IT Jobs Meta

Data pipeline and meta-analysis dashboard for IT job postings from the web.

This application serves as a data analysis service based on postings from No Fluff Jobs, which is one of the most popular job walls for IT specialists in Poland. The data is gathered once a week and presented in a dashboard in form of interactive plots, graphs, and maps. The meta-analysis of IT markets provides insights into leading technologies, salaries, job experience, and work locations distributions. The knowledge distilled from the analysis can help with finding a job, evaluating salary for a vacancy, or planning a career in the IT sector. Have fun exploring the data!

Methodology and project structure

The idea behind data analysis is described in the data_exploration.ipynb Jupyter Notebook in the analysis directory.

The data processing pipeline and application operation scheme follow the flowchart below:

flowchart
subgraph Data pipeline
  DS[Job postings on the web<br><a href=http://www.github.com>No Fluff Jobs</a>] --> |Scrap| RD[Raw data]
  RD --> |Extract| T[Transformation<br>pipeline]
end

T --> |Load| DW[Data warehouse]
RD --> |Archive| DL[Data lake]
DW --> |Provide data| DD

subgraph DD[Data dashboard]
  DWB[Dashboard website]
end

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Notice that the terms data lake and data warehouse are used in a rather loose/naive way in the following descriptions.

Setup

At least Python version 3.10 is required to run this application. The setup configuration is stored in the setup.cfg file.

Installation

You can install this application with pip, either from a local repository:

git clone [email protected]:maciejzj/it-jobs-meta.github
pip install ./it_jobs_meta

or directly from the GitHub repository with:

pip install [email protected]:maciejzj/it-jobs-meta.git

All runtime dependencies will be installed alongside the application. From now, you should be able to call it with it-jobs-meta (if it doesn't work double check pip installation path and PATH environmental variable).

The application can also be used without installation, in a development setup. Refer to further sections of this file for advice on that.

Usage

Resort to the command line help to discover available options:

$ it-jobs-meta -h
usage: it-jobs-meta [-h] [-v {debug,info,warning,critical,error}]
                    [-l LOG_PATH]
                    {pipeline,dashboard} ...

Data pipeline and meta-analysis dashboard for IT job postings

positional arguments:
  {pipeline,dashboard}

options:
  -h, --help            show this help message and exit
  -v {debug,info,warning,critical,error}, --log-level {debug,info,warning,critical,error}
                        set verbosity/log level of the program (default: info)
  -l LOG_PATH, --log-path LOG_PATH
                        path to the log file (default: var/it_jobs_meta.log)

📝 Notice: If you don't want to store the log output in a file redirect it to /dev/null (e.g. it-jobs-meta -l /dev/null ...)

The pipeline subcommand is used to scrap the job postings data from the web, store it in the data lake in a raw form, and in the data warehouse in a processed form (ready to be used by the dashboard later).

$ it-jobs-meta -h
usage: it-jobs-meta pipeline [-h] [-c CRON_EXPRESSION] [-a URL] [-r CONFIG_PATH | -b CONFIG_PATH] (-m CONFIG_PATH | -s CONFIG_PATH)

Run data pipeline once or periodically, scrap data, store it in the data lake, load processed data to the data warehouse.

options:
  -h, --help            show this help message and exit
  -c CRON_EXPRESSION, --schedule CRON_EXPRESSION
                        schedule pipeline to run periodically with a cron expression
  -a URL, --from-archive URL
                        obtain postings data from archive (URL must point to JSON in data lake storage format)
  -r CONFIG_PATH, --redis CONFIG_PATH
                        choose Redis as the data lake with the given config file
  -b CONFIG_PATH, --s3-bucket CONFIG_PATH
                        choose S3 Bucket as the data lake with the given config file
  -m CONFIG_PATH, --mongodb CONFIG_PATH
                        choose MongoDB as the data warehouse with the given config file
  -s CONFIG_PATH, --sql CONFIG_PATH
                        choose MariaDB as the data warehouse with the given config file

The dashboard subcommand runs the dashboard server; use it to visualize the data after the data is scrapped with the pipeline subcommand.

$ it-jobs-meta -h
usage: it-jobs-meta dashboard [-h] [-w] [-l LABEL] -m CONFIG_PATH

options:
  -h, --help            show this help message and exit
  -w, --with-wsgi       run dashboard server with WSGI (in deployment mode)
  -l LABEL, --label LABEL
                        extra label to be displayed at the top navbar
  -m CONFIG_PATH, --mongodb CONFIG_PATH
                        choose MongoDb as the data provider with the given config file

There are several backend implementations for the data lake and the data warehouse. It is preferred to use Redis for development and AWS S3 Bucket in deployment. Preprocessed data can be stored either as NoSQL with MongDB or SQL with MariaDB. The dashboard supports only the MongoDB database as the data provider.

Configuration files

There are several options that require passing a path to a yaml config path as an argument. The reference config files are stored in the config directory.

❗️ Warning: The config files match the services configuration in the docker-compose.yml file. If you wish to run this app publicly change the login credentials.

Development

You can run the program without installing it with a package manager. Install the prerequisites with pip install -r requirements.txt (using virtual environment is recommended). To install extra development tools compatible with the project (test tools, type checker, etc.) run: pip install -r requirements-dev.txt.

The server-side services for development can be set up with Docker Compose. Install docker, docker-compose, and run docker-compose up in the project directory to set up the services.

The application can be run with python -m it_jobs_meta. Since running the data pipeline is going to download data from the web, it is not recommended to run it as a whole during the development. The run-from-archive option can be used with the supplied data sample in the test directory (./it_jobs_meta/data_pipeline/test/1640874783_nofluffjobs.json) to run the pipeline offline. Some modules include demo versions of parts of the application, resort to using them and unit tests during the development process.

Development tools

The development tools packages are stored in requirements-dev.txt. After installation run:

• pytest it_jobs_meta to run unit tests
• flake8 it_jobs_meta to lint
• mypy it_jobs_meta to type check
• isort it_jobs_meta to sort imports
• black it_jobs_meta to format the code

Tools configuration is stored in the pyproject.toml file.

Deployment

The application is not bound to any specific deployment environment; however, AWS is used for running the main instance. The setup for creating AWS infrastructure for the application using Terraform and Ansible deployment is placed int the deployment directory.

License

There is no explicit license with this software meaning that the following applies.