We extract market statistics, industry reports, forecast data, and raw chart metrics from Statista. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Market Statistics objects from statista.com. All fields typed and schema-versioned.
"statistic_id": "264810", "title": "Number of smartphone users worldwide from 2013 to 2028", "category": "Technology & Telecommunications", "publication_date": "2023-11-14", "region": "Worldwide", "survey_time": "2013 to 2023", "premium_flag": false, "raw_data_points": "['2013: 1310', '2014: 1570', '2015: 1860']"
| # | statistic_id | title | category | sub_category | publication_date | source_name |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Industry Reports objects from statista.com. All fields typed and schema-versioned.
"report_id": "14285", "title": "Artificial Intelligence (AI) in Healthcare", "industry": "Healthcare", "pages": 84, "publication_date": "2024-01-12", "price_usd": 495.0, "author": "Statista Research Department"
| # | report_id | title | industry | pages | publication_date | format |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Consumer Insights objects from statista.com. All fields typed and schema-versioned.
"insight_id": "ci_9821", "topic": "Online Shopping Behaviour", "country": "United Kingdom", "audience_size": 2045, "survey_method": "Online Survey", "field_period": "Q3 2023", "questions_asked": 42
| # | insight_id | topic | country | audience_size | survey_method | field_period |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Company Insights objects from statista.com. All fields typed and schema-versioned.
"company_id": "comp_102", "name": "Apple Inc.", "hq_location": "Cupertino, CA", "revenue_usd": 383285000000, "employees": 161000, "industry_sector": "Consumer Electronics", "stock_ticker": "AAPL"
| # | company_id | name | hq_location | revenue_usd | employees | industry_sector |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search & Discovery objects from statista.com. All fields typed and schema-versioned.
"keyword": "electric vehicles", "result_position": 1, "result_type": "statistic", "item_id": "270538", "title": "Global electric vehicle sales from 2010 to 2023", "premium_flag": false, "release_date": "2024-02-05"
| # | keyword | result_position | result_type | item_id | title | snippet |
|---|---|---|---|---|---|---|
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Statista embeds its actual data points inside complex JavaScript objects and charting libraries. Our pipeline parses the underlying state, bypassing the visual layer to deliver structured metrics directly to your database.
We parse Highcharts configuration objects and embedded JSON to extract exact numerical values, dates, and categories rather than estimating from visual charts.
Extract publication dates, survey methodologies, sample sizes, and original source links for every statistic.
Scrape dossier metadata, tables of contents, pricing, and report descriptions across all industry verticals.
Extract survey structures, demographic splits, and audience sizes from Statista Consumer Insights data.
Capture revenue figures, employee counts, headquarters locations, and competitor lists from company insight pages.
Iterate through thousands of search results for specific keywords to build comprehensive datasets on niche topics.
Extract and standardise country and region tags to allow cross-border market comparisons.
Automatically identify which statistics are free and which require premium access, saving compute on inaccessible URLs.
Monitor specific industries or keywords for new report publications and statistic updates on a daily or weekly basis.
Brief in. Clean data out.
Provide categories, search terms, or specific statistic URLs. We design the extraction schema together.
We configure Scrapy parsers to target Statista embedded JSON objects and bypass bot protection layers.
Schema validation, unit normalisation, and data completeness checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Statista requires parsing complex DOM structures and bypassing strict rate limits. Here is how we maintain reliable pipelines.
Statista renders charts using JavaScript libraries. Standard HTML parsing fails to capture the exact data points. We intercept the underlying JSON state objects injected into the page source to extract precise numerical values and labels.
Statista employs strict rate limiting and automated bot detection. Our crawlers distribute requests across residential ISP proxies with realistic browser fingerprints to maintain access without triggering blocks.
Statista frequently updates its frontend architecture. Our selector strategy relies on structured data extraction and regex pattern matching within script tags, ensuring layout changes do not break your data pipeline.
Many statistics are gated behind premium accounts. Our pipeline detects paywall elements early in the request cycle, tagging records appropriately and preventing wasted compute on inaccessible data.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in chart data arrays and respond immediately. SLA uptime is contractual.
Consultancies aggregate statistics across industries to build comprehensive market sizing models and trend analyses.
Private equity firms extract forecast data and historical growth rates to validate investment opportunities in emerging sectors.
Strategy teams monitor company insights and market share statistics to benchmark performance against industry leaders.
Machine learning teams ingest structured market data and metadata to train financial models and predictive algorithms.
Universities compile historical demographic and economic data points for large scale longitudinal studies.
Media organisations track new statistic publications to automate data journalism and report generation.
"Statista aggregates global market intelligence into a single platform, but building automated models requires extracting the underlying chart data at scale."
Most teams fail at scraping Statista because the actual data points are embedded in complex JavaScript chart objects or hidden behind dynamic paywalls. DataFlirt parses the underlying state objects, handles session management, and structures the raw metrics so your analysts can focus on modelling rather than parsing HTML.
Everything supported by our statista.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication while custom middleware extracts and parses the embedded JSON objects containing the raw chart data.
We maintain pools of residential ISP proxies. Rotation happens per request with sticky sessions where required to navigate strict rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About statista.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We do not rely on OCR or visual scraping. Statista embeds the raw data points used to render the charts within the page source as JSON objects. Our pipeline intercepts and parses these objects to deliver exact numerical values.
Our standard pipeline targets publicly accessible statistics and metadata. We can identify and tag premium statistics, but we do not circumvent authentication walls or scrape data requiring a paid Corporate subscription.
If the historical data points are present within the current statistic page source, we extract them. We also maintain a time-series table of statistics from the date your pipeline is commissioned.
We extract all available metadata for industry reports, including titles, descriptions, pricing, and tables of contents. We do not download or parse the gated PDF files.
Pipelines can be configured to monitor specific categories or keywords daily. Full category refreshes typically complete within a 12-hour window depending on the requested volume.
Yes. We provide a sample run of up to 500 statistics or a specific category as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of a specific industry vertical or continuous monitoring of market forecasts, we scope, build, and operate the pipeline. Tell us what you need.
