ngods-stocks VS practical-data-engineering

I've worked for 3-4 years in positions where I helped structure ETLs, DWs and alike. However, I'm now on the cusp of being hired to help structure the area in a big investment fund here, helping the research area have an easier time focusing on their models. My previous experience led me to grasp DBT, SQL, and most of my experience came from using a Microsoft stack with SSIS, Analysis Services and the like. I'm feeling wayyyy over my head to start building this, and the multitude of possible stacks make me very afraid that I might overengineer this, and I will initially be alone in the area. What do I do? Fake it till I make it? I never lied in my resume, so it's not like they expect a senior with plenty of experience but still... I read this: https://github.com/zsvoboda/ngods-stocks And it seems like a good starter, albeit overly complex for our use case. I could use suggestions, people to talk to, etc. Please help

Hi, I've created an opensource demo of a Docker-based local analytics stack that includes Apache Iceberg, Trino, Spark, Dagster (orchestration), Cube.dev (analytics model), Metabase (reports and dashboards), and Jupyter (data science notebook). I think that this is a pretty good starting point for Iceberg projects.  Feel free to check it out at GitHub.

I’m guessing that you use the Spark JDBC dataframes. Trino is in my opinion easier to use. You get SQL access to all pgsql tables with this simple config file. No need to write a piece of code for each table. The config above just maps the pgsql schema to a Trino schema. Then you configure Iceberg with another config file and you can do cross-schema SQL queries like create table pgsql.xyz from select * from iceberg.abc. Or you can use dbt that is based on SQL.

Or in a side project: I combined an extensive amount of tools for web-scraping real-estates, uploading them to S3 with MinIO, Spark, and Delta Lake, adding some Data Science magic with Jupyter Notebooks, ingesting into Data Warehouse Apache Druid, visualizing dashboards with Superset and managing everything with Dagster (blog, github)

2️⃣ Building a Data Engineering Project in 20 Minutes

The source-code you can find on practical-data-engineering for the data pipeline or in data-engineering-devops with all it’s details to set things up. Although not all is finished, you can observe the current status of the project on real-estate-project.