data-engineer

To design scalable data pipelines, architect ETL or ELT processes that satisfy data freshness and pipeline SLAs. This involves structuring infrastructure to reliably ingest and process continuous streams using big data technologies like Spark and Kafka.

Optimizing a data warehouse to reduce query times and cut storage costs requires analyzing and restructuring your existing data architecture. This approach targets cloud data platforms to improve efficiency and significantly lower operational expenses per TB.

Yes, you can build ETL processes leveraging cloud data platforms like Snowflake and BigQuery. The architecture integrates these platforms to manage data infrastructure, ensuring reliable pipelines and meeting specific data quality and cost optimization metrics.

Cost optimization for big data infrastructure works by refining data architecture and pipeline SLAs to reduce infrastructure and operational costs. It targets a lower cost per TB while maintaining required data freshness and quality across cloud platforms.

The difference between ETL and ELT in data pipeline architecture relates to where data transformation occurs. ETL transforms data before loading, while ELT loads raw data first and transforms it within the target big data or cloud platform.

You should not use Spark or Kafka for data engineering when your project lacks scalability requirements or real-time streaming needs. If data freshness SLAs are flexible and infrastructure costs strictly outweigh big data processing benefits, simpler architectures suffice.