CHALLENGES:
High Cardinality and Extreme Scale
Blockchain data, distinct from traditional financial data, is decentralized, exhibits high cardinality, undergoes constant mutation, and needs real-time analysis.
Rapid Data Mutation and Complexity
The inherent nature of blockchain data, with its high-cardinality attributes, makes indexing and querying computationally intensive.
Continuous and Incremental Processing
In contrast to batch ETL paradigms, crypto data analysis demands continuous and incremental processing.
Hybrid Query Workloads
Crypto applications typically require support for both low-latency point queries and complex analytical queries. Optimizing these diverse query patterns over high-cardinality, constantly updating datasets, without resorting to full table scans, is a significant technical hurdle.
SolutioN
Timeplus supports mutable streams for high-cardinality, fast-mutating data. Developers can build secondary indexes on mutable streams for high-performance and flexible queries.
Timeplus also suppors changelog-based incremental aggregation and hybrid aggregation powered by hybrid hash tables
Step 1
Create Web Socket or HTTP Stream to acquire market data, or use Kafka/NATs/REST
Step 2
Apply stream processing, mutable data schema and JOIN
Step 3
Create real-time alerts or visualization
SEE IT IN ACTION:
data lineage:
The cryptocurrency ecosystem is characterized by the generation of high-volume, high-velocity, and frequently mutable data.
Timeplus facilitates the ingestion of real-time market data through various mechanisms, including WebSockets, HTTP Streams, or API push notifications, enabling the construction of sophisticated data pipelines for deriving immediate insights.