Prefect Reviews & Product Details

Prefect is one of the low costing and best orchestration tool in market. We can integrate lots of platforms with prefect. Databricks and snowflake jobs can be ran through prefect. Review collected by and hosted on G2.com.

Need Python knowledge to work on prefect and need to have some more features where Airflow is ahead Review collected by and hosted on G2.com.

Prefect is one of the best orchasteration tool. Using source git repo we can customize integration our own way. Easy to use even for beginners using local setup. Knowledge on python is sufficient. Review collected by and hosted on G2.com.

Other major competitor to prefect has pre built hooks and connectors where prefect is behind cause we shoud develop the connectors Review collected by and hosted on G2.com.

The UI is the best part. All of our production processes, prefect or not, send error messages to slack. Our team members rotate being on call, so if an error message persists in slack, whoever is on call has to investigate. With most of our processes, the on call person has to get out their laptop, track down logs, etc. If they are in the middle of something, such as dinner, friends, etc, they have to go back to their laptop to handle things. With prefect, I am easily able to go on my phone when a prefect error comes in, log into prefect, and take care of everything I need to right there. An ios app for prefect would make this even better than it already is. Review collected by and hosted on G2.com.

Documentation is terrible. Not enough examples, especially with the more complicated things like work pools and blocks. Any examples that are there are way to simple to actually help you implement it on something real. The great design of the UI give you better clues as to how things work than the documentation does. Review collected by and hosted on G2.com.

Easy to understand framework and concepts for new DataOps position, comparing to Airflow

Supportive and helpful through Slack channel, typically get response within a hour Review collected by and hosted on G2.com.

Nothing so far, we are happy with Prefect Free Versions Review collected by and hosted on G2.com.

The thing our team has enjoyed the best about the prefect is how easy it is to convert any python code into a working and automated pipeline via the prefect decorators. We were able to migrate our cloud function workflows into prefect in just a couple of days. The declarative deployments yaml file is also easy to understand and when used in our CI/CD pipelines. Review collected by and hosted on G2.com.

I wish that prefect had more integrations but I do understand that it is growing and the team is working hard to grow this area. One thing that I wish prefect cloud had a native integration with Kafka and integrations with DataHub. Review collected by and hosted on G2.com.

Prefect is all about taking the idea of having scheduled Python scripts and easily and expertly tacking on all the things that make an engineer's life easier: UI-based scheduling, parameterization, flow visualization, detailed logging, exception handling, notifications, webhooks, containerization and easy deployments, concurrency. It's like a pure (and massive) value-add system with almost no need to make concessions in how you like to connect to data, transform data, or deliver data. As such I think it's especially powerful when you have a lot of non-standard ETL pipelines going in all directions (i.e. a mountain of tech debt) and you want to clean things up without being forced into restrictive design decisions.

Prefect is really flexible, not just in terms of how you deploy the execution agents, but also in terms of how you want to structure your code and what Prefect features you want to use heavily or lightly. There's no limitation on data sources or integrations: it just fits into whatever you're already doing with Python. It doesn't force you into any mold beyond just using Python, although the concepts of tasks, and the way Prefect handles concurrency, is super helpful if you want to make use of these things. It makes it easy to containerize Python scripts and make every aspect of a data flow deployment modular and flexible. And once you have your settings the way you like them, deploying changes or new flows is easy.

We run about 200 scheduled Prefect flows a day, sending and receiving data between over 50 different systems at our university. Prefect has been a lifesaver. Documentation, support, and the Prefect Slack space have all been super helpful. Review collected by and hosted on G2.com.

I can't complain hardly at all about Prefect. Having said that, I'm an experienced engineer who likes a lot of control. If you're already writing scheduled Python scripts to move data, Prefect will be a great fit, I think. But it isn't designed to address questions like how to extract data from X system or load it into Y system or how to transform data. It's about simplifying all the scaffolding around the guts of your Python code so you can focus on doing what you need to do. Review collected by and hosted on G2.com.

It is pythonic and therefore easy to implement.

Version 2.0 has great features, like deploy.

This allows us to develop remotely, collaboratively but in an orderly and simple way.

Once you understand the essentials, i.e. flows, tasks, work-pool, workers and deployment, everything becomes very simple.

It has a great community, very active. In general, the problems I've had already have several comments and solutions on Slack. Review collected by and hosted on G2.com.

We had to read and test for a couple of days to get the development environment up and running and connect to the cloud.

In the documentation, more examples are missing, such as the connection with Gitlab (there are only examples with Github) and there are a couple of differences that we solved with the community Review collected by and hosted on G2.com.

I've followed Prefect since version 1, and have happily observed as my suggestions on their (very responsive) Slack-channel have made it into production.

Very little initial code is required to register a flow, subflows and subtasks, but the options are vast. I do relatively simple ETL-jobs, but find myself eliminating a lot of the manual checks as I can trigger flows based on events in others.

The API is decently documented.

Elegant retries, flow-naming and options for task-runners.

If your are like me and found Airflow daunting - this is a really good choice. Review collected by and hosted on G2.com.

It's in rapid development, so pay attention to the release logs. As an example, I used to deploy using Python objects, but now there preferred option is the prefect.yaml file.

Some of the development is not always reflected in the documentation, but they are catching up.

The Flow-view can be a bit cluttered if you have some jobs that run for a few minutes and others for hours. Review collected by and hosted on G2.com.

It is quite easy to anotate existing python code and convert it in a prefect flow without changing a lot of code.

The hybrid architecture which uses agents or workers allows you to have a lot of flexibility on where and how the code is executed. This also allows you to have isolated environments for every flow in a cleaner way than something like airflow. Review collected by and hosted on G2.com.

Prefect 2 is still a young framework and it is still under construction so expect it to have a lot of constant updates and posible breaking changes.

Its community is not as big as something liker "Airflow" so it lacks a lot of connectors and also some of them are not frequently updated. Review collected by and hosted on G2.com.

I recently had the opportunity to adopt a Prefect centric ETL orchestration pattern and I have to say, it has been a game-changer. It was perfect for our situation where we had multiple locations with different setups, but wanted a common logging and deployment pattern.

What impressed me the most was how user-friendly Prefect is. It's much more straightforward compared to complex solutions like Argo Events, and or relatively mature products like Airflow. If youre in the python space working on ETL, you should be using this product.

But what really got me excited was Prefect's recent introduction of their AI tool called Marvin. It's fantastic for small "AI functions" and it allows us to leverage chatGPT results as a structured object type. This has added a whole new level of efficiency to our ETL processes cleaning up free text fields into structured categories.

Overall, I can confidently say that Prefect has exceeded our expectations. It's been a breeze to use and has greatly improved our ETL workflow. I highly recommend it to anyone in need of a reliable ETL orchestrator. Review collected by and hosted on G2.com.

There have been several breaking changes pushed over minor releases. Additionally they seem to have siliently stopped fixing bugs related to the "agents" in favor of the "workers" approach. The fact that there are known bugs with agents that will not be fixed in favor of the user adopting an entirely new pattern does get frustrating as a paying customer. Review collected by and hosted on G2.com.