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Buyer's Guide
Data Integration Tools
September 2022
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Managing Director at a consultancy with 11-50 employees
Reseller
Top 10
Frees staff to focus on data workflow and on what can be done with data, and away from the details of the technology
Pros and Cons
  • "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."
  • "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."
  • "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."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

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.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

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.

What was our ROI?

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.

What's my experience with pricing, setup cost, and licensing?

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.

Which other solutions did I evaluate?

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. 

What other advice do I have?

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.

Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor. The reviewer's company has a business relationship with this vendor other than being a customer: Reseller.
Dan Peacock - PeerSpot reviewer
Enterprise Data Architect at a manufacturing company with 201-500 employees
Real User
Top 10
It's flexible and can do almost anything I want it to do
Pros and Cons
  • "Lumada has allowed us to interact with our employees more effectively and compensate them properly. One of the cool things is that we use it to generate commissions for our salespeople and bonuses for our warehouse people. It allows us to get information out to them in a timely fashion. We can also see where they're at and how they're doing."
  • "Some of the scheduling features about Lumada drive me buggy. The one issue that always drives me up the wall is when Daylight Savings Time changes. It doesn't take that into account elegantly. Every time it changes, I have to do something. It's not a big deal, but it's annoying."

What is our primary use case?

We mainly use Lumada to load our operational systems into our data warehouse, but we also use it for monthly reporting out of the data warehouse, so it's to and from. We use some of Lumada's other features within the business to move data around. It's become quite the Swiss army knife.

We're primarily doing batch-type reports that go out. Not many people want to sift through data and pick it to join it in other things. There are a few, but again, I usually wind up doing it. The self-serve feature is not as big a seller to me because of our user base. Most of the people looking at it are salespeople.

Lumada has allowed us to interact with our employees more effectively and compensate them properly. One of the cool aspects is that we use it to generate commissions for our salespeople and bonuses for our warehouse people. It allows us to get information out to them in a timely fashion. We can also see where they're at and how they're doing. 

The process that Lumada replaced was arcane. The sentiment among our employees, particularly the warehouse personnel, was that it was punitive. They would say, "I didn't get a bonus this month because the warehouse manager didn't like me." Now we can show them the numbers and say, "You didn't get a bonus because you were slacking off compared to everybody else." It's allowed us to be very transparent in how we're doing these tasks. Previously, that was all done behind the vest. I want people to trust the numbers, and these tools allow me to do that because I can instantly show that the information is correct.

That is a huge win for us. When we first rolled it out, I spent a third of my time justifying the numbers. Now, I rarely have to do that. It's all there, and they can see it, so they trust what the information is. If something is wrong, it's not a case of "Why is this being computed wrong?" It's more like: "What didn't report?"

We have 200 stores that communicate to our central hub each night. If one of them doesn't send any data, somebody notices now. That wasn't the case in the past. They're saying, "Was there something wrong with the store?" instead of, "There's something wrong with the data."

With Lumada's single end-to-end data management, we no longer need some of the other tools that we developed in-house. Before that, everything was in-house. We had a build-versus-buy mentality. It simplified many aspects that we were already doing and made that process quicker. It has made a world of difference. 

This is primarily anecdotal, but there were times where I'd get an IM from one of the managers saying, "I'm looking at this in the sales meeting and calling out what somebody is saying. I want to make sure that this is what I'm seeing." I made a couple of people mad. Let's say they're no longer working for us, and we'll leave it at that. If you're not making somebody mad, you're not doing BI right. You're not asking the right questions.

Having a single platform for data management experience is crucial for me. It lets me know when something goes wrong from a data standpoint. I know when a load fails due to bad data and don't need to hunt for it. I've got a status board, so I can say, "Everything looks good this morning." I don't have to dig into it, and that has made my job easier. 

What's more, I don't waste time arguing about why the numbers on this report don't match the ones on another because it's all coming from the same place. Before, they were coming from various places, and they wouldn't match for whatever reason. Maybe there's some piece of code in one report that isn't being accounted for in the other. Now, they're all coming from the same place. So everything is on the same level.

What is most valuable?

