We extract macroeconomic indicators, time-series datasets, sovereign debt figures, and procurement contracts from worldbank.org. 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 Global Indicators objects from worldbank.org. All fields typed and schema-versioned.
"indicator_code": "NY.GDP.MKTP.CD", "indicator_name": "GDP (current US$)", "country_code": "IND", "country_name": "India", "year": 2023, "value": 3549918900000.0, "source_organization": "World Bank national accounts data", "last_updated": "2024-05-10"
| # | indicator_code | indicator_name | country_code | country_name | year | value |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Project Data objects from worldbank.org. All fields typed and schema-versioned.
"project_id": "P173702", "project_name": "India COVID-19 Emergency Response", "country": "India", "status": "Closed", "approval_date": "2020-04-02", "closing_date": "2022-12-31", "commitment_amt": 1000000000.0, "sector": "Health"
| # | project_id | project_name | country | region | status | approval_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Microdata Catalogue objects from worldbank.org. All fields typed and schema-versioned.
"study_id": "IND_2018_PLFS_v01_M", "title": "Periodic Labour Force Survey 2018-2019", "country": "India", "year_start": 2018, "year_end": 2019, "data_access": "Public Use", "unit_of_analysis": "Household/Individual", "producer": "National Statistical Office"
| # | study_id | title | country | year_start | year_end | collection_mode |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Climate Data objects from worldbank.org. All fields typed and schema-versioned.
"country_iso": "BGD", "variable": "tas", "model": "Ensemble", "scenario": "SSP3-7.0", "time_period": "2040-2059", "annual_mean": 26.8, "unit": "Celsius", "min_val": 25.9
| # | country_iso | variable | model | scenario | time_period | annual_mean |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Procurement Contracts objects from worldbank.org. All fields typed and schema-versioned.
"contract_id": "1248992", "project_id": "P164961", "supplier_name": "Larsen & Toubro", "supplier_country": "India", "award_date": "2021-08-14", "contract_amount": 14500000.0, "currency": "USD", "procurement_method": "International Competitive Bidding"
| # | contract_id | project_id | supplier_name | supplier_country | award_date | contract_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
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The World Bank data portal relies on complex internal APIs, heavy nested JSON/XML payloads, and fragmented historical datasets. We normalise this into clean, warehouse-ready schemas.
We flatten complex multi-year indicator arrays into standard relational rows (indicator, country, year, value) for direct SQL querying.
Automated downloading, extraction, and parsing of large ZIP and CSV data dumps published in the World Bank Data Catalogue.
Extract tabular data from published economic updates and project appraisal documents using OCR and coordinate-based extraction.
Monitor retrospective adjustments to GDP and GNI figures. We capture the delta between previous and current releases.
Automatically map World Bank internal region codes and country names to standard ISO 3166-1 alpha-2 and alpha-3 codes.
Extract IBRD and IDA loan statements, outstanding balances, and disbursement schedules at the sovereign level.
Capture survey methodology, sampling frameworks, and unit-of-analysis metadata from the Microdata Library.
Extract projected temperature and precipitation anomalies across multiple SSP scenarios and climate models.
Navigate deep pagination limits in the World Bank Developer APIs to ensure 100% dataset coverage without truncation.
Brief in. Clean data out.
Provide indicator codes, country lists, or project sectors. We design the extraction schema together.
We configure Scrapy crawlers, API polling, and file extraction logic for worldbank.org.
Schema validation, null-rate checks, historical continuity verification, and sample datasets before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Public data portals present unique architectural challenges, including rate limits, schema drift, and fragmented file formats. Here is how we maintain pipeline stability.
World Bank APIs enforce strict rate limits per IP. We distribute requests across our infrastructure with exponential backoff and jitter to ensure continuous extraction without triggering 429 Too Many Requests errors.
Many datasets are only available as multi-gigabyte ZIP archives containing poorly formatted CSVs. Our workers stream these files into memory, parse the contents, standardise the headers, and write directly to Parquet.
Different World Bank departments publish data using inconsistent date formats, country identifiers, and null values (e.g., '..', 'NA', or blank). We apply strict normalisation rules before delivery.
Crucial project financials and appraisal matrices often exist only inside PDF reports. We deploy machine learning models to detect tables, extract cell data, and reconstruct the relational structure.
Macroeconomic data is frequently revised retroactively. We hash every time-series point and compare it against the previous run, emitting only the records that have changed.
Quant funds and economists ingest GDP, inflation, and trade indicators to train predictive models for sovereign debt markets.
Asset managers use World Bank climate projections, poverty rates, and governance indicators to construct sovereign ESG scores.
Logistics firms monitor infrastructure investment projects and trade logistics indices to forecast regional supply chain viability.
Universities and think tanks rely on clean microdata and household surveys to evaluate the efficacy of development policies.
Fixed income analysts track IBRD/IDA loan commitments and debt service metrics to assess country creditworthiness.
Multinational corporations analyze demographic trends, literacy rates, and GNI per capita to identify emerging market expansion opportunities.
"The World Bank publishes the most authoritative economic data on earth, but extracting it from fragmented APIs and bulk CSVs requires dedicated infrastructure."
Most data teams waste weeks writing custom parsers for World Bank ZIP files, PDFs, and rate-limited APIs. DataFlirt absorbs the complexity of extraction, normalisation, and historical revision tracking so your analysts can query clean Parquet tables immediately.
Everything supported by our worldbank.org 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 HTML portal crawling and PDF discovery, while custom Python clients manage authenticated API polling and rate-limit compliance.
Large CSV and ZIP datasets are processed in-memory on AWS ECS containers, avoiding disk I/O bottlenecks and ensuring high-throughput normalisation.
Pipelines run on Kubernetes. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About worldbank.org scraping, legality, and pipeline operations.
Ask us directly →Yes. The vast majority of datasets on worldbank.org are published under open access licenses (such as CC BY 4.0) intended for public consumption and automated retrieval. DataFlirt extracts only public indicators, metadata, and project documents. We do not bypass authentication for confidential internal systems.
The World Bank frequently updates historical data retroactively. We maintain a time-series database and run periodic sweeps over historical years. When a value changes, our pipeline emits a delta record highlighting the revision, ensuring your warehouse reflects the latest official figures.
Yes. We use coordinate-based extraction and machine learning models to parse tabular data from Project Appraisal Documents (PADs) and economic updates, converting them into structured JSON or CSV.
Pipeline frequency is configurable. We can poll specific API endpoints daily for fast-moving project data, or run monthly/quarterly sweeps for macroeconomic indicators aligned with the World Bank's publication schedule.
Yes. The World Bank often uses internal region codes and varied country spellings. We map all geographic entities to standard ISO 3166-1 alpha-2 and alpha-3 codes before delivery.
We can extract all public metadata, survey methodologies, and variable dictionaries from the Microdata Library. However, actual dataset files that require a formal researcher application and signed licensing agreement must be accessed manually by your team.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full dump of the World Bank Data Catalogue or continuous updates on specific project financials — we scope, build, and operate the pipeline. Tell us what you need.
