We asked business professionals to review the solutions they use. Here are some excerpts of what they said:. Alteryx is a self-service data analytics solution, that provides a platform that can prep, blend, and analyze all of your data, then deploy and share analytics in hours. It can automate time-consuming and manual data tasks while performing predictive, statistical, and spatial analytics in the same workflow. It uses a drag and drop tool with an intuitive user interface with no coding and programming required.
Alteryx licenses are subscription-based and include all product updates and support to their customers. Sign In. Compare Alteryx vs.
Alteryx is rated 9. The top reviewer of Alteryx writes "Does a good job of end-to-end integration as well as accessing data from multiple sources or email modes". On the other hand, the top reviewer of KNIME writes "Has good machine learning and big data connectivity but the scheduler needs improvement ". See our Alteryx vs. KNIME report. Cancel You must select at least 2 products to compare! Read 12 Alteryx reviews. It is a free open-source tool that performs very similarly to other expensive tools.
KNIME has been great for me over the years. It allows me to Free Report: Alteryx vs. Find out what your peers are saying about Alteryx vs. KNIME and other solutions. Updated: March Download now. Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs. See Recommendations.
KNIME vs. Dataiku Data Science Studio vs. Microsoft BI vs. Alteryx vs. RapidMiner vs. Weka vs. Learn More. Top Industries. Company Size. 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.Thursday, July 26, It has the goal to make the application stacks of large enterprises which have evolved over decades simpler and more powerful by providing a versatile mediation system.
In particular its support for historization of events and their visualization provenance, lineage sets it apart from competing products. After an introduction to Apache Nifi and its underlying concepts you will learn about a case study from the telecommunications sector which shows it's performance and easy extensibility via custom data processors.
It was originally developed at Airbnb, today it is very popular and used by hundreds of companies and organizations.
Compare Alteryx vs. KNIME
This talk briefly introduces the concepts in Airflow, gives some tips and tricks about deploying and operating Airflow, and shows how Airflow is used at SimScale. Christine K. Stefan S. Afaq Alam A. Ahmad A.
Alex T. Alexander S. Skip to content COVID advisory For the health and safety of Meetup communities, we're advising that all events be hosted online in the coming weeks. Learn more. Hosted by Stefan S. Munich Data Engineering Meetup.
Public group? Attendees See all. Go to Attendee List Christine K. Munich Data Engineering Meetup See more events. Past event.Developers describe Airflow as " A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb ". Use Airflow to author workflows as directed acyclic graphs DAGs of tasks.
The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
On the other hand, Apache NiFi is detailed as " A reliable system to process and distribute data ". An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic. Airflow is an open source tool with Here's a link to Airflow's open source repository on GitHub. Airflow Stacks. Apache NiFi Stacks. Need advice about which tool to choose?
Ask the StackShare community! Apache NiFi. Airflow vs Apache NiFi: What are the differences? Some of the features offered by Airflow are: Dynamic: Airflow pipelines are configuration as code Pythonallowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment. Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine. On the other hand, Apache NiFi provides the following key features: Web-based user interface Highly configurable Data Provenance Airflow is an open source tool with What is Airflow? What is Apache NiFi?
Why do developers choose Airflow? Why do developers choose Apache NiFi?In this first part we will start learning with simple examples how to record and query experiments, packaging Machine Learning models so they can be reproducible and ran on any platform using MLflow.
Machine Learning ML is not easy, but creating a good workflow which you can reproduce, revisit and deploy to production is even harder. There has been many advances towards creating a good platform or managing solution for ML. These packages are great, but not so easy to follow. Maybe the solution is a mix of these three, or something like that. MLflow is designed to work with any ML library, algorithm, deployment tool or language.
And this is according to the creators. But I faced several issues while installing it. So here are my recommendations if you can run mlflow in your terminal after installing ignore :. And the way of solving that was not very easy.
Installing protoc API for protocol buffers using modern Haskell language and library patterns. So what have we done so far? The first one logs the passed-in parameter under the current run, creating a run if necessary, the second one logs the passed-in metric under the current run, creating a run if necessary, and the last one log a local file or directory as an artifact of the currently active run. So with this simple example we learned how to save the log of params, metrics and files in our lifecycle.
Both keys and values are strings. The goal is to model wine quality based on physicochemical tests. After running you will see in the terminal this:. You will see:. And you will have this for each run, so you can track everything you do.
Also the model have a pkl file and a YAML for deployment, reproduction and sharing. Data Scientist. Physicist and computational engineer. I have a…. Favio has 15 jobs jobs listed on…. He has a passion for science, philosophy, programming, and music.
Right now he is working on data science, machine learning and big data as the Principal Data Scientist at Oxxo. He loves new challenges, working with a good team and having interesting problems to solve. He loves applying his knowledge and expertise in science, data analysis, visualization, and automatic learning to help the world become a better place.Nearly four years after this was written, there is still no satisfying solution to providence tracking in a data science workflow.
The few solutions that do exist tend to be too opinionated to catch on. I've gotten a lot of mileage out of hacking some provenance tracking into a plain-old-Makefile workflow, but it's hard to balance human-readable makefiles and the amount of hackery it takes. I would love to know what you think about Domino Data Lab's solution. Here's an older video describing it. We now track things like git checkouts and dataset versions too. It looks like the right approach for building a platform from scratch, the problem is that I am working with an existing in-house platform.
Makefiles are so un-opinionated that they fit in quite nicely, all I really want is a more modern Make. I've tried a few other techniques, but there is something that is really hard to beat about the plain Makefile.
