Talend Data Quality Reviews

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.

Three to five years.

Better control and flexibility to add/custom define features, to tailor to your needs by modifying its Java generated code.

While building Customer registry for a global hi-tech manufacturing firm few years back, to establish single view of customer across geographies, Talend data quality was used in conjunction with other tools to track and manage data quality metrics, with minimum investment. DQ Scorecards were published for daily updates to DMO(data management operations) and steering committee.

I’ve been part of defining and building similar solutions and been part of this specific solution involving Talend for close to an year.

Yes, and we did overcome the same by custom defining/development efforts.

Switch to Talend came natural since working on a custom data quality solution then and encountered difficulty managing the same.

An application dev/consulting firm supported with custom development efforts, not a Talend data quality vendor, intuitive UI and features of product helped the team to learn quickly and work on this platform.

We've evaluated multiple options including building/maintaining a custom solution. Cost, maintenance and custom development requirements were primary factors for choosing Talend.

Account for Java developers/ custom development efforts apart from DQ functional/technical expertise, to use Talen DQ product to the fullest.

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.

One to three years.

Fuzzy matching lookups.

Talend has allowed us to systematically organise/structure and deploy our staged ETL transformation processes from Development into production, we have tracked our data quality efforts during our runs and supplied comprehensive feedback during our development.

Toolbox/component integration, performance (optimal memory performance) bench marks / manual across 64bit 32 bit architectures not existent.

2-4 years.

No.

Sometimes when working with larger datasets (possibly due to insufficient memory).

No.

Customer Service:

Excellent

Technical Support:

Excellent

Yes I have, found Talend less fussy with different data and debugging tools. It is a superior solution once you are acquainted with it.

Straightforward.

In-house,

100%.

No setup costs or usage costs. Talend open studio.

Yes, SSIS and Pentaho.

Platform/Technology specific decisions need to be made upfront before considering this solution.