LAHARI CHUNDURI

Hardworking and passionate job seeker with strong organizational skills eager to secure entry-level Data Engineer position. Ready to help team achieve company goals.

Pipeline for Movie Data

Objective: Build an ETL (Extract, Transform, Load) pipeline to process movie data from different sources, perform some transformations, and load it into a database for analysis.

Steps:

Data Extraction: Choose at least two different sources of movie data. This could be CSV files, JSON files, or APIs. IMDb dataset, The Movie Database (TMDb) API, or any other movie-related dataset available online.

Data Transformation: Clean and preprocess the data. Handle missing values, duplicates, and any inconsistencies. Merge or join datasets if you have chosen multiple sources.

Data Loading: Choose a relational database (e.g., SQLite, MySQL, PostgreSQL) or a NoSQL database (e.g., MongoDB) to store the processed data.
Automation: Create a script or program that automates the entire ETL process.
Schedule the script to run at regular intervals (e.g., daily, weekly) to keep the database up-to-date with the latest movie data.

Analysis: Write SQL queries to extract insights from your data.
For example, you could analyze trends over time, identify the highest-rated movies, or explore the distribution of genres.

Visualization: Create visualizations using a tool like Matplotlib, Seaborn, or Plotly to represent your findings.
This step is optional but can add an extra layer of appeal to your project.

Project Title: Weather Data Processing and Visualization

API Access:

• Register for an API key from the chosen weather API.
• Learn how to make API requests to retrieve weather data.

Data Extraction:

• Fetch historical weather data for a specific location (e.g., a city) from the API.
• Understand the structure of the data and the information available.

Data Transformation:

• Clean and preprocess the data to handle any inconsistencies.
• Transform the data to make it suitable for analysis. Consider aggregating data on a daily, weekly, or monthly basis.

Database Setup:

• Set up a local database (e.g., SQLite, MySQL, or PostgreSQL).
• Design a schema to store the processed weather data.

ETL Pipeline Implementation:

• Write Python scripts to automate the ETL process.
• Extract data from the API, transform it, and load it into your database.

Visualization:

• Use a visualization library to create charts and graphs that represent different aspects of the weather data.
• Examples could include line charts for temperature trends, bar charts for rainfall, or scatter plots for correlation analysis.