Tag Archives: popular


Data pipeline for Lyft trip data (18k+ stars on GitHub): This extensive project builds a data pipeline to ingest, transform, and analyze over 1.5 billion Lyft ride-hailing trips. The ETL pipeline loads raw CSV data from S3 into Redshift, enriches it with additional data from other sources, and stores aggregated metrics in a data warehouse. Visualizations of the cleaned data are then generated using Tableau. Some key aspects of the project include:

Building Lambda functions to load and transform data in batches using Python and AWS Glue ETL jobs
Designing Redshift database schemas and tables to optimize for queries
Calculating metrics like total rides and revenues by city and over time periods
Deploying the ETL pipelines, database, and visualizations on AWS
Documenting all steps and components of the data pipeline

This would be an excellent capstone project due to the large scale of real-world data, complex ETL process, and end-to-end deployment on cloud infrastructure. Students could learn a lot about architecting production-grade data pipelines.

Data pipeline for NYC taxi trip data (10k+ stars): Similar to the Lyft project but for NYC taxi data, this project builds a streaming real-time ETL pipeline instead of batch processing. It ingests raw taxi trip data from Kafka topics, enriches it with spatial data using Flink jobs, and loads enriched events into Druid and ClickHouse for real-time analytics. It also includes a dashboard visualizing live statistics. Key aspects include:

Setting up a Kafka cluster to act as the data lake
Developing Flink jobs to streamingly join trip data with location data
Configuring Druid and ClickHouse databases for real-time queryability
Deploying the streaming pipeline on Kubernetes
Building a real-time dashboard using Grafana

This project focuses on streaming ETL and real-time analytics capabilities which are highly valuable skills for data engineers. It provides an end-to-end view of architecting streaming data pipelines.

Data pipeline for Wikipedia page view statistics (6k+ stars): This project builds an automated monthly pipeline to gather Wikipedia page view statistics from CSV dumps, process them through Spark jobs, and load preprocessed page view counts into Druid. Some key components:

Downloading and validating raw Wikipedia page view dumps
Developing Spark DataFrame jobs to filter, cleanse and aggregate data
Configuring Druid clusters and ingesting aggregated page counts
Running Spark jobs through Airflow and monitoring executions
Integrating Druid with Superset for analytics and visualizations

By utilizing Spark, Druid, Airflow and cloud infrastructure, this project showcases techniques for building scalable batch data pipelines. It also focuses on automating and monitoring the end-to-end workflow.

Each of these representative GitHub projects have received thousands of stars due to their relevance, quality, and educational value for aspiring data engineers. They demonstrate best practices for architecting, implementing and deploying real-world data pipelines on modern data infrastructure. A student undertaking one of these projects as a capstone would have the opportunity to dive deep into essential data engineering skills while gaining exposure to modern cloud technologies and following industry standards. They also provide complete documentation for replicating the systems from start to finish. Projects like these could serve as excellent foundations and inspiration for high-quality data engineering capstone projects.

The three example GitHub projects detailed above showcase important patterns for building data pipelines at scale. They involve ingesting, transforming and analyzing large volumes of real public data using modern data processing frameworks. Key aspects covered include distributed batch and stream processing, automating pipelines, deploying on cloud infrastructure, and setting up databases for analytics and visualization. By modeling a capstone project after one of these highly rated examples, a student would learn valuable skills around architecting end-to-end data workflows following best practices. The projects also demonstrate applying data engineering techniques to solve real problems with public, non-sensitive datasets.


Some of the most commonly used tools and technologies for building mobile apps in a capstone project include:

Programming Languages: The programming language used will depend on whether the app is being developed for iOS or Android. For iOS, Swift and Objective-C are the main languages used, while Android apps are typically developed using Java and Kotlin. Other cross-platform languages like Flutter, React Native and Xamarin can be used to develop apps that run on both platforms.

Development Environments: For iOS development, Xcode is Apple’s official IDE (Integrated Development Environment) used for building iOS, watchOS, tvOS, and macOS software and includes tools for coding, designing user interfaces, and managing projects. For Android development, Android Studio is the official IDE which is based on the JetBrains IntelliJ IDEA software and includes emulator capabilities and tools for code editing, debugging, and testing. Visual Studio Code is another popular cross-platform code editor used along with plugins.

User Interface Design Tools: Sketch and Figma are popular UI/UX design tools used for wireframing and prototyping mobile app interfaces before development. Adobe Photoshop and Illustrator are also commonly used for graphics design aspects. During development, UI elements are coded using XML layout files and UI kit frameworks.

Databases: Most apps require databases for storing persistent data. Popular cross-platform options include SQLite (for local storage), and remote cloud databases like Firebase (NoSQL) and AWS. Realm is another powerful cross-platform mobile database that supports both offline and synchronized data.

Networking/APIs: APIs enable apps to pull in remote data from the web and connect to backend services. Common RESTful API frameworks used include Retrofit/Retrofit2 (Android), and Alamofire (iOS/Swift). For calling external APIs, JSON parsing libraries like Gson, Moshi and SwiftyJSON are helpful.

Testing Tools: Testing frameworks like JUnit (Java), XCTest (iOS), and Espresso (Android) help automatically test app functions. Additional tools for GUI testing include Appium, Calabash, and UI Automator. Beta testing platforms allow distributing pre-release builds for crowd-sourced feedback.

