LinkedIn companies use datasets to access public company data for machine learning, ecosystem mapping, and strategic decisions. Popular use cases include competitive analysis, CRM enrichment, and lead generation.

Use our LinkedIn Companies Information dataset to access comprehensive data on companies worldwide, including business size, industry, employee profiles, and corporate activity. This dataset provides key company insights, organizational structure, and competitive landscape, tailored for market researchers, HR professionals, business analysts, and recruiters.

Leverage the LinkedIn Companies dataset to track company growth, analyze industry trends, and refine your recruitment strategies. By understanding company dynamics and employee movements, you can optimize sourcing efforts, enhance business development opportunities, and gain a strategic edge in your market. Stay informed and make data-backed decisions with this essential resource for understanding global company ecosystems.

Dataset Features

• timestamp: Represents the date and time when the company data was collected.
• id: Unique identifier for each company in the dataset.
• company_id: Identifier linking the company to an external database or internal system.
• url: Website or URL for more information about the company.
• name: The name of the company.
• about: Brief description of the company.
• description: More detailed information about the company's operations and offerings.
• organization_type: Type of the organization (e.g., private, public).
• industries: List of industries the company operates in.
• followers: Number of followers on the company's platform.
• headquarters: Location of the company's headquarters.
• country_code: Code for the country where the company is located.
• country_codes_array: List of country codes associated with the company (may represent various locations or markets).
• locations: Locations where the company operates.
• get_directions_url: URL to get directions to the company's location(s).
• formatted_locations: Human-readable format of the company's locations.
• website: The official website of the company.
• website_simplified: A simplified version of the company's website URL.
• company_size: Number of employees or company size.
• employees_in_linkedin: Number of employees listed on LinkedIn.
• employees: URL of employees.
• specialties: List of the company’s specializations or services.
• updates: Recent updates or news related to the company.
• crunchbase_url: Link to the company’s profile on Crunchbase.
• founded: Year when the company was founded.
• funding: Information on funding rounds or financial data.
• investors: Investors who have funded the company.
• alumni: Notable alumni from the company.
• alumni_information: Details about the alumni, their roles, or achievements.
• stock_info: Stock market information for publicly traded companies.
• affiliated: Companies or organizations affiliated with the company.
• image: Image representing the company.
• logo: URL of the official logo of the company.
• slogan: Company’s slogan or tagline.
• similar: URL of companies similar to this one.

Distribution

• Data Volume: 56.51M rows and 35 columns.
• Structure: Tabular format (CSV, Excel).

Usage

This dataset is ideal for:
- Market Research: Identifying key trends and patterns across different industries and geographies.
- Business Development: Analyzing potential partners, competitors, or customers.
- Investment Analysis: Assessing investment potential based on company size, funding, and industries.
- Recruitment & Talent Analytics: Understanding the workforce size and specialties of various companies.

Coverage

• Geographic Coverage: Global, with company locations and headquarters spanning multiple countries.
• Time Range: Data likely covers both current and historical information about companies.
• Demographics: Focuses on company attributes rather than demographics, but may contain information about the company's workforce.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For building models, conducting research, or enhancing machine learning algorithms with business data.
• Researchers: For academic analysis in fields like economics, business, or technology.
• Businesses: For analysis, competitive benchmarking, and strategic development.
• Investors: For identifying and evaluating potential investment opportunities.

Dataset Name Ideas

• Global Company Profile Database
• **Business Intellige

This dataset is designed for analyzing various product categories within the Japanese market. It provides information about each product category's size, growth rate, market share, competitor market shares, average price, customer demographics, online presence, and market saturation. Here's a breakdown of each column:

Product Category: The type of products or services being analyzed within the Japanese market.

Total Market Size (in USD): The estimated total market size in terms of US dollars for each product category. This figure reflects the overall revenue potential for that category.

Market Growth Rate (%): The projected annual growth rate of each product category's market. This percentage indicates how much the market is expected to expand or contract over time.

Market Share (%): The percentage of the total market size that each product category holds. This reflects the relative importance of each category within the overall market.

Competitor 1 Market Share (%): The market share percentage of the first major competitor within each product category. This helps to understand the competitive landscape.

Competitor 2 Market Share (%): The market share percentage of the second major competitor within each product category. Similar to the previous column, this provides insight into the competitive environment.

Average Price (in USD): The average price of products or services within each product category. This information helps understand the pricing dynamics of the category.

Customer Demographics: The primary target audience or customer segments for each product category. Understanding the demographics helps in tailoring marketing efforts.

Online Presence (%): The percentage of businesses within each product category that have an online presence. This includes websites, social media, and other digital platforms.

Market Saturation (%): An estimate of how much of the potential market demand has already been captured by existing products or services within each category. A higher percentage indicates a more saturated market.

