meltano VS Pandas

- https://github.com/meltano/meltano

We'd love to hear what you think of Meltano (Cloud). If you join the Beta, you get 100 free credits (200 hourly or 100 daily runs) and a 20% discount on the pricing at GA (June 27). The first 100 to sign up get 1,000 credits -- that's 83 days of hourly runs or 3 years of dailies!

The team and I will be checking in here throughout the day, so don't hesitate to ask questions! If we don't get to you, feel free to join 3,500+ Meltano fans on https://meltano.com/slack and we'll chat there!

Meltano[0] might be of interest to you. Easy way to move data that should be very familiar for software engineers. If a connector doesn't exist our SDK makes it easy to build it.

[0] https://github.com/meltano/meltano

(disclaimer - I work at Meltano)

We currently don't do any process optimization on a per-stream basis when doing an extract. We have seen folks in the community running each tap separately for each stream which can speed it up. We've got an issue around this (Melturbo).

Meltano

I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)

I'm learning https://meltano.com/ right now, so am building custom Taps, mostly for fun. I'm enjoying it. I'm pulling in a variety of data from https://www.geonames.org/ and Canadian weather/climate data into BigQuery

https://meltano.com/ is ELT but I like it

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.