Likelihood to Recommend If you can load your data first into your warehouse, dbt is excellent. It does the T(ransformation) part of ELT brilliantly but does not do the E(xtract) or L(oad) part. If you know SQL or your development team knows SQL, it's a framework and extension around that. So, it's easy to learn and easy to hire people with that technical skill (as opposed to specific Informatica,
SnapLogic , etc. experience). dbt uses plain text files and integrates with GitHub. You can easily see the changes made between versions. In GUI-based UIs it was always hard to tell what someone had changed. Each "model" is essentially a "SELECT" statement. You never need to do a "CREATE TABLE" or "CREATE VIEW" - it's all done for you, leaving you to work on the business logic. Instead of saying "FROM specific_db.schema.table" you indicate "FROM ref('my_other_model')". It creates an internal dependency diagram you can view in a DAG. When you deploy, the dependencies work like magic in your various environments. They also have great documentation, an active slack community, training, and support. I like the enhancements they have been making and I believe they are headed in a good direction.
Read full review Helps to increase productivity, optimize costs, and democratize data across multiple cloud environments with cloud ETL and ELT. Capacity to integrate data sources at scale and with ease. Has cloud data integration capabilities that cover diverse sets of patterns, use cases, and users ensuring we have well-architected and seamless automated data pipelines.
Read full review Pros user experience makes it easy to work with SQL and version control customer success team and the dbt (data build tool) community help establish best practices thorough and clear documentation Read full review The matching algorithms in IDQ are very powerful if you understand the different types that they offer (e.g., Hamming Distance, Jaro, Bigram, etc..). We had to play around with it to see which best suit our own needs of identifying and eliminating duplicate customers. Setting up the whole process (e.g., creating the KeyGenerator Transformation, setting up the matching threshold, etc..) can be somewhat time consuming and a challenge if you don't first standardize your data. The integration with PowerCenter is great if you have both. You can either import your mappings directly to PowerCenter or to an XML file. The only downside is that some of the transformations are unique to IDQ, so you are not really able to edit them once in PowerCenter. The standardizer transformation was key in helping us standardize our customer data (e.g., names, addresses, etc..). It was helpful due to having create a reference table containing the standardized value and the associated unstandardized values. What was great was that if you used Informatica Analyst, a business analyst could login and correct any of the values. Read full review Cons Slow load times of the dbt cloud environment (they're working on it via a new UI though) More out-of-the-box solutions for managing procedures, functions, etc would be nice to have, but honestly, it's pretty easy to figure out how to adapt dbt macros Read full review Several partnerships diminishing the value of technologies Unable to get list of objects from Repository (like sources & targets) that don't have any dependency Scheduling: The built-in scheduling tool has many constraints such as handling Unix/VB scripts etc. Most enterprises use third party tools for this. Read full review Likelihood to Renew As pointed out earlier, due all the robust features IDQ has, our use f the product is successful and stable. IDQ is being used in multiple sources (from CRM application and in batch mode). As this is an iterative process, we are looking to improve our system efficiency using IDQ.
Read full review Usability Easy to use not only for developers but also business users
Read full review Reliability and Availability The application works well except an occasional error out while using the system. It usually gets fixed when restarting the Infa server
Read full review Performance Performance works just fine. It was able to load 200+ business terms, 150+ DQ automation, etc. very well.
Read full review Alternatives Considered Most ETL pipeline products have a T layer, but dbt just does it better. The transformation is on steroids compared to the others. Also, just allows much more Adhoc solutions for very specific projects. Those ETL tools are probably better on the T part if you don't need too many transforms - also dbt is pretty much free dependent on how you work it, also extremely scalable.
Read full review Informatica Data Quality has a wide range of cleansing features, that are detailed, professional, and accurate in scaling down the required database. Further, Informatica Data Quality ensures there is proper collaboration, and this fosters businesses to have the freedom of working closely with several programs. Finally, Informatica Data Quality design is authentic and allows personalization.
Read full review Scalability Scalability works as expected and it is truly an enterprise system.
Read full review Return on Investment Simplified our BI layer for faster load times Increased the quality of data reaching our end users Makes complex transformations manageable Read full review Integration with tools like PowerCenter helped faster delivery of product, and at the same time conversion Reduce overall project cost due to bad data , bad quality, exceptions identified nearing go-live and post production Employee efficiency is increased exponentially due to more automated, customized tool Read full review ScreenShots