The number of shares outstanding, shown on a company’s balance sheet under the heading “capital stock” is used to calculate key metrics including market capitalization figures, earnings per share (EPS) and share stakes for regulatory reporting levels. The Share Outstanding service consists of two datasets, Official Shares Outstanding and Daily-Adjusted Shares Outstanding.

Official Shares Outstanding Providing official figures sourced directly from local exchanges or company sources, as soon as they are published. The frequency of official updates varies from market to market. Updates can also range from daily to annually.

Adjusted Share Outstanding When corporate actions occur prior to the release of official updates, the number of shares outstanding can be impacted drastically. Use the adjusted shares outstanding dataset to provide the figures for events including:
Bonus Bonus rights Buyback Capital Reduction Consolidation Conversion Demerger Divestment Entitlement Redemption Rights Sub-division

Once the official figures have been released by the exchange, then receive reverted figures. The Worldwide Shares Outstanding service provides up-to-date figures from over 100 countries worldwide enabling companies to efficiently calculate figures to comply with exchange regulations or portfolio holding levels.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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For more datasets, click here.

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

&g...

Envestnet®| Yodlee®'s Consumer Behavior Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Analysis of ‘📊 Financial market screener’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pierrelouisdanieau/financial-market-screener on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

--- Original source retains full ownership of the source dataset ---

The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

Snapshot img

US Enterprise Data Management Market Size 2024-2028

The US enterprise data management market size is forecast to increase by USD 5.59 billion at a CAGR of 13.6% between 2023 and 2028.

The market, including Enterprise Data Management (EDM) software, is experiencing significant growth due to increasing demand for data integration and visual analytics. The BFSI industry's reliance on data warehousing and data security continues to drive market expansion. Technological advancements, such as artificial intelligence and machine learning are revolutionizing EDM solutions, offering enhanced capabilities for data processing and analysis. However, the high cost of implementing these advanced EDM solutions remains a challenge for some organizations. Additionally, data security concerns and the need for regulatory compliance are ongoing challenges that require continuous attention and investment. In the telecom sector, the trend towards digital transformation and the generation of vast amounts of data are fueling the demand for strong EDM solutions. Overall, the EDM software market is expected to continue its growth trajectory, driven by these market trends and challenges.

What will be the size of the US Enterprise Data Management Market during the forecast period?

Request Free Sample

The Enterprise Data Management (EDM) market in the BFSI sector is experiencing significant growth due to the industry's expansion and strict regulations. With the increasing volume, velocity, and complexity of data, IT organizations in banks and other financial institutions are prioritizing EDM solutions to handle massive datasets and ensure information accuracy. These systems enable data synchronization, address validation, and single-source reporting, addressing data conflicts and silos that hinder effective business operations. EDM solutions are essential for both internal applications and external communication, allowing for leveraging analytics to gain a competitive edge. In the BFSI sector, where risk control is paramount, EDM plays a crucial role in managing and consuming datasets efficiently.
The market is characterized by a competitive environment, with IT investments focused on multiuser functionality and Big Data capabilities to meet the diverse needs of various business verticals, including manufacturing and services industries. Overall, EDM is a strategic imperative for businesses seeking to stay competitive and compliant in today's data-driven economy.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud


Ownership

  Large enterprise
  Small and medium enterprise


End-user

  Commercial banks
  Savings institutions


Geography

  US

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. The BFSI sector in the US is witnessing a significant expansion in the enterprise data management market, driven by strict regulations and the competitive environment. Large organizations, including commercial banks, insurance companies, and non-banking financial institutions, are prioritizing data management to ensure information accuracy and risk control. Enterprise Data Management (EDM) solutions are crucial for internal applications and external communication, enabling data synchronization and business operations. Leveraging analytics, IT organizations manage vast datasets and datasets' consumption, addressing data conflicts and ensuring data quality for reporting. EDM encompasses handling massive data through Business Analytics, ETL tools, data pipelines, and data warehouses, as well as data visualization tools.

Get a glance at the market share of various segments Request Free Sample

The on-premises segment was valued at USD 2.9 billion in 2018 and showed a gradual increase during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Enterprise Data Management Market?

Growing demand for data integration and visual analytics is the key driver of the market. In the BFSI sector, strict regulations necessitate the effective management of large volumes of structured and unstructured data. The industry's expansion and competitive environment necessitate the need for advanced data management solutions. Enterprises are leveraging Enterprise Data Management (EDM) systems to address the challenges of data synchronization, internal

Context

I always wanted to have a program that fetch the whole stock market data at once without concerning about new companies that went public recently. So, here it is.

Content

This dataset contains 2 python scripts which one can fetch the data from on their own machine without any special requirements by just running the collect.py . I have done this part in May/21/2021 (Version 2). So, the data is available until then. If one wants to have extend that period, they can run the collect.py .

