This datasets specifies the transaction (Buys,Sells,Awards) done between companies and their employess internally. There is no stock exchange data of the public level . As the dataset is collected through public API provided by rapid API platform .

RapidAPI is a comprehensive platform that functions as the world's largest API Hub, serving as a marketplace and management platform for Application Programming Interfaces (APIs). Its primary purpose is to connect developers (API consumers) with a vast array of APIs provided by various developers and companies (API providers).

***key terms :- ***

**Stock Options **:- A stock option is a type of employee benefit that gives you the right to buy company shares at a fixed price usually lower than the market price after a certain period of time.

Restricted Stock Units (RSUs) :- This are the stock grants provided by the company to employees as an compensation which keeps the motivation of the workers high.

Talking about the quality of the dataset , so i had made some filters to their datatype , representation . the size of the dataset is small around 1300 (toy datasets) especially useful and helpful to perform the beginner friendly Exploratory data analysis. There is no primary key for the dataset we need to create an synthetic one .

Columns:-

1. symbol :- It just only shows the ticker symbol of the company's stock.

2. symbolName :- Full Name of the company corresponding to the ticker.

3. fullName :- Name of the company's insider making the transaction.

4. shortJobTitle:- Position of the insider who is making the stock transaction.

5. transactionType:- Type of the transaction ---- Buy, sell & Award.

6. amount :- Number of shares traded in the transaction

7. reportedPrice:- Current price per share reported for the transaction

8. usdValue :- Total amount in dollars for the current transaction.

9. eodHolding :- Insider’s end-of-day holding after the transaction (number of shares remaining).

10. transactionDate:- Date on which transaction has been done.

11. symbolCode:- Type of security traded (e.g., STK for stock, UIT for unit trust).

12. hasOptions:- Indicates if the insider has stock options (Yes/No).

13. symbolType:- Numeric code representing the type of instrument or classification (often internal or system-defined).

Github Link to source code of data collection through API :- https://github.com/Aryan83699/yahoo-stock-exchange

Provides hourly grid limit price information and demand forecast values for the mainland and Jeju. Each hour is represented by the end point of that unit period (i.e. trading time 06:00 represents a period starting immediately after 05:00 and ending at 06:00) Data updated once a day, around 20:00 . The existing grid limit price inquiry API will be deleted in the future.

This dataset provides comprehensive stock market data sourced from Polygon API and Finviz. It includes two main components:

1. Aggregated Data:

   • In this sheet contains day-wise data for a specific stock ticker with columns for:
   • DATE: Date of the stock data
   • DAY: Day of the week for the stock
   • TICKER: Stock symbol
   • Prev Close: Previous day's close price at 16:00
   • PM Hi: Highest price during pre-market hours (4:00 - 9:29)
   • PM Hi Time: Time of the highest pre-market price
   • PM total vol to PM High: Total volume up to the highest pre-market price
   • PM Volume: Total volume during pre-market hours
   • Open $: Opening price at 9:30
   • Open 1 min Vol: Total volume in the first minute after opening
   • Open 2 min Vol: Total volume in the first two minutes after opening
   • ∑ Vol: PM to intra Hi: Total volume from pre-market to highest intra-day price
   • ∑ Vol: Open to intra Hi: Total volume from market open to highest intra-day price
   • HOD: High of the day from 9:29 to 16:00
   • HOD Time: Time of the high of the day
   • HOD 1 Min Vol: Volume in the minute when the high of the day occurred
   • EOD $: End-of-day price at 16:00
   • Volume: Total volume from 4:00 to 19:00
   • Market Cap: Market capitalization (sourced from Finviz)
   • Float: Float shares (sourced from Finviz)
   • Shares O/S: Outstanding shares (sourced from Finviz)
   • Short Float %: Percentage of shares shorted (sourced from Finviz)
   • Inst Own %: Institutional ownership percentage (sourced from Finviz)
   • Insider Own %: Insider ownership percentage (sourced from Finviz)

2. Ticker-Specific Sheets:

   • Each sheet contains minute-wise data for a specific stock ticker with columns for:
     • Time: Timestamp of the record
     • Low Price: Lowest price during the minute
     • High Price: Highest price during the minute
     • Open Price: Opening price during the minute
     • Close Price: Closing price during the minute

Tags: #StockMarketData #FinancialData #MinuteWiseData #PolygonAPI #Finviz #StockAnalysis #TradingData #MarketMetrics #StockSymbols #FinancialAnalysis

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

EDI's history of corporate action events dates back to January 2007 and uses unique Security IDs that can track the history of events by issuer since January 2007.

