Updated June 27, 2023
Introduction to Data Engineer Projects
The following article provides an outline for Data Engineer Projects. The demand for data engineers is growing in tandem with the demand for big data. In most cases, a true data engineering project has numerous components. Setting up a data engineering project while adhering to industry best practices can take a long time. Acquiring and sourcing data from different sources is one of the jobs of a data engineer. Get rid of unnecessary data and inaccuracies by cleaning the data. Finally, remove duplicates from the provided data and transform it into the appropriate format.
Top Data Engineer Projects and Ideas
Companies continuously search for talented data engineers who can help them develop new data engineering initiatives. Working on real-time data engineering projects is the best thing you can do if you’re a newbie.
You will not only be able to test your skills and limitations while you work on data engineering projects, but you will also acquire exposure that will help you further your career. This is because you will need to complete the projects appropriately.
The following are the most important:
- Python’s application in large data.
- ETL (Extract, Transform, and Load) solutions.
- Big data technologies such as Hadoop and others.
- The idea of data pipelines(from Business requirements).
- Apache Airflow, AWS S3.
The full pipeline is deployed in each of our Big Data projects. – data ingestion -> data storage -> analytics cluster (such as databricks) -> data storage -> visualisation.
ETL:
Define the Extract, Transform, and Load (ETL) process and explain the precise order in which ETL activities should be performed. Next, make basic ETL software.
Have you ever wondered how data from several sources were merged to create a single source of information? Batch processing is a type of data collection; we’ll see a type of batch processing called Extract, Transform, and Load.
It’s the process of extracting vast amounts of data from various sources and formats and converting it to a single format before loading it into a database or target file. Let’s imagine you’re the CEO of a start-up that has developed an AI that can identify whether someone is at risk for diabetes based on their height and weight. Few data are stored in CSV files, while others are stored in JSON files. You must combine this information into a single file for the AI to read. Because your data is in imperial units, but the AI utilizes metric units, you’ll need to convert it.
The data in CSV format:
Let’s use Python to implement the following ETL. Let’s look at some easy examples of the extraction step. Let’s look at the composite functions of the extract function first.
Let’s have a look at the glob function in the glob module.
Code:
Import glob
List_csv=glob.glob(‘ *.csv’)
Def extract ( file_to_process)
Dataframe=pd.reas_csv()
Return DataframeThese data engineering projects will give ideas to grow in the field:
1. Weather Data Accesible using Kafka
- Managing data sources and version control systems. Kafka connects all the events(REST Proxy).
- Python functions and modules for processing Kaggle data, combining tweets and weather photos into a single file that serves as a streaming data source, and writing a Python program to submit requests to an Azure API endpoint. A streaming data pipeline using an Azure Function as a backend consumes tweets from a local source client using Azure API administration.
- As a message queuing service, Azure Event Hub is used.
- Messages from Azure Event Hub are written to Azure Cosmos Database using an Azure Function.
- Tools required are WSL2, Python Pandas, Docker, Azure SDK, and Power BI.
2. With Apache Spark and AWS -EMR
- Building a Data Lake to store all types of data in a pipeline. We may need to obtain some log data for our research because this is Apache log format data. It would be great to regularly copy the log data to S3.
- Amazon S3 is a data lake that can store and retrieve unlimited data from anywhere on the internet anytime. In this project, utilize Airflow to schedule Big Data ETLs, which can be used to manage data pipelines. Choose Airflow for the following reasons: It gives you a good view of your daily runs, easy failure, and recovery from a crash.
- For providing the data to analysts, we chose Apache Zeppelin. Apache Zeppelin is a web-based notebook that allows us to examine our data using built-in visualizations and supports SQL, Scala, and Python programming languages. Apache Zeppelin comes with some basic charts that display the results of our data processing by default.
3. Apache Cassandra
- Apache Cassandra is a NoSQL database management system that allows users to work with large amounts of data.
- Its key advantage is that it allows you to use data distributed over numerous commodity servers, reducing the chance of failure.
- You’ll have to use Cassandra to accomplish data modeling in this project.
- To begin, ensure that your data is evenly distributed.
- It’s one of the most popular data engineering projects right now. Second, utilize the minimum number of partitions the software reads while modeling.
4. Big Data Ocean
- A firm records each fishery. We’ll assume there are two different devices sending data in this case.
- The nutshell has a meter communicating data about each time — duration, depth, and the lever pulled up.
- Customers pay for a second device, which sends data about the money. Finally, check to see that your tooling is reliable and in use, and establish a set of techniques for extracting value from the Big Data Ocean.
Conclusion
Data engineering is one of the hottest fields in technology right now. Data engineers have high job satisfaction, a wide range of creative challenges, and the opportunity to work with rapidly changing technologies. Knowing these ideas will make using any data warehouse, data engineering tool, or framework correctly a breeze and will add value to your resume.
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