Building a scalable data analytics pipeline involves several key steps. First, design a robust data ingestion layer to collect data from various sources in real-time or batch modes. Next, implement data processing components such as data transformation, cleaning, and enrichment. Use scalable frameworks like Apache Kafka or Apache Spark for handling large datasets efficiently. Store processed data in a scalable database like Amazon Redshift or Google BigQuery. For analysis, integrate business intelligence tools and machine learning models. Ensure pipeline scalability with cloud platforms, automate with orchestration tools like Apache Airflow, and focus on monitoring and maintenance for continuous improvement.
