This dataset represents a snapshot of the FRED catalog, captured on 2025-03-24.

What is FRED? As per the FRED website,

Short for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. FRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. In essence, FRED helps users tell their data stories. The purpose of this article is to guide the potential (or current) FRED user through the various aspects and tools of the database.

The FRED database is an abolute gold mine of economic data time series. Thousands of such series are published on the FRED website, organized by category and avialable for viewing and downloading. In fact, a number of these economic datasets have been uploaded to kaggle. With in the current notebook, however, we are not interested in the individual time series; rather, we are focused on catalog itself.

The FRED API has been used for gaining access to the catalog. The catalog consists of two files

• categories.csv - the heirarchy of categories used for organizing the time series
• series.csv - the list of avialable time series themselves

A given category is identified by a category_id. And, in a similar fashion, a given series is identified by a series_id. In a given category, one may find both a group of series and a set of sub-categories. As such every series record contains a category_id to identify the immediate category under which it is found category record contains a parent_id to indicate where in the category heirarchy it resides

Blockchain data query: FED Rates API - query_3805954

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Martin Sanchez on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Jaanus Jagomägi on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Federal Reserve Bank Of New York Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Real-Time Bank Feed APIs Market Outlook

According to our latest research, the global Real-Time Bank Feed APIs market size reached USD 2.14 billion in 2024, reflecting robust adoption across banking and financial sectors. The market is projected to grow at a CAGR of 19.2% from 2025 to 2033, reaching an estimated USD 10.78 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for seamless financial data integration, enhanced digital banking experiences, and the need for real-time transaction processing in a globally interconnected financial ecosystem.

One of the key growth factors fueling the Real-Time Bank Feed APIs market is the accelerating digital transformation initiatives within the banking and financial services industry. Banks and financial institutions are under immense pressure to modernize their legacy systems and deliver customer-centric digital solutions. Real-Time Bank Feed APIs enable seamless data exchange between banks and third-party applications, facilitating instant access to account balances, transaction histories, and payment statuses. This capability not only improves operational efficiency but also enhances customer experience by providing up-to-date financial information, which is critical in an era where consumers expect immediate access to their banking data.

Another significant driver is the proliferation of open banking regulations and standards across major economies. Regulatory frameworks such as PSD2 in Europe and similar initiatives in Asia Pacific and North America mandate banks to provide secure API access to customer data, provided customer consent is obtained. These regulations have catalyzed the adoption of Real-Time Bank Feed APIs by encouraging innovation and competition among financial service providers. Fintech companies, in particular, leverage these APIs to develop new financial products, streamline payment processing, and offer advanced analytics, thereby expanding the overall use cases and market penetration of Real-Time Bank Feed APIs.

The rapid growth of the fintech ecosystem is also contributing to the expansion of the Real-Time Bank Feed APIs market. Fintech startups and established technology firms are increasingly collaborating with banks to create integrated financial management platforms, automated accounting tools, and real-time fraud detection systems. The ability of Real-Time Bank Feed APIs to provide accurate, up-to-the-minute financial data is essential for these applications, driving their widespread adoption. Furthermore, the increasing use of artificial intelligence and machine learning in financial services amplifies the demand for real-time data feeds, as these technologies rely on timely and accurate information to deliver predictive insights and automated decision-making.

From a regional perspective, North America currently dominates the Real-Time Bank Feed APIs market, accounting for the largest share due to its mature banking infrastructure, high digital literacy, and strong presence of leading fintech innovators. Europe follows closely, propelled by stringent open banking regulations and a rapidly evolving financial services landscape. The Asia Pacific region is witnessing the fastest growth, driven by burgeoning digital banking adoption, supportive regulatory environments, and a large unbanked population transitioning to digital financial services. Latin America and the Middle East & Africa are gradually emerging as promising markets, fueled by increasing investments in digital infrastructure and growing demand for efficient banking solutions.

Component Analysis

The Real-Time Bank Feed APIs market is segmented by component into Software and Services, each playing a crucial role in the overall value chain. The software segment encompasses the core API platforms, integration tools, and middleware that enable the secure and efficient exchange of financial data between banks and third-party applications. These solutions are designed to

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Philip Veater on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Federal Reserve Bank Of Ny Eroc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Graph and download economic data for Secured Overnight Financing Rate (SOFR) from 2018-04-03 to 2025-12-01 about financing, overnight, securities, rate, and USA.

