Developed a strong understanding of the Databricks Lakehouse Platform - including its architecture, workspace, and capabilities - enabling effective use of the Lakehouse for foundational data engineering tasks in real-world analytics environments.

Learned to build and manage multi-hop ETL pipelines using Apache Spark SQL and Python for both batch and incremental processing, skills that translate directly to designing scalable data ingestion and transformation workflows.

Gained the ability to productionize ETL pipelines, SQL queries, and dashboards while maintaining proper permissions, preparing you to support operational data pipelines and deliver reliable insights across enterprise data teams.

Learned how to use Snowflake’s architecture, build SQL-based data pipelines, and design scalable data models. The course included hands-on labs where I queried data, managed schemas, and optimized storage.

Can apply these skills to build and maintain cloud-based ETL workflows in real-world analytics or BI projects.

This certification proves I can work with modern data warehousing tools and handle real-time data workflows efficiently.

Built a strong foundation in Python for data science, covering pandas, scikit-learn, statistics, and machine learning. Through projects and coding exercises, I practiced everything from data wrangling to model training.

Can apply this in business settings to analyze trends, make predictions, and drive decisions using data.

This certification shows that I can independently manage end-to-end data science workflows.

Learned to build ETL and ELT pipelines using Python libraries. I practiced extracting data from APIs, transforming it, and loading it into databases in hands-on exercises.

Can now automate data movement and transformation across systems—useful for any data engineering or analytics project.

This certification proves my ability to handle real-world data pipeline challenges using Python.

Learned how to deploy and secure Databricks on AWS, from single workspaces to multi-region enterprise environments.

Studied VPC peering, IAM roles, encryption, and other cloud-native security practices.

Integrated Databricks with S3, Redshift, and AWS KMS for a smooth cloud-native data pipeline

Learned how to configure and manage Azure Databricks workspaces with enterprise-scale security and governance.

Covered networking configurations such as VNet injection, secure cluster connectivity, and role-based access control.

Integrated Azure Databricks with services like Azure Data Lake, Key Vault, and Log Analytics for streamlined operations.

Learned how to deploy and manage Databricks workspaces on Google Cloud, with a focus on multi-region scalability and enterprise setup.

Explored networking and security best practices, including VPCs, IAM, and private connectivity.

Gained skills in integrating Databricks with GCP services like BigQuery and Cloud Storage for secure and optimized data workflows.

Explored large language models, prompt engineering, and Google’s AI tools like Vertex AI. I also completed labs where I built and tested GenAI applications using real datasets.

Can use these skills to create AI-powered apps, chatbots, and content generation tools using Google Cloud.

This certification shows I understand how to responsibly develop and deploy GenAI solutions at scale.

Learned the basics of generative AI, large language models, and real-world use cases in business. The course covered responsible AI, risks, and how GenAI fits into the Databricks ecosystem.

Can use these insights to guide GenAI adoption in enterprise tools, especially within the Lakehouse architecture.

This certification shows I understand the potential and limitations of GenAI from both a technical and business perspective.

Learned core administration tasks across all three clouds: identity management, workspace setup, access controls, and audit logging.

Gained hands-on knowledge in managing compute resources, clusters, jobs, and optimizing platform usage.

Explored how to set up and manage CI/CD pipelines within Databricks to enable efficient DevOps practices.

Gained a solid understanding of the Databricks Lakehouse Platform, including Spark, Delta Lake, MLflow, and Unity Catalog. I passed an accreditation quiz that tested my grasp of platform features and architecture.

Can now support data workflows that integrate analytics, data engineering, and machine learning on a unified platform.

This certification validates that I can contribute meaningfully to projects using Databricks for data and AI development.

Completed a job simulation involving AI-powered financial chatbot development for BCG's GenAI Consulting team.

Gained experience in Python programming, including the use of libraries such as pandas for data manipulation.

Integrated and interpreted complex financial data from 10-K and 10-Q reports, employing rule-based logic to create a chatbot that provides user-friendly financial insights and analysis.