Storm emphasized that data sovereignty involves having full control over the data pipeline. This concept not only extends to owning an open data set but also encompasses the entire process of managing data. By leveraging open-source tools and principles, individuals can run their data analyses locally on their machines. This represents a significant shift from relying on cloud services or proprietary databases, which can be resource-intensive and complex.
2. Benefits of Open-Source Tools
Storm highlighted that the use of open-source data tools comes with both ideological and practical benefits. Ideologically, it aligns with the philosophy of making data free and open. Practically, it allows for faster and simpler data workflows, as these tools can be integrated robustly and evolve continuously. This is invaluable, especially for small teams or solo operators in cryptocurrency settings who often lack extensive data resources.
3. Streamlined Infrastructure with Low Maintenance
According to Storm, one of the primary advantages of adopting data sovereignty is the reduced maintenance burden on infrastructure. With most crypto companies lacking large data teams, having a simple, easily manageable system increases productivity. This concept appeals to single data practitioners who need efficient workflows without the complexity of team-based management systems.
4. Enhancing the EIP Process with Data
Storm articulated that better data accessibility and analysis would improve the Ethereum Improvement Proposal (EIP) process. By examining relevant data, stakeholders can assess the implications of proposed changes more effectively. This could lead to EIPs that are better informed and more aligned with the core issues they aim to address, thus strengthening the overall governance of the ecosystem.
5. Modular Approach to Data Collection
During his presentation, Storm shared insights on how tools like cryo work in data extraction and analysis. He explained that cryo allows users to gather data locally by transforming it into manageable datasets formatted in parquet. This modular approach not only enhances ease of use but also allows users to choose various configurations based on their specific needs, whether utilizing local or third-party resources.
6. Efficient Local Data Processing
Storm demonstrated that local processing of large datasets can be achieved effectively without requiring extensive computational power. He showcased how one can query data on WBTC transfers efficiently and perform complex calculations on datasets that exceed the machine’s memory size. This example underscored the power of local data governance in running high-performance analytics without relying on external systems.
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