The IP address lookup market is experiencing robust growth, driven by the increasing reliance on location-based services, cybersecurity advancements, and the expanding digital footprint across various industries. The market, currently valued at approximately $250 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising demand for precise geolocation data for targeted advertising, fraud prevention, and personalized user experiences is a major catalyst. Secondly, the heightened concern regarding data privacy and security necessitates advanced IP address lookup solutions, leading to increased adoption among businesses of all sizes. Finally, the proliferation of IoT devices and the expanding use of cloud-based services further amplify the need for efficient and accurate IP address lookups. The market is segmented by application (SMEs and large enterprises) and by type (cloud-based and on-premises solutions), with cloud-based solutions dominating due to their scalability, cost-effectiveness, and ease of implementation. The competitive landscape features a mix of established players and emerging vendors, each offering diverse solutions to meet varying market demands. While some companies focus on comprehensive databases offering granular location details, others concentrate on providing APIs for seamless integration into existing systems. Regional growth varies, with North America and Europe currently holding a significant market share. However, rapidly developing economies in Asia-Pacific are expected to exhibit accelerated growth in the coming years, driven by increasing internet penetration and digital transformation initiatives. Restraints on market growth include concerns about data accuracy, potential privacy violations, and the emergence of new technologies that may offer alternative approaches to geolocation. Despite these challenges, the overall outlook for the IP address lookup market remains positive, with strong growth anticipated throughout the forecast period.

The IP address lookup market is experiencing robust growth, driven by increasing demand for geolocation services, cybersecurity solutions, and fraud prevention measures across various industries. The market's expansion is fueled by the proliferation of internet-connected devices, the rise of big data analytics, and the growing need for accurate user identification and location tracking. While cloud-based solutions dominate due to scalability and cost-effectiveness, on-premises deployments remain significant for organizations with stringent data security requirements. Large enterprises are major consumers, leveraging IP address lookups for targeted advertising, network security, and compliance purposes. SMEs, however, are increasingly adopting these solutions to improve customer experience and optimize online operations. The market is geographically diverse, with North America and Europe currently leading, but Asia-Pacific is projected to witness significant growth in the coming years due to rapid digitalization and expanding internet penetration. Competition is intense, with a mix of established players and emerging startups offering a range of services, from basic IP geolocation to advanced data intelligence and fraud detection tools. Factors hindering market growth include data privacy concerns, increasing regulatory scrutiny, and the potential for inaccurate or outdated IP data. The forecast period (2025-2033) anticipates continued market expansion, with a projected Compound Annual Growth Rate (CAGR) driving substantial revenue increases. This growth will be fueled by technological advancements in IP geolocation accuracy, integration with other analytical tools, and the emergence of innovative applications within industries like e-commerce, fintech, and advertising. While challenges related to data privacy and accuracy persist, ongoing innovation and industry consolidation are poised to mitigate these risks and drive wider adoption. The market segmentation will likely remain stable, with the continued dominance of cloud-based solutions and strong demand from both large enterprises and SMEs. Regional variations will persist, reflecting differing levels of digital infrastructure and regulatory frameworks across the globe. However, emerging economies in Asia-Pacific are likely to significantly increase their market share throughout the forecast period.

Geo Open is an IP address geolocation per country in MMDB format. The database can be used as a replacement for software using the MMDB format. Information about MMDB format: https://maxmind.github.io/MaxMind-DB/ Open source server using Geo Open: https://github.com/adulau/mmdb-server Open source library to read MMDB file: https://github.com/maxmind/MaxMind-DB-Reader-python Historical dataset: https://cra.circl.lu/opendata/geo-open/ The database is automatically generated from public BGP AS announces matching the country code. The precision is at country level.

Country-Specific Domains Whois Database presents an extensive pricing list for purchasing country-specific whois database files. These databases are categorized by country and include whois information for domain names registered within each country. The pricing packages are customized for each country's database, providing a range of choices for buyers.

United States FB: AS: IBF Only: DB: IP: US Addressees data was reported at 6.000 USD mn in Dec 2019. This records a decrease from the previous number of 7.000 USD mn for Sep 2019. United States FB: AS: IBF Only: DB: IP: US Addressees data is updated quarterly, averaging 18.500 USD mn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 630.000 USD mn in Mar 2016 and a record low of 0.000 USD mn in Dec 2015. United States FB: AS: IBF Only: DB: IP: US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB043: Balance Sheet: Foreign Banks: All States.

