This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

DESCRIPTION

VERSIONS

version1.0.1 fixes problem with functions

version1.0.2 added table dbeel_rivers.rn_rivermouth with GEREM basin, distance to Gibraltar and link to CCM.

version1.0.3 fixes problem with functions

version1.0.4 adds views rn_rna and rn_rne to the database

The SUDOANG project aims at providing common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). VISUANG is the SUDOANG Interactive Web Application that host all these tools . The application consists of an eel distribution atlas (GT1), assessments of mortalities caused by turbines and an atlas showing obstacles to migration (GT2), estimates of recruitment and exploitation rate (GT3) and escapement (chosen as a target by the EC for the Eel Management Plans) (GT4). In addition, it includes an interactive map showing sampling results from the pilot basin network produced by GT6.

The eel abundance for the eel atlas and escapement has been obtained using the Eel Density Analysis model (EDA, GT4's product). EDA extrapolates the abundance of eel in sampled river segments to other segments taking into account how the abundance, sex and size of the eels change depending on different parameters. Thus, EDA requires two main data sources: those related to the river characteristics and those related to eel abundance and characteristics.

However, in both cases, data availability was uneven in the SUDOE area. In addition, this information was dispersed among several managers and in different formats due to different sampling sources: Water Framework Directive (WFD), Community Framework for the Collection, Management and Use of Data in the Fisheries Sector (EUMAP), Eel Management Plans, research groups, scientific papers and technical reports. Therefore, the first step towards having eel abundance estimations including the whole SUDOE area, was to have a joint river and eel database. In this report we will describe the database corresponding to the river’s characteristics in the SUDOE area and the eel abundances and their characteristics.

In the case of rivers, two types of information has been collected:

River topology (RN table): a compilation of data on rivers and their topological and hydrographic characteristics in the three countries.

River attributes (RNA table): contains physical attributes that have fed the SUDOANG models.

The estimation of eel abundance and characteristic (size, biomass, sex-ratio and silver) distribution at different scales (river segment, basin, Eel Management Unit (EMU), and country) in the SUDOE area obtained with the implementation of the EDA2.3 model has been compiled in the RNE table (eel predictions).

CURRENT ACTIVE PROJECT

The project is currently active here : gitlab forgemia

TECHNICAL DESCRIPTION TO BUILD THE POSTGRES DATABASE

1. Build the database in postgres.

All tables are in ESPG:3035 (European LAEA). The format is postgreSQL database. You can download other formats (shapefiles, csv), here SUDOANG gt1 database.

Initial command

open a shell with command CMD

Move to the place where you have downloaded the file using the following command

cd c:/path/to/my/folder

note psql must be accessible, in windows you can add the path to the postgres

bin folder, otherwise you need to add the full path to the postgres bin folder see link to instructions below

createdb -U postgres eda2.3 psql -U postgres eda2.3

this will open a command with # where you can launch the commands in the next box

Within the psql command

create extension "postgis"; create extension "dblink"; create extension "ltree"; create extension "tablefunc"; create schema dbeel_rivers; create schema france; create schema spain; create schema portugal; -- type \q to quit the psql shell

Now the database is ready to receive the differents dumps. The dump file are large. You might not need the part including unit basins or waterbodies. All the tables except waterbodies and unit basins are described in the Atlas. You might need to understand what is inheritance in a database. https://www.postgresql.org/docs/12/tutorial-inheritance.html

1. RN (riversegments)

These layers contain the topology (see Atlas for detail)

dbeel_rivers.rn

france.rn

spain.rn

portugal.rn

Columns (see Atlas)

    gid


    idsegment


    source


    target


    lengthm


    nextdownidsegment


    path


    isfrontier


    issource


    seaidsegment


    issea


    geom


    isendoreic


    isinternational


    country

dbeel_rivers.rn_rivermouth

    seaidsegment


    geom (polygon)


    gerem_zone_3


    gerem_zone_4 (used in EDA)


    gerem_zone_5


    ccm_wso_id


    country


    emu_name_short


    geom_outlet (point)


    name_basin


    dist_from_gibraltar_km


    name_coast


    basin_name

dbeel_rivers.rn ! mandatory => table at the international level from which

the other table inherit

even if you don't want to use other countries

(In many cases you should ... there are transboundary catchments) download this first.

