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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
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                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

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            [continent] => Array
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                            [de] => Nordamerika
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                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
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                    [iso_code] => US
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [longitude] => -83.0061
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
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                            [iso_code] => OH
                            [names] => Array
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                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
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                    [ip_address] => 216.73.216.109
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
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                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                    [ip_address] => 216.73.216.109
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                    [network] => 216.73.216.0/22
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                    [15] => mobileCountryCode
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                )

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                    [geoname_id] => 4509177
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                            [pt-BR] => Columbus
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    [location:protected] => GeoIp2\Record\Location Object
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
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                    [4] => metroCode
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                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
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                    [0] => code
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    [subdivisions:protected] => Array
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            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Disney Plus Streaming Dataset - Web Scraping Disney Plus OTT Data

Scrape Disney+ data insights with comprehensive Disney+ datasets, including viewing habits, user preferences, and trending shows. Use Disney+ data scraping and Disney+ media content scraping for detailed streaming analysis. Uncover

  • Title
  • Genre
  • Release Date
  • Ratings

Disney Datasets Explained

Disney datasets encompass data obtained through Disney streaming service data scraping, including movie and TV show data scraping, content catalog scraping, and metadata scraping. These datasets provide insights into Disney content for analytics and strategic decision-making. We guarantee accurate and current information on:

  • Title
  • Genre
  • Release Year
  • Director
  • Cast
  • Duration
  • Rating
  • Languages
  • Country
  • Awards
  • Viewer Ratings
  • Streaming Quality
  • Availability
  • Watch History
  • Recommendations
  • Viewing Devices
  • Playback Settings
  • User Profiles
  • Search History
  • Content Tags
  • Content Updates
  • Streaming Metrics
  • Content Popularity
  • Content Engagement
  • Content Trends
  • Content Recommendations
  • Content Licensing
  • Content Restrictions
  • Viewer Feedback
  • User Interactions
Datasets-Explained
IdPlatform_NamePlatform_URLContent_idCategorySub_CategoryTitleDescriptionSeasonsRatingURL
1Disney+https://www.disneyplus.comC001SeriesFantasyThe AcolyteStar Wars prequel; crime‑mystery at High Republic era14.2★https://www.disneyplus.com/series/the-acolyte/4YhxyzA-
2Disney+https://www.disneyplus.comC002SeriesSuperheroAgatha All AlongMarvel miniseries spin‑off from WandaVision14.4★https://www.disneyplus.com/series/agatha-all-along/abcdef-
Get Started
Flexible-Data-Delivery

Flexible Data Delivery

Customized for your Disney data scraping requirements, we offer flexibility in output formats, storage preferences, and delivery timelines:

  • Scrape Disney datasets in preferred formats such as JSON and CSV;
  • Retrieve data via SFTP or seamlessly integrate with cloud storage solutions like Google Cloud Storage, AWS S3, and others;
  • Choose from one-time, monthly, quarterly, or bi-annual extraction frequencies to align with your specific needs.
Get Started

Secure Premium Industry Data with Ease

Premium-Industry-Datasets-on-Demand
Premium Industry Datasets on Demand

Access Actowiz's curated enterprise datasets for precise data insights, sourced by top web data specialists.

Efficiency Meets Expertise
Efficiency Meets Expertise

Save time; our team expertly extracts data for analysis, aligning with strategic business goals cost-effectively.

Tailored Data Solutions for Your Enterprise
Tailored Data Solutions for Your Enterprise

Receive tailored industry datasets; our flexible methods align with unique business needs for optimal decisions.

Commitment-to-Ethical-Data-Practices
Commitment to Ethical Data Practices

Actowiz leads Ethical Web Data Collection, upholding GDPR and CCPA, setting standards for responsible data practices./p>

Pricing Overview

Standard-Pricing

Standard Pricing

  • Standardized Data Offerings with readily accessible datasets.
  • Consistent Data Framework that ensures clarity and consistency.
  • Top-tier Data Integrity from the most intricate data channels.

Flexible Delivery Options:

  • Monthly Updates
  • Quarterly Deliveries
  • One-off Purchases
Get Started
Recommended
Custom-Pricing

Custom Pricing

  • Tailored Data Retrieval precisely aligned with your business needs.
  • Adaptable Data Framework crafted to fit your unique needs.
  • Scalable & Dynamic Offerings to ensure flexibility and scalability.
  • Streamlined Communication ensures smooth and instant communication.

Varied Delivery Options:

  • Daily Updates
  • Weekly Snapshots
  • Monthly Insights
  • Quarterly Reports
  • Customized Frequencies to Suit You
Get Started

Inclusive Benefits Across All Plans:

Get Started
  • Expert Data Extraction
  • Regulatory Assurance
  • Personalized Support
  • Broad Data Coverage

Why Choose Actowiz Solutions’ Datasets?

Assessing-Your-Data-Requirements

Assessing Your Data Requirements:

Our first step is to delve into your company's specifics and business goals, ensuring we align perfectly with your data aspirations.

