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Genet Shanko Dekebo
Highlights
Highlights
End-to-End-Web3-dApps-Alogarand
End-to-End-Web3-dApps-Alogarand
The project designed on the way to develop a scalable web3 dApps that is used by any of the organization or company that perform generation of certification’s, distribute and value transfer by using the idea Algorand NFTs and smart contracts. In the project react , flask as API and smart contract used.
Approaches
Approaches
Frontend -react is used as a client-side application that the browser fetches from the host server and renders to the user.
Backend -the Flask- API is as a server-side application. Usually, an application that takes over all the heavy logic, including managing the database, interacts with the client-side through the API.
Smart contract: a smart contract prepared that is used as an agreement between a parts. We used are snippets of code (Algorand-NFTs-smart Contracts) that live on the block chain. It operate within the block chain and are unchangeable. A transaction will happen if the rules written in smart contracts are executed accordingly
Web 3
Web 3
Nowadays, the terms “web2” and “web3” are relatively new concepts used to market blockchain technologies. Blockchain refers to technology that stores and shares digital information across a peer-to-peer network of computer servers. Those with access to a blockchain cannot alter or delete any existing data, but they can view it, use existing data, or add new data.
Data Engineering: Data warehouse tech stack with MySQL, DBT, Airflow
Data Engineering: Data warehouse tech stack with MySQL, DBT, Airflow
The project intended to design and create a scalable data warehouse that will host the vehicle trajectory data extracted by analyzing footage taken by swarm drones and static roadside cameras. The developed application outcome planned to use as a solution for real world problem. Tools used: airflow, Redsh, dbt, PostgreSQL and python
APPROACH
Airflow: Apache Airflow is an open-source workflow management platform for data engineering. In our case, we will manage the workflow of the ELT pipeline.
Python: An object oriented programming language used to write the data extracting, loading and transforming scripts. -DBT(Data built tool): is a data transformation tool that enables data analysts and engineers to transform, test, and document data in the cloud data warehouse.
PostgreSQL: is a free and open-source relational database management system emphasizing extensibility and SQL compliance.
Redash: is an open source web application. It’s used for clearing databases and visualizing the results
ETL:
The use of ETL in the real world problem solving through the way of create a useful pipeline that saves data in a data warehouse (Postgres) and transforms it using dbt once it's been stored. Airflow will direct the project's flow.
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