How Leveraging SonarX's Snowflake Data Shares is a Better Solution for Analytical Read Layer

In the rapidly evolving world of blockchain and Web3 technologies, access to accurate and comprehensive data is critical for making informed decisions. Whether you are a crypto enthusiast, a developer, or a business looking to navigate the decentralized finance landscape, having reliable data at your fingertips is crucial. While blockchain networks provide transparency, extracting actionable insights from these networks can be a formidable challenge. Traditional approaches to ingesting data from blockchain networks, such as using RPC (Remote Procedure Call) nodes, have their limitations. These nodes can be slow, unreliable, and expensive to maintain, especially when dealing with multiple chains. For instance, the average cost of running your own node across multiple chains can reach up to $40,000/month!

SonarX (formerly Sonarverse), powered by Snowflake, removes the complexity of engineering pipelines to access blockchain data. Customers can focus on insights, not pipelines.

Building an enterprise data infrastructure to enable data analytics on blockchain is a sizable technical challenge that will grow in cost over time as the amount of data grows.

By leveraging Snowflake, a cloud-based data warehousing platform renowned for its scalability, performance, and ease of use, SonarX allows customers to directly access fully curated on-chain datasets without needing to pay for storage and ingestion costs. Snowflake enables ANSI SQL access to query and analyze data historically that allows millions/billions row scans in a performant manner, minimizing idle usage. Warehouse compute is billed by the second on Snowflake so once the query is done, the user does not have to pay for idle usage on the cluster.

Snowflake Data Share allows users to access SonarX’s full historical on-chain data as though it was their own database without needing to copy the data over. Additionally, there is no need to backfill blocks from node providers, clean and curate data, and build and maintain a load-intensive, costly data infrastructure. All of the historical data is available right at the user’s fingertips, enabling faster analytics and insights.