The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

This database is an adaptation for DADA2 of : Vasselon, Valentin; Rimet, Frederic; Bouchez, Agnès, 2018, "Rsyst::diatom_rbcl_align_312bp database: a database adapted to DNA metabarcoding (version v7: 23-02-2018)", https://doi.org/10.15454/HYRVUH, Portail Data Inra, V1 This version is adapted from Diat.barcode version 7 https://data.inra.fr/dataset.xhtml?persistentId=doi:10.15454/HYRVUH

Select from any of our pre-built bundles: - HR decision makers in APAC - HR decision makers in US - IT decision-makers in APAC - IT decision makers in US - C-Level, VP & Directors in APAC - C-Level, VP & Directors in US

All records include: - Contact name - Job title - Contact email address - Contact phone number - Contact location - Organisation name - Organisation type - Organisation headcount - Primary industry

Additional information like social media handles, secondary industries, and organisation websites may be provided where available.

All bundles are verified by Leadbook's proprietary A.I. powered data technology and provide business contact information inlcuding contact name, organisation name, contact location, industry, job title and email address.

Sudan Imports: Textiles: Ready Made Clothes data was reported at 10,752.000 USD th in Mar 2018. This records an increase from the previous number of 8,455.000 USD th for Feb 2018. Sudan Imports: Textiles: Ready Made Clothes data is updated monthly, averaging 12,833.000 USD th from Jan 2003 (Median) to Mar 2018, with 183 observations. The data reached an all-time high of 88,620.000 USD th in Jan 2010 and a record low of 0.000 USD th in Mar 2008. Sudan Imports: Textiles: Ready Made Clothes data remains active status in CEIC and is reported by Central Bank of Sudan. The data is categorized under Global Database’s Sudan – Table SD.JA007: Imports: by Commodity.

This database is an adaptation for DADA2 of Diat.barcode v9. Length of sequences is 263 bp ------------------------- Rimet, Frederic; Chonova, Teofana; Gassiole, Gilles; Gusev, Evgenuy; Kahlert, Maria; Keck, François; Kelly, Martyn; Kulikovskiy, Maxim; Maltsev, Yevhen; Mann, David; Pfannkuchen, Martin; Trobajo, Rosa; Vasselon, Valentin; Wetzel, Carlos; Zimmermann, Jonas; Bouchez, Agnès, 2018, "Diat.barcode, an open-access barcode library for diatoms", https://doi.org/10.15454/TOMBYZ

Egypt Industrial Production: Public: Ready Made Clothes data was reported at 13.800 Piece mn in 2016. This records an increase from the previous number of 13.700 Piece mn for 2015. Egypt Industrial Production: Public: Ready Made Clothes data is updated yearly, averaging 14.000 Piece mn from Jun 1992 (Median) to 2016, with 24 observations. The data reached an all-time high of 258.000 Piece mn in 2013 and a record low of 10.600 Piece mn in 2007. Egypt Industrial Production: Public: Ready Made Clothes data remains active status in CEIC and is reported by Ministry of Planning. The data is categorized under Global Database’s Egypt – Table EG.B005: Industrial Production: Annual.

• 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied

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We were able to do so by obtaining data from various locations around the world. As a result, our database is widely accessible, and anyone can use it from any location on the planet. Please contact us if you want the best eCommerce leads .

We sell eCommerce leads that can be filtered by industry. We know what you’re going through and what you’ll need for your next project. As a result, we’ve compiled a list of eCommerce leads that are exactly what you require. With the most potential data we provide, you can grow your business and achieve your business goals. All of our eCommerce leads are generated professionally, with real people – not bots – entering data.

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786 Global import shipment records of Readymade Garment with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Company Datasets for valuable business insights!

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These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

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You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

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72 Global import shipment records of Ready Made Dresses with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Dataset: The files on your computer.

