Crypto Analytics Tool for MakerDAO

A fully automated ETL pipeline that drive data-driven decisions

About MakerDAO

MakerDAO (San Francisco, California) is one of the most well-known projects in the world focusing on decentralized finance. It’s main product is DAI — a collateralized, decentralized digital currency pegged to the USD. Currently, MakerDAO has $5 billion locked as collateral and $2 billion DAI in circulation. DAI is particularly useful in unbanked communities with currency depreciation and increasing inflation, where people lack access to conventional financial services.

Industry Context

The Maker Protocol is one of the largest decentralized applications (dapps) on Ethereum, being also the first DeFi application significantly adopted worldwide. Such DeFi tools connect users with services that don’t involve any centralized point of authority. The system makes the entire process more efficient, safer, and completely transparent. However, there are still some roadblocks preventing the implementation of this technology.

Business Challenges

MakerDAO dApp is an attractive source of high-quality business data like cryptocurrency rates, exchange rates, interest rates showing fluctuations in the market, etc. All this information may become very handy for effective decision making, if properly structured and presented. But this is a challenging process, since:


Data sourced from blockchain is highly technical and detailed.


Manually gathering crypto related data is a daunting task, highly prone to data loss and inaccuracy.


Collecting large datasets for building numerous reports and analytics requires specific skills, software, and financial costs.

Technological Challenge

The organization of data transport could be implemented using traditional means (through the use of an API) or the construction of an ETL (Extract, Transform, and Load) pipeline.

ETL scripts are flexible yet very powerful tools for collecting, structuring, and presenting blockchain data. In order to set up an ETL pipeline, one should have a solid knowledge of the software environment for executing the ETL scripts, databases, and blockchain operations.

Transformation Engine
ETL Pipeline

Due to the sheer demand for this expertise, MakerDao turned to Unicsoft.


On the part of the Unicsoft team, a highly-skilled data scientist was committed to working on this project. He made a great effort to create an automated ETL pipeline:


Developed a system that takes blockchain data, integrates and processes it, and creates data repositories that are used for fast financial reporting.


Integrated the pipeline’s scripts with data sources and transformed the data into the formats required for storage in the client’s central database.


Synchronized work on the Flask server and APScheduler to avoid data duplication, improved the client’s scripts, and wrote a new method for loading data to the Postgres database.


Once the scripts were sorted out, analysis became easy and the entire process rolled along with end-to-end automation.

  • PostgreSQL
  • MySQL
  • Google
  • BigQuery DB
Transformation Engine
  • Python Flask
  • APScheduler
  • Pandas
  • SQLAlchemy
  • PostgreSQL DB
ETL Pipeline

The Result

At the end of their collaboration with Unicsoft, MakerDAO got an ETL pipeline with a built-in web interface to monitor how it works and set up custom reports. It’s a solution that assists with extracting data from different sources, transforms it into the required format, and loads it into the client’s database. Today, the entire process is automated, such that it requires minimal support from the client’s side.

It’s a solution that assists with extracting data from different sources, transforms it into the required format, and loads it into the client’s database.

Data source
Data source
Data source
Data source
Data source
Data transformation in a required format

“They quickly reacted to our request and provided an interesting suite of candidates. During the cooperation period, they were always available and ready to help. Our overall experience was very good. Highly recommended.”

Tomasz Mierzwa
Lead Data Architect at MakerDAO
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