How is data warehousing adapting to accommodate the needs of Web3
Use cases, challenges, and solutions
Web3 thrives on a diverse and dynamic data ecosystem. On-chain data from blockchains, like transaction history and smart contract interactions, offers unparalleled transparency and immutability. Off-chain data, encompassing user activity, social media interactions, and DeFi protocols, paints a richer picture of user behaviour and market dynamics. To handle this data deluge, robust and flexible data storage and analysis solutions are necessary.
Data warehousing, traditionally associated with centralisation, is adapting to the principles of decentralisation in Web3, providing a foundation for the efficient and secure storage, retrieval, and analysis of vast amounts of data. Data warehousing facilitates advanced analytics and business intelligence in the Web3 environment. By providing a structured and organised data repository, it enables developers and businesses to gain insights into user behaviour, market trends, and the performance of their decentralised applications.
Cloud data warehouses (CDWs) have undeniable advantages:
Scalability: Handle massive datasets efficiently, crucial for analysing the continuously growing Web3 data volume.
Flexibility: Integrate diverse data sources, both on-chain and off-chain, for a holistic view of the Web3 ecosystem.
Accessibility: Provide user-friendly interfaces and tools for data exploration and analysis.
Use cases
By adapting to the Web3 landscape, CDWs can unlock many possibilities:
DApp development and optimisation: Developers can utilise CDWs to analyse user behaviour and smart contract performance, optimise their dApps for user experience, and identify potential growth opportunities.
Market intelligence and DeFi insights: Investors and DeFi participants can gain valuable insights into market trends, identify promising projects, and make informed investment decisions based on data-driven analysis.
Personal data management: Users can leverage CDWs to store and manage their Web3 data, granting them control over their digital footprint and enabling them to monetise it through data marketplaces.
Fraud detection and security enhancement: CDWs can facilitate the identification of anomalous activity and potential security breaches across the Web3 ecosystem, enabling proactive measures to protect users and their assets.
Data challenges faced by Web3 and blockchain companies
As companies in the Web3 and blockchain space deal with vast amounts of decentralised data, they face unique data warehousing challenges. Here are some of them:
Data integration and access: Web3 and blockchain companies must integrate data from multiple sources, such as decentralised exchanges, wallets, and smart contracts, across multiple nodes and distributed ledgers. However, the lack of a unified data schema and the complexity of the data models can make it challenging to access and retrieve data in real-time, and bring all the data together in a single data warehouse.
Data security and privacy: Data security is critical in the Web3 and blockchain space because of the sensitivity and value of the data stored. In addition, blockchain data is often pseudonymous, meaning that it can’t be tied directly to individuals. When storing and processing this data, companies need to ensure that data warehousing solutions are secure, abide by the data privacy laws, and that only authorised parties can access the data.
Data consistency: Immutability is a key feature of blockchain data, ensuring its consistency by preventing any changes to the data once it is added to the blockchain. Ensuring data consistency in a data warehouse that interacts with the blockchain can be a challenge, particularly when data is updated or deleted in real-time.
How ByteHouse helps overcome these challenges
Data integration and access: ByteHouse can connect to multiple data sources, like HDFS, Amazon S3, Hive, real-time streaming sources. It offers a single source of truth with the latest and the most complete dataset. ByteHouse can handle large volumes of data at the petabyte scale and can process and analyse large amounts of data in real-time.
Data security and privacy: ByteHouse takes the security of its users’ data seriously and is committed to maintaining the highest standards of information security. ByteHouse provides enterprise level security, with features such as Role Based Access Control, Column-Level Access Control, Dynamic Data Masking, and features for managing users and permissions. It enforces network security and IP filtering. ByteHouse has passed ISO 20000, ISO 22301, ISO 9001, ISO 27001, ISO 27017 certification, and has established a scientific and effective management system as a guarantee for information security management.
Data consistency: ByteHouse implicitly encapsulates each statement as a transaction. The transaction provides atomicity, consistency, isolation, and durability (ACID) properties. They guarantee data validity despite errors, network failures, machine failures, and other mishaps. All data written in one statement is atomic, and the other statements don’t see partial data. For a write statement, all the write data becomes visible at the same time and is persistent after the write statement succeeds. Until then, any data it writes is invisible to other statements. If the write statement fails, ByteHouse will roll back the current transaction and automatically clean the intermediate data written by this statement.