Columns Description

tickers.csv contains ticker names along with some additional data such as name of the company, sector, industry, and the country of the company.

Each CSV file in stocksData folder named as the company's ticker name. Each file has 8 columns: - Date: as an index. - Open, Close, High, Low: which is in dollars. - Volume: which is number of shares that traded in specific date. - Stock Splits: Show if there is a stock split in specific day as the split ratio. - Dividends: which is in dollars. If a company doesn’t provide dividends for their share holders, this column can be dropped.

Acknowledgements

I've used finviz site and yfinance package to gather this rich data.

Inspiration

I hope one can find this helpful and interesting. If you have any questions don't hesitate to contact me at milad@miladtabrizi.com .

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Business Analysis Services Market Outlook

As of 2023, the global market size for Business Analysis Services is estimated to be approximately USD 15 billion, with a projected growth to reach USD 30 billion by 2032, reflecting a CAGR of around 8%. Key factors driving this growth include the increasing complexity of business operations, a surge in demand for data-driven decision-making, and the rising adoption of digital transformation across various industries.

One of the notable growth factors for the Business Analysis Services market is the rapid digital transformation taking place across multiple industries. Organizations are increasingly leveraging technology to streamline operations, improve customer experience, and gain a competitive edge. Business Analysis Services play a crucial role in guiding these digital initiatives by providing insights and strategies grounded in comprehensive data analysis. As more companies embark on digital transformation journeys, the demand for business analysis services is expected to rise significantly.

Another significant driver of market growth is the heightened need for data-driven decision-making. In today's competitive landscape, organizations are relying more heavily on data to make informed decisions. Business analysts are essential in interpreting complex data sets and translating them into actionable business strategies. This trend is especially prominent in sectors such as BFSI, healthcare, and retail, where data analytics can provide critical insights into customer behavior, market trends, and operational efficiencies. Consequently, the demand for skilled business analysis services is projected to grow at a robust pace.

The increasing complexity of business operations is also propelling the demand for Business Analysis Services. As businesses expand globally, operational challenges become more intricate, necessitating expert analysis to navigate regulatory environments, optimize supply chains, and enhance overall efficiency. Business analysts provide essential support in these areas by identifying bottlenecks, recommending process improvements, and ensuring compliance with industry standards. This operational complexity underscores the indispensable role of business analysis services in achieving organizational success.

In this evolving landscape, Business-Outcome-Driven Enterprise Architecture Consulting and Solutions have emerged as pivotal in aligning business strategies with technological advancements. These solutions focus on creating a cohesive framework that not only supports current business operations but also anticipates future challenges and opportunities. By leveraging enterprise architecture, organizations can ensure that their IT infrastructure is agile, scalable, and aligned with their strategic goals. This approach not only enhances operational efficiency but also drives innovation by enabling businesses to quickly adapt to market changes and technological disruptions. As digital transformation continues to accelerate, the demand for enterprise architecture consulting is expected to grow, offering organizations a strategic advantage in navigating the complexities of modern business environments.

From a regional perspective, North America holds the largest share of the Business Analysis Services market, driven by the presence of a significant number of established enterprises and the advanced state of digital transformation in the region. Europe follows closely, with a strong emphasis on regulatory compliance and operational efficiency fueling demand. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, spurred by rapid economic development and increasing adoption of technology-driven business solutions. Latin America and the Middle East & Africa are also emerging as promising markets due to growing awareness of the benefits of business analysis services.

Service Type Analysis

The Business Analysis Services market can be segmented by service type into Consulting, Training, Outsourcing, and Others. Consulting services dominate the market, accounting for the largest share. This segment involves providing expert advice to organizations on how to optimize their operations, improve efficiency, and implement effective strategies. The need for specialized knowledge and experience in addressing complex business challenges makes consulting an indispensable component of the market. Co

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 66 companies listed on the Nairobi Securities Exchange (XNAI) in Kenya. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Kenya:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Kenya:

Nairobi Securities Exchange All Share Index (NASI): The main index that tracks the performance of all companies listed on the Nairobi Securities Exchange (NSE). NASI provides insights into the overall market performance in Kenya.

Nairobi Securities Exchange 20 Share Index (NSE 20): An index that tracks the performance of the top 20 companies by market capitalization listed on the NSE. NSE 20 is an important benchmark for the Kenyan stock market.

Safaricom PLC: A leading telecommunications company in Kenya, offering mobile and internet services. Safaricom is one of the largest and most actively traded companies on the NSE.

Equity Group Holdings PLC: A prominent financial institution in Kenya, providing banking and financial services. Equity Group is a significant player in the Kenyan financial sector and is listed on the NSE.