Choose to receive accurate corporate actions data via an SFTP connection either 4x daily or end-of-day. Proprietary format. ISO 15022 message standard, providing MT564 & 568 announcements.

To support global trading schedules, EDI offers seven daily data feeds at 03:30, 07:00, 09:00, 11:00, 13:00, 15:00, and 17:15 GMT, ensuring continuous access to accurate, market-aligned data.

Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.

Extensive and dependable pricing information spanning the entire range of financial markets. Encompassing worldwide coverage from stock exchanges, trading platforms, indicative contributed prices, assessed valuations, expert third-party sources, and our enhanced data offerings. User-friendly request-response, bulk access, and tailored desktop interfaces to meet nearly any organizational or application data need. Worldwide, real-time, delayed streaming, intraday updates, and meticulously curated end-of-day pricing information.

Argus is a prominent source of pricing evaluations and business insights extensively utilized in the energy and commodity sectors, specifically for physical supply agreements and the settlement and clearing of financial derivatives. Argus pricing is also employed as a benchmark in swaps markets, for mark-to-market valuations, project financing, taxation, royalties, and risk management. Argus provides comprehensive services globally and continuously develops new assessments to mirror evolving market dynamics and trends. Covered assets encompass Energy, Oil, Refined Products, Power, Gas, Generation fuels, Petrochemicals, Transport, and Metals.

API Security Platform Market Outlook

According to our latest research, the API Security Platform market size reached USD 1.97 billion in 2024, reflecting a robust surge in demand for advanced security solutions to protect digital interfaces. The market is set to expand at a remarkable CAGR of 27.5% from 2025 to 2033, with the forecasted market size expected to reach USD 17.24 billion by 2033. This significant growth is primarily driven by the escalating frequency and sophistication of API-based cyberattacks, the proliferation of cloud-native applications, and stringent regulatory mandates compelling organizations to prioritize API security across sectors.

The primary growth factor fueling the API Security Platform market is the exponential increase in API adoption across enterprises, which has inevitably expanded the attack surface for malicious actors. Organizations are increasingly leveraging APIs to enable digital transformation, streamline operations, and foster innovation. However, this surge in API usage has also led to a corresponding rise in security vulnerabilities, including unauthorized access, data breaches, and Denial-of-Service attacks. As a result, businesses are investing heavily in comprehensive API security platforms that offer real-time threat detection, automated policy enforcement, and end-to-end visibility into API traffic. This heightened focus on securing APIs is further amplified by the growing awareness among C-level executives regarding the reputational and financial risks associated with API breaches.

Another significant growth driver is the rapid migration of workloads to cloud environments and the adoption of microservices architecture. With enterprises embracing cloud-native development and deploying applications across hybrid and multi-cloud infrastructures, APIs have become the backbone of digital ecosystems. This shift has necessitated the deployment of advanced API security solutions capable of protecting APIs throughout their lifecycle, from development and testing to production and deprecation. Modern API security platforms are now integrating artificial intelligence and machine learning to proactively identify anomalous behaviors, mitigate zero-day threats, and automate incident response, thereby enhancing the overall security posture of organizations operating in highly dynamic environments.

Regulatory compliance and data privacy mandates are also playing a pivotal role in shaping the API Security Platform market. Governments and regulatory bodies worldwide are introducing stringent frameworks, such as GDPR, CCPA, and PSD2, which require organizations to implement robust security controls for APIs handling sensitive information. Non-compliance can result in hefty fines and reputational damage, compelling businesses to prioritize API security investments. Furthermore, industry-specific regulations in sectors like BFSI and healthcare are driving the adoption of specialized API security platforms that ensure data integrity, confidentiality, and traceability, thereby fostering trust among customers and stakeholders.