the rn network must be restored firt !

table rne and rna refer to it by foreign keys.

pg_restore -U postgres -d eda2.3 "dbeel_rivers.rn.backup"

france

pg_restore -U postgres -d eda2.3 "france.rn.backup"

spain

pg_restore -U postgres -d eda2.3 "spain.rn.backup"

portugal

pg_restore -U postgres -d eda2.3 "portugal.rn.backup"

rivermouth and basins, this file contains GEREM basins, distance to Gibraltar, the link to CCM id

for each basin flowing to the sea. pg_restore -U postgres -d eda2.3 "dbeel_rivers.rn_rivermouth.backup"

with the schema you will probably want to be able to use the functions, but launch this only after

restoring rna in the next step

psql -U postgres -d eda2.3 -f "function_dbeel_rivers.sql"

1. RNA (Attributes)

This corresponds to tables

dbeel_rivers.rna

france.rna

spain.rna

portugal.rna

Columns (See Atlas)

    idsegment


    altitudem


    distanceseam


    distancesourcem


    cumnbdam


    medianflowm3ps


    surfaceunitbvm2


    surfacebvm2


    strahler


    shreeve


    codesea


    name


    pfafriver


    pfafsegment


    basin


    riverwidthm


    temperature


    temperaturejan


    temperaturejul


    wettedsurfacem2


    wettedsurfaceotherm2


    lengthriverm


    emu


    cumheightdam


    riverwidthmsource


    slope


    dis_m3_pyr_riveratlas


    dis_m3_pmn_riveratlas


    dis_m3_pmx_riveratlas


    drought


    drought_type_calc

Code :

pg_restore -U postgres -d eda2.3 "dbeel_rivers.rna.backup" pg_restore -U postgres -d eda2.3 "france.rna.backup" pg_restore -U postgres -d eda2.3 "spain.rna.backup"
pg_restore -U postgres -d eda2.3 "portugal.rna.backup"

1. RNE (eel predictions)

These layers contain eel data (see Atlas for detail)

dbeel_rivers.rne

france.rne

spain.rne

portugal.rne

Columns (see Atlas)

    idsegment


    surfaceunitbvm2


    surfacebvm2


    delta


    gamma


    density


    neel


    beel


    peel150


    peel150300


    peel300450


    peel450600


    peel600750


    peel750


    nsilver


    bsilver


    psilver150300


    psilver300450


    psilver450600


    psilver600750


    psilver750


    psilver


    pmale150300


    pmale300450


    pmale450600


    pfemale300450


    pfemale450600


    pfemale600750


    pfemale750


    pmale


    pfemale


    sex_ratio


    cnfemale300450


    cnfemale450600


    cnfemale600750


    cnfemale750


    cnmale150300


    cnmale300450


    cnmale450600


    cnsilver150300


    cnsilver300450


    cnsilver450600


    cnsilver600750


    cnsilver750


    cnsilver


    delta_tr


    gamma_tr


    type_fit_delta_tr


    type_fit_gamma_tr


    density_tr


    density_pmax_tr


    neel_pmax_tr


    nsilver_pmax_tr


    density_wd


    neel_wd


    beel_wd


    nsilver_wd


    bsilver_wd


    sector_tr


    year_tr


    is_current_distribution_area


    is_pristine_distribution_area_1985

Code for restauration

pg_restore -U postgres -d eda2.3 "dbeel_rivers.rne.backup" pg_restore -U postgres -d eda2.3 "france.rne.backup" pg_restore -U postgres -d eda2.3 "spain.rne.backup"
pg_restore -U postgres -d eda2.3 "portugal.rne.backup"

1. Unit basins

Units basins are not described in the Altas. They correspond to the following tables :

dbeel_rivers.basinunit_bu

france.basinunit_bu

spain.basinunit_bu

portugal.basinunit_bu

france.basinunitout_buo

spain.basinunitout_buo

portugal.basinunitout_buo

The unit basins is the simple basin that surrounds a segment. It correspond to the topography unit from which unit segment have been calculated. ESPG 3035. Tables bu_unitbv, and bu_unitbvout inherit from dbeel_rivers.unit_bv. The first table intersects with a segment, the second table does not, it corresponds to basin polygons which do not have a riversegment.

