Subsquid Raises $17M To Build A New Era for Blockchain Data Indexing

In this exclusive interview, Dmitry Zhelezov, co-founder of Subsquid (SQD), discusses the platform's recent $17 million funding round, its groundbreaking "modular indexing" technology, and the future of decentralized data access.

1. Ishan Pandey: Hi Dmitry, welcome to our interview series. SQD recently launched its SQD token and has raised over $17 million in funding. Can you share the vision behind Subsquid and how the SQD token fits into your ecosystem?

   
   

Dmitry Zhelezov: A lot of effort and billions of VC funding have been put into scaling the throughput of blockchains, namely how fast you put in new transactions. But at the same time, at the internet scale, the data is read on average 10-100 times more frequently than it’s written – you can think of how many twitter posts you read and compare to how many you write. Yet, the read access to real-time and historical blockchain data is more like an afterthought. Our mission at SQD is to enable permissionless, trustless access to the entirety of Web3 data at the Internet scale.

2. Ishan Pandey: Indexing platforms are crucial for Web3 scalability. How does SQDd's approach to indexing differentiate itself from other solutions in the market, and what unique benefits does it offer to developers and users?

   
   

Dmitry Zhelezov: Data Indexing is a complex multi-step process and is not dissimilar to how crude oil is refined into high-octane petrol. And likewise specialization is key to scale data indexing to the industrial scale. We at SQD pioneered what we call modular indexing design, where the data indexing pipeline is split into multiple completely independent steps. The key bottleneck we addressed is the step to extract the raw data. We are replacing the highly inefficient RPC nodes with read-optimized SQD Network for data access. The SQD Network is not only decentralized but is at least 100x more performant than the traditional way of indexing data.

3. Ishan Pandey: SQD has recently partnered with Deutsche Telekom to run dedicated worker nodes. How do you see this collaboration enhancing the security and efficiency of blockchain data retrieval, and what does it mean for the future of decentralized infrastructure?

Dmitry Zhelezov: Deutsche Telekom is one of the most notable players in the Web3 Infra space. For us, it’s not merely a stamp of quality approval but a strong boost for network liveness and decentralization. By design, the data in the SQD is spread across all nodes in the network with the maximal redundancy, so with the top-tier DT Infra team joining the network, we feel confidence in the quality of service it provides.

4. Ishan Pandey: Your platform started in the Polkadot ecosystem but has since expanded to Ethereum and is now in beta for Solana. How do you manage the challenges of cross-chain indexing, and what opportunities does this multi-chain approach present?

   
   

Dmitry Zhelezov: The SQD Network is designed to address the data access problem across the whole Web3 space, and can accommodate virtually any sort of on-chain data. This is the power of the modular indexing design, where we managed to make the SQD Network (the base data access layer part) truly chain-agnostic, putting the work of data decoding and transformation on the client side, close to the application business logic.

5. Ishan Pandey: The concept of a decentralized "data lake" is intriguing. Can you explain how this works in practice and how it contributes to the overall efficiency and resilience of Web3 applications?

   
   

Dmitry Zhelezov: Well, in fact SQD Network is more like a “Data Ocean” rather than a “Data Lake”. You can think of it as a planetary-scale database distributed across more than a thousand nodes each holding a chunk of data. The protocol ensures that there’s enough redundancy that no data is lost, and the nodes themselves act as mini-databases to query the chunk data they have. The access to the data as well as the rewards for the nodes is powered by the $SQD token, ensuring censorship-resistant, non-KYC access to the entirety of the data from anywhere in the world. Finally, the advanced cryptography and economic security guarantess that the data served by the nodes is not corrupted and sound.

6. Ishan Pandey: As blockchain networks continue to grow and generate more data, what do you see as the biggest challenges for indexing platforms in the coming years, and how is SQD preparing to address these challenges?

   
   

Dmitry Zhelezov: The exponential pace at which the amount of historical data is growing is probably the biggest challenge by far. Unfortunately, many ecosystems, like Solana Foundation or like Lens protocol, go for stop-gap centralized solutions like putting their data into Google Big Query. While it may solve some immediate developer needs, having a permissionless, extremely low-cost way to query and access this data is a must.

7. Ishan Pandey: Looking ahead, how do you envision the role of indexing platforms evolving in the Web3 ecosystem, and what potential breakthroughs or innovations in this space excite you the most?

   
   

Dmitry Zhelezov: The most exciting innovations are definitely coming from the advancements in modern cryptography. The two things I am particularly looking forward to is the further reduction of ZK proof generation by another two orders of magnitude (going from from affordable to extremely cheap), and then the mainstream adoption of the Fully Homorphic Encryption.

The progress on ZK would allow it to fully leverage the compute power of very low-cost, untrusted nodes combined with the mathematical level of confidence in the validity of the data queries. Right now we are almost there, yet one has to resort to some trade-offs in terms to mitigate to yet still relatively high cost of generating a ZK proof.

The FHE is even more exciting as it will allow it to incorporate private data into the SQD Network, utilize the full power of its query engine to filter and aggregate the data, without compromising the privacy. Only the clients then will be able to decode the query results.

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