15 ETL Project Ideas for Practice in 2024

Learn how data is loaded into data warehouses by gaining hands-on experience on these amazing ETL project ideas in 2024.

15 ETL Project Ideas for Practice in 2024
 |  BY Daivi

The big data analytics market is expected to grow at a CAGR of 13.2 percent, reaching USD 549.73 billion in 2028. This indicates that more businesses will adopt the tools and methodologies useful in big data analytics, including implementing the ETL pipeline. Data engineers are in charge of developing data models, constructing data pipelines, and monitoring ETL (extract, transform, load). Furthermore, data scientists must be familiar with the data sets they will be working on, and for improved data handling, they must have a thorough understanding of the entire ETL process. Let us now understand why the ETL pipelines hold such great value in Data Science and Analytics. 


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Why is ETL used in Data Science?

 

ETL stands for Extract, Transform, and Load. It entails gathering data from numerous sources, converting it, and then storing it in a new single data warehouse. This data warehouse is accessible to data analysts and scientists and helps them perform data science tasks like data visualization, statistical analysis, machine learning model creation, etc. Anyone who works with data, whether a programmer, a business analyst, or a database developer, creates ETL pipelines, either directly or indirectly. ETL is a must-have for data-driven businesses.

The transition to cloud-based software services and enhanced ETL pipelines can ease data processing for businesses. Companies that use batch processing can now switch to continuous processing without interrupting their current operations. ETL pipelines help data scientists to prepare data for analytics and business intelligence. Data from multiple systems (CRMs, social media platforms, Web reporting, etc.) are migrated, aggregated, and modified to meet the parameters of the destination database to deliver significant insights.

 

There are various reasons to implement ETL pipelines in Data Science. An ETL pipeline can help with the following tasks-

  • Centralizes and standardizes data, making it more accessible to analysts and decision-makers.

  • Allows developers to focus on essential tasks by relieving them from technical implementation activities for data migration.

  • Supports data migration to a data warehouse from existing systems, etc.

15 ETL Projects Ideas For Big Data Professionals

Below is a list of 15 ETL projects ideas curated for big data experts, divided into various levels- beginners, intermediate and advanced.

 

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ETL Projects for Beginners

Yelp Data Analysis using Azure Databricks

This beginner-level project is one of the most helpful ETL projects ideas for data analysts. It will help you understand the ETL process, which includes acquiring, cleaning, and transforming data to obtain actionable insights. You'll also have the opportunity to know more about Azure Databricks, Data Factory, and Storage services. The Yelp dataset consists of information on Yelp's companies, user reviews, and other information that has been made freely available for personal, educational, and scholarly use. This dataset covers 6,685,900 reviews, 192,609 businesses, and 200,000 photos across ten metropolitan areas.

 

Source Code- Yelp Data Analysis using Azure Databricks

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Olber Cab Service Real-time Data Analytics

This ETL project aims to create an end-to-end stream processing pipeline. Extract, transform, load, and report are the four stages of this workflow. In real-time, the ETL pipeline gathers data from two sources, joins relevant records from each stream, enhances the output, and generates an average. You will also be working with Azure Databricks in this data analytics project.

Olber, a cab service firm, collects data on each cab trip, and two distinct gadgets generate additional data per journey. Each trip's length, distance, and pick-up and drop-off locations are sent via the cab meter. The cab service wishes to determine the average tip per kilometer driven in real-time for each location to observe passenger behaviors.

 

Source Code- Olber Cab Service Real-time Data Analytics

ETL Pipeline for Aviation Data Analysis

In this beginner-level ETL project idea, you'll learn how to obtain streaming data from an API, cleanse it, convert it for insights, and finally visualize it in a dashboard. The first stage in this ETL  project is to use NiFi to collect streaming data from the Airline API and Sqoop to batch data from AWS Redshift. Then, create a data pipeline that uses Apache Hive and Druid to analyze the data. After that, you'll compare the results and use AWS Quicksight to visualize the data and explore hive optimization approaches.

