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We use this tool for B2B and B2C customer de-duplication/matching, generating a golden version of our customers and for householding. 

We use Melissa Data Matchup for SSIS to de-duplicate our customer data on a daily basis so that we were able to reduce marketing costs and increase the quality of communication with customers.

It replaced a weekly primitive custom de-duplication (record level) matching process.

Its survivor-ship logic handles very complex column-level rules efficiently providing us with a first-time for a single version of truth for our customer data. It's inherent intelligence into name and address parsing provides a very accurate exact match with no false positives and no unexpected false negatives. We are continually impressed by its sophistication and ease of use. The tool does not requires a middle tier or specialized staff like every other tool on the market.

The high value in this tool is its relatively low cost, ease of use, tight integration with SSIS, superior performance (compared to competitors), and attribute-level advanced survivor-ship logic. There's no separate server needed and no separate application to maintain.

This vendor offers a large variety of components from on-prem to cloud SaaS as well as hybrid of cloud and on-prem. This review is specific to the "MatchUp for SSIS" component.

For us, this tool had very high value due to the fact that we didn't have to become experts in some overly complicated DQ tool. And because it is fully integrated with our EDW ETL rather than having to originate and integrate an external application.

We are using it for daily 1) direct matching, 2) column-level survivor-ship and 3) mail house-holding. We started with B2C customers and later added B2B customers. The tool supports unique matching specific to organization names and individual names (as well as a variety of other specialized types of data values) and works well in both cases. For example it can pull out nicknames and match on those.

One of the business and operational benefits for us is feeding the end result to Adobe Campaign for marketing automation. But the primary output is simply creating and managing an analytical golden record for our customer data. This has provided a very effective, holistic, maintenance-free, and extremely cost effective solution for us.

The initial POC was up and running in just a few days with no training needed. The plug-in into our ETL tool was seamless and fully integrated into our existing processes. Most of our effort was due to the need to identify customer survivor-ship requirements and validation. Any needed adjustment changes could be done very quickly allowing us to focus on business requirements instead of implementing technology.

- Scalability is a limitation as it is single threaded.  You can bypass this limitation by partitioning your data (say by alphabetic ranges) into multiple dataflows but even within a single dataflow the tool starts to really bog down if you are doing survivorship on a lot of columns.  It's just very old technology written that's starting to show its age since it's been fundamentally the same for many years.  To stay relavent they will need to replace it with either ADF or SSIS-IR compliant version.  

- Licensing could be greatly simplified. As soon as a license expires (which is specific to each server) the product stops functioning without prior notice and requires a new license by contacting the vendor. And updating the license is overly complicated. 

- The tool needs to provide resizable forms/windows like all other SSIS windows. Vendor claims its an SSIS limitation but that isn't true since pretty much all SSIS components are resizable except theirs! This is just an annoyance but needless impact on productivity when developing new data flows.

- The tool needs to provide for incremental matching using the MatchUp for SSIS tool (they provide this for other solutions such as standalone tool and MatchUp web service). We had to code our own incremental logic to work around this.

- Tool needs ability to sort mapped columns in the GUI when using advanced survivorship (only allowed when not using column-level survivorship).

- It should provide an option for a procedural language (such as C# or VB) for survivor-ship expressions rather than relying on SSIS expression language.

- It should provide a more sophisticated ability to concatenate groups of data fields into common blocks of data for advanced survivor-ship prioritization (we do most of this in SQL prior to feeding the data to the tool).

- It should provide the ability to only do survivor-ship with no matching (matching is currently required when running data through the tool).

- Tool should provide a component similar to BDD to enable the ability to split into multiple thread matches based on data partitions for matching and survivor-ship rather than requiring custom coding a parallel capable solution.  We broke down customer data by first letter of last name into ranges of last names so we could run parallel data flows.

- Documentation needs to be provided that is specific to MatchUp for SSIS.  Most of their wiki pages were written for the web service API MatchUp Object rather than the SSIS component.