App Distribution: Releasing the finished app involves building release configurations for distribution through official app stores. For Android, the built APK file needs to be uploaded to the Google Play Store. iOS apps are archived and submitted to Apple’s TestFlight Beta Testing system before final release on the App Store. Alternatives include direct distribution through other app markets or as an enterprise app.

Version Control: Git is universally used for managing the source code history and changes through versions. Popular hosting platforms are GitHub, GitLab and Bitbucket for open source collaboration during development. Integrating continuous integration (CI) through services like Jenkins, Travis CI or GitHub Actions automates things like running tests on code commits.

3rd Party Libraries/SDKs: Common third-party open source libraries integrated through dependency managers massively boost productivity. Popular examples for Android include, but are not limited to, SQLite, Glide, Retrofit, Google Play Services, Firebase etc. Equivalents for iOS include CoreData, Alamofire, Kingfisher, Fabric etc. Various other SDKs may integrate additional functionalities from third parties.

App Analytics: Tracking usage metrics and diagnosing crashes is important for improvement and monitoring real-world performance. Popular analytics services include Google Analytics, Firebase Analytics, and Fabric Crashlytics for both platforms. These help analyze app health, usage patterns, identify issues and measure the impact of changes.

DevOps Automation: Tools for automating deployments, configurations and infrastructure provisioning. Popular examples are Docker (containerization), Ansible, AWS Amplify, GitHub Actions, Kubernetes, Terraform etc. Help smoothly manage release workflows in production environments.

Some additional factors to consider include app monetization strategies if needed, security best practices, compliance and localization aspects. While the specific tools may differ between platforms or use cases, the above covers many of the core technologies and frameworks commonly leveraged in modern mobile application development projects including capstone or thesis projects. Adopting best practices around design, development workflows, testing and data ensures student projects meet industry standards and help demonstrate skills to potential employers.


Climate and weather modeling: Some of the most well-known MPI applications are used for modeling global and regional climate patterns as well as forecasting weather. Examples include NCAR’s Community Atmosphere Model (CAM), NASA’s Goddard Earth Observing System Model (GEOS), NOAA’s Weather Research and Forecasting (WRF) model, and EC-Earth used by European climate institutes. These models break the global domain into sections that can be run simultaneously across many nodes, with MPI used to pass boundary data between sections during runtime. Accurate climate and weather prediction is crucial and requires using massive supercomputing clusters with tens of thousands or more cores.

Computational fluid dynamics (CFD): Simulating fluid flows around objects is important for engineering applications like aircraft and vehicle design. CFD codes that use MPI include OpenFOAM, ANSYS Fluent, and Star-CCM+. These break the simulation domain into subdomains that can be computed in parallel. Core tasks like calculating pressures, velocities, and temperatures across mesh points require frequent inter-process communication with MPI. Applications include modeling aerodynamics, combustion, heat transfer, and more. CFD simulations can utilizes massive core counts on today’s largest supercomputers.

Materials modeling: Understanding material properties and behavior at an atomic level drives research in materials science, physics, and chemistry. Popular molecular dynamics codes that employ MPI include LAMMPS, GROMACS, NAMD, and VMD. These simulate collections of atoms and molecules over time using inter-atomic potentials. The simulation box containing atoms is split among processes, with MPI used to handle interactions across process boundaries. This allows modeling extremely large systems with billions of atoms for long time periods to capture phenomena like phase changes, self-assembly, and protein folding. Understanding new materials often relies on national-scale HPC resources.

Astrophysics simulations: Modeling phenomena in astrophysics and cosmology requires extreme computational capabilities. Examples of MPI-based codes include Enzo for cosmological simulations, FLASH for astrophysical hydrodynamics, and GADGET for cosmological structure formation. These divide the spatial domain into smaller subvolumes assigned to processes. As the simulation progresses, processes bordering subvolumes must coordinate across inter-process boundaries with MPI to handle gravity calculations, fluid interactions, and other physics. Following the evolution of the universe and modeling astronomical phenomena demands exascale machines with immense parallelism.

NuComputational genomics: As genome sequencing abilities advance, analyzing and understanding the massive amounts of genomic and genetic data produced requires supercomputing. BWA-MEM and Bowtie2 use MPI to align DNA sequences to a reference genome across many nodes to accelerate this core bioinformatics task. Similarly, simulations exploring protein-folding, molecular interactions, and other genetic phenomena employ MPI frameworks like GROMACS to enable exascale-level biomolecular modeling. Genomics and personalized medicine continue to drive enormous data growth and computational demands across biomedicine.

The above are just a sampling of major HPC application domains that leverage MPI for its ability to partition large parallel workloads and coordinate processes across many thousands or more processing elements. MPI enables solving problems at massive scale in fields as diverse as weather/climate modeling, materials development, biological and biomedical discoveries, and advancing fundamental science. With exascale supercomputing now on the horizon, these kinds of MPI-based applications are poised to make even greater strides by pushing the limits of extreme-scale simulation.

MPI has emerged as an indispensable tool enabling high performance computing and the large-scale scientific and engineering simulations that drive innovation across numerous important domains. Whether modeling aspects of our planet, designing new materials and technologies, or advancing our understanding of nature at the most minute and vast of scales, MPI underpins some of our most computationally intensive and impactful work. This makes it a cornerstone technology propelling discovery and progress through academic research as well as applications with direct benefits to society, the economy and national interests.