From a regional perspective, North America continues to dominate the API Security Platform market, accounting for the largest share in 2024. The region's leadership is attributed to the presence of major technology providers, early adoption of digital transformation initiatives, and a mature cybersecurity ecosystem. Europe and Asia Pacific are also witnessing substantial growth, with Asia Pacific expected to register the fastest CAGR during the forecast period. This growth is driven by rapid digitization, increasing investments in cloud infrastructure, and the rising incidence of API-related cyber threats across emerging economies. Latin America and the Middle East & Africa are gradually catching up, propelled by government-led digital initiatives and growing awareness of API security best practices.

Component Analysis

The API Security Platform market by component is segmented into Solutions and Services, with solutions commanding a significant share in 2024. API security solutions encompass a

Permutable AI’s Global Macro Sentiment API provides aggregated sentiment data for global macroeconomic topics, including inflation, GDP, monetary policy, fiscal policy, geopolitics, and natural disasters. With support for Python, R, and Java client libraries, plus webhook integration, the API allows developers and analysts to retrieve structured insights from news sources within custom date ranges. Parameters include start and end dates (30-day lookback), filtering by sources, and strict real-time extraction options. Data outputs include sentiment scores, topic classifications, and aggregated publication timestamps—ideal for market insights, trading strategies, and research applications. Full API reference documentation is available at copilot-api.permutable.ai/redoc .

Network API Security Market Outlook

According to our latest research, the global Network API Security market size reached USD 1.47 billion in 2024, reflecting a robust surge in demand for advanced security solutions across digital ecosystems. The market is projected to grow at a CAGR of 19.2% from 2025 to 2033, reaching a forecasted value of USD 6.33 billion by 2033. The rapid proliferation of APIs in cloud-native architectures and the escalating frequency of sophisticated cyber threats are key factors propelling this growth. As digital transformation accelerates across industries, the need for comprehensive API security frameworks is becoming increasingly critical to safeguard sensitive data and ensure operational continuity.

One of the primary growth factors driving the Network API Security market is the exponential increase in API usage across various industry verticals. Organizations are increasingly adopting microservices and cloud-native applications, leading to a significant rise in the number of exposed APIs. This surge has made APIs attractive targets for cybercriminals, resulting in a sharp uptick in data breaches and security incidents. Enterprises are thus prioritizing the implementation of advanced API security solutions that offer real-time threat detection, automated policy enforcement, and robust authentication mechanisms. The growing awareness of regulatory compliance requirements, such as GDPR and CCPA, further compels organizations to invest in sophisticated API security measures to avoid legal repercussions and reputational damage.

Another significant factor fueling the expansion of the Network API Security market is the evolution of API-based business models. As organizations leverage APIs to enable third-party integrations, foster innovation, and streamline business operations, the attack surface expands considerably. This has led to a paradigm shift in security strategies, with a pronounced focus on API lifecycle management and end-to-end security orchestration. Vendors in the market are responding by introducing AI-driven security platforms capable of identifying anomalous behavior, mitigating zero-day vulnerabilities, and providing granular visibility into API traffic. The convergence of API security with broader cybersecurity frameworks is also driving the adoption of unified solutions that can seamlessly integrate with existing IT infrastructures, delivering enhanced protection and operational efficiency.

The increasing adoption of digital banking, e-commerce, and telehealth services is also contributing to the robust growth of the Network API Security market. These sectors are particularly vulnerable to API-based attacks due to the sensitive nature of the data they handle and the necessity for seamless interoperability between multiple systems. As a result, sector-specific regulations and industry standards are becoming more stringent, compelling organizations to adopt proactive API security measures. The market is witnessing a surge in demand for managed security services, API gateways, and threat intelligence solutions tailored to the unique requirements of these high-risk industries. This trend is expected to accelerate further as digital ecosystems become more interconnected and the threat landscape continues to evolve.

From a regional perspective, North America currently dominates the global Network API Security market, accounting for the largest revenue share in 2024. The region's leadership is underpinned by the presence of major technology providers, a mature cybersecurity ecosystem, and a high concentration of digital enterprises. Europe and Asia Pacific are also witnessing significant growth, driven by increasing regulatory mandates and rapid digitalization across key sectors. The Asia Pacific region, in particular, is expected to exhibit the highest CAGR during the forecast period, fueled by large-scale investments in cloud infrastructure and the proliferation of fintech and e-commerce platforms. As organizations worldwide continue to prioritize API security, the market is poised for sustained expansion across all major geographies.

