I will discuss the challenges faced and solutions implemented in architecting data engineering solutions for a trading desk. The key challenges included improving the efficiency of transaction data maintenance in an Oracle database and storing historical data for advanced analytics on the Cloudera Hadoop platform.

To address these, I optimized data structures within the Oracle database and developed data engineering frameworks to migrate terabytes of historical data into Cloudera Hadoop. This included creating data products in Hadoop for sub-second latency queries over large datasets.

Key actions included:
1) Restructuring Oracle tables for better performance.
2) Collaborating with the trading team to strategize data product development.
3) Domain-driven data engineering design for trading.
4) Designing innovative data pipelines and frameworks for efficient data handling.
5) Creating a hybrid low-latency data architecture for real-time and historical data analytics.
6) Technologies used included Sqoop, Hive, Spark, Kafka, HBase, Druid, Unix scripting, and Python.

The results were significant: improved query performance by 20%, faster data access times (10 times faster than Oracle), and reduced P&L calculation times from 45 minutes to less than 5 minutes. The novel hybrid data architecture tailored for the trading desk facilitated faster historical data processing via Hadoop. It alleviated the computational burden for transaction data queries on the Oracle database by 70%, facilitating better trading decisions and increased profitability.

The talk will go through some key architectural considerations, optimization methodologies, and data pipeline strategies for hybrid data architecture.

I will also add content on how AI can enable latency Hybrid Data Pipeline Architecture for Trading to improve productivity.