Palantir Technologies Inc. is a software company specialized in big data analytics. Some of its most famous products are Palantir Gotham, Palantir Apollo, and Palantir Foundry. In 2023, the American software company's operating expenses amounted to over *** billion U.S. dollars, of which ***** million U.S. dollars for research and development.

Palantir Technologies reported $816.48M in Operating Expenses for its fiscal quarter ending in December of 2024. Data for Palantir Technologies | PLTR - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Palantir Technologies pre-paid expenses for the quarter ending March 31, 2025 were $0.127B, a 56.08% increase year-over-year. Palantir Technologies pre-paid expenses for 2024 were $0.129B, a 29.7% increase from 2023. Palantir Technologies pre-paid expenses for 2023 were $0.1B, a 33.37% decline from 2022. Palantir Technologies pre-paid expenses for 2022 were $0.15B, a 34.89% increase from 2021.

Palantir Technologies reported $136K in Interest Expense on Debt for its fiscal quarter ending in December of 2023. Data for Palantir Technologies | PLTR - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Palantir Technologies total non-operating income/expense for the twelve months ending March 31, 2025 was $0.196B, a 50.62% increase year-over-year. Palantir Technologies annual total non-operating income/expense for 2024 was $0.179B, a 52.63% increase from 2023. Palantir Technologies annual total non-operating income/expense for 2023 was $0.117B, a 158.61% decline from 2022. Palantir Technologies annual total non-operating income/expense for 2022 was $-0.2B, a 158.01% increase from 2021.

Palantir Technologies reported $470.44M in Selling and Administration Expenses for its fiscal quarter ending in December of 2024. Data for Palantir Technologies | PLTR - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(PLTR) Palantir: Data-Driven Destiny?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Palantir's Prospects: A Glimpse at PLTR's Future?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Palantir Technologies Spese Operative - Valori correnti, dati storici, previsioni, statistiche, grafici e calendario economico - Jul 2025.Data for Palantir Technologies | Spese Operative including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Palantir: The Stock to Watch in 2023

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Palantir Technologies Despesas De Vendas E Administração - Valores atuais, dados históricos, previsões, estatísticas, gráficos e calendário econômico - Jul 2025.Data for Palantir Technologies | Despesas De Vendas E Administração including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Palantir Technologies 营业费用 - 当前值，历史数据，预测，统计，图表和经济日历 - Jul 2025.Data for Palantir Technologies | 营业费用 including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Palantir Technologies Despesas Operacionais - Valores atuais, dados históricos, previsões, estatísticas, gráficos e calendário econômico - Jul 2025.Data for Palantir Technologies | Despesas Operacionais including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The Law Enforcement Software market is experiencing robust growth, projected to reach a significant market size by 2033. A compound annual growth rate (CAGR) of 8.50% from 2025 to 2033 indicates a consistently expanding market driven by several key factors. Increasing crime rates and the need for enhanced public safety are compelling law enforcement agencies to adopt advanced software solutions for improved efficiency and effectiveness. Furthermore, the growing adoption of cloud-based technologies, the increasing need for data analytics in criminal investigations, and the integration of artificial intelligence (AI) and machine learning (ML) for predictive policing are major drivers. The market is segmented by software type (e.g., records management systems, crime analysis software, body-worn camera solutions), deployment mode (cloud, on-premise), and end-user (federal, state, local agencies). While data privacy concerns and the high initial investment costs represent potential restraints, the long-term benefits of improved crime prevention, investigation efficiency, and resource optimization are outweighing these concerns. The competitive landscape is populated by both established technology giants like IBM and Accenture, and specialized vendors such as Axon Enterprise and Palantir Technologies, leading to innovation and competitive pricing. This dynamic interplay of factors suggests a continued, strong upward trajectory for the Law Enforcement Software market in the coming years. The market's expansion is further fueled by government initiatives promoting technological advancement within law enforcement. The demand for interoperability between different systems is also increasing, necessitating the adoption of standardized software solutions. The rising adoption of mobile technologies, providing officers with real-time access to critical information, is another substantial driver. Future growth will likely be influenced by the development and adoption of more sophisticated AI-powered tools, improved data security measures, and increased focus on community policing initiatives that benefit from integrated software platforms. The market’s regional distribution will see variations, with North America and Europe likely maintaining a significant share due to their advanced technological infrastructure and higher adoption rates. However, developing regions are also expected to witness substantial growth, driven by increasing government investments and rising awareness of the benefits of law enforcement software. Key drivers for this market are: , Increasing Smart City Initiatives; Increasing Adoption of AI and IoT for Public Safety; Growing Adoption of Cloud-based Solutions in SMEs. Potential restraints include: , Increasing Smart City Initiatives; Increasing Adoption of AI and IoT for Public Safety; Growing Adoption of Cloud-based Solutions in SMEs. Notable trends are: Video Analytics is Expected to Gain Popularity.