TLDs-Specific Domains Whois Database offers a comprehensive pricing list for purchasing domain-specific whois database files based on top-level domains (TLDs). Each database contains whois information for domain names registered under a particular TLD, along with total domain counts and pricing packages unique to each TLD database.

United States FB: IL: IBF Only: DB: IP: US Addressees data was reported at 0.000 USD mn in Dec 2019. This stayed constant from the previous number of 0.000 USD mn for Sep 2019. United States FB: IL: IBF Only: DB: IP: US Addressees data is updated quarterly, averaging 0.000 USD mn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 0.000 USD mn in Dec 2019 and a record low of 0.000 USD mn in Dec 2019. United States FB: IL: IBF Only: DB: IP: US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB046: Balance Sheet: Foreign Banks: Illinois.

Maxmind IP Geolocation Archival Data Because of GDPR concerns, Maxmind doesn't provide historical data. We have used this data to do historical studies of IP data for MTurk, etc. and it is quite possible that such data would be useful elsewhere. Maxmind changed its db format from geolite to geolite2 and you will need to use its respective packages for the two formats to read the binary files. The data is provided for research purposes alone.

Registrar-Specific Domains Whois Database provides a detailed pricing list for purchasing registrar-specific whois database files. These databases are organized based on registrars and include whois information for domain names registered with each registrar. The pricing packages are tailored to each registrar's database, offering a variety of options.

United States FB: NY: IBF Only: DB: IP: US Addressees data was reported at 2.000 USD mn in Dec 2019. This records a decrease from the previous number of 3.000 USD mn for Sep 2019. United States FB: NY: IBF Only: DB: IP: US Addressees data is updated quarterly, averaging 15.500 USD mn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 630.000 USD mn in Mar 2016 and a record low of 0.000 USD mn in Dec 2015. United States FB: NY: IBF Only: DB: IP: US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB044: Balance Sheet: Foreign Banks: New York.

Ip Grishin D B Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

United States FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data was reported at 2.919 USD bn in Dec 2019. This records an increase from the previous number of 2.463 USD bn for Sep 2019. United States FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data is updated quarterly, averaging 5.128 USD bn from Sep 2009 (Median) to Dec 2019, with 42 observations. The data reached an all-time high of 10.906 USD bn in Sep 2009 and a record low of 2.463 USD bn in Sep 2019. United States FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB043: Balance Sheet: Foreign Banks: All States.

Full whois database lists different subscription packages about databases. But there is no price listing, so you will have to directly contact us. It lists full whois databse with Active domains whois databse, IP whois databases and ASN whois database.

United States FB: NY: Excl IBF: DB: IP: US Addressees data was reported at 735.631 USD bn in Dec 2019. This records an increase from the previous number of 700.333 USD bn for Sep 2019. United States FB: NY: Excl IBF: DB: IP: US Addressees data is updated quarterly, averaging 694.370 USD bn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 805.216 USD bn in Sep 2014 and a record low of 574.237 USD bn in Dec 2016. United States FB: NY: Excl IBF: DB: IP: US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB044: Balance Sheet: Foreign Banks: New York.

United States FB: NY: Excl IBF: DB: IP: Non-US Addressees data was reported at 98.365 USD bn in Dec 2019. This records a decrease from the previous number of 103.863 USD bn for Sep 2019. United States FB: NY: Excl IBF: DB: IP: Non-US Addressees data is updated quarterly, averaging 79.410 USD bn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 103.863 USD bn in Sep 2019 and a record low of 40.918 USD bn in Mar 2013. United States FB: NY: Excl IBF: DB: IP: Non-US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB044: Balance Sheet: Foreign Banks: New York.