Source :

Portugal

https://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.ziphttps://sniambgeoviewer.apambiente.pt/Geodocs/gml/inspire/HY_PhysicalWaters_DrainageBasinGeoCod.zip

France

In france unit bv corresponds to the RHT (Pella et al., 2012)

Spain

http://www.mapama.gob.es/ide/metadatos/index.html?srv=metadata.show&uuid=898f0ff8-f06c-4c14-88f7-43ea90e48233

pg_restore -U postgres -d eda2.3 'dbeel_rivers.basinunit_bu.backup'

france

pg_restore -U postgres -d eda2.3

DVD rental database to demonstrate the features of PostgreSQL.

There are 15 tables in the DVD Rental database:

• actor – stores actors data including first name and last name.
• film – stores film data such as title, release year, length, rating, etc.
• film_actor – stores the relationships between films and actors.
• category – stores film’s categories data.
• film_category- stores the relationships between films and categories.
• store – contains the store data including manager staff and address.
• inventory – stores inventory data.
• rental – stores rental data.
• payment – stores customer’s payments.
• staff – stores staff data.
• customer – stores customer data.
• address – stores address data for staff and customers
• city – stores city names.
• country – stores country names.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F428950174ca8917693d9a125242c9a02%2F2.png?generation=1688974937835056&alt=media" alt="">

Launch pagila-schema.sql code in PgAdmin 4 and then launch pagila-insert-data.sql

Don't forget to switch on auto-commit mode.

This is a dump generated by pg_dump -Fc of the IMDb data used in the "How Good are Query Optimizers, Really?" paper. PostgreSQL compatible SQL queries and scripts to automatically create a VM with this dataset can be found here: https://git.io/imdb

An example sample sheet containing samples information that is used to start an analysis in VarGenius. (TSV 330 bytes)

This HydroShare resource provides a complete example of camera-based streamflow monitoring data collection and automated segmentation processing for the Blacksmith Fork site, demonstrated on one day of real image data. It includes both example image inputs and compute outputs generated using a containerized cloud-based inference pipeline. The processing workflow uses the Segment Anything deep learning model, deployed in a serverless environment with AWS Lambda and S3. Each image is segmented to identify regions of interest (ROIs) and calculate water-relevant pixel statistics. Ground truth comparison supports quality assurance using Intersection over Union (IoU) scores. Results are automatically uploaded and stored in a PostgreSQL database for hydrologic analysis. This dataset supports the reproducibility of the modeling approaches described in submitted manuscripts to Environmental Modelling & Software, offering transparency into the full data processing pipeline from raw image ingestion to output storage. It serves as a reference implementation for camera-based environmental monitoring at scale.

The Forager.ai Global Install Base Data set is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 22M total records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

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, encourages 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. Based on the results, we proposed and evaluated Julynter, a linting tool for Jupyter Notebooks.

Papers:

• PIMENTEL, J. F.; MURTA, L.; BRAGANHOLO, V.; FREIRE, J.; A large-scale study about quality and reproducibility of jupyter notebooks. In: International Conference on Mining Software Repositories (MSR), 2019, Montreal, Canada.
• PIMENTEL, J. F.; MURTA, L.; BRAGANHOLO, V.; FREIRE, J.; Understanding and Improving the Quality and Reproducibility of Jupyter Notebooks. Empirical Software Engineering, 2021 (in press)

This repository contains three files:

• db2020-09-22.dump.gz
• sample.tar.gz
• julynter_reproducility.tar.gz

Reproducing the Notebook Study

The db2020-09-22.dump.gz file contains a PostgreSQL dump of the database, with all the data we extracted from notebooks. For loading it, run:

gunzip -c db2020-09-22.dump.gz | psql jupyter

Note that this file contains only the database with the extracted data. The actual repositories are available in a google drive folder, which also contains the docker images we used in the reproducibility study. The repositories are stored as content/{hash_dir1}/{hash_dir2}.tar.bz2, where hash_dir1 and hash_dir2 are columns of repositories in the database.