KCB Group PLC: Another major financial institution in Kenya, offering banking and financial services. KCB Group is also listed on the NSE and is among the key players in the country's banking industry.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Kenya, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Kenya ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Kenya?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Kenya exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and se...

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

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Why Choose Success.ai?

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• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
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Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
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• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

Survey of innovation and business strategy, market share of highest selling good or service in the enterprise's main market, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2009 to today.

By 2025, the managed database services market will likely hit USD 445,020.1 million and grow to USD 1,497,335 million by 2035, with a CAGR of 12.9%. The rise of using multi-cloud and mixed cloud plans, rising AI use for smart database upkeep, and more people using Database-as-a-Service are guiding the future of the industry. Also, more worry about keeping data safe and following rules is driving market growth.

Metric	Value
Market Size (2025E)	USD 445,020.1 Million
Market Value (2035F)	USD 1,497,335 Million
CAGR (2025 to 2035)	12.9%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	13.1%

Country	CAGR (2025 to 2035)
UK	12.7%

Region	CAGR (2025 to 2035)
European Union (EU)	12.9%

Country	CAGR (2025 to 2035)
Japan	13.0%

Country	CAGR (2025 to 2035)
South Korea	13.2%

Managed Database Services Market - Segmentation Outlook

Service	Market Share (2025)
Database Administration	38.0%

Application	Market Share (2025)
Customer Relationship Management (CRM)	46.0%

Competitive Outlook

Company Name	Estimated Market Share (%)
Amazon Web Services (AWS)	18-22%
Microsoft Corporation (Azure)	14-18%
Google Cloud Platform (GCP)	12-16%
Oracle Corporation	10-14%
IBM Corporation	6-10%
Other Companies (combined)	30-40%

Forecast: T-Mobile Telecom Company Market Share in the US 2022 - 2026 Discover more data with ReportLinker!

Database Management Services Market Outlook

The global database management services market size was estimated at USD 20.5 billion in 2023 and is projected to reach USD 40.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.6% during the forecast period. A significant growth factor propelling this market includes the increasing digital transformation initiatives across various industries, driving the need for robust database management solutions.

One of the primary growth drivers for the database management services market is the exponential growth of data generated globally. Enterprises are increasingly digitizing their operations, generating massive volumes of data that need efficient management. Furthermore, the proliferation of cloud computing has made the storage and management of data more flexible and scalable, fueling the adoption of cloud-based database management services. Another critical aspect is the advent of big data analytics, which demands advanced database management systems to handle and process large datasets effectively.

The increasing adoption of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is also contributing significantly to the market's growth. These technologies require robust database management systems to store and analyze the vast amounts of data they generate. Businesses are recognizing the value of data-driven insights for making informed decisions, thereby accelerating the demand for sophisticated database management services. Additionally, regulatory requirements for data storage and management are becoming more stringent, compelling organizations to adopt advanced database management systems to ensure compliance.

The growing trend of remote work and the need for real-time data access also play a crucial role in the market's expansion. With more employees working remotely, the demand for seamless and secure data access has surged, leading to a higher need for effective database management solutions. Moreover, the rise of e-commerce and online services has led to an increased demand for efficient and scalable database management systems to handle customer data, transactions, and other critical information.

From a regional perspective, North America holds a significant share of the database management services market, primarily due to the presence of major technology companies and early adoption of advanced technologies. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid industrialization, increasing digitalization, and growing investments in IT infrastructure. Europe and Latin America are also experiencing steady growth, with organizations in these regions increasingly adopting database management solutions to enhance operational efficiency and drive business growth.

Service Type Analysis

Database management services can be segmented by service type into consulting, implementation, maintenance, and support. Consulting services involve providing expert advice and strategies for database management tailored to an organization’s specific needs. As businesses strive to integrate more sophisticated data solutions, the demand for consulting services is expected to grow. Consultants help identify the most suitable database management systems, optimize existing infrastructure, and ensure that data policies comply with regulatory standards, thus driving the segment's growth.

Implementation services encompass the deployment of database management systems and solutions within an organization. This segment is poised for significant growth as companies move towards modernizing their IT infrastructures. Implementation services ensure seamless integration of new systems with existing technologies, minimizing disruption and enhancing data accessibility and security. With the rise of cloud computing, implementation services are increasingly focused on migrating on-premises databases to cloud-based solutions, which offers scalability and cost-efficiency.

Maintenance services involve the ongoing management and upkeep of database systems to ensure their optimal performance. This includes regular updates, security patches, and troubleshooting to prevent downtime and data loss. As businesses become more reliant on data-driven operations, the importance of maintenance services cannot be overstated. These services ensure that databases remain functional, secure, and efficient, thereby supporting continuous business operations and data availabilit

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!