Crab is a command line tool for Mac and Windows that scans file data into a SQLite database, so you can run SQL queries over it.

e.g. (Win)    C:> crab C:somepathMyProject
or (Mac)    $ crab /some/path/MyProject

You get a CRAB> prompt where you can enter SQL queries on the data, e.g. Count files by extension

SELECT extension, count(*) 
FROM files 
GROUP BY extension;

e.g. List the 5 biggest directories

SELECT parentpath, sum(bytes)/1e9 as GB 
FROM files 
GROUP BY parentpath 
ORDER BY sum(bytes) DESC LIMIT 5;

Crab provides a virtual table, fileslines, which exposes file contents to SQL

e.g. Count TODO and FIXME entries in any .c files, recursively

SELECT fullpath, count(*) FROM fileslines 
WHERE parentpath like '/Users/GN/HL3/%' and extension = '.c'
  and (data like '%TODO%' or data like '%FIXME%')
GROUP BY fullpath;

As well there are functions to run programs or shell commands on any subset of files, or lines within files e.g. (Mac) unzip all the .zip files, recursively

SELECT exec('unzip', '-n', fullpath, '-d', '/Users/johnsmith/Target Dir/') 
FROM files 
WHERE parentpath like '/Users/johnsmith/Source Dir/%' and extension = '.zip';

(Here -n tells unzip not to overwrite anything, and -d specifies target directory)

There is also a function to write query output to file, e.g. (Win) Sort the lines of all the .txt files in a directory and write them to a new file

SELECT writeln('C:UsersSJohnsondictionary2.txt', data) 
FROM fileslines 
WHERE parentpath = 'C:UsersSJohnson' and extension = '.txt'
ORDER BY data;

In place of the interactive prompt you can run queries in batch mode. E.g. Here is a one-liner that returns the full path all the files in the current directory

C:> crab -batch -maxdepth 1 . "SELECT fullpath FROM files"

Crab SQL can also be used in Windows batch files, or Bash scripts, e.g. for ETL processing.

Crab is free for personal use, $5/mo commercial

See more details here (mac): [http://etia.co.uk/][1] or here (win): [http://etia.co.uk/win/about/][2]

An example SQLite database (Mac data) has been uploaded for you to play with. It includes an example files table for the directory tree you get when downloading the Project Gutenberg corpus, which contains 95k directories and 123k files.

To scan your own files, and get access to the virtual tables and support functions you have to use the Crab SQLite shell, available for download from this page (Mac): [http://etia.co.uk/download/][3] or this page (Win): [http://etia.co.uk/win/download/][4]

Content

FILES TABLE

The FILES table contains details of every item scanned, file or directory. All columns are indexed except 'mode'

COLUMNS
 fileid (int) primary key -- files table row number, a unique id for each item
 name (text)        -- item name e.g. 'Hei.ttf'
 bytes (int)        -- item size in bytes e.g. 7502752
 depth (int)        -- how far scan recursed to find the item, starts at 0
 accessed (text)      -- datetime item was accessed
 modified (text)      -- datetime item was modified
 basename (text)      -- item name without path or extension, e.g. 'Hei'
 extension (text)     -- item extension including the dot, e.g. '.ttf'
 type (text)        -- item type, 'f' for file or 'd' for directory
 mode (text)        -- further type info and permissions, e.g. 'drwxr-xr-x'
 parentpath (text)     -- absolute path of directory containing the item, e.g. '/Library/Fonts/'
 fullpath (text) unique  -- parentpath of the item concatenated with its name, e.g. '/Library/Fonts/Hei.ttf'

PATHS
1) parentpath and fullpath don't support abbreviations such as ~ . or .. They're just strings.
2) Directory paths all have a '/' on the end.

FILESLINES TABLE

The FILESLINES table is for querying data content of files. It has line number and data columns, with one row for each line of data in each file scanned by Crab.

This table isn't available in the example dataset, because it's a virtual table and doesn't physically contain data.

COLUMNS
 linenumber (int) -- line number within file, restarts count from 1 at the first line of each file
 data (text)    -- data content of the files, one entry for each line

FILESLINES also duplicates the columns of the FILES table: fileid, name, bytes, depth, accessed, modified, basename, extension, type, mode, parentpath, and fullpath. This way you can restrict which files are searched without having to join tables.

Example Gutenberg data

An example SQLite database (Mac data), database.sqlite, has been uploaded for you to play with. It includes an example files table...

Thailand Domestic Sales: Concrete: Ready Made Concrete Floor data was reported at 42,202.290 sq m in Jun 2018. This records an increase from the previous number of 41,713.030 sq m for May 2018. Thailand Domestic Sales: Concrete: Ready Made Concrete Floor data is updated monthly, averaging 36,437.735 sq m from Jan 2000 (Median) to Jun 2018, with 222 observations. The data reached an all-time high of 68,420.480 sq m in Mar 2015 and a record low of 13,244.843 sq m in Jan 2000. Thailand Domestic Sales: Concrete: Ready Made Concrete Floor data remains active status in CEIC and is reported by Office of Industrial Economics. The data is categorized under Global Database’s Thailand – Table TH.H004: Domestic Sales: Office of Industrial Economics (ISIC Rev. 4).