For scripts, notebooks, and detailed instructions on how to analyze or reproduce the data collection, please check the instructions on the Jupyter Archaeology repository (tag 1.0.0)

The sample.tar.gz file contains the repositories obtained during the manual sampling.

Reproducing the Julynter Experiment

The julynter_reproducility.tar.gz file contains all the data collected in the Julynter experiment and the analysis notebooks. Reproducing the analysis is straightforward:

• Uncompress the file: $ tar zxvf julynter_reproducibility.tar.gz
• Install the dependencies: $ pip install julynter/requirements.txt
• Run the notebooks in order: J1.Data.Collection.ipynb; J2.Recommendations.ipynb; J3.Usability.ipynb.

The collected data is stored in the julynter/data folder.

Changelog

2019/01/14 - Version 1 - Initial version
2019/01/22 - Version 2 - Update N8.Execution.ipynb to calculate the rate of failure for each reason
2019/03/13 - Version 3 - Update package for camera ready. Add columns to db to detect duplicates, change notebooks to consider them, and add N1.Skip.Notebook.ipynb and N11.Repository.With.Notebook.Restriction.ipynb.
2021/03/15 - Version 4 - Add Julynter experiment; Update database dump to include new data collected for the second paper; remove scripts and analysis notebooks from this package (moved to GitHub), add a link to Google Drive with collected repository files

The Northwind database is a sample database that was originally created by Microsoft and used as the basis for their tutorials in a variety of database products for decades. The Northwind database contains the sales data for a fictitious company called “Northwind Traders,” which imports and exports specialty foods from around the world. The Northwind database is an excellent tutorial schema for a small-business ERP, with customers, orders, inventory, purchasing, suppliers, shipping, employees, and single-entry accounting. The Northwind database has since been ported to a variety of non-Microsoft databases, including PostgreSQL.

The Northwind dataset includes sample data for the following.

• Suppliers: Suppliers and vendors of Northwind
• Customers: Customers who buy products from Northwind
• Employees: Employee details of Northwind traders
• Products: Product information
• Shippers: The details of the shippers who ship the products from the traders to the end-customers
• Orders and Order_Details: Sales Order transactions taking place between the customers & the company

Serverless PostgreSQL Market Outlook

According to our latest research, the global serverless PostgreSQL market size reached USD 1.25 billion in 2024, reflecting robust adoption across industries. The market is poised to expand at a CAGR of 22.1% from 2025 to 2033, projecting a significant rise to USD 8.82 billion by 2033. This rapid growth is primarily driven by the increasing demand for scalable, cost-efficient, and low-maintenance database solutions, as enterprises accelerate their cloud migration and digital transformation journeys.

A key growth factor for the serverless PostgreSQL market is the compelling need for operational agility and cost optimization in database management. Traditional database systems require significant upfront investments in hardware, software, and skilled personnel for maintenance and scaling. In contrast, serverless PostgreSQL solutions eliminate the burden of infrastructure management, allowing organizations to focus on application development and innovation. The pay-as-you-go pricing model and automated scaling capabilities are particularly attractive for businesses with fluctuating workloads, enabling them to optimize resource utilization and reduce total cost of ownership. This paradigm shift is further fueled by the proliferation of cloud-native application architectures and the growing adoption of DevOps practices, which emphasize agility, automation, and continuous delivery.

Another critical driver is the rising demand for real-time data analytics and the integration of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). Serverless PostgreSQL offers seamless scalability and high availability, making it an ideal choice for data-intensive applications that require rapid ingestion, processing, and analysis of large data volumes. As organizations increasingly leverage data-driven insights to gain a competitive edge, the need for robust, flexible, and easily manageable database solutions continues to surge. Additionally, the open-source nature of PostgreSQL fosters innovation and customization, enabling enterprises to tailor their database environments to specific business requirements without vendor lock-in.

