AWS Glue vs. Fivetran vs. Informatica Intelligent Data Management Cloud

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

Incentivized

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

Incentivized

Informatica Cloud is a great tool for use when data must be formatted consistently. Once configured, it is very robust and reliable. It is also well-suited for an organization without a robust IT staff to maintain a full server infrastructure. It offers a cost-effective approach to high-quality data integration for even the largest organizations. Organizations without staff experienced in data analytics may find it challenging to take advantage of the more complex results of this tool.

Incentivized

• It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
• As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
• It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

Incentivized

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

Incentivized

• Once the secure connection is established it’s quite easy to operate and create new jobs. The controls are simple, and we appreciate the fact there are not a lot of complex fine-tunings required. Navigation is also easy, and we enjoy the ability to open multiple tabs in the browser to work on multiple projects.
• The monitoring functionality works well to help track the progress of the jobs, again, without too much complication. In a fast dev environment, speed is essential and we quickly seeing the status/progress of jobs as well as any errors if the jobs fail helps us maintain speed.
• The web interface is a lot easier to interact with than the client/on-prem version. Putting much of the heavy lifting of interacting with the tool onto the shoulders of the browser makes it easier to keep multiple sessions open and get in/out quickly without having to VPN into the office.

Incentivized

• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

Incentivized

• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

Incentivized

• The User Interface can be a bit difficult to get in initial stages.
• Error messages are hard to understand which sometimes takes a lot of time for the resolution
• Cannot Implement complex SQL queries which should be fixed in future release.

Incentivized

While easy to set up and manage monitoring for large datasets, its complexity can be a barrier for new users. Integration with AWS Ecosystem, Managed Monitoring, Dashboards and monitoring tools for AWS Glue are generally easy to set up and maintain, Automated Data Pipelines. Automates data pipeline creation, making it efficient for certain data integration

Incentivized

Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

Incentivized

Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.

Incentivized

I've never had trouble getting into contact with Informatica's support for technical help. I give it a nine because it does pretty well for mid to enterprise-scale workflows.

Incentivized

AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.

We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

Incentivized

First, the wizard is easy to use making the learning curve for simple ETL tasks nice. Second, since Informatica is mature there are a good variety of connectors available. Finally, we have driven some fairly complex ETL solutions using only the cloud.

Incentivized

• We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
• One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing

Incentivized

• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

Incentivized

• Cut costs on the number of staff we need for data import.
• Cut costs for automation that can be done for data that no longer has to be manually imported.
• We've saved countless dollars by eliminating duplicates in our system with this tool.

Incentivized