Myanmar Production: Made Up Article: Shirts & Ready made Garments data was reported at 9.000 Unit th in 2017. This records a decrease from the previous number of 689.000 Unit th for 2016. Myanmar Production: Made Up Article: Shirts & Ready made Garments data is updated yearly, averaging 1,390.500 Unit th from Mar 1986 (Median) to 2017, with 24 observations. The data reached an all-time high of 146,600.000 Unit th in 1986 and a record low of 9.000 Unit th in 2017. Myanmar Production: Made Up Article: Shirts & Ready made Garments data remains active status in CEIC and is reported by Central Statistical Organization. The data is categorized under Global Database’s Myanmar – Table MM.B002: Production by Commodity: Annual.

This database is an adaptation for DADA2 of Diat.barcode v12. Length of sequences is 263 bp ------------------------- Rimet, Frederic et al., 2018, "Diat.barcode, an open-access barcode library for diatoms", https://doi.org/10.15454/TOMBYZ

Moldova Industrial Production: Volume: Animals Ready Made Forage data was reported at 87.500 Tonne th in 2017. This records a decrease from the previous number of 95.400 Tonne th for 2016. Moldova Industrial Production: Volume: Animals Ready Made Forage data is updated yearly, averaging 71.600 Tonne th from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 229.300 Tonne th in 1997 and a record low of 25.700 Tonne th in 2003. Moldova Industrial Production: Volume: Animals Ready Made Forage data remains active status in CEIC and is reported by National Bureau of Statistics of the Republic of Moldova. The data is categorized under Global Database’s Moldova – Table MD.B005: Industrial Production: Volume: Annual.

Open Food Facts Database

  What is 🍊 Open Food Facts?





  A food products database

Open Food Facts is a database of food products with ingredients, allergens, nutrition facts and all the tidbits of information we can find on product labels.

  Made by everyone

Open Food Facts is a non-profit association of volunteers. 25.000+ contributors like you have added 1.7 million + products from 150 countries using our Android or iPhone app or their camera to scan… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/product-database.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Belarus Consumer Price Index (CPI): Prev Month=100: Non Food Products: Ready Made Garments data was reported at 101.060 Prev Mth=100 in Feb 2025. This records an increase from the previous number of 99.720 Prev Mth=100 for Jan 2025. Belarus Consumer Price Index (CPI): Prev Month=100: Non Food Products: Ready Made Garments data is updated monthly, averaging 100.300 Prev Mth=100 from Mar 2009 (Median) to Feb 2025, with 192 observations. The data reached an all-time high of 113.900 Prev Mth=100 in Sep 2011 and a record low of 98.180 Prev Mth=100 in Jun 2020. Belarus Consumer Price Index (CPI): Prev Month=100: Non Food Products: Ready Made Garments data remains active status in CEIC and is reported by National Statistical Committee of the Republic of Belarus. The data is categorized under Global Database’s Belarus – Table BY.I005: Consumer Price Index: by Products and Services: Previous Month=100.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This database was built to identify taxa in metagenome samples using the CCMetagen pipeline. The whole NCBI nt collection allows a complete taxonomic overview, including from microbial eukaryotes that may be present in the dataset. This database is already indexed, ready to use with KMA and CCMetagen.

A manual describing how to use this dataset can be found at: https://github.com/vrmarcelino/CCMetagen

Additionally, a tutorial on the whole analysis of a set of metatranscriptome samples can be found at: https://github.com/vrmarcelino/CCMetagen/tree/master/tutorial

The database was built as follows:

The partially non-redundant nucleotide database was downloaded from the NCBI website (ftp://ftp.ncbi.nih.gov/blast/db/FASTA/nt.gz) in January 2018. This database was formatted to include taxids in sequence headers.

Indexing was then performed with KMA using the commands:

kma_index -i nt_taxid.fas -o ncbi_nt -NI -Sparse TG

Three indexed databases are provided:

1. NCBI nucleotide collection
2. RefSeq database of bacterial and fungal genomes