Component Analysis

The Component segment of the Network API Security market is primarily divided into Solutions and Services. Solutions encompass a wide range of software platforms and tools designed to protect APIs from unauthorized access, data leakage, and various forms

Historic data updated on 07/14/2023. Q4 of 2023 and data for all years on systems allowing parking outside of a docking station updated on 06/04/2024. Bikeshare ridership by system, year, month, and day for bikeshare systems with docking stations. Data available by month starting in January 2019. Months are rearranged to include the same number of days of the week across years (see below). Data designed to show the impacts of COVID-19 on bikeshare ridership as featured at https://maps.dot.gov/BTS/dockedbikeshare-COVID/ Ridership data not available for all docked bikeshare systems. Only docked bikeshare systems with ridership data shown. Some systems included in the data permit users to leave a bicycle outside of a docking station; these trips are not counted. Trips defined as rides from point A to B. If user makes trip from B to A on same day, counted as a second trip. Trips labeled as round trips in Metro Bike Share and Indego trip files counted as 2 trips. Trips with no trip time are not counted. Trips with no start station identifier and/or end station id are not counted in totals. Trips shorter than 1 minute or greater than 2 hours excluded. Days aligned to include the same days of weeks in 2019 and 2020. Days included in each month are as follows: Assigned month can be found in the attachments (https://data.bts.gov/api/views/6cfa-ipzd/files/36fde1b8-57c3-4d31-b9dc-bbc896ba346e?download=true&filename=days_included_in_docked_bikeshare_monthly_summaries.xlsx) Trips beginning on 12/31/2019 but ending on 01/01/2020 not included in totals. Interactive map application featuring data: https://maps.dot.gov/BTS/dockedbikeshare-COVID/

📊 Alibaba Stock Price Dataset (2014-2025) 📈

📌 Dataset Overview

This dataset contains historical stock price data for Alibaba Group (NYSE: BABA), extracted using the Yahoo Finance API (yfinance). The data spans from 2014 to 2025, capturing Alibaba's stock performance over a decade, including key financial metrics such as opening and closing prices, highest and lowest trading values, volume, and adjusted closing price.

📅 Time Period:

• Start Date: January 1, 2014
• End Date: February 2025 (latest available data)

📈 Use Cases:

This dataset is valuable for:
✅ Time Series Analysis (predicting future trends using machine learning)
✅ Technical Analysis (identifying trading opportunities based on past patterns)
✅ Algorithmic Trading (building automated trading models)
✅ EDA & Visualization (analyzing stock price movements)

📂 Data Details

📦 Data Source

This dataset was obtained using the Yahoo Finance API (yfinance), a powerful Python library for extracting real-time and historical stock market data.

🟦 What this is Synthetic, lineage-verified OHLC bars computed from decoded DEX swaps and pool states. Each row is a time bucket for a specific pool and token direction (token_in → token_out), with open/high/low/close, volumes, and trade counts.

Key traits • Schema-stable, versioned, audit-ready • Real-time (WSS) and historical/EOD delivery • Verifiable lineage to pools, tokens, swaps/logs

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Coverage includes: • Uniswap V2, V3, V4 • Balancer V2, PancakeSwap, Solidly, Maverick, Aerodrome, and others

📑 Schema Columns as delivered (stable names/types): • id BIGINT - surrogate row id (PK) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) Lineage (ids): • tracing_id BYTEA NOT NULL - row identity (proof-of-derivation) • parent_tracing_ids BYTEA NOT NULL - immediate sources (packed hashes) • genesis_tracing_ids BYTEA NOT NULL - ultimate on-chain sources (packed hashes) Lineage (chain position, window anchors): • first_genesis_block_number BIGINT NOT NULL - first event in bucket • first_genesis_tx_index INTEGER NOT NULL • first_genesis_log_index INTEGER NOT NULL • last_genesis_block_number BIGINT NOT NULL - last event in bucket • last_genesis_tx_index INTEGER NOT NULL • last_genesis_log_index INTEGER NOT NULL Bucket definition: • bucket_start TIMESTAMPTZ NOT NULL - inclusive bucket start (UTC) • bucket_seconds INTEGER NOT NULL - one of {60,300,900,1800,3600,14400,86400} for 1m,5m,15m,30m,1h,4h,1d Pair & mid snapshot: • token_in BYTEA NOT NULL - 20B (FK → erc20_tokens) • token_out BYTEA NOT NULL - 20B (FK → erc20_tokens) OHLC (prices are decimals-adjusted; token_out per 1 token_in): • open NUMERIC(78,18) NOT NULL • high NUMERIC(78,18) NOT NULL • low NUMERIC(78,18) NOT NULL • close NUMERIC(78,18) NOT NULL Volumes (token units are decimals-adjusted): • volume_in NUMERIC(78, 18) NOT NULL - sum of amount_in within bucket • volume_out NUMERIC(78, 18) NOT NULL - sum of amount_out within bucket • trades_count BIGINT NOT NULL - swap count in bucket

