Latest news with #ApacheKafka
Yahoo
3 days ago
- Business
- Yahoo
1 Under-the-Radar AI Stock With 50% Upside Potential
In today's increasingly digital world, the speed at which data is captured, processed, and acted upon is critical. That's where Confluent (CFLT), a technology company valued at a market capitalization of $8.1 billion, enters the picture. Founded by the original creators of Apache Kafka, Confluent provides a cloud-native data infrastructure platform that enables organizations to connect, store, and process real-time data streams at scale. Confluent stock has dipped 14% year-to-date (YTD), compared to the S&P 500 Index's ($SPX) gain of 1.9% YTD. Nonetheless, Wall Street believes CFLT stock has more than 50% upside potential over the next year. Let's see whether the stock is currently a buy. Confluent follows a hybrid business model, providing both a self-managed software platform and a fully managed cloud offering. Subscriptions and usage-based pricing, which are increasingly popular among large enterprises due to their flexibility and scalability, generate revenue for the company. Is Palantir Stock Poised to Surge Amidst the Israel-Iran Conflict? CoreWeave Stock Is Too 'Expensive' According to Analysts. Should You Sell CRWV Now? Grains, Unrest, & Gold: What Middle East Tensions Mean for Your Portfolio Now Our exclusive Barchart Brief newsletter is your FREE midday guide to what's moving stocks, sectors, and investor sentiment - delivered right when you need the info most. Subscribe today! Despite a cautious enterprise cloud-spending environment, the company's first-quarter results for fiscal 2025 showed increasing momentum in subscription revenue, hybrid deployments, and new-generation offerings such as Apache Flink and Tableflow. Total revenue increased 25% year on year to $271.1 million, with adjusted earnings increasing by an impressive 60% to $0.08 per share. During the Q1 earