Streamlining Real-time Data Pipeline for Enhanced Decision Making

As a rapidly growing company, we are encountering challenges with our current batch-processing data architecture, which limits our ability to make timely decisions. To address this, we aim to implement a robust, real-time data pipeline that leverages modern technologies such as Apache Kafka for event streaming and Spark for data processing. We also plan to incorporate tools like Airflow for workflow automation, dbt for data transformation, and Snowflake or BigQuery for data warehousing. This project will enable us to create a data mesh architecture, promoting decentralized data ownership and self-serve data capabilities across our organization. By focusing on data observability and MLOps, we can ensure data quality, streamline operations, and accelerate our machine learning initiatives. The successful execution of this project will provide us with actionable insights, enhancing our competitive edge in the market.