The Cleveland Fed maintains a broad range of indicators and datasets that are available for download, including median CPI, median PCE inflation, inflation expectations, yield curve and GDP growth, and simple monetary policy rules.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Fed Construction Llc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Fed Doo Zen Ca Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Fed Supremetech Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Low Bridge Clearance Digital Feed APIs Market Outlook

According to our latest research, the global Low Bridge Clearance Digital Feed APIs market size reached USD 1.24 billion in 2024, reflecting robust demand across transportation and logistics sectors. The market is forecasted to expand at a CAGR of 13.7% from 2025 to 2033, reaching a projected value of USD 4.02 billion by 2033. This impressive growth is primarily driven by the increasing need for real-time, accurate clearance data to enhance route planning, reduce accidents, and optimize logistics operations worldwide.

One of the primary growth factors for the Low Bridge Clearance Digital Feed APIs market is the rapid advancement in connected vehicle technologies and the proliferation of smart transportation infrastructure. As commercial fleets and logistics providers increasingly integrate digital solutions into their operations, the demand for APIs that deliver up-to-date clearance data has surged. These APIs enable seamless integration with navigation systems, fleet management platforms, and mapping services, providing actionable insights on bridge heights and potential hazards. The emphasis on reducing vehicle-bridge collision rates, which cause significant financial and operational losses, further accelerates adoption. Additionally, regulatory mandates in several regions requiring the use of advanced navigational aids for oversized vehicles have spurred market growth, as compliance becomes a critical operational requirement for transportation companies.

Another crucial driver is the growing focus on operational efficiency and cost reduction within the logistics and transportation industries. By leveraging Low Bridge Clearance Digital Feed APIs, fleet operators can avoid costly detours, property damage, and legal liabilities associated with bridge strikes. These APIs facilitate dynamic route planning, allowing vehicles to automatically reroute based on real-time clearance data, traffic conditions, and road closures. The integration of artificial intelligence and machine learning into these APIs further enhances their predictive capabilities, offering proactive risk mitigation and route optimization. As the industry shifts towards digital transformation and smart mobility, the adoption of robust digital feed APIs is becoming a competitive differentiator for logistics providers and transportation companies.

Furthermore, the increasing penetration of cloud computing and mobile technologies has made it easier for organizations of all sizes to access and deploy Low Bridge Clearance Digital Feed APIs. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, enabling even small and medium-sized enterprises to benefit from advanced clearance data solutions. The expansion of smart city initiatives and infrastructure modernization projects, especially in emerging markets, is also fueling demand for digital feed APIs that support urban mobility, public transportation, and municipal planning. As governments and municipalities invest in intelligent transportation systems, the integration of clearance data APIs into public and private sector applications is set to become more widespread, underpinning the market’s sustained growth trajectory.

From a regional perspective, North America currently dominates the Low Bridge Clearance Digital Feed APIs market, supported by a mature transportation infrastructure, high adoption of fleet management technologies, and stringent regulatory frameworks. Europe follows closely, driven by cross-border logistics activities and a strong emphasis on road safety. Asia Pacific is emerging as a high-growth region, propelled by rapid urbanization, expanding logistics networks, and government investments in smart transportation solutions. Latin America and the Middle East & Africa, while smaller in market share, are witnessing increasing adoption as digital transformation initiatives gain momentum and infrastructure modernization accelerates. Collectively, these regional dynamics underscore the global relevance and expanding footprint of the Low Bridge Clearance Digital Feed APIs market.

Component Analysis

The Component segment of the Low Bridge Clearance Digital Feed APIs market is broadly categorized into Software, Hardware, and Services. Software solutions form the backbone of the market, providing the core API functionalities that collect, process, and disseminate real-time clearance data. These soft

Fed Asia Limited Southeast Rep Office Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The flood risk forecast is produced by the Flood Forecasting Centre (FFC) on a daily basis. It is issued more frequently when serious flooding is forecast. It provides the indication of the potential for flooding for five days: the day on which it is issued and the subsequent four days ahead.

The forecast highlights flood risk on a county by county basis and includes a short commentary on the situation. It covers flooding from rivers, the sea, surface water and groundwater for Wales and England.

This dataset is produced in partnership with the Met Office Flood Forecasting Centre and the Environment Agency and is intended to complement the Flood Warning Service provided by Natural Resources Wales.

Fed Y E Ve Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Content

This series is constructed as Advance Retail and Food Services Sales (https://fred.stlouisfed.org/series/RSAFS) deflated using the Consumer Price Index for All Urban Consumers (1982-84=100) (https://fred.stlouisfed.org/series/CPIAUCSL).

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1992-01-01

• Observation End : 2019-10-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ive Erhard on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Discover the booming bank feed market! Our comprehensive analysis reveals a CAGR of X% (estimated based on market trends), driven by cloud accounting software and automation. Explore market segmentation, key players (Xero, QuickBooks, etc.), and regional insights for North America, Europe, and beyond. Learn about growth drivers, restraints, and future predictions for the period 2019-2033.