Bottom sediments

FB: CA: Excl IBF: DB: IP: Non-US Addressees data was reported at 2.312 USD bn in Dec 2019. This records an increase from the previous number of 2.149 USD bn for Sep 2019. FB: CA: Excl IBF: DB: IP: Non-US Addressees data is updated quarterly, averaging 2.276 USD bn from Mar 2013 (Median) to Dec 2019, with 28 observations. The data reached an all-time high of 3.483 USD bn in Jun 2017 and a record low of 1.843 USD bn in Mar 2014. FB: CA: Excl IBF: DB: IP: Non-US Addressees data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB045: Balance Sheet: Foreign Banks: California.

No description is available. Visit https://dataone.org/datasets/156e1c7b908b4b750d3233138ad5de9f for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/e17418806826a43a84f238d913f5680f for complete metadata about this dataset.

The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of Jupyter Notebooks. At the same time, there has been growing criticism that the way notebooks are being used leads to unexpected behavior, encourage poor coding practices and that their results can be hard to reproduce. To understand good and bad practices used in the development of real notebooks, we analyzed 1.4 million notebooks from GitHub.

Paper: https://2019.msrconf.org/event/msr-2019-papers-a-large-scale-study-about-quality-and-reproducibility-of-jupyter-notebooks

This repository contains two files:

• dump.tar.bz2
• jupyter_reproducibility.tar.bz2

The dump.tar.bz2 file contains a PostgreSQL dump of the database, with all the data we extracted from the notebooks.

The jupyter_reproducibility.tar.bz2 file contains all the scripts we used to query and download Jupyter Notebooks, extract data from them, and analyze the data. It is organized as follows:

• analyses: this folder has all the notebooks we use to analyze the data in the PostgreSQL database.
• archaeology: this folder has all the scripts we use to query, download, and extract data from GitHub notebooks.
• paper: empty. The notebook analyses/N12.To.Paper.ipynb moves data to it

In the remaining of this text, we give instructions for reproducing the analyses, by using the data provided in the dump and reproducing the collection, by collecting data from GitHub again.

Reproducing the Analysis

This section shows how to load the data in the database and run the analyses notebooks. In the analysis, we used the following environment:

Ubuntu 18.04.1 LTS
PostgreSQL 10.6
Conda 4.5.11
Python 3.7.2
PdfCrop 2012/11/02 v1.38

First, download dump.tar.bz2 and extract it:

tar -xjf dump.tar.bz2

It extracts the file db2019-03-13.dump. Create a database in PostgreSQL (we call it "jupyter"), and use psql to restore the dump:

psql jupyter < db2019-03-13.dump

It populates the database with the dump. Now, configure the connection string for sqlalchemy by setting the environment variable JUP_DB_CONNECTTION:

export JUP_DB_CONNECTION="postgresql://user:password@hostname/jupyter";

Download and extract jupyter_reproducibility.tar.bz2:

tar -xjf jupyter_reproducibility.tar.bz2

Create a conda environment with Python 3.7:

conda create -n analyses python=3.7
conda activate analyses

Go to the analyses folder and install all the dependencies of the requirements.txt

cd jupyter_reproducibility/analyses
pip install -r requirements.txt

For reproducing the analyses, run jupyter on this folder:

jupyter notebook

Execute the notebooks on this order:

• Index.ipynb
• N0.Repository.ipynb
• N1.Skip.Notebook.ipynb
• N2.Notebook.ipynb
• N3.Cell.ipynb
• N4.Features.ipynb
• N5.Modules.ipynb
• N6.AST.ipynb
• N7.Name.ipynb
• N8.Execution.ipynb
• N9.Cell.Execution.Order.ipynb
• N10.Markdown.ipynb
• N11.Repository.With.Notebook.Restriction.ipynb
• N12.To.Paper.ipynb

Reproducing or Expanding the Collection

The collection demands more steps to reproduce and takes much longer to run (months). It also involves running arbitrary code on your machine. Proceed with caution.

