Talend Data Quality Reviews

The primary use case is for data ingestion. We current have HDP 2.6 installed on Ubuntu 16.04.

Has reduced manual effort to import data.

Data ingestion

Not enough material is available for beginners.

We use it to load our big data system with S3 and Redshift. We also use it to process in HL7 from hospitals in real-time.

It lowers the amount of time in development from weeks to a day.

The ease of transforming data with inputs to TMaps and tJavaRow makes life so easy.

There is one place where I would appreciate an upgrade, if it is possible. If the SQL input controls could dynamically determine the schema-based on the SQL alone, it would simplify the steps of having to use a manually created and saved schema for use in the TMap for the Postgres and Redshift components. This would make things even easier. When it does guess the schema it tends to bring back every column from every table or every column from the table specified in the table name in the component. Sometimes, the SQL comes from multiple tables and has some transformations of data. 

I do not know if it would even be possible, but if this could be figured out automatically for the column names and types, that would be amazing.

I have not run into anything we could not use Talend to find a solution for.

• Fixing data by using regular expressions or synonyms and Data Stewardship.
• Using data profiling to gauge the quality of the data before and after it’s used/needed.
• Master Data Management - Authoring and matching survivorship, including Data Stewardship.

It allows our customers to master and expand their products to an international scale. In addition, it enables customers to consolidate multiple, disparate sources of data into a centralized, master data hub which can used for operations or analytics.

The solution enables robust data matching, merging, survivorship, and Data Stewardship that can be a part of data quality workflows or true master data management.

Needs integrated data governance in terms of dictionaries, glossaries, data lineage, and impact analysis. It also needs operationalization of meta data.

We are a marketing and advertising company. We use this tool to fetch data from Google, Bing, and Adobe. We receive marketing data daily via email, FTP, and API, then process the data into MySQL tables.

Coming into the department with no knowledge of Talend, the interface has been user-friendly enough to allow me to come up to speed in four to five months on almost all its functions and use it like a pro.

• The file fetch process is impeccable. 
• We are able to get emails from URLs very easily using this function when others fail. 
• tLogRows are also great for finding bad data.

NullPointerExceptions are going to be the death of me and are a big reason for our transition away from Talend. One day, it is fine with a 1000 blank rows, then the next day, it will find one blank cell and it breaks down. When we are dealing with millions of rows of data, this can be super hard to find. 

Heap space issues also plague us consistently. We maxed it out and it runs fine, then it doesn’t, then it does. 

Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using. 

Inconsistency is a big issue.

JRules, TMap, TParallel, ELT, etc

It has provided the feature wherein the business could make the changes as requested without performing the ETL deployment code to production.

I think the memory issues we faced when using the 3GB RAM compared to the 4GB RAM computers caused lot of issues. Probably can improve in that.

4 years - Talend Open Studio 3.1.2, 4.1.3, 5.0, Talend Integration Suite 4.1.3, Talend Data Quality 4

Intially we did encountered issues with the deployment, but over the period of time we were able to find the proper way to perform the deployment and also used a tool called HERMES for the deployment.

No issues

No issues

Very nice customer service

Excellent support from the technical support team

Yes earlier we had Ab Initio but switched to Talend because initially it was an Open Studio with no cost involved and also it was supported by the JRules component.

It was not straight forward as it was pretty new to everyone among our team, but over the period of time when we had hands on the tool everything got smooth.

It was a in-house team.

Ab Initio, Informatica etc.

I would definitely recommend others to implement this product as it is really helpful, easy to learn, user friendly, provides lot of enhanced features, etc.

Data Quality easily identifiable instead of manual finding and cleaning the data through Excel (earlier used to follow) before ETL

Currently the best open source data quality tool available as compared to other open DQ tools ('DataCleaner', 'Open Source Data Quality & Profiling') for of a variety of reasons:

1. Vast connectors to different DB, Web, CRM, etc
2. Custom code is allowed
3. Wide range of advanced algorithms
4. Recommended for advanced users
5. Detailed analysis, etc
6. Large community of users

The most valuable features for us are: custom code, connectors, algorithms.

As it is a open source tool, some minor bugs are there.

Fairly straightforward. Lots of user guides and tutorials are available to get started.

The best part is that it is open source.

Great product, surely give it a try.

Maybe the best thing is the product's easy start-up level when you are familiar with Java. Also job creation is fast compared to some other tools. One more good thing is that tables' metadata is easy to bring into the tool and utilize. Last thing to mention here is flexibility to use Java code inside the job.

These are: fast job creation from start to finish which improves ROI, good and easy debugging functions.

First, We faced problems with stability of the products. Also some components were clearly not tested well, which meant that there were bugs. Better tools for geo-data are needed. Documentation was poor in the beginning but it got better over time.

Talend Enterprise Data Integration 5.1 (1) and Talend Platform for
Data Services (2)

2 years by one customer (without Data Quality (1)), 6 months in other customer (with Data Quality(2))

At the customer deployment to the production environment from the test one was a bit exhausting. This could be because they didn't use/know the best-practices.

Yes we had issues. Quite often the server needed rebooting as if there were memory leaks. Sometimes the CVS version management got stuck.

No issues. Only issues were with the Java memory which is scalable and changeable from the job settings.

Customer service was good most of the time. Answers came in a timely fashion.

It was good most of the time. Answers came in a timely fashion.

It was pretty straightforward. Memory settings by the client needed some modification in the first place. From the server point of view I cannot say.

In house team.

Yes. We evaluated IBM DataStage.

Data migration (database to database using direct DB access and commands or using web services).

It has definitely streamlined certain processes.

The ability to build the interface using clear components and access the code (Java) to validate and trace any error. The wide range of components which suits a variety of purposes and provides a flexible development environment to the coder.

The ability to change the code when debugging the JavaScript could be improved.