I'm a database guy, not a programmer, so Lumada's ability to create low-code pipelines without custom coding is crucial for me. I don't need to do any Java customization. I've had to write SQL scripts and occasionally a Javascript within it, but those are few and far between. I can do everything else within the tool itself. I got into databases because I was sick and tired of getting errors when I compiled something. 

What needs improvement?

Some of the scheduling features about Lumada drive me buggy. The one issue that always drives me up the wall is when Daylight Savings Time changes. It doesn't take that into account elegantly. Every time it changes, I have to do something. It's not a big deal, but it's annoying. That's the one issue, but I see the limitation, and it might not be easily solvable. 

For how long have I used the solution?

I started working with Lumada long before it was acquired by Hitachi. It's been about 11 years now. I'm the primary person in the company who works with it. A few people know the solution tangentially. Aside from very basic elements, most tasks related to Lumada usually fall in my lap.

What do I think about the stability of the solution?

Lumada's stability and performance are pretty good. The limitations I run into are usually with the database that I'm trying to write to rather than read from. The only time I have a real issue is when an incredibly complex query takes 20 minutes to start returning data. It's sitting there going, "All right. Give me something to do." But then again, I've got it running on a machine that's got 64 gigs of memory.

What do I think about the scalability of the solution?

Scaling out our processes hasn't been a big deal. We're a relatively small shop with only a couple of production databases. We're more of a regional enterprise, and I haven't had any issues with performance yet. It's always been some other product or solution that has gotten in the way. Lumada can handle anything we throw at it. Every night I run reports on our part ledger. That includes 200 million records, and Lumada can chew through it in about an hour and a half. 

I know we can extend processing into the Spark realm if we need to. We've thought about that but never really needed it. It's something we keep in our back pocket. Someone suggested trying it out, but it never really got off the ground because other more pressing needs came up. From what I've seen, it'll scale out to whatever I need it to do. Any limitations are in the backend rather than the software. I've done some metrics on it. It's the database that I have to wait on more than the software. It's not doing a whole lot CPU-wise. My limitations are elsewhere, usually.

Right now, we have about 100 users working with Lumada. About 100 people log in to the system, but probably 200 people get reports from it. Only about 50 use the analysis tools, including the top sales managers and all of the buying group. There are also some analysts from various groups who use it constantly. 

How are customer service and support?

I'd give Lumada support a nine out of 10. It has been exceptional historically, but there was a rough patch about a year and a half ago shortly after Hitachi took over. They were in a transition period, but it has been very responsive since. I usually don't need help. When I do, I get a response the same day, and somebody's working on it. I'm not too worried about things going wrong, like an outage. I've never had that happen.

Sometimes when we do upgrades, and I'm in my test environment, I'll contact them and say, "I ran into this weird issue, and it's not doing what it should. What do you make of it?" They'll tell me, "You got to do this, that, and the other thing." They've been good about it.

Which solution did I use previously and why did I switch?

Before Lumada, we had a variety of homegrown solutions. Most of it was centered on our warehouse management system because that was our primary focus. There were also reports within the point of sale system, and the two never crossed paths. Now they're integrated. There was also an analysis tool they had before I came on board. I can't remember the name of it. The company had something, but it didn't do what they thought it would do, and the project fizzled.

Part of the problem was that they didn't have somebody in-house who understood business intelligence until they brought me on. They were very operationally focused before that. The management was like, "We need more insight into what we're doing and how we're doing it." That was phase two of the big data warehouse push. The management here is relatively conservative in that regard, so they're somewhat slow to say, "Hey. We need to do something along these lines." But when they decide to go, get out of the way because here we come.

I used a different tool at my previous job called Informatica. Lumada has less of a learning curve for deployment. Lumada was similar enough to Informatica that it's like, "Okay. This makes sense," but there were a few differences. Once I figured out the difference, it made a lot of sense to me. The entire chain of steps Lumada allows you to do is intuitive.

Informatica was a lot more tedious to use. You had to hook every column up from its source to its target. With Lumada, it's the name that matters and its position. It made aspects a whole lot easier and less tedious. Every so often, it bites me in the butt. If I get a column out of order, it'll let me know I did something wrong. But it's much less error-prone because I don't have to hook every column up from its source to its target anymore. With Informatica, there were times where I spent 20 minutes just sitting there trying not to drool on myself. It was terrible. 

