The goal of this project was to build an ETL system collecting and computing data about trades and trading positions for various cryptocurrencies from different trading platforms.
The data about trades and trading positions was fetched via multiple REST APIs and web sockets (depending on the trading platform). The data was then normalized, and some elements were aggregated, computed, and estimated. Finally, the normalized data was loaded into a common data model in a relational database (Exasol DB).
The goal of this project was to build an ETL system collecting, normalizing, and aggregating data about popularity index (search volume) of various keywords from Google Trends. The system had two subsystems one for pulling historical data in bulk, the other for current data. After fetching RAW data, it is then normalized to align short and long term values. Following, the data is aggregated for different time granularity. The data was finally loaded into a relational database (Exasol DB).