We performed a comparison between AWS Glue and Jitterbit Harmony based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), MuleSoft, Matillion and others in Cloud Data Integration."AWS Glue is fast and managed by AWS. Hence, you don't have to worry about capacity and the performance of Glue jobs. It has integrations with other data stores of AWS. The product offers metadata management, logging, and ETL processing capabilities. It comes with a powerful feature, Glue Studio, which helps to do queries interactively within the community. It is a managed service and very secure. Another popular and mature service is S3."
"The most valuable feature of AWS Glue is scalability."
"I like that it's flexible, powerful, and allows you to write your own queries and scripts to get the needed transformations."
"The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team."
"AWS Glue is quite better than other tools, but you have to learn it properly before you start using it."
"It is AWS-integrated. There is end-to-end integration with the other AWS services. It is also user-friendly."
"The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features."
"AWS Glue's most valuable features are the data catalog, including crawlers and tables, and Glue Studio, which means you don't have to use custom code."
"It is very easy to build integrations and processes to pull and push data."
"Jitterbit handles the most lines of data in a .csv and loads the quickest of any data loader I have tried."
"Jitterbit provides the ability to quickly map data between files and databases."
"It runs like an appliance and has tremendous throughput."
"Easy integration with Salesforce"
"It is a scalable solution."
"Integrity, ease of use, user-friendly user interface, and errorless logs are the most valuable features."
"The Cloud Console for monitoring and troubleshooting data operations."
"Currently, it supports only two languages in the background: Python and Scala. From our customization point of view, it would be helpful if it can also support Java in the background."
"The start-up time is really high right now. For instance, when you start up a new job, you have to wait for five or eight minutes before it starts. If the start-up time is reduced to one or two minutes, it will be great. It will be better to have a direct linkage to Redshift in AWS. If we can use data catalogs from Redshift, it will be so easy to create some data catalogs. Currently, we can only use data catalogs from S3."
"It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options."
"The technical support for this solution could be improved. In future, we would like to connect more services like Athena or Kinesis to help control more loads of data."
"There is a learning curve to this tool."
"I would like to see stable libraries at the moment they are not there."
"The interface for AWS Glue could improve, they do not put a lot of details. You can write the code, in PySpark or in Scala, which is a big advantage, it is only easy to use for a developer. It will be difficult for new users to enter the cloud environment."
"We face performance issues when using AWS Glue for data transformation and integration."
"Sometimes additional connectors are needed."
"There were some bugs in the product. For example if you run a delete query and test, it deletes the actual data."
"In the past few months, there have been some server downtimes during work hours that have affected some critical scheduled integrations."
"You need to have some development skills or hire a Jitterbit engineer to make changes."
"I know with Salesforce updating the UI like they did, it slowed it down a lot."
"Its API management capabilities need improvement."
"I would like the ability to offer a dedicated cloud version for security."
"The initial setup can be a little bit difficult."
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Jitterbit Harmony is ranked 19th in Cloud Data Integration with 13 reviews. AWS Glue is rated 7.8, while Jitterbit Harmony is rated 8.6. The top reviewer of AWS Glue writes "Provides serverless mechanism, easy data transformation and automated infrastructure management". On the other hand, the top reviewer of Jitterbit Harmony writes "An easy-to-setup solution with good stability ". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, Informatica Cloud Data Integration, SSIS and Talend Open Studio, whereas Jitterbit Harmony is most compared with Microsoft Azure Logic Apps, Azure Data Factory, Mule Anypoint Platform, MuleSoft Composer and SnapLogic.
See our list of best Cloud Data Integration vendors.
We monitor all Cloud Data Integration 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.