Crafting-Tailored-Data-Solutions

Crafting Tailored Data Solutions

Leveraging our robust in-house web scraping tools, we design a bespoke strategy tailored to your data extraction needs.

Sample-Data-Preview

Sample Data Preview

Experience firsthand with a sample of our company data. Gauge its quality and familiarize yourself with our seamless data delivery mechanism.

Steadfast-Data-Streamlining

Steadfast Data Streamlining

Once aligned, we initiate regular data dispatches based on the frequency and specifications we've mutually agreed upon.

Frequently Asked Questions

Disney datasets refer to structured collections of data obtained through Disney data scraping and Disney video streaming data extraction. These datasets encompass a wide range of information related to Disney's content library, user interactions, and platform performance. They include details such as movie and TV show titles, genres, release years, director and cast information, viewer ratings, streaming quality options, availability in different regions, user preferences, and viewing histories. Disney datasets are valuable for market analysis, content recommendation systems, viewer behavior analytics, and improving content delivery strategies.
Choosing Disney datasets over traditional web scraping offers structured and reliable data specifically tailored for comprehensive analysis of Disney's media content and streaming service. Disney datasets, obtained through methods like Disney media content scraping, Disney streaming service data scraping, and Disney movie and TV show data scraping, provide curated information such as detailed metadata, viewer preferences, and content performance metrics. Unlike ad-hoc web scraping, Disney datasets ensure consistent data quality and format, facilitating easier integration into analytical tools and databases. This approach saves time and resources while ensuring accurate insights for optimizing content strategies, enhancing user experience, and making informed business decisions.
You can obtain Disney data from various sources, primarily through methods like Disney content catalog scraping and metadata scraping for Disney. These techniques involve gathering comprehensive information about Disney's media content, including movie and TV show titles, genres, release years, director and cast details, ratings, and viewer reviews. Utilizing APIs provided by Disney or employing web scraping tools can ensure accurate and up-to-date data retrieval. This data is crucial for market analysis, content recommendation systems, viewer behavior analytics, and improving content delivery strategies.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
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2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
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1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All
Blog
Case Studies
Infographics
Report
Aug 18, 2025

Monthly API Insights for Quick Commerce Startups – Real-Time Data & Performance Metrics

Get Monthly API Insights for Quick Commerce Startups, tracking real-time performance, data trends, and analytics to optimize growth and operational efficiency.

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How Daily Grocery Dataset Updates for Startup Transformed a Grocery Business in Faridabad

Discover how Daily Grocery Dataset Updates for Startup helped a Faridabad grocery business boost pricing accuracy, inventory management, and competitive market positioning.

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Weekly Tracking of Job Role Demand via Indeed & LinkedIn in Chicago

weekly tracking of job role demand via Indeed & LinkedIn in Chicago, analyzing hiring trends, role popularity, and market demand shifts.

Aug 18, 2025

Monthly API Insights for Quick Commerce Startups – Real-Time Data & Performance Metrics

Get Monthly API Insights for Quick Commerce Startups, tracking real-time performance, data trends, and analytics to optimize growth and operational efficiency.

Aug 17, 2025

Scraping Government Portals in India - Opportunities, Compliance, and Best Practices

Explore Scraping Government Portals in India, uncover opportunities, ensure compliance, and leverage data for insights while following legal and ethical best practices.

Aug 16, 2025

Grocery Discount Tracking on Instacart for Competitive Pricing

Actowiz Solutions tracks Instacart grocery discounts in real time to help brands, retailers, and analysts optimize competitive pricing strategies.

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How Daily Grocery Dataset Updates for Startup Transformed a Grocery Business in Faridabad

Discover how Daily Grocery Dataset Updates for Startup helped a Faridabad grocery business boost pricing accuracy, inventory management, and competitive market positioning.

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How to Scrape Integrate Vivino Wine Data to Improve Product Matching for an Alcohol Marketplace

Discover how alcohol marketplaces boost accuracy with our Case Study - Scrape Integrate Vivino Wine Data, improving product matching, discovery, and conversions.

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Scrape Zepto Sales Data to Inform Quick Commerce Expansion Strategy in Mumbai

Discover how Actowiz Solutions used Case Study - Scrape Zepto Sales Data to unlock insights, guiding quick commerce expansion strategies in Mumbai for higher growth.

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Weekly Tracking of Job Role Demand via Indeed & LinkedIn in Chicago

weekly tracking of job role demand via Indeed & LinkedIn in Chicago, analyzing hiring trends, role popularity, and market demand shifts.

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Monthly Tracking of Property Prices in NYC via Realtor.com

monthly tracking of property prices in NYC, using Realtor.com data to analyze market trends, price shifts, and neighborhood-level changes.

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Weekly Uber Eats Data Tracking of Vendor Activity in New York

Analyze vendor trends with Weekly Uber Eats data in New York, tracking menus, pricing, and activity for strategic food delivery insights.