Big Data Tools ETL Tools Structured Data 
ProjectPro
JUNE 6, 2025
Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange. This indicates the growing use of the ETL process and various ETL tools and techniques across multiple industries.
ProjectPro
JUNE 6, 2025
Project Idea : Build a data engineering pipeline to ingest and transform data, focusing on runs, wickets, and strike rates. Use the ESPNcricinfo Ball-by-Ball Dataset to process match data. Store raw data in AWS S3, preprocess it using AWS Lambda, and query structured data in Amazon Athena.
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ProjectPro
JUNE 6, 2025
It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. Step 1- Automating the Lakehouse's data intake.
Knowledge Hut
DECEMBER 26, 2023
The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout.
ProjectPro
JANUARY 27, 2023
Over the past few years, data-driven enterprises have succeeded with the Extract Transform Load (ETL) process to promote seamless enterprise data exchange. This indicates the growing use of the ETL process and various ETL tools and techniques across multiple industries.
ProjectPro
JUNE 6, 2025
Furthermore, you will find a few sections on data engineer interview questions commonly asked in various companies leveraging the power of big data and data engineering. SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.
ProjectPro
JUNE 6, 2025
Identifying patterns is one of the key purposes of statistical data analysis. For instance, it can be helpful in the retail industry to find patterns in unstructured and semi-structured data to help make more effective decisions to improve the customer experience.
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