How was the initial setup?

Setting up Lumada was pretty straightforward. We just rolled it out and went from proof of concept to live in about a year. I was relatively new to the organization at the time and was still getting a feel for it — knowing where data was and what all these things mean. My experience at a shoe company didn't exactly translate to an auto parts business. I went to classes down in Orlando to learn the product, then we went from there and just tried it. We had a few faux pas here and there, but we knew.

What was our ROI?

Lumada has also significantly reduced our ETL development time. It depends on the project, but if someone comes to me with a new data source, I can typically integrate it within a week, whereas it used to take a month. It's a 4-to-1 reduction. It's allowed our IT department to stay lean. I worked at another company with 70 IT people, 50 of which were programmers. My current workplace has 12 people, and six are programmers. The others are UI-type developers, and there are about six database people, including me. We save the equivalent of a full-time employee, so that's anywhere from $50,000 to $75,000 a year.

What's my experience with pricing, setup cost, and licensing?

I think Lumada's price is fair compared to some of the others, like BusinessObjects, which is was the other solution that I used at my previous job. BusinessObject's price was more reasonable before SAP acquired it. They jacked the price up significantly. Oracle's OBIEE tool was also prohibitively expensive. We felt the value was much greater than the cost, and the value for the money was much better than if we had gone with other solutions.

Which other solutions did I evaluate?

We didn't consider other options besides Lumada because we are members of an auto parts trade association, and they were using the Pentaho tool before it was Hitachi to do some ETL tasks. They recommended it, so we started using it. I evaluated a couple of other ones, but they cost more than we were willing to spend to try out this type of solution. Once we figured out what it could do for us, then it's like, "Okay. Now, we can do some real work here."

What other advice do I have?

I rate Lumada nine out of 10. The aspect I like about Lumada is its flexibility. I can make it do pretty much whatever I want. It's not perfect, but I haven't run into a tool that is yet. I haven't used every aspect of it, but there's very little that I can't make it do. I haven't run into a scenario where it couldn't handle a challenge we put in front of it. It's been a solid performer for us. I rarely have a problem that is due to Lumada. The issues I have with my loads are never because of the software.

If you plan to implement Lumada, I recommend going to the classes. Don't be afraid to ask dumb questions of support because many of them used to be consultants. They've all been there, done that. One of the guys I talk to regularly lives about 80 miles to the north of me. I have a rapport with him. They're willing to go above and beyond to make you successful.

Which deployment model are you using for this solution?

On-premises
Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
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Michael Lutz - PeerSpot reviewer
Sr. Data Warehouse Developer at Fox Chase Cancer Center
Real User
Top 10
It's a powerful tool that does a lot, but it has its annoyances
Pros and Cons
  • "Data Services' table comparison mechanism is very powerful. It's pretty hard to find a similar feature in other solutions."
  • "Source code control is another headache. When your source code base gets too large, managing the source code becomes cumbersome."

What is our primary use case?

We use Data Services to pull data from a hospital system and transfer it to a centralized Oracle database. Then we use the tool to transform the data for reporting and analytics.

How has it helped my organization?

Data Services enables hospital staff to understand the data across disparate systems within the enterprise, helping the company to make more intelligent business decisions.

What is most valuable?

Data Services' table comparison mechanism is very powerful. It's pretty hard to find a similar feature in other solutions. There isn't really a great solution in Raw SQL. Data Services' general programming features are strong. The programming language is pretty rich. You can significantly transform the data. It also performs well when pushing SQL down to the database server. The tool tries to do as much work in a database as possible, but there are times when it can't do that as well. In those cases, it will process the data in its own engine. All the connectors and the panelization capabilities are pretty useful as well.

What needs improvement?

Data Services' UI can be annoying. Searching through long lists of things takes time because of some limitations. Source code control is another headache. When your source code base gets too large, managing the source code becomes cumbersome. Also, it's not possible to generate the code. You have to code everything manually. There's no way they're going to fix this. It would be nice to generate code from another program, but you can't do that. 