Furthermore, the expanding ecosystem of cloud service providers and managed database platforms is accelerating the adoption of serverless PostgreSQL on a global scale. Leading cloud vendors are continuously enhancing their offerings with advanced features such as automated backups, security compliance, multi-region replication, and integrated monitoring tools. These advancements simplify database operations and enhance reliability, security, and performance, making serverless PostgreSQL a preferred choice for mission-critical applications across diverse industry verticals. The growing emphasis on digital transformation, coupled with the rising trend of remote work and distributed teams, is expected to sustain the momentum of market growth in the coming years.

From a regional perspective, North America continues to dominate the serverless PostgreSQL market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major cloud service providers, early adoption of advanced technologies, and a mature IT infrastructure. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing cloud investments, and a burgeoning startup ecosystem. Europe also represents a significant market, supported by stringent data protection regulations and a growing focus on cloud-based innovation. Latin America and the Middle East & Africa are gradually catching up, propelled by government initiatives and rising awareness of cloud benefits, though their market shares remain relatively modest compared to the leading regions.

Deployment Type Analysis

The deployment type segment of the serverless PostgreSQL market is categorized into public cloud, private cloud, and hybrid cloud. The public

This database supports the "SmartTraffic_Lakehouse_for_HCMC" project, designed to improve traffic management in Ho Chi Minh City by leveraging big data and modern lakehouse architecture.

This database manages operations for a parking lot system in Ho Chi Minh City, Vietnam, tracking everything from parking records to customer feedback. The database contains operational data for managing parking facilities, including vehicle tracking, payment processing, customer management, and staff scheduling. It's an excellent example of a comprehensive system for managing a modern parking infrastructure, handling different vehicle types (cars, motorbikes, and bicycles) and various payment methods.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13779146%2F40c8bbd9fd27a7b9fbe7c77598512cf2%2FParkingTransaction.png?generation=1735218498592627&alt=media" alt="">

The parking management database includes sample data for the following: - Owner: Customer information including contact details, enabling personalized service and feedback tracking - Vehicle: Detailed vehicle information linked to owners, including license plates, types, colors, and brands - ParkingLot: Information about different parking facilities at shopping malls, including capacity management for different vehicle types and hourly rates - ParkingRecord: Tracks vehicle entry/exit times and calculated parking fees - Payment: Records payment transactions with various payment methods (Cash, E-Wallet) - Feedback: Stores customer ratings and comments about parking services - Promotion: Manages promotional campaigns with discount rates and valid periods - Staff: Manages parking facility employees, including roles, contact information, and shift schedules

The design reflects real-world requirements for managing complex parking operations in a busy metropolitan area. The system can track occupancy rates, process payments, manage staff schedules, and handle customer relations across multiple locations.

Note: This database is part of the SmartTraffic_Lakehouse_for_HCMC project, designed to improve urban mobility management in Ho Chi Minh City. All data contained within is simulated for demonstration and development purposes. The project was created by Nguyen Trung Nghia (ren294) and is available on GitHub.

About my project: - Project: SmartTraffic_Lakehouse_for_HCMC - Author: Nguyen Trung Nghia (ren294) - Contact: trungnghia294@gmail.com - GitHub: Ren294

