ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) represent two distinct architectural patterns for data integration, fundamental for constructing analytical systems that process crypto trading data. They define the sequence of operations applied to data as it moves from source to a target data repository.
Mechanism
ETL operates by extracting raw data, transforming it into a structured format on a dedicated processing server, then loading the refined data into a data warehouse. Conversely, ELT extracts data and loads it directly into a data lake or modern data warehouse, with transformations performed subsequently within the target system utilizing its computational power.
Methodology
The selection between ETL and ELT depends on factors such as data volume, velocity, and the computational capabilities of the target data platform. ETL is often favored for structured data with strict schema requirements, while ELT is advantageous for large, diverse datasets in cloud environments, providing flexibility for schema-on-read applications and on-demand transformations in crypto analytics.
Building a cat data analytics platform requires architecting a scalable system to master the immense variety and velocity of its composite data streams.
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