ngods-stocks
udacity_bike_share_datalake_project
ngods-stocks | udacity_bike_share_datalake_project | |
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3 | 1 | |
373 | 0 | |
- | - | |
0.0 | 5.3 | |
over 1 year ago | 8 months ago | |
Jupyter Notebook | Jupyter Notebook | |
BSD 3-clause "New" or "Revised" License | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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ngods-stocks
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I'm way over my head
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
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Apache Iceberg-based opensource analytics stack demo
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.
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Iceberg + Spark + Trino + Dagster: modern, open-source data stack installation
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.
udacity_bike_share_datalake_project
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Unveiling the Azure Data Lake for Bike Share Data Analytics
You can find the code related to this project in my GitHub repository.
What are some alternatives?
practical-data-engineering - Practical Data Engineering: A Hands-On Real-Estate Project Guide
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
amazon-emr-with-delta-lake - Amazon EMR Notebook to show how to read from and write to Delta tables with Amazon EMR
vulcan-sql-examples - Curated VulcanSQL show cases
synapse-azure-data-explorer-101 - Getting started with Azure Synapse and Azure Data Explorer
BigDL - Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max). A PyTorch LLM library that seamlessly integrates with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, etc.
dbt-metabase - dbt + Metabase integration
project-atlas-sao-paulo - A project for the development of rich geospatial data from the city of São Paulo for use in Machine Learning models.
data-engineering-zoomcamp - Free Data Engineering course!
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
DE-ZOOMCAMP-PROJECT