PySpark Customs Data Analysis on Databricks
Description:
Developed and implemented a scalable data processing and analysis pipeline using PySpark on Databricks to analyze large datasets from the customs database. This project facilitated a deep understanding of international trade patterns, company-level import/export transactions, and commodity flows. The processed and analyzed data was then ingested into the Orbis database, enhancing its customs-related information for customers. This project optimized Databricks compute resources, workflows, and data lineage options to ensure efficient and reliable data processing.
Key Technologies:
- Databricks (Apache Spark, Delta Lake)
- PySpark (Spark SQL, Spark DataFrames)
- SQL (Databricks SQL, Orbis Database)
- Datamyne Database
- Databricks Workflows and Data Lineage
- Cloud Storage: AWS S3 or Azure Blob Storage
- Notebooks: Jupyter/Databricks Notebooks
- Version Control: Git
Project Overview:
- Data Ingestion: Extracted large datasets from the customs database and ingested them into Databricks using efficient data connectors.
- Data Transformation: Employed PySpark and Delta Lake to perform complex data transformations, aggregations, and cleaning operations, preparing the data for analysis.
- Data Analysis: Utilized Spark SQL and Spark DataFrames to analyze trade flows between countries and companies, identify key imported/exported commodities, and derive meaningful insights from the customs data.
- Compute Resource Optimization: Optimized Databricks compute resources by tuning Spark configurations, selecting appropriate cluster types, and implementing efficient data partitioning strategies.
- Workflow Orchestration: Developed and implemented Databricks Workflows to automate the data processing pipeline, ensuring reliable and repeatable execution.
- Data Lineage Tracking: Leveraged Databricks data lineage capabilities to track data transformations and dependencies, ensuring data integrity and traceability.
- Data Ingestion into Orbis: Successfully ingested the processed and analyzed data into the Orbis database, enriching customer profiles with comprehensive customs information.
- Cloud Storage Integration: Utilized cloud storage (AWS S3 or Azure Blob Storage) to store and manage large datasets, ensuring scalability and cost-effectiveness.
Key Achievements:
- Scalable Data Processing: Implemented a scalable data processing pipeline capable of handling large volumes of Datamyne customs data.
- Optimized Databricks Performance: Optimized Databricks compute resources, resulting in significant improvements in data processing speed and efficiency.
- Enhanced Data Insights: Provided valuable insights into international trade patterns and company-level transactions, enhancing the Orbis database with comprehensive customs information.
- Automated Data Pipeline: Implemented Databricks Workflows to automate the data processing pipeline, ensuring reliable and repeatable execution.
- Improved Data Lineage: Leveraged Databricks data lineage capabilities to ensure data integrity and traceability.
- Efficient Data Ingestion: Successfully ingested the processed data into the Orbis database, enriching customer profiles with valuable customs information.
- Cost Optimization: Effectively managed and optimized databricks compute resources to reduce costs.
Project Context:
This project addressed the need for efficient and scalable analysis of large customs datasets to provide valuable insights into international trade patterns. By leveraging Databricks and PySpark, this project enabled the processing and analysis of complex data, enhancing the Orbis database with comprehensive customs information for its customers.
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