 

Source Code- ETL Pipeline for Aviation Data Analysis

 

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Intermediate ETL Project Ideas for Practice

Oil Field Data Analytics using Spark, HBase, and Phoenix

Using Apache Spark, HBase, and Apache Phoenix, create a Real-Time Streaming Data Pipeline for a system that analyzes oil wells. Create an oil-well monitoring application. Sensors on oil rigs generate streaming data processed by Spark and stored in HBase for analysis and reporting by various tools. Load the dataset into HDFS storage after downloading it. Using Spark, read data from HDFS storage and write it to an HBase database. To evaluate data using SQL queries, create a Phoenix view on an HBase table.

 

Source Code- Oil Field Data Analytics using Spark, HBase, and Phoenix

YouTube Data Analytics using AWS Glue, Lambda, and Athena

In this ETL project, you will use Athena, Glue, and Lambda to create an ETL Data Pipeline in Python for YouTube Data. Start by importing data into Amazon S3, then set up AWS Glue jobs for ETL purposes. Use Glue crawler for adding/modifying tables in the data catalog. AWS Lambda will allow you to create functions, making it easier to execute your code.  Also, use AWS Athena to generate interactive queries and AWS IAM services to access all the AWS resources securely.

 

This project will enable you to manage, simplify, and analyze structured and semi-structured YouTube video data based on video categories and trending metrics securely and efficiently. The dataset contains data (in CSV files) of daily popular YouTube videos. Every day, up to 200 popular videos for various locations are uploaded. Each region's data is stored in a separate file. The data includes video title, channel title, publishing time, tags, views, likes and dislikes, description, etc. The JSON file associated with the region also has a category_id field that varies with the area.

 

Source Code-  YouTube Data Analytics using AWS Glue, Lambda, and Athena

Retail Analytics using Sqoop, HDFS, and Hive

This ETL project will show you how to leverage Sqoop, HDFS, and Hive to apply data analytics in the retail industry. This project uses the Walmart store sales data to determine each store's minimum and maximum sales, the stores with the highest standard deviation, etc.

Start building the data pipeline by loading data from the database into Hive using Sqoop. Also, use Hive to transform data for further analysis and reporting. The AWS EC2 instance helps deploy the application on a virtual server (cloud environment).

 

Source Code- Retail Analytics using Sqoop, HDFS, and Hive

Real-Time AWS Log Analytics

You will create an end-to-end log analytics solution to gather, ingest, and process data in this project. Use the processed data to keep track of the health of AWS production systems. This project analyzes log data from various sources, including websites, mobile devices, sensors, and apps. You can use log analytics to track application availability, detect fraud, and monitor service level agreements (SLAs). For easier query processing, convert logs from various sources to a standard format.

 

Source Code- Real-Time AWS Log Analytics

Advanced ETL Projects for Experienced Professionals

Amazon Customer Reviews Analysis using Redshift ETL, AWS Glue, and Step Functions

This advanced-level ETL project uses AWS Glue and Step Functions to acquire source data and gain faster analytical insights on Amazon Redshift Cluster. Use in-house AWS technologies to execute end-to-end loading and derive business insights for this project's ETL implementation. The cluster reads data from S3 and loads it into an Amazon Redshift table using Amazon Redshift Spectrum. The cluster runs an aggregation query and uses UNLOAD to export the results to another Amazon S3 location. The state machine will notify an Amazon Simple Notification Service topic in case of pipeline failure.

 

Source Code- Amazon Customer Reviews Analysis using Redshift ETL, AWS Glue, and Step Functions

 

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Real-Time E-commerce Dashboard with Spark, Grafana, and InfluxDB

You will create a real-time e-commerce user analytics dashboard in this project. This project generates user purchase events in Avro format over Kafka for the ETL pipeline. Spark Streaming Framework performs batch and real-time join operations on user purchase and demographic type events. The Spark Streaming Framework receives these events and generates a range of points for time series and dashboards. The events from the Kafka streams are pushed to influxDB through Kafka connect. Grafana generates graphs by connecting to various sources such as influxDB and MySQL.