The European AI in defense market, valued at approximately €1.5 billion in 2025, is projected to experience robust growth, driven by escalating geopolitical tensions and the increasing need for advanced military capabilities. A Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033 indicates a significant expansion, reaching an estimated market size of over €3 billion by 2033. Key drivers include the adoption of AI-powered systems for enhanced situational awareness, improved decision-making, and autonomous weapon systems. The market is segmented across hardware, software, and platforms (land, air, and naval), with applications spanning cybersecurity, battlefield healthcare, and warfare platform optimization. Leading companies like BAE Systems, Lockheed Martin, and Thales are heavily investing in R&D, fostering innovation and competition. The UK, Germany, and France are expected to be the largest national markets within Europe, due to their robust defense budgets and advanced technological infrastructure. However, challenges remain, including data privacy concerns, ethical implications of autonomous weapons, and the need for robust cybersecurity measures to protect AI systems from adversarial attacks. The substantial growth is fueled by several trends. Governments across Europe are prioritizing the modernization of their defense forces, incorporating AI technologies to gain a competitive advantage. The increasing availability of large datasets suitable for AI training, coupled with advancements in deep learning and machine learning algorithms, are further accelerating market expansion. Furthermore, the rising demand for AI-powered solutions for predictive maintenance, logistics optimization, and intelligence gathering is significantly impacting market growth. Despite these positive factors, restraints include the high cost of AI development and implementation, along with concerns about potential job displacement due to automation in the defense sector. Nevertheless, the long-term outlook for the European AI in defense market remains overwhelmingly positive, with significant opportunities for growth and innovation. Recent developments include: Jul 2022: The Defense Ministry of France announced that it had authorized the go-ahead for the final phase of new artificial intelligence and big data processing capability, which is being developed by the company Athea, a joint venture between Thales and Atos. The main aim of such a project will be to provide France with secure and sovereign artificial intelligence and big data platforms that can analyze massive data generated by military equipment as well as other sensors., May 2022: Palantir Technologies Inc., a data analytics company, announced that it successfully won a USD 12.5 million contract with the Defense Ministry of the United Kingdom. Plantir Technologies Inc., under the contract, will be providing support for its Foundry platform, which will enable the users to cut down on their costs by work automation and reduction in the time required to process the data.. Notable trends are: Increasing Investments In Artificial Intelligence Will Drive The Market During The Forecast Period.

Palantir Technologies reported $884M in Sales Revenues for its fiscal quarter ending in March of 2025. Data for Palantir Technologies | PLTR - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The global Law Enforcement Case Handling Software market is experiencing robust growth, with a market size of $1098.3 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 10.5% from 2025 to 2033. This expansion is driven by several key factors. Increasing crime rates and the subsequent need for efficient case management solutions are major contributors. Furthermore, the rising adoption of cloud-based solutions enhances accessibility, scalability, and data security, fueling market growth. Law enforcement agencies are increasingly recognizing the value of data analytics and predictive policing capabilities embedded within these software systems to improve investigative efficiency and resource allocation. The shift towards digital transformation within law enforcement organizations globally is another significant driver. Government initiatives focused on modernizing policing methods and enhancing inter-agency collaboration are also positively impacting market expansion. Segmentation reveals strong demand across various applications, including public safety agencies, courts, and procuratorates. The market's competitive landscape is characterized by a mix of established players like Motorola Solutions and Oracle, and emerging technology companies like Palantir Technologies and DFLABS, offering a range of solutions tailored to specific agency needs. The regional distribution of the market indicates substantial opportunities across North America and Europe, driven by advanced technological infrastructure and high levels of government investment in public safety. However, growth is expected across all regions, with emerging economies in Asia-Pacific witnessing increasing adoption due to rising crime rates and the need to modernize law enforcement systems. While the market faces challenges such as high initial investment costs and the need for robust cybersecurity measures, the overall positive trends strongly suggest sustained growth and market expansion throughout the forecast period. The continuous development of advanced features such as AI-powered investigative tools and improved data integration capabilities will further drive market expansion.

The global Operational Data Fusion market is experiencing robust growth, driven by increasing demand for real-time intelligence and improved decision-making across diverse sectors. While precise market size figures for 2025 are unavailable, considering a plausible CAGR of 15% (a conservative estimate given the technological advancements in this field) and a base year value (let's assume a 2024 market size of $5 billion based on industry reports on similar markets), the 2025 market size would be approximately $5.75 billion. This growth trajectory is anticipated to continue, pushing the market to a substantial size by 2033. Key drivers include the escalating adoption of IoT devices generating vast amounts of data, the need for enhanced cybersecurity measures leading to sophisticated data fusion techniques, and increasing government investments in defense and intelligence applications. The market is segmented by deployment (cloud, on-premise), application (defense & intelligence, cybersecurity, transportation & logistics, finance), and component (software, hardware, services). Leading players such as Thomson Reuters, Palantir Technologies, and LexisNexis are shaping the market with their advanced solutions. However, challenges like data privacy concerns, high implementation costs, and the complexity of integrating diverse data sources hinder broader adoption. The forecast period (2025-2033) presents significant opportunities for market expansion. The ongoing development of advanced analytics, AI, and machine learning is expected to further enhance data fusion capabilities, creating more efficient and effective solutions. The rise of edge computing is also poised to play a crucial role, enabling faster processing and analysis of real-time data closer to the source. Despite potential restraints, the market is predicted to maintain a healthy CAGR, driven by the continued growth of data volumes and the increasing importance of data-driven decision-making across various industries. A strategic focus on overcoming technological hurdles and addressing data security concerns will be crucial for sustained growth.