Notes • Prices are decimals-adjusted (token_out per 1 token_in). • Volumes are decimals-adjusted • Direction is implied by token_in → token_out. For the reverse, a separate row exists with tokens swapped.

🔑 Keys & Joins • Primary key: id • Idempotency: (pool_uid, token_in, token_out, bucket_start, bucket_seconds) • Foreign keys: • pool_uid → liquidity_pools(uid) • token_in/token_out → erc20_tokens(contract_address) • first_genesis_ and last_genesis_ triples → logs(block_number, tx_index, log_index)

🔗 Joins to Dependency Products • Liquidity Pools Catalog (liquidity_pools) - pool metadata (fee tier, type, tokens). • ERC-20 Tokens Catalog (erc20_tokens) - symbol, decimals, names. • Swaps / Logs - provenance checks and drill-downs.

🧬 Lineage & Reproducibility Every bar’s lineage is cryptographically linked to its inputs: • tracing_id - deterministic identity of this OHLC row • parent_tracing_ids - contributing swaps/states used in the bucket • genesis_tracing_ids - ultimate raw on-chain sources Anchors to the first and last events in the bucket enable exact replay and audit.

📈 Common uses • Charting & analytics (1m → 1d); volatility, and signal engineering • Backtesting and factor research with stable, reproducible bars • Routing heuristics and execution scheduling by time of day • Monitoring: liquidity/price regime shifts at multiple horizons

🚚 Delivery By default • WebSocket (WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

🗂️ Files (time-partitioned in UTC, compressed) • Parquet • CSV • XLS • JSON

💡 Quality and operations • Reorg-aware ingestion. • 99.95% uptime target SLA. • Backfills to chain genesis. • Versioned, schema-stable datasets; changes are additive and announced.

🔄 Change policy Schema is stable. Any breaking change ships as a new version (e.g., token_to_token_prices_ohlc_v2) with migration notes. Content updates are additive; types aren’t changed in place.

API Threat Intelligence Market Outlook

According to our latest research, the global API Threat Intelligence market size reached USD 1.86 billion in 2024, reflecting the rapid adoption of advanced security solutions across verticals. The market is experiencing robust momentum, expanding at a CAGR of 17.8% from 2025 to 2033. By the end of 2033, the API Threat Intelligence market is expected to achieve a valuation of USD 8.19 billion. This impressive growth is primarily driven by the escalating sophistication of cyber threats targeting APIs, the proliferation of digital transformation initiatives, and the increasing regulatory focus on data security and privacy compliance.

The growth trajectory of the API Threat Intelligence market is underpinned by several pivotal factors. One of the primary drivers is the exponential rise in API usage across enterprises, which has inadvertently expanded the attack surface for cybercriminals. Modern businesses are leveraging APIs to enable seamless integrations, support mobile applications, and facilitate cloud-native architectures. However, this increased interconnectivity has made APIs a lucrative target for attackers exploiting vulnerabilities to access sensitive data and disrupt business operations. As a result, organizations are investing heavily in API threat intelligence solutions that offer real-time monitoring, automated threat detection, and actionable insights to mitigate evolving risks.

