Real-Time Data Pipeline Optimization for Enhanced Business Intelligence

In an increasingly data-driven market, our company is seeking to enhance its data engineering capabilities to support real-time analytics and business intelligence. As an SME in the Data Analytics & Science industry, our current data pipeline is limited by latency issues and lacks the agility to process data in real-time. The project involves designing and implementing an optimized data pipeline that leverages state-of-the-art technologies including Apache Kafka for event streaming, Spark for real-time processing, and Airflow for workflow orchestration. Additionally, the project will incorporate tools such as dbt for data transformation and Snowflake or BigQuery for data warehousing. The goal is to create an efficient and scalable data architecture that supports a data mesh approach, increasing data observability and enabling MLOps practices. By achieving this, we aim to transform our data into a truly strategic asset, allowing for faster decision-making and enhanced business outcomes.