• Data Pipeline Development: o Building efficient ETL/ELT pipelines using Databricks and Delta Lake for structured, semi structured, and unstructured data. o Transforming raw data into consumable datasets for analytics and machine learning.
• Data Optimization: o Improving performance by implementing best practices like partitioning, caching, and Delta Lake optimizations. o Resolving bottlenecks and ensuring scalability.
• Data Integration: o Integrating data from various sources such as APIs, databases, and cloud storage systems (e.g., AWS S3, Azure Data Lake).
• Real-Time Streaming: o Designing and deploying real-time data streaming solutions using Databricks Structured Streaming.
• Data Quality and Governance: o Implementing data validation, schema enforcement, and monitoring to ensure high-quality data delivery. o Using Unity CatLog to manage metadata, access permissions, and data lineage. •
• Collaboration and Documentation: o Collaborating with data analysts, data scientists, and other stakeholders to meet business needs. o Documenting pipelines, workflows, and technical solutions. 3. Deliverables
• Fully functional and documented data pipelines. •
• Optimized and scalable data workflows on Databricks.
• Real-time streaming solutions integrated with downstream systems.
• Detailed documentation for implemented solutions and best practices.