For some tasks you need to do as an ETL developer, it doesn't make sense to code them all one at a time. You want to be able to read the metadata from something and generate the code. But the architecture of the tool doesn't play nicely with that. The ability to generate code would be one feature that would be nice to have. 

I'd also like to see a smoother code promotion check-in and check-out process for large codebases and fixes to the comparison mechanism. The tool allows you to compare your code against the essential repositories, but the comparison mechanism is buggy. It sometimes reports false positives for what has changed. SAP could maybe improve the performance diagnostics for the production environment. Data Services has some rudimentary performance analysis tools, but they're pretty basic. There's not a dashboard to view where you're doing things well and not well performance-wise. A comprehensive performance dashboard would be nice.

For how long have I used the solution?

I've been using SAP Data Services for about six years.

What do I think about the stability of the solution?

Data Services is pretty solid. I would give it almost a perfect 10 for stability.

What do I think about the scalability of the solution?

Data Services is highly scalable. Right now, there are a small number of direct users. They're all developers and software engineers. This isn't a user-facing tool like BOE or Webby that reports business objects. Data Services is an ETL tool that IT people use, so the user base is small. We are using it extensively, though, with about 150 jobs that run every night, but we are planning on decreasing our usage of the tool. However, that is because of what's going on in the hospital system rather than the tool's fault. Due to system changes, we don't have as much need for ETL work. So it will be probably decreasing over time. But we've used it extensively for six years.

How are customer service and support?

SAP's support is good, but the process can be complex. If I were giving it a letter grade, I would say B-minus. 

Which solution did I use previously and why did I switch?

My company was using SSIS for a short time before I came on board. They switched because my boss knows Oracle well, and this seemed like the best option. He did an entire evaluation, and they decided to go with SAP Data Services because it is a fully functional reporting suite. Data Services operates well with BOE and its reporting solution. However, the primary factor in the decision was that my boss isn't a SQL Server person. He's an Oracle person, and this tool is more robust in the Oracle realm. In addition, Data Services isn't overly expensive. I believe that it's considerably less costly than Oracle Data Integrator or Informatica PowerCenter. He would've gone with those two tools, but they were too expensive.

How was the initial setup?

Setting up Data Services is moderately complex. It's a big tool with a lot of components that need to be configured. Data Services is an enterprise-level data extraction and transformation load tool. There are just a lot of pieces. We've struggled because we were trying to install it on Solaris. I just finished an upgrade. I set up our environment five years ago, and we just upgraded it again. 

It took much longer because they didn't have good support for Solaris. They say that they support Solaris, but they really don't. Since we moved over to Linux, we've been a lot better off. But it's a pretty sizable effort to set up the tool and configure it. You have to have DevTest and PRIDE. There are a lot of moving parts and various repository job servers. You have to understand the topology and architecture of the tool to know how to install and configure it. It's not like installing Microsoft Word.

The total amount of time needed to deploy depends on your environment. Generally, it should take about a month to get it up and running. In actuality, it took a lot longer than that. But once we got on the right OS, it was quicker. Our deployment strategy was to have three instances of the tool. First, we have DevTest and PRIDE. Then each developer has their own repository where they can do their work. So we have a development, test, and production repository. Every developer first runs their code in their own repository, which is where your code is stored. The code is then promoted to a common dev area. After that, it goes to a common test area, and finally, it heads to a common production area.

We've deployed Data Services several times, and each time we've had to do a net new install and transfer things over. We ran into some limitations on being able to upgrade in place. We have one person managing and maintaining the solution, but that person's workload fluctuates. It can be very demanding at times. It can use a lot of one person's time for short periods when you do upgrades. You have to know how to get onto SAP's website, open tickets, and work with SAP Support. That's another thing that adds to the time of the deployment. The amount of time you spend with SAP support is non-trivial. You have to understand the ticketing system and how to contact the engineers. It takes a little while to get used to that.

What about the implementation team?

When Data Services was first deployed, I was not working at the company. At the time, they used consultants. Since I came on, I've been handling it all in-house.

What other advice do I have?

I rate SAP Data Services seven out of 10. It's a powerful tool that does a lot. It has a lot of strengths, but there are some annoyances that slow down the programmer. It gets frustrating over time. It's also crucial that your repository database technology performs well. I would say seven out of 10 because it's powerful. It would be a nine if it weren't for the clunky source code management and a few other hassles like its inability to generate code.

I probably wouldn't choose this tool for what we're doing because I don't think SAP views this tool as their go-forward technology. SAP now has HANA and a new set of tools that operate in the cloud. I believe the company is primarily in a maintenance situation with Data Services. So if I were starting a new ETL project from scratch, I wouldn't go with SAP Data Services.

Which deployment model are you using for this solution?

Private Cloud
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Data Integration Specialist/CTO at Asset messages
Real User
Top 20
A complete product with good integrations and excellent flexibility
Pros and Cons
  • "The solution has a good balance between automated items and the ability for a developer to integrate and extend what he needs. Other competing tools do not offer the same grade of flexibility when you need to go beyond what is provided by the tool. Talend, on the other hand, allows you to expand very easily."
  • "The server-side should be completely revamped."

What is our primary use case?

I primarily use the solution for integration. I consolidate data from several different databases and spreadsheets and merge systems into Amazon Redshift.

What is most valuable?

The solution has a good balance between automated items and the ability for a developer to integrate and extend what he needs. Other competing tools do not offer the same grade of flexibility when you need to go beyond what is provided by the tool. Talend, on the other hand, allows you to expand very easily.

We have a good integration with Artifactory and a good integration with GitHub. 

I don't see the need for anything. At the moment, Talend is a good complete product while at the same time not being overwhelming.

What needs improvement?

What I really don't like is the TAC, which is the Talent Administration Console. It's currently slow, old technology. It's obsolete and ugly to use. The studio is great, whereas TAC is on the other side of the spectrum. Actually, in AWS, in many cases, we are ditching the TAC and we are using Amazon-provided services, like Lambda functions, to code the Talend files produced by Talend Studio.

The server-side should be completely revamped.

For how long have I used the solution?

I've been dealing with the solution for more than three years.

What do I think about the scalability of the solution?

There are two pairs of version keys that parallelize, which is not available in the free version. However, when you can parallelize your jobs you can achieve a good degree of scalability. This is one of the points where Talend shines compared to, for example, Informatica. Informatica is designed to do one record at a time. It requires expert support and general competence in the product.

Talend, by default, can be very efficient, especially using the bulk load and bulk insert components, which, at the moment we are using. We are moving to approximately 10 terabytes of data daily. Notwithstanding the load, we had to tweak very, very little. Using standard components we could achieve our overall needs.

How are customer service and technical support?

The fact that the technical support is out of China means that sometimes the engineers don't have a good command of the English language. That could hinder the overall experience. Apart from that, most of the incidents were resolved in 48 hours. Therefore, we're pretty satisfied with the level of service we've received.

Which solution did I use previously and why did I switch?

I have previously worked with Informatica and TIBCO. I still believe Talent is superior. 

How was the initial setup?

Obviously, being on AWS, we're using Linux AMI machines or vertical machines, using two machines. The setup was very easy. The only difficult part was to set up to the current time zone, however, this is nothing to do with Talend. We were having issues when Talend jobs were reporting the wrong date when they arrived because the set up was ETC rather than the local time zone.

Setting up Talend and setting up the administration console at the Tac server was very, very easy for somebody with a Linux System Administration skillset.

What about the implementation team?

I set up the solution on my own.

What's my experience with pricing, setup cost, and licensing?

I don't deal with corporate and pricing. I am just an IT Consultant. I honestly don't know the costs to run the solution. I don't know the price. I know people in the finance department are often complaining about the price, however, I don't know what the exact number is. 

It's all relative, after all. I have a very strong Oracle background and the Oracle price is totally outrageous. I don't believe that Talend prices are in the same ballpark as Oracle. I don't understand why people are complaining. That said, I don't deal with signing contracts and processes so I really don't know.

What other advice do I have?

I am an IT Consultant. I use it currently in my job. I'm providing services to a company in Australia, using Talend.

I would advise others to use the Studio. If you have to pay for some license to use the key parallelize component, it's a good idea to do it. However, don't use the TAC. Use the other orchestration services like Control-M or AWS functions such as AWS Lambda. Don't use the TAC. The TAC is really ugly.

Overall, I'd rate the solution eight out of ten. I'd rate it higher, however, the TAC is unreliable. It's a big part of the solution, and, while I do really appreciate Talend Studio and the ability to link into a producer to make a Java code, which is ugly, I don't care because the job is done anyway. The fact that the orchestration is creating execution plans, from the TAC it's fine. However, when the running of the execution plan, sometimes there are issues. The job remains stuck on the actual server. I've had a lot of issues with TAC. 

Which deployment model are you using for this solution?

Private Cloud

If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

Amazon Web Services (AWS)
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Data/Solution Architect at a computer software company with 51-200 employees
Real User
Robust, easy to use, has a simple error logging mechanism, and works very well for huge volumes of data
Pros and Cons
  • "As a data integration platform, it is easy to use. It is quite robust and useful for volumetric analysis when you have huge volumes of data. We have tested it for up to ten million rows, and it is robust enough to process ten million rows internally with its parallel processing. Its error logging mechanism is far simpler and easier to understand than other data integration tools. The newer version of InfoSphere has the data catalog and IDC lineage. They are helpful in the easy traceability of columns and tables."
  • "Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate. In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere."

What is our primary use case?

We use it for creating a pattern for data integration with our data vault. We have also used it for creating APIs.

What is most valuable?

As a data integration platform, it is easy to use. It is quite robust and useful for volumetric analysis when you have huge volumes of data. We have tested it for up to ten million rows, and it is robust enough to process ten million rows internally with its parallel processing. 

Its error logging mechanism is far simpler and easier to understand than other data integration tools.

The newer version of InfoSphere has the data catalog and IDC lineage. They are helpful in the easy traceability of columns and tables.

What needs improvement?

Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate.

In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere.

For how long have I used the solution?

It was DataStage previously, and then it became InfoSphere. I have used DataStage for ten years and InfoSphere for one year.

What do I think about the stability of the solution?

It is quite stable. In the newer components of InfoSphere, you have a mapping tool called FastTrack and a metadata generator, which can have issues from time to time, but they get resolved.

What do I think about the scalability of the solution?

It is not that easy to scale on-premises. I have worked on the ones deployed on Windows or Unix, and scalability is often dependent on whether you can add more CPUs or boxes. On the cloud, it would have been easier to scale. However, the current version can only be deployed on Windows or Unix.

How are customer service and technical support?

I have not been in touch with them recently. Earlier, I was in touch with their technical support and had raised tickets because some weird errors, such as fantom error, were being logged in the error log, which made no sense. We used to get in touch with their support team to understand these.

Which solution did I use previously and why did I switch?

I have used Informatica and SAS CA. IBM InfoSphere has the highest cost of licensing as compared to others. It is not very widely used, and it is very difficult to find people who have this sort of knowledge. 

The newer version of Informatica is on the cloud and is much more user-friendly than InfoSphere because it provides profiling information in nice graphs and charts. It also provides a lot of templates. For example, if I want to build a whole dimensional kind of structure, Informatica has a template. I just need to use that template. So, the ease of use is far better in Informatica, and it has everything that InfoSphere has. The only thing is that Informatica comes in bundles. That's the reason sometimes organizations don't go for it. For example, the data integration is a separate section, and the data quality is a separate section. They have separate pricing.

How was the initial setup?

The initial setup is quite simple. It didn't take more than half an hour to set it up on my laptop.

What about the implementation team?

I implemented it myself. In terms of maintenance, a particular version might not require any maintenance. There could be bug fixes and minor versions going in for some versions.

What's my experience with pricing, setup cost, and licensing?

It is quite expensive.

What other advice do I have?

I would recommend this solution for large-scale implementation where you need a complex transformation and data integration to happen according to a structured format, either a data vault or a dimension model. It is suitable for big companies because of the cost. It is a very valuable platform for data in large volumes. For small volumes, you have other open-source tools that can do the same thing for you.

I am part of a consultancy, and I have deployed this product for companies. We have five to eight developers. Because InfoSphere is a licensed product, and its licenses cost a lot, there are not many InfoSphere developers.

I would rate IBM InfoSphere DataStage an eight out of ten.

Which deployment model are you using for this solution?

On-premises
Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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