Eventually, it can become unwieldy, but for small-medium projects, it's a life saver. Combine that with git, and you've got a pretty robust system. The only real downside is that when you have very large initial inputs, it isn't practical to store those in the same repository.
That is my goto combinations for bioinformatics workflows that don't need to run on a cluster. A Makefile variant of mine is my go-to for clusters. I've experimented with Docker workflows, which while in theory are nice, you'll always require some sort of organizing script to document the analysis.
I treat the cloud resources as immutable once created so that the "pointer" file's modified time is consistent with the modified time of the data on the cloud. At my company we use mixes of R, python and Scala, together with airflow.
We have been toying with our own templates, and should release them eventually. Yes, I actually only use Python for notebooks, so I mostly ignore the Python specific parts. I don't actually use their template, I just use the directory layout as guidance. Yeah I tend to be pretty meticulous about this too, but it takes a large amount of effort and there's no incentive for most people to do it unfortunately. I've been writing Makefiles too, but I don't feel like I can explain with a straight face to others that they should do it too It is open source, language agnostic, and distributed.
Plus it automatically tracks the provenance of all of your data pipelines, regardless of language or parallelism over your data. That way you have versioned sets of your data e.
Apache Nifi & Apache Airflow
Things like Airflow and Luigi are, no doubt, useful for data pipelining and some workflows depending on what language you are working with. However, by combining pipelining and data versioning in a unified way, Pachyderm naturally lets you handle provenance of complicated pipelines, have exact reproducibility, and even do interesting things like incremental processing.
You should also check out Pachyderm, github. Pachyderm builds provenance tracking directly into your data so every results has provenance through every bit of data and code that was used to create it. Luigi works pretty well for workflow and versioning, if not complete provenance. Personally moved to improve on the ideas in SciLuigi, but replacing Luigi with Go's concurrency primitives, in SciPipe .
In SciPipe, an accompanying ". Among lightweight solutions, the popular ones these days seem to be NextFlow , Snakemake , BPipe  and others.
You'd really have to check out the "awesome pipelines"  list, to get any kind of overview. When more infrastructure setup is feasible, I think PachyDerm  mentioned elsewhere in the thread looks really powerful with its "Git for data" approach. Something I'd wish to use as an overarching solution within which to run my SciPipe workflows. Luigi doesn't support incremental builds, does it? I think that was my main contention the last time I looked into it.
What do you mean by incremental builds?DataOps is very important in data science, and that my opinion is that data scientists should pay more attention to DataOps.
At the moment we normally are versioning code with something like Gitand more people and organizations are starting to version their models. But what about data? In this blog post, I provide a transcript of the interview.
You might find interesting the ideas behind DVC and how Dmitry sees the future of data science and data engineering. You can hear the podcast here:. We need to pay more attention on how we organize our work. We need to pay more attention how we structure our project, where we need to find the places where we waste our time instead of doing actual work. And is very important to be more organized, more productive as a data scientist, because today, we are still on the Wild West.
Disclaimer: This transcript is a result of listening to the podcast and writing what I heard. I used some software to help me in the transcription but most of the work is made by my ears and hands, so please if you can improve this transcription feel free to leave a comment below :. So Dmitry, could you start by introducing yourself?
It is pleasure to be on your show. I am Dmitry Petrov. I have a mixed background in data science and software engineering. About 10 years ago I worked at academia and sometimes I say, I work with machine learning for more than 10 years but you probably know that 10 years ago machine learning was mostly about like linear regression and logistic regression laughing.
This is pretty much what I was working on. Then I switched to software engineering. I write some production code. At that time, data science was not a thing. Around five years ago, I switched back to quantitative area.
I became a data scientist at Microsoft. And I saw how modern data science looks like.
Data version control with DVC. What do the authors have to say?
Today we are working on DVC and I basically build tools for machine learning. Do you remember how you first got introduced to Python? It happened in I believe. And later I found myself using Python in every research project that I was working on.
So but I still used Python for ad-hoc projects for automation.Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. Use Airflow to author workflows as directed acyclic graphs DAGs of tasks.Lightning Talks: Dan Whitenack: Pachyderm - Machine Learning and Data Pipelines
The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed; MLflow: An open source machine learning platform.
MLflow is an open source platform for managing the end-to-end machine learning lifecycle. Airflow and MLflow are both open source tools.
It seems that Airflow with Airflow Stacks. MLflow 39 Stacks. Need advice about which tool to choose? Ask the StackShare community! Airflow vs MLflow: What are the differences? Some of the features offered by Airflow are: Dynamic: Airflow pipelines are configuration as code Pythonallowing for dynamic pipeline generation.
This allows for writting code that instantiate pipelines dynamically. Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment. Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine. On the other hand, MLflow provides the following key features: Track experiments to record and compare parameters and results Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms Airflow and MLflow are both open source tools.
What is Airflow? The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
What is MLflow? Why do developers choose Airflow? Why do developers choose MLflow? Sign up to add, upvote and see more pros Make informed product decisions. What are the cons of using Airflow? Be the first to leave a con. What are the cons of using MLflow?
Explainable. Repeatable. Scalable.
What companies use Airflow? What companies use MLflow? Sign up to get full access to all the companies Make informed product decisions.
What tools integrate with Airflow? What tools integrate with MLflow? What are some alternatives to Airflow and MLflow?
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. An easy to use, powerful, and reliable system to process and distribute data.
It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.