Another significant growth catalyst is the dynamic regulatory environment that mandates stringent security measures for protecting digital assets and customer information. Regulatory frameworks such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and industry-specific standards like PCI DSS are compelling organizations to enhance their security postures. API threat intelligence platforms are becoming indispensable tools for achieving compliance by providing visibility into API traffic, identifying anomalous behaviors, and generating compliance reports. Furthermore, the rise of open banking, healthcare interoperability, and digital government services is accelerating the demand for advanced threat intelligence solutions tailored to sector-specific needs.

Technological advancements in artificial intelligence, machine learning, and automation are also fueling the expansion of the API Threat Intelligence market. Vendors are integrating these technologies to deliver proactive threat hunting, predictive analytics, and automated incident response capabilities. This evolution is empowering security teams to detect zero-day attacks, prevent data breaches, and respond to threats with greater agility. Additionally, the trend toward API-first development and the adoption of microservices architectures are reshaping security strategies, with organizations prioritizing API-centric threat intelligence to safeguard their digital ecosystems.

From a regional perspective, North America continues to dominate the API Threat Intelligence market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the high concentration of technology-driven enterprises, early adoption of cloud and digital platforms, and a mature cybersecurity landscape. Europe and Asia Pacific are also witnessing significant growth, driven by increasing investments in digital infrastructure, rising cybercrime incidents, and heightened regulatory scrutiny. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, as organizations in these regions recognize the strategic importance of securing APIs to support their digital transformation journeys.

Component Analysis

The Component segment of the API Threat Intelligence market is bifurcated into Solutions and Services. Solutions represent the software platforms and tools designed to identify, analyze, and mitigate threats targeting APIs. This sub-segment dominates the market, accounting for a significant portion of the overall revenue in 2024. Solutions encompass a wide array of functionalities, including real-time API traffic monitoring, anomaly detection, threat intelligence feeds, and automated policy enforcement. As organizations increasingly prioritize proactive security, the demand for comprehensive solutions that can seamlessly integrate with existing IT environments is on the rise. Vendors are continuously enhancing their off

Network API Security Market Outlook

According to our latest research, the global Network API Security market size reached USD 2.13 billion in 2024, reflecting the growing importance of API protection within digital infrastructure. The market is expected to expand at a robust CAGR of 24.7% from 2025 to 2033, projecting a value of approximately USD 17.18 billion by the end of the forecast period. This strong growth trajectory is primarily driven by the rapid proliferation of APIs in modern application architectures, increasing sophistication of cyber threats, and an urgent need to safeguard sensitive data and ensure business continuity in the digital era.

A primary growth factor for the Network API Security market is the explosive adoption of digital transformation initiatives across all industry verticals. As organizations accelerate their migration to cloud environments and microservices-based architectures, APIs have become the backbone for enabling seamless data exchange and integration between disparate systems. However, this increased reliance on APIs has also expanded the attack surface, making them a prime target for cybercriminals. High-profile breaches exploiting API vulnerabilities have heightened awareness among enterprises, prompting significant investments in advanced API security solutions. The rising regulatory scrutiny around data privacy and compliance, such as GDPR and CCPA, further compels organizations to prioritize comprehensive API security frameworks, fueling market growth.

Another significant driver is the evolution of sophisticated attack methodologies targeting APIs, including injection attacks, data exfiltration, and business logic abuse. Traditional web application firewalls (WAFs) and legacy security tools are often insufficient to counter these evolving threats, creating a demand for specialized network API security solutions that offer real-time monitoring, threat intelligence, behavioral analytics, and automated response capabilities. The integration of artificial intelligence and machine learning in API security platforms has further enhanced their ability to detect anomalies and respond to zero-day vulnerabilities, thereby strengthening the overall security posture of organizations and driving the adoption of these solutions.

Additionally, the surge in remote work, mobile applications, and IoT deployments has fundamentally altered network perimeters, making API security a critical aspect of enterprise security strategies. As organizations increasingly adopt DevOps and agile development practices, the need for secure API development, testing, and deployment has become paramount. Security teams are collaborating closely with developers to embed security controls early in the API lifecycle, resulting in a growing demand for comprehensive API security platforms that support continuous integration and delivery pipelines. This shift towards a "security by design" approach is expected to sustain long-term market growth.