We present a compilation and analysis of 1099 Holocene relative shore-level (RSL) indicators including 867 relative sea-level data points and 232 data points from the Ancylus Lake and the following transitional phase from 10.7 to 8.5 ka BP located around the Baltic Sea. The spatial distribution covers the Baltic Sea and near-coastal areas fairly well, but some gaps remain mainly in Sweden. RSL data follow the standardized HOLSEA format and, thus, are ready for spatially comprehensive applications in, e.g., glacial isostatic adjustment (GIA) modelling. Sampling method The data set is a compilation of rather different samples from geological, geomorphological and archaeological studies. Most of the data was already published in different formats. In this compilation we homogenized the meta information of the available information according to the HOLSEA database format, https://www.holsea.org/archive-your-data, which is a modification of the recommendations given in Hijma et al. (2015). In addition to the reformatting, the majority of samples with radiocarbon dating were recalibrated with oxcal-software using the calib13 and marine13 curves. Furthermore, all sample descriptions were critically checked for consistency in positioning, levelling and indicative meaning by experts of the respective geographic region see Supplement 2. Analytical method In principle, it is a compilation, recalibration and revision of already published data. Data Processing Data of individual compilations were revised and imported into a relational database system. Therein, the data was transferred into the HOLSEA format by specified rules. By this procedure, a homogeneous categorisation was achieved without losing the original data. Also this is stored in the relational database system allowing for later updates of the transfer procedure or a recalibration of the data. Description of data table HOLSEA-baltic-yymmdd.xlsx The workbook in excel format contains 5 sheets, see https://www.holsea.org/archive-your-data: · Long-form, containing the complete information available for each sample · Short-form, a subset of attributes of the Long-form sheet · Radiocarbon, containing the radiocarbon dating information of the respective samples · U-series, a corresponding table containing the respective information of Uranium dating · References, a complete reference list of the primary publications in which the individual data sampling is described. All online sources for the compilation are included in the metadata. A full list of source references is provided in the data description file.

Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

Direct mobile numbers

Current job title, company, and department

Full career history + education background

Location data + LinkedIn profiles

Company size, industry, and revenue

✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

Who Uses This Data?

Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

Recruiters: Source passive candidates with up-to-date contact intel.

Data Vendors: License premium datasets to enhance your product.

Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

Flexible Delivery, Instant Results

API (REST): Real-time integration for CRMs, dialers, or marketing stacks

CSV/JSON: Campaign-ready files.

PostgreSQL: Custom databases for large-scale enrichment

Compliance: Full audit trails + opt-out management

Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

Need Proof? Contact us to see why Fortune 500 companies and startups alike trust Forager.ai for mission-critical outreach.

The global Data Base Management Systems market was valued at USD 50.5 billion in 2022 and is projected to reach USD 120.6 Billion by 2030, registering a CAGR of 11.5 % for the forecast period 2023-2030. Factors Affecting Data Base Management Systems Market Growth

Growing inclination of organizations towards adoption of advanced technologies like cloud-based technology favours the growth of global DBMS market

The cloud-based data base management system solutions offer the organizations with an ability to scale their database infrastructure up or down as per requirement. In a crucial business environment data volume can vary over time. Here, the cloud allows organizations to allocate resources in a dynamic and systematic manner, thereby, ensuring optimal performance without underutilization. In addition, these cloud-based solutions are cost-efficient. As, these cloud-based DBMS solutions eliminate the need for companies to maintain and invest in physical infrastructure and hardware. It helps in reducing ongoing operational costs and upfront capital expenditures. Organizations can choose pay-as-you-go pricing models, where they need to pay only for the resources they consume. Therefore, it has been a cost-efficient option for both smaller businesses and large-enterprises. Moreover, the cloud-based data base management system platforms usually come with management tools which streamline administrative tasks such as backup, provisioning, recovery, and monitoring. It allows IT teams to concentrate on more of strategic tasks rather than routine maintenance activities, thereby, enhancing operational efficiency. Whereas, these cloud-based data base management systems allow users to remote access and collaboration among teams, irrespective of their physical locations. Thus, in regards with today's work environment, which focuses on distributed and remote workforces. These cloud-based DBMS solution enables to access data and update in real-time through authorized personnel, allowing collaboration and better decision-making. Thus, owing to all the above factors, the rising adoption of advanced technologies like cloud-based DBMS is favouring the market growth.

Availability of open-source solutions is likely to restrain the global data base management systems market growth

Open-source data base management system solutions such as PostgreSQL, MongoDB, and MySQL, offer strong functionality at minimal or no licensing costs. It makes open-source solutions an attractive option for companies, especially start-ups or smaller businesses with limited budgets. As these open-source solutions offer similar capabilities to various commercial DBMS offerings, various organizations may opt for this solutions in order to save costs. The open-source solutions may benefit from active developer communities which contribute to their development, enhancement, and maintenance. This type of collaborative environment supports continuous innovation and improvement, which results into solutions that are slightly competitive with commercial offerings in terms of performance and features. Thus, the open-source solutions create competition for commercial DBMS market, they thrive in the market by offering unique value propositions, addressing needs of organizations which prioritize professional support, seamless integration into complex IT ecosystems, and advanced features. Introduction of Data Base Management Systems

