Dileep A

• Designed and implemented real-time streaming data pipelines using Azure Event Hubs, Azure Stream Analytics, and Apache Spark, reducing data latency by 40%.
• Developed ETL pipelines with Azure Data Factory (ADF) and PySpark, automating data ingestion and transformation for large-scale datasets.
• Built and optimized Azure Synapse Analytics-based data warehouses, improving query performance by 35% through indexing, partitioning, and caching strategies.
• Integrated machine learning models into data pipelines using Azure Databricks, enabling automated insights for business intelligence and analytics teams.
• Designed and managed data orchestration workflows using Azure Data Factory (ADF) and Apache Airflow, ensuring automated and fault-tolerant data processing.
• Implemented data monitoring and logging frameworks using Azure Monitor, Log Analytics, and the ELK stack, improving system reliability and debugging efficiency.

Technologies: Azure Data Factory (ADF), Azure Synapse Analytics, Azure Databricks, PySpark, Apache Kafka, Azure Event Hubs, Airflow, Azure Blob Storage, Azure Monitor, ELK Stack, Docker, and Kubernetes.

• Developed ETL pipelines for ingesting and processing satellite imagery and environmental data using Python, Airflow, and SQL.
• Designed scalable workflows to support news analytics and web scraping.
• Optimized data workflows using Apache Airflow, reducing manual processing time by 40%.
• Managed geospatial datasets with PostgreSQL/PostGIS and QGIS, improving spatial analysis capabilities.
• Deployed data workflows on cloud platforms (Azure), ensuring high-performance data processing. Developed and maintained scalable ETL pipelines for ingesting and processing hazelnut crop yield datasets, improving data accessibility for analytics.
• Designed real-time data ingestion solutions using Azure Functions and Azure Cosmos DB, reducing manual data processing efforts by 50%.
• Created interactive dashboards using Tableau and Power BI, leveraging SQL-based queries for data extraction and visualization.
• Implemented data preprocessing techniques for satellite imagery and climate data, enhancing the predictive capabilities of crop yield models.
• Worked on spatial analytics using GIS data, integrating location-based insights into agricultural strategy planning.

Technologies: Python, SQL, Tableau, Power BI, AWS Lambda, DynamoDB, Airflow, GIS

• Developed and optimized Apache Spark (PySpark) jobs on Hadoop clusters, improving data retrieval performance, and efficiently handling large-scale datasets.
• Designed and implemented HDFS-based data pipelines, ensuring efficient storage and processing of structured and unstructured data.
• Tuned Hive and Impala queries, reducing query execution time, and improving overall system performance for analytics use cases.
• Integrated Apache Kafka for real-time event processing, enabling seamless data ingestion and stream processing.
• Monitored and troubleshot YARN-based job executions, identifying performance bottlenecks, and optimizing resource allocation for better job execution efficiency.

Technologies : Hadoop,Pyspark,SQL,Apache Kafka,Cloudera,Apache Hive,Apache Pig.