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 any duplicates from the provided data and transform it into the appropriate format.
Top Data Engineer Projects and Ideas
Companies are continuously on the 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.
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 at a type of batch processing called Extract, Transform, and Load.
It’s the process of extracting vast amounts of data from a variety of 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 of the data is stored in CSV files, while others are stored in JSON files. You must combine all of 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.
Def extract ( file_to_process)
These data engineering projects will give an 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 source of streaming data, and writing a Python program to submit requests to an Azure API endpoint. A streaming data pipeline in using an Azure Function as a backend, it 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 if we could copy the log data to S3 at regular times.
- Using Amazon S3 as a data lake, which can be used to store and retrieve an unlimited quantity of data from anywhere on the internet at any time. In this project, utilise 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 visualisations 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 that is distributed over numerous commodity servers, reducing the chance of failure.
- You’ll have to use Cassandra to accomplish data modelling 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, while modelling, utilise the minimum number of partitions the software reads.
4. Big Data Ocean
- Each fishery is recorded by a firm. We’ll assume there are two different devices sending data in this case.
- The nutshell has a metre that communicates data about each time — duration, depth, and lever pulled up.
- Customers make payments to 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.
Data engineering is one of the hottest fields in technology right now. Data engineers have a high level of 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.
This is a guide to Data Engineer Projects. Here we discuss the introduction and top data engineer projects and ideas respectively. You may also have a look at the following articles to learn more –