Requirements

This time, we have extra requirements:

All the analysis requirements
lbzip2 2.5
gcc 7.3.0
Github account
Gmail account

Environment

First, set the following environment variables:

export JUP_MACHINE="db"; # machine identifier
export JUP_BASE_DIR="/mnt/jupyter/github"; # place to store the repositories
export JUP_LOGS_DIR="/home/jupyter/logs"; # log files
export JUP_COMPRESSION="lbzip2"; # compression program
export JUP_VERBOSE="5"; # verbose level
export JUP_DB_CONNECTION="postgresql://user:password@hostname/jupyter"; # sqlchemy connection
export JUP_GITHUB_USERNAME="github_username"; # your github username
export JUP_GITHUB_PASSWORD="github_password"; # your github password
export JUP_MAX_SIZE="8000.0"; # maximum size of the repositories directory (in GB)
export JUP_FIRST_DATE="2013-01-01"; # initial date to query github
export JUP_EMAIL_LOGIN="gmail@gmail.com"; # your gmail address
export JUP_EMAIL_TO="target@email.com"; # email that receives notifications
export JUP_OAUTH_FILE="~/oauth2_creds.json" # oauth2 auhentication file
export JUP_NOTEBOOK_INTERVAL=""; # notebook id interval for this machine. Leave it in blank
export JUP_REPOSITORY_INTERVAL=""; # repository id interval for this machine. Leave it in blank
export JUP_WITH_EXECUTION="1"; # run execute python notebooks
export JUP_WITH_DEPENDENCY="0"; # run notebooks with and without declared dependnecies
export JUP_EXECUTION_MODE="-1"; # run following the execution order
export JUP_EXECUTION_DIR="/home/jupyter/execution"; # temporary directory for running notebooks
export JUP_ANACONDA_PATH="~/anaconda3"; # conda installation path
export JUP_MOUNT_BASE="/home/jupyter/mount_ghstudy.sh"; # bash script to mount base dir
export JUP_UMOUNT_BASE="/home/jupyter/umount_ghstudy.sh"; # bash script to umount base dir
export JUP_NOTEBOOK_TIMEOUT="300"; # timeout the extraction


# Frequenci of log report
export JUP_ASTROID_FREQUENCY="5";
export JUP_IPYTHON_FREQUENCY="5";
export JUP_NOTEBOOKS_FREQUENCY="5";
export JUP_REQUIREMENT_FREQUENCY="5";
export JUP_CRAWLER_FREQUENCY="1";
export JUP_CLONE_FREQUENCY="1";
export JUP_COMPRESS_FREQUENCY="5";

export JUP_DB_IP="localhost"; # postgres database IP

Then, configure the file ~/oauth2_creds.json, according to yagmail documentation: https://media.readthedocs.org/pdf/yagmail/latest/yagmail.pdf

Configure the mount_ghstudy.sh and umount_ghstudy.sh scripts. The first one should mount the folder that stores the directories. The second one should umount it. You can leave the scripts in blank, but it is not advisable, as the reproducibility study runs arbitrary code on your machine and you may lose your data.

Scripts

Download and extract jupyter_reproducibility.tar.bz2:

tar -xjf jupyter_reproducibility.tar.bz2

Install 5 conda environments and 5 anaconda environments, for each python version. In each of them, upgrade pip, install pipenv, and install the archaeology package (Note that it is a local package that has not been published to pypi. Make sure to use the -e option):

Conda 2.7

conda create -n raw27 python=2.7 -y
conda activate raw27
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 2.7

conda create -n py27 python=2.7 anaconda -y
conda activate py27
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.4

It requires a manual jupyter and pathlib2 installation due to some incompatibilities found on the default installation.

conda create -n raw34 python=3.4 -y
conda activate raw34
conda install jupyter -c conda-forge -y
conda uninstall jupyter -y
pip install --upgrade pip
pip install jupyter
pip install pipenv
pip install -e jupyter_reproducibility/archaeology
pip install pathlib2

Anaconda 3.4

conda create -n py34 python=3.4 anaconda -y
conda activate py34
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.5

conda create -n raw35 python=3.5 -y
conda activate raw35
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 3.5

It requires the manual installation of other anaconda packages.

conda create -n py35 python=3.5 anaconda -y
conda install -y appdirs atomicwrites keyring secretstorage libuuid navigator-updater prometheus_client pyasn1 pyasn1-modules spyder-kernels tqdm jeepney automat constantly anaconda-navigator
conda activate py35
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.6

conda create -n raw36 python=3.6 -y
conda activate raw36
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 3.6

conda create -n py36 python=3.6 anaconda -y
conda activate py36
conda install -y anaconda-navigator jupyterlab_server navigator-updater
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.7

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