Analytics Portfolio

IBM : Introduction to Data Engineering

• Gained a solid foundation in data engineering concepts, processes, and the roles of data engineers, scientists, and analysts.

• Learned about data ecosystems: data types, file formats, sources, and how different data repositories (RDBMS, NoSQL, data warehouses, data lakes) are used.

• Explored data pipelines, ETL/ELT, and integration processes; also big data processing tools like Hadoop and Spark.

• Covered data platform lifecycle: architecture, ingesting data, wrangling, querying, and governance/compliance.

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Tools & Techniques:

• Relational databases & SQL for storage and querying

• NoSQL databases and data warehouse / data lake architectures 

• Big data processing frameworks: Apache Hadoop, Spark

• ETL / ELT pipelines & data integration methods 

• Data governance, security, compliance principles