We performed a comparison between Databricks and Informatica PowerCenter based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: PeerSpot users consistently feel Databricks is a more complete solution, providing better integrations, features, and ease of use. The cloud-based architecture makes scaling seamless.
"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"The solution is very simple and stable."
"The solution is an impressive tool for data migration and integration."
"Databricks helps crunch petabytes of data in a very short period of time."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"I work in the data science field and I found Databricks to be very useful."
"Has a good visual tool for data mapping."
"It is very comprehensive in terms of connector and transformation capabilities from both a source and target perspective."
"The setup is straightforward."
"The technical support for Informatica PowerCenter is good."
"If the systems get migrated or upgraded, the amount of resources required are very minimal. We can change the connections and establish a new connection. It's very helpful."
"UI-based ability to create data mapping."
"Informatica PowerCenter is a very good ETL tool."
"The support is valuable. There are also open-source ETL products, which work very well, but there is no support. When we face a production problem, being able to get support is valuable, and it brings efficiency. With an open-source solution, we can't engage anyone to resolve the problem as quickly as possible."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"The integration of data could be a bit better."
"There is room for improvement in visualization."
"In the next release, I would like to see more optimization features."
"It should have more compatible and more advanced visualization and machine learning libraries."
"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"It should be more cloud-centric than on-prem-centric."
"Its licensing can be improved. It should be features-wise and not bundle-wise. A bundle will definitely be costly. In addition, we might use one or two features. That's why the pricing model should be based on the features. The model should be flexible enough based on the features. Their support should also be more responsive to premium customers."
"Some of the conversions are done inside the product. We use work tables that are created by the engine itself, but the names of the work tables are very long, and they don't have any meaning, which makes it a bit difficult to understand and follow exactly what is happening inside."
"This solution needs the functionality to do batch processing of data. It also lacks connectivity to NoSQL, unstructured data sources."
"Lacks ability to calculate cost of the product."
"It would be good to recreate the entire interface to make it easier for users to build workflows."
"In the future, I would like to see Informatica PowerCenter integrate a more powerful dashboard."
"Informatica PowerCenter is outdated and would benefit from modernization. They should have a very good migration strategy from Informatica PowerCenter to AACF. Informatica PowerCenter there is no point in using it, you have to use a cloud version."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Informatica PowerCenter is ranked 3rd in Data Integration with 78 reviews. Databricks is rated 8.2, while Informatica PowerCenter is rated 8.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Informatica PowerCenter writes "Stable, provides good support, and integrating it with other systems is very fast, but its pricing is expensive". Databricks is most compared with Amazon SageMaker, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio, Dremio and Azure Stream Analytics, whereas Informatica PowerCenter is most compared with Informatica Cloud Data Integration, Azure Data Factory, SSIS, AWS Glue and Oracle Data Integrator (ODI). See our Databricks vs. Informatica PowerCenter report.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.