Blockchain Data Stacks 

Optimizing data to suit a particular use case is very critical. Businesses now work with vast amounts of blockchain data, which makes data management systems that streamline information more important. This post explores three distinct data handling approaches, each offering unique advantages tailored to different needs, helping businesses align data workflows with specific goals. The three methods are:

• The Lean Stack
• Subgraphs or Indexing Engines
• Data Lake

The Lean Stack 

The Lean Stack offers an optimized approach that delivers only essential metrics through streamlined request pipelines, decoding, and transformation processes. It ensures efficiency by minimizing unnecessary data extraction and processing.

In the Lean Stack, request pipelines, decoding processes, and transformation engines are carefully crafted to deliver only the necessary metrics without excess data processing or storage. This approach is the least flexible, as the stack is tailored specifically to handle the data it was originally designed for and nothing beyond that.

It is highly customizable, employs any language, and follows the classic Extract, Transform, Load (ETL) process. Therefore, it is lean but highly efficient.

Indexing Engines (Subgraphs) 

Subgraphs provide a standardized method for structuring and querying blockchain data, offering enhanced flexibility and reusability across applications. Using predefined rules and user-defined scripts, these engines transform raw data into structured entities accessible via GraphQL APIs, making them perfect for applications with frequent and diverse data query needs.

Subgraphs’ predefined data flows make the management system ideal for outsourced data processing. Indexing engines allow businesses to outsource operations to an “Indexer” who manages the indexing service and ensures it stays online and functional. This approach is particularly beneficial for small teams, as it removes the need for dedicated data infrastructure.

Blockchain Data Lake

A data lake stack employs the Extract, Load, Transform (ELT) process and relies on data houses to store large amounts of raw and decoded blockchain data. Compared to the other two stacks, it is considerably elaborate and clearly separates processes such as request pipelines, decoding, transformations, and storage.

Since users who choose the data lake stack can outsource raw and decoded streaming services, they improve efficiency by avoiding the data requisition and decoding processes altogether.

Generating metrics using a data lake stack involves:

• Exploration – A data lake stack uses considerably more data, so the first process is to explore the pool to identify relevant events and contracts to the desired outcome.
• Filtering – This entails refining large datasets to include only the contracts and functions that a protocol needs. The filtering process helps minimize dataset size and reduce computational costs. It is done on demand and focuses only on the data that will be utilized.
• Modeling – This process includes building models for your protocol using data from various sources, such as raw, decoded, offline, and custom-ingested data. You can generate multiple models as needed, and saved transformations can be reused for aggregation, enabling thorough and adaptable data analysis.

Aside from these three stacks, this piece also looks at Online SQL. While Online SQL tools are not a stack, they are integral in analyzing and leveraging the vast amounts of data that blockchain networks generate.

Online SQL tools offer the infrastructure needed to make the typically expensive and complex process of using a data lake stack accessible to regular users. These platforms allow users to analyze, transform, and generate metrics from blockchain data in the back-end and provide additional features like visualization tools and dashboards to display insights.

Conclusion

In the varied realm of crypto and blockchain data, finding a stack that strictly follows just one approach is uncommon. Most data providers and organizations use a hybrid strategy, combining multiple solutions to suit their needs. This often involves mixing different stacks and outsourcing data processes to optimize business goals, scalability, and cost-effectiveness. Blending these methods enables data players to customize their systems. This approach ensures they can adapt to the ever-changing blockchain landscape and deliver accurate, reliable insights across various applications.

If you like this blockchain and crypto data series, Read more on the 2077 Research website.