A Database Management System (DBMS) is a software which is specifically designed to organize and manage data in a structured manner. This system allows users to create, modify, and query a database, and also manage the security and access controls for that particular database. The DBMS offers tools for creating and modifying data models, that define the structure and relationships of data in a database. This system is also responsible for storing and retrieving data from the database, and also provide several methods for searching and querying the data. The data base management system also offers mechanisms to control concurrent access to the database, in order to ensure that number of users may access the data. The DBMS provides tools to enforce security constraints and data integrity, such as the constraints on the value of data and access controls that restricts who can access the data. The data base management system also provides mechanisms for recovering and backing up the data when a system failure occurs....

The Forager.ai Global Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 70M total records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

Neotoma Database snapshot. Can be restored from the commandline using pg_restore (https://www.postgresql.org/docs/current/app-pgrestore.html). Current as of June 8, 2021.

Forager.ai's Global Job Postings Dataset stands unmatched! We meticulously track every public posting and capture all publicly available information for each record.

| Volume and Stats |

• 850M+ total records, constantly growing.
• 97M+ updates/month, the highest refresh rate in the industry.
• AI-driven data curation — we fuel the world's top sales and recruitment platforms with our robust data.
• Our delivery frequency is the industry's fastest, offering hourly updates.
• Delivery formats: JSONL, CSV.

| Datapoints |

• Key fields: Job Title, Normalized Job Title, Job Description, Company, Employment Type, Industries, Salary, Recruiter, Benefits, Company Address, Required Experience, Required Qualifications.
• Easily linkable to Forager's Company Dataset for deeper insights into company firmographics.

| Use Cases |

Sales Platforms, ABM Vendors, Intent Data Companies, AdTech and more: - Integrate our job postings data to provide your end-users with real-time insights into which companies are actively hiring. - Extract intent data based on future company departmental growth. - Advertising tech: Deliver targeted ads to potential candidates for recruiting clients, in combination with our people data.

Venture Capital and Private Equity: - Discover emerging startups that are beginning to hire. - Gain insights into high-growth startups' departmental growth plans. - Keep track of your portfolio companies' hiring strategies and job interest through job post lifecycle. - Empower your portfolio companies with competitor hiring practices insights. - Identify trending, high-demand industries with rapid hiring activities.

HR Tech, ATS Platforms, Recruitment Solutions, Executive Search Agencies: - Embed our job postings data into your platform to give your end-users unprecedented business opportunities. - Generate outreach signals for recruiters and business development teams using job postings open for over 60 days. - Identify new companies that could benefit from executive placement or headhunting services. - Match open roles for existing executive clients.

| Delivery Options | - Flat files via S3 or GCP - PostgreSQL Shared Database - PostgreSQL Managed Database - REST API - Other options available upon request, depending on the scale required.

Tags: Jobs Data, Job Postings, Workforce Intelligence, Economic Signals, Talent, Jobs Database, Hiring Data, Intent Data.

RCGroups.com is a model aircraft, truck, boat and general hobbyist forum board. In 2011, a poll and thread was started about climate change. This thread has continued for over 10 years and includes over 60,000 unique posts about climate science denial and its associated arguments from both sides of the debate. The scraped data includes authors, dates, and post content. In total there are almost 500 unique users who participated by posting replies in this thread, and almost 1,000 who voted in the poll.

We also have the results of the poll, so we are able to include a file that lists users and their viewpoints as a AGW denier, believer, or "unsure". This is great for classifying the users!

Link: [ https://www.rcgroups.com/forums/showthread.php?1452521 ] Archive: [ https://archive.ph/IuTMp ]

See code section for notebook with stopwords to clean up text column and start sentiment analysis. Also in code section: sample PostgreSQL database setup and sample queries. We include a script in code section to create two new columns that describe the sentiment of each post.

Note: the thread has continued since scraping this data. Posts newer than April 2022 are not in this dataset.