From a regional perspective, North America continues to dominate the Network API Security market, accounting for the largest revenue share in 2024. This leadership is attributed to the high concentration of technology-driven enterprises, advanced cybersecurity infrastructure, and early adoption of API-centric business models in the region. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, increasing adoption of cloud services, and rising awareness of API vulnerabilities among enterprises. Europe also demonstrates substantial market potential, driven by stringent regulatory frameworks and a strong focus on data protection. Latin America and the Middle East & Africa are gradually catching up, supported by growing investments in digital infrastructure and cybersecurity.

Component Analysis

The Component segment of the Network API Security market is broadly categorized into Solutions and Services. The solutions sub-segment encompasses core API security offerings such as API

The Future Schedules API is perfect for: • Travel agency and flight booking websites where users are expected to submit a date and view available flights • Websites, tools or apps to display scheduled flights on a given date • Flight schedule and airway traffic analysis based on region or dates

We have developed many filters you can use in the input to request the exact data you need without having to filter the data on your end.

The data includes: - Departure and arrival airport information: IATA codes - Weekday: The day of the week of the flight, "1" being Monday - Terminal and gate: The most common terminal and the gate number of the departing/arriving flight - Take-off information: Scheduled departure or arrival time of the flight - Aircraft details: Model code and text - Airline details: Name, IATA and ICAO codes - Flight information: Flight number with flight IATA and ICAO codes

1) Request For the departure schedule of a certain airport on a certain future date.

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=departure&iataCode=BER&date=YYYY-MM-DD

For the arrival schedule of a certain airport on a certain future date.

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=arrival&iataCode=BER&date=YYYY-MM-DD

For the flights that are scheduled to arrive at a certain airport on a certain date (out of a departure schedule).

GET http://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=departure&iataCode=BER&arr_iataCode=ORY&date=YYYY-MM-DD

For the flights that are scheduled to depart from a certain airport on a certain date (out of an arrival schedule).

GET https://aviation-edge.com/v2/public/flightsFuture?key=[API_KEY]&type=arrival&iataCode=BER&dep_iataCode=ory&date=YYYY-MM-DD

2) Filters &iata_code= (obligatory) Departure or arrival airport IATA code depending on the "&type=" value &type= (obligatory) Either "departure" or "arrival" as both within the same query is not possible &date= (obligatory) Future date in YYYY-MM-DD format

&dep_iataCode= filter of departure airport if "arrival" for "&type=" was chosen, based on the airport IATA code &dep_icaoCode= filter of departure airport if "arrival" for "&type=" was chosen, based on the airport ICAO code &arr_iataCode= filter of arrival airport if "departure" for "&type=" was chosen, based on the airport IATA code &arr_icaoCode= filter of arrival airport if "departure" for "&type=" was chosen, based on the airport ICAO code &airline_iata= option to filter airline based on airline IATA code &airline=icao= option to filter airline based on airline ICAO code &flight_num= option to filter a specific flight based on its flight number

3) Example Output: [ {"weekday": "1", "departure": { "iataCode": "mty", "icaoCode": "mmmy", "terminal": "c", "gate": "f2", "scheduledTime": "20:35" }, "arrival": {"iataCode": "iah", "icaoCode": "kiah", "terminal": "d", "gate": "d12", "scheduledTime": "22:00" }, "aircraft": {"modelCode": "a320", "modelText": "airbus a320-232" }, "airline": {"name": "vivaaerobus", "iataCode": "vb", "icaoCode": "viv"}, "flight": {"number": "616", "iataNumber": "vb616", "icaoNumber": "viv616"} } ]

Note: Schedules that are up to 1 year ahead of the current date are available.

API Threat Protection for Mobile Market Outlook

According to our latest research, the API Threat Protection for Mobile market size reached USD 1.62 billion globally in 2024, demonstrating robust growth amid rising security concerns in mobile environments. The market is expected to expand at a CAGR of 17.9% from 2025 to 2033, reaching an estimated USD 7.02 billion by the end of the forecast period. This impressive growth is primarily driven by the escalating frequency and sophistication of API-based attacks targeting mobile applications, which has compelled organizations across industries to prioritize advanced API threat protection solutions.