 

Source Code- Real-Time E-commerce Dashboard with Spark, Grafana, and InfluxDB

Build an End-to-End ETL Pipeline on AWS EMR Cluster

Sales data aids in decision-making, better knowledge of your clients, and enhances future performance inside your company. Working on this project will allow you to understand better how to create a Big Data pipeline on AWS from scratch. This advanced-level ETL project contains all the elements that a data engineer should be familiar with, including evaluating sales data utilizing highly competitive big data technology stacks such as Amazon S3, EMR, and Tableau. Begin by exporting the raw sales data to AWS S3. Then, on AWS, create an EMR cluster with the necessary parameters. For staging purposes, create an external Hive table on top of S3. You'll use Hive as an ETL tool, i.e., create several ETL pipelines for storing the processed data in a table using Hive. Finally, use Tableau to show the cleansed and modified data in various graphs.

 

Source Code- Build an End-to-End ETL Pipeline on AWS EMR Cluster

AWS Snowflake Data Pipeline using Kinesis and Airflow

For this ETL project, create a data pipeline starting with EC2 logs. Use Snowflake and Amazon S3 for storing data after transformation. Use Airflow DAGs for processing the data. Use Amazon Managed Workflows for Apache Airflow (MWAA) to quickly set up and operate an ETL pipeline in the cloud. Managed Workflows allows you to create workflows with Airflow and Python without worrying about the core infrastructure's scalability, accessibility, or reliability. In addition, Amazon Kinesis Data Firehose sends live streaming data to Amazon S3.

 

Source Code-  AWS Snowflake Data Pipeline using Kinesis and Airflow

ETL Projects in Healthcare Domain

Covid-19 Data Analysis using PySpark and Hive

In this Big Data project, you'll build a large-scale Big Data pipeline on AWS. For this project, use the Covid-19 dataset, and transmit the data in real-time from an external API using NiFi. Also, NiFi will help you parse the complex JSON data into CSV format and store the result in HDFS.

Then, using PySpark, deliver this data to Kafka for processing. Spark will then consume the processed data and put it in HDFS. On top of HDFS, construct an external Hive table. Finally, you will clean, process, and store the data in a data lake. After that, use Tableau and AWS QuickSight to visualize the data.

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Healthcare Centre Data Analytics using AWS Glue and Hive

This ETL project uses patient and medication data from multiple healthcare facilities for this project. You will create a data warehouse for managing and reporting drug inventory, healthcare services, patient data, marketing efforts, and other topics. The project aims to develop high-quality population health analytics. Begin by importing data into Amazon S3, then use AWS Glue jobs for creating the ETL pipeline. Use Hive to process data for additional analysis and reporting. The AWS EC2 instance lets you deploy the project on a virtual server. 

ETL Projects in Banking Domain

Credit Card Fraud Analysis using Apache Kafka, Hadoop, and Amazon S3

This ETL project will enable you to analyze the credit card transaction dataset and detect any fraudulent transactions that might occur. To begin, gather data and enter it into Kafka. Kafka automatically publishes new messages to the Kafka topic by adding rows to the source table, allowing for a real-time data stream. The ETL pipeline takes messages from a Kafka topic and converts them to KStream objects. Stream the data to Amazon S3 after loading it into the target system. Transfer ETL and storage operations to MapR-powered Hadoop, drastically reducing costs and timeframes.

 

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ETL Pipeline for Customer Segmentation

This project will enable banks to analyze vast amounts of data to reveal consumer behavior and preferences trends. It will allow you to generate customized customer profiles that will aid in bridging the gap between bankers and their customers. The initial step in this ETL project is to gather customer information and batch data from AWS Redshift using Sqoop. Next, build a data pipeline that analyses the data using Apache Hive.

 

These interesting ETL projects for practice will help you excel in your Big Data analytics career. If you are willing to explore more useful big data projects, check out ProjectPro’s solved end-to-end Data Science and Big Data projects that will help you enhance your data science skillset in no time. Always remember, ‘practice makes a man perfect’!

FAQs

What is ETL example?

An example of ETL is the processing and analyzing customer reviews data for an e-commerce platform. 

How long does an ETL migration project take?

It depends on various factors such as-

  • The amount of data you are transferring,
  • The method you are using for the data transfer,
  • The amount of data transformation needed, and
  • Whether your process is efficient or not.  

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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