Forager.ai - Global B2B Person Data Set is a comprehensive and AI-powered collection of over 720M professional LinkedIn profiles. Our dataset is refreshed bi-weekly (2x/month) to ensure the most up-to-date and dynamic information, setting the industry standard for data accuracy and coverage. Delivered via JSON or CSV formats, it captures publicly available information on professional profiles across industries and geographies.

| Volume and Stats | 755M+ Global Records, continually growing. Each record is refreshed twice a month, ensuring high data fidelity. Powered by first-party data curation, supporting leading sales and recruitment platforms. Hourly delivery, providing near-real-time data access. Multiple data formats: JSONL, CSV for seamless integration.

| Datapoints | 150+ unique data points available, including: Current Title, Current Company, Work History, Educational Background, location and contact details. with high accuracy +95%. Linkage to other social networks and contact data for added insights.

| Use Cases | Sales Platforms, ABM Vendors, and Intent Data Companies Fuel your platforms with fresh, accurate professional data. Gain insights from job changes and update your database in real time. Enhance contact enrichment for targeted marketing and sales outreach. Venture Capital (VC) and Private Equity (PE) Firms Track employees and founders in your portfolio companies and be the first to know when they change roles. Access employee growth trends to benchmark against competitors. Discover new talent for portfolio companies, optimizing recruitment efforts. HR Tech, ATS Platforms, and Recruitment Solutions Build effective, industry-agnostic recruitment platforms with a wealth of professional data. Track job transitions and automatically refresh profiles to eliminate outdated information. Identify top talent through work history, educational background, and skills analysis.

| Delivery Options | Flat files via S3 or Snowflake PostgreSQL Shared/Managed Database REST API Custom delivery options available upon request.

| Key Features | Over 180M U.S. Professional Profiles. 150+ Data Fields available upon request. Free data samples for evaluation purposes. Bi-Weekly Updates Data accuracy +95%

Tags: LinkedIn Data, Professional Data, Employee Data, Firmographic Data, Work Experience, Education Data, Account-Based Marketing (ABM), Intent Data, Identity Resolution, Talent Sourcing, Sales Database, Recruitment Solutions, Contact Enrichment.

🎟️ BookMyShow SQL Data Analysis 🎯 Objective This project leverages SQL-based analysis to gain actionable insights into user engagement, movie performance, theater efficiency, payment systems, and customer satisfaction on the BookMyShow platform. The goal is to enhance platform performance, boost revenue, and optimize user experience through data-driven strategies.

📊 Key Analysis Areas 1. 👥 User Behavior & Engagement Identify most active users and repeat customers Track unique monthly users Analyze peak booking times and average tickets per user Drive engagement strategies and boost customer retention 2. 🎬 Movie Performance Analysis Highlight top-rated and most booked movies Analyze popular languages and high-revenue genres Study average occupancy rates Focus marketing on high-performing genres and content 3. 🏢 Theater & Show Performance Pinpoint theaters with highest/lowest bookings Evaluate popular show timings Measure theater-wise revenue contribution and occupancy Improve theater scheduling and resource allocation 4. 💵 Booking & Revenue Insights Track total revenue, top spenders, and monthly booking patterns Discover most used payment methods Calculate average price per booking and bookings per user Optimize revenue generation and spending strategies 5. 🪑 Seat Utilization & Pricing Strategy Identify most booked seat types and their revenue impact Analyze seat pricing variations and price elasticity Align pricing strategy with demand patterns for higher revenue 6. ✅❌ Payment & Transaction Analysis Distinguish successful vs. failed transactions Track refund frequency and payment delays Evaluate revenue lost due to failures Enhance payment processing systems 7. ⭐ User Reviews & Sentiment Analysis Measure average ratings per movie Identify top and lowest-rated content Analyze review volume and sentiment trends Leverage feedback to refine content offerings 🧰 Tech Stack Query Language: SQL (MySQL/PostgreSQL) Database Tools: DBeaver, pgAdmin, or any SQL IDE Visualization (Optional): Power BI / Tableau for presenting insights Version Control: Git & GitHub