The surge in mobile device adoption, coupled with the proliferation of mobile applications across critical sectors such as banking, healthcare, retail, and government, is a significant growth driver for the API Threat Protection for Mobile market. Organizations are increasingly integrating mobile channels into their digital transformation strategies, creating a larger attack surface for cybercriminals. As APIs serve as essential conduits for data exchange between mobile apps and backend systems, the risk of data breaches, unauthorized access, and service disruptions has grown exponentially. This heightened threat landscape is prompting enterprises to invest in comprehensive API threat protection solutions that offer real-time detection, automated mitigation, and continuous monitoring, thereby safeguarding sensitive data and ensuring uninterrupted mobile service delivery.

Another key growth factor is the evolving regulatory environment, which mandates stringent data protection and privacy standards for mobile applications. Regulations such as GDPR, CCPA, and industry-specific mandates in banking and healthcare require organizations to implement robust security measures that extend to API endpoints. Non-compliance can result in severe financial penalties and reputational damage, motivating enterprises to adopt advanced API security frameworks. Furthermore, the increasing reliance on third-party APIs and the adoption of microservices architectures in mobile app development have introduced new vulnerabilities, making API threat protection a critical component of enterprise security strategies. The demand for solutions that provide centralized visibility, automated threat intelligence, and seamless integration with existing security infrastructure is set to drive sustained growth in this market.

The rapid advancement in artificial intelligence and machine learning technologies is also shaping the future of API Threat Protection for Mobile. Vendors are leveraging AI-driven analytics to detect anomalous API behaviors, identify zero-day threats, and automate response mechanisms, enhancing the effectiveness of threat protection while reducing the burden on security teams. The integration of AI with API security platforms enables real-time risk assessment and adaptive policy enforcement, allowing organizations to stay ahead of emerging threats. As mobile applications continue to evolve in complexity and scale, the adoption of intelligent, automated API threat protection solutions will become indispensable for enterprises aiming to maintain robust security postures in the digital era.

Regionally, North America remains the largest market for API Threat Protection for Mobile, accounting for over 38% of global revenue in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology-driven enterprises, early adoption of advanced security solutions, and a stringent regulatory framework have positioned North America as a leader in this space. Meanwhile, Asia Pacific is witnessing the fastest growth, fueled by rapid digitalization, increasing mobile penetration, and rising awareness of API security risks among enterprises and governments. Europe’s market expansion is underpinned by strong data protection laws and growing investments in cybersecurity infrastructure. As organizations worldwide continue to prioritize mobile security, the demand for API threat protection solutions is expected to surge across all major regions.

Component Analysis

The API Threat Protection for Mobile market is segmented by component into software and services, each playing a pivotal role in addressing the complex security challenges posed by mobile APIs. Software solutions form the backbone of API threat protection, encompas

Approximately 20% of Winnipeg Transit buses are equipped with automated passenger counting sensors at each door that count the number of people entering (boarding) and exiting (alighting) the bus along with the relevant bus stop information.

This data is extrapolated into an estimate of the average daily boardings and alightings, aggregated by route, stop, and time of day, for each day type (weekday, Saturday, Sunday). Data is collected over time ranges corresponding to regular seasonal schedules (September-December, December-April, April-June, June-September), and will be uploaded within 30 days after the end of each seasonal schedule.

The time of day field for weekdays is defined as AM Peak (05:00-09:00), Mid-Day (09:00-15:30), PM Peak (15:30-18:30), Evening (18:30-22:30), and Night (22:30-end of service). For Saturdays and Sundays, it is defined as Morning (05:00-11:00), Afternoon (11:00-19:00), Evening (19:00-22:30), and Night (22:30-end of service).

Due to detection errors and small sample sizes in some cases, boarding numbers may not exactly match alighting numbers. On-request passenger counts are not included in this data set.

More transit data can be found on Winnipeg Transit's Open Data Web Service, located here: https://api.winnipegtransit.com/home/api/v3

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The system marginal price refers to the electricity market price (KRW/kWh) for the amount of electricity applied by trading hour, and you can search for the system marginal price information by hour, divided into the mainland and Jeju regions. ㅇ Note 1: The trading time 0:00 of the API indicates the period starting immediately after 0:00 and ending at 01:00. ㅇ Note 2: The API will be deleted in the future, and we recommend using the Korea Power Exchange_System Marginal Price and Demand Forecast (for one-day-ahead power generation plan) API. ㅇ Updated to OPENAPI User Guide v1.5 on 2024.11.29