Equalum is approximately five times faster to develop than an integration without Equalum. Even with competitor's tools to Equalum, Equalum is still a more productive environment to work in. It is about five times faster than not using a tool and probably twice as fast as using a competitor's tool.

The unlimited license is a couple of million dollars a year. So, we aren't expecting a return on investment for more than a year.

Equalum is rather expensive compared to its competitors but the comparison may not be fair because Equalum has features that save time that competitors do not. So you have to make up that cost in time savings, and we haven done that. Equalum has shortened our development cycle by a factor of 5.

There are additional costs for their professional services.

We had StreamSets and were very familiar with it already. However, we were specifically looking for other products that would provide additional functionality above and beyond StreamSets. 

StreamSets has a very similar user interface to Equalum. If you were looking at the interface between the two, you wouldn't be able distinguish them very much as products.

StreamSets has more connectors to it. Equalum has some more flexibility around the number of transformations that can be done. However, the big reason for going with Equalum has been the change data capture.

An advantage of Equalum is its underlying architecture. It runs on top of Spark and Kafka. We were looking at the roadmap for Equalum to be able to deploy on external Spark engines, and we see this solution as a more scalable architecture than StreamSets.

There was one other open-source product that we looked at, but it didn't have a user interface. It was mostly through configuration. While it was much cheaper than Equalum, it didn't come with the user interface and productivity, so we eliminated it.

Make sure that your use cases for Equalum include change data capture. This is the sweet spot for Equalum and where it will save you time and money.

We use the solution’s Oracle Binary Log Parser, which is one of our primary use cases. We have a lot of databases on shared Oracle servers and configuring the Log Parser on those servers is brought up often as a security issue because an owner of one database can potentially see the data of another database. Therefore, we have had to make some adjustments on how we organize our Oracle Databases because of it. However, I think this would be an issue with any tool using this methodology.

I would rate this solution as a nine out of 10.