- They need to update their wiki site documentation as much of it is not kept current. Its also very very basic offering very little in terms of guidelines. For example, the tool is single-threaded so getting great performance requires running multiple parallel data flows or BDD in a data flow which you can figure out on your own but many SSIS practitioners aren't familiar with those techniques.

- The tool can hang or crash on rare occasions for unknown reason. Restarting the package resolves the problem. I suspect they have something to do with running on VM (vendor doesn't recommend running on VM) but have no evidence to support it.  When it crashes it creates dump file with just vague message saying the executable stopped running.

We have been using this product for over 7 years.  

No as long as you don't try to match on null last names or lots of duplicate (exact match) records or try to run it in the default 64 bit mode of SSIS (issue here is only with new versions).

We can run 9 million customer record exact matches in 10 minutes using 5 partitions/parallel dataflows. Survivorship takes another 50 minutes. I'm sure you could run faster with dedicated hardware and running more parallel dataflows. The tool starts to exponentially slow down once you pass about 2 million customers in a single dataflow so its best to keep it at or under that number although mileage will vary depending on the complexity of your matching.  Its unfortunate that the vendor hasn't built in parallelism which would both eliminate the need to do this yourself.  They should be able to auto-scale it based on # of CPU's your running.

Even with that limitation this tool is magnitudes faster than the last matching tool I used and it wasn't a simple plug-in to an ETL tool. I recently heard of a competing tool that takes longer to match just a few thousand customers than this tool takes to run millions of them.

Note:

We probably run higher volumes than many organizations. For B2B and daily matching you could probably process a delta in a matter of a few minutes with this tool.  

Note:  I suspect an essential ingredient when considering scalability is whether you're calling a web service for matching or just on-prem. Their SSIS component is only on-prem but they offer a web service as well which we have not tested.

Combining survivorship and matching in the same data flow slows performance. We got much better performance by running in two separate dataflows - the first for just matching and then another for just survivorship (re-using the previous grouping numbers in the first match) to make it perform to our requirements.

Customer Service:

Fairly typical vendor support. They are immediately attentive to problems and provide email notifications of software versions. The main technical contact we work with has been there for the last decade which is very refreshing!

Technical Support:

They regularly release new versions of the product with bug fixes and enhancements although just the matchup tool itself has changed very little in the past 5 years. 

However unless you can interact directly with the development team problems may not get resolved in a timely manner. I have usually been left coming up with my own solution in the time I was waiting for their support to provide answers from their support team.

I have used Datamentors and SAS Dataflux in the past with good success although I would easily take this product over those products for just matching/survivorship purposes. We had tested Oracle's cloud-based Fusion product which wasn't actually a functioning product at the time. The MelissaData tool is light-years ahead of Datamentors, far easier to use and the price can't be compared. The SAS tool was very expensive.  All other matching tools require separate middle tier application verses this product which is just a plug-in to SSIS.

Initial setup on the first install was VERY easy. Propagating the matching rules to the next server was easy IF you know which file to copy which isn't well documented. The tool is extremely easy to use when you know just a few little things which aren't documented. Their development staff were very helpful in providing simple tips on how to set it up.

This was in-house implementation. The vendor was very responsive in answering questions.

I have no numbers for ROI but it's avoided having to spend 6 figures for similar functionality in another tool.  Plus since it's fully integrated with SSIS there is no need for separate server - more money saved. 

This vendor has no equal in pricing for equivalent functionality. First no one else offers this level of integration with SSIS. Second other vendors with equal functionality all cost many times the cost of this tool. Third it doesn't require a separate server or large learning curve of new software. Fourth, this is one of the "go to" vendors for matching purposes as some master data and data quality tools are actually calling MelissaData Matchup object in the backend then charging you a lot for their pretty GUI to do this for you.

I evaluated Microsoft's DQS which could not scale over 100,000 customer records. DQS actually supported calling MelissaData Matchup in the old Microsoft Marketplace (no longer available) to use it's more sophisticated matching but it was a moot point if DQS can't handle the volume.  

