Though starting a career as a data engineer is exciting, it is not as simple as learning a few programming languages and practicing with data engineer interview questions. The resume is the first step in evaluating a candidate for a data engineering job. Most recruiters search for real-world data engineering project experience and filter candidates based on actual data engineering project expertise. However, suppose you want your CV for a data engineer position shortlisted for further consideration. In that case, you must have an extensive understanding of various data engineering technologies and processes other than having a data engineer certification.

1) Twitter Sentiment Analysis in Real-Time using Spark

As a result, marketers have ample opportunity on Twitter. The term “Twitter sentiment” refers to analyzing users’ opinions in their tweets. Companies can benefit from examining user attitudes on Twitter for their product, mainly focused on social media trends, user feelings, and future opinions of the online community.

This data engineering project’s data pipeline contains five stages: data ingestion, the NiFi GetTwitter processor, which receives real-time tweets from Twitter and ingests them into a messaging queue, and data output. The Kafka subject is where collection takes place. For determining each tweet’s sentiment, real-time data will be processed using Spark structured streaming API and evaluated using Spark MLib. The processed and aggregated results are saved in MongoDB. Using Python’s Plotly and Dash tools, the results are shown as interactive dashboards. Also, know about Product Engineering Services.

2) Spark Streaming with Kafka Project for Log Analytics

In this project, you will use the Apache NiFi dataflow management framework to acquire server log data, preprocess it, and store it in a dependable distributed storage HDFS. This data engineering project entails cleaning and manipulating data with Apache Spark to gain insights into server activity, such as the most frequent hosts hitting the server and which country or city generates the most network traffic. You will next use a Plotly-Dash to display these occurrences. It also helps build a tale about what’s going on the server.

3) Using Azure Databricks to analyze Yelp data

The Yelp dataset is made up of information on Yelp’s companies, user reviews, and other information that has been made freely available for personal, educational, and scholarly use. Use it to learn NLP for sample production data, available as JSON files. In ten metropolitan areas, this dataset contains 6,685,900 reviews, 192,609 businesses, and 200,000 photos. During this Azure project, you will learn to comprehend the ETL process, including acquiring, cleaning, and transforming data to obtain business insights. You will have the perfect opportunity of learning about Azure Databricks, Data Factory, and Storage services. It is an important factor in a data engineer career after getting the data engineer certification.

4) Olber Cab Service uses Databricks for real-time data analytics

Older, 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. Customers make payments through a mobile application, transmitting information about fares. The taxi firm wishes to determine the average tip per kilometer driven in real-time for each location to notice passenger trends.

5) AWS EMR Cluster ETL Pipeline

Sales data aids in decision-making, better knowledge of your clients, and enhances future performance inside your company. Sales leaders must evaluate data and apply what they learn to better their plan. This data engineering project evaluates sales data using highly competitive big data technology stacks such as Amazon S3, EMR, and Tableau to derive metrics from existing data. Finally, Tableau shows the cleansed and modified data in various graphs.

6) Using Big Data Tools to Analyze Aviation Data

Aviation data may categorize passengers, track their habits, and reach out to them with relevant and tailored offers.

It helps the airline improve customer service, increase customer loyalty, and generate new revenue streams.

7) AWS ELK Stack Event Data Analysis

New York City agencies and other partners distribute free public data as part of the NYC Open Data initiative. This data engineering initiative gives data enthusiasts a chance to participate in the information created and used by the New York City government. You will investigate the accidents that occur in New York City. Data extraction, data cleansing, transformation, exploratory analysis, visualization, and data flow orchestration of event data on the cloud are all part of this end-to-end big data project.

8) Demand Forecasting for Shipping and Distribution

After getting the data engineer certification, joining in this data engineering project proposal helps you learn the uses of previous demand data to estimate future demand for various consumers, products, and destinations. When a logistics company needs to anticipate the quantities of different products consumers desire to be delivered at various places in the future, this data engineering project has a real-world application. Demand projections can be fed into an allocation mechanism by the company. The allocation tool can optimize operations in the long run, such as delivery vehicle routing and capacity planning. When a vendor or insurer wants to know how many products will be returned due to failures, this is one example.

9) Data Analysis for COVID-19

It is fascinating that the data engineer portfolio has a project example. You will learn to preprocess and merge datasets to prepare them for analysis using the Live COVID19 API dataset. You’ll visualize data in several Dashboards after preprocessing, cleansing, and data transformation.

10) IoT Infrastructure that is Smart

You will cover a general design for constructing smart IoT infrastructure in this IoT project. Technology has enabled us to handle a significant volume of data consumed at a high rate, thanks to the rising advancement of IoT in every aspect of life. With a sample use case, this big data project explores IoT architecture.

Getting a data engineer certification is not just enough to make a successful career. It would help if you had extra factors of learning from projects to become a pro faster.