This tool is a dream compared to my previous experience with batch matching/de-duplication tools. And the pricing is incredible given its functionality and simplicity. High value and very lost cost. If you're an SSIS shop (they support other ETL tools also however) and you need to de-duplicate, household and/or do column-level survivorship then this tool can't be beat.

I highly advise running parallel threads by splitting your dataflow into multiple paths.  This allow parallel matching and increaes throuput significantly.

Since the beginning of my career, I have been working on Talend, and I have been working with Qlik Talend Cloud for the last five years.

My main use cases for Qlik Talend Cloud include multiple scenarios. We have developed frameworks where we listen to Kafka streams, receive Kafka messages, ingest them, and after transformation, load those Kafka streams to the Snowflake data warehouse. Other use cases include API integrations, where we read data in the form of JSON from our clients and transform it as needed before loading it into the database. In case of any failure or no data, we trigger an email using SMTP settings. Additionally, we have a data vault integration within our Snowflake data warehouse where we have built a procedure to move data from one layer to another and execute those stored procedures using Talend. We have created a Talend framework that helps us execute Snowflake procedures, and in case of any issues, we have an audit logging job and framework to write all logs to the database, ensuring we can track whether everything is working fine.

Regarding Qlik Talend Cloud, for the Kafka framework, we have multiple topics to read data from Kafka. We built a generic framework in Talend Data Integration where we just need to configure the topic. We receive data in the form of JSON by listening to that Kafka topic. Once authenticated, we consume the messages from Kafka through Talend and load them into Snowflake. This generic framework allows us to easily onboard any new topic within one hour, just by configuring the Kafka topic consumer and the target table name.

There are multiple use cases with one of them being the Kafka framework I just mentioned.

The best features of Qlik Talend Cloud are tough to distinguish, but the Talend Management Cloud (TMC) platform allows us to schedule or monitor our jobs easily and in a user-friendly manner. You can build your job, monitor it, create a plan, and receive notifications for both failures and successes, making it a valuable feature. Another highlight is that whenever a new technology enters the market, Talend tends to be the first product to provide its connector.

Out of all the features, I find myself relying the most on quick connectors to new technologies. Whenever a new technology comes into the market, Talend is quick to offer a connector to connect and consume the data.

Talend has plenty of platforms including Talend Data Preparation, Talend Data Stewardship, and support for Big Data, which are significant.

Qlik Talend Cloud has positively impacted my organization, HCL, where there are multiple projects running, and we have a Center of Excellence team with over 50 Talend resources, 30 of whom are Talend certified developers. Talend is widely used across many projects, and it is contributing positively to the organization's growth and revenue.

Qlik Talend Cloud is doing well, but one improvement could be the integration of development functionalities we use in Talend Studio directly onto the Talend browser. Currently, that would allow us to perform drag-and-drop tasks online without needing to switch back to the studio.

The support in my experience is good, and the documentation is also helpful. However, having Talend Studio functionalities accessible through the browser would be a significant improvement.

In my current field, I am working for over 12 plus years.

Qlik Talend Cloud is quite stable now. It was not as stable when we were using TAC and on-premise systems, but currently, with Qlik Talend Cloud version 8.3 or 8.1, it is stable.

The scalability of Qlik Talend Cloud runs smoothly, and we are not facing any challenges.

The support in my experience is good, and the documentation is also helpful.

Customer support is good. Whenever we need assistance, we can raise a ticket, and responses are quite fast.

Positive

I previously used Informatica about 8 to 10 years ago, which was quite challenging as it had multiple windows to manage separately. In contrast, Talend provided a more integrated experience within a single window, making it easier to work with, offering plenty of components and transformations.

I evaluated Informatica a few years back as part of my decision-making process.

My advice for others considering Qlik Talend Cloud is that it is a good product that has everything required, even when compared to other well-known products. Talend is user-friendly and has many components and connectors, which makes it a great choice. New technologies are readily supported with connectors offered quickly by Talend. Skill development for new resources is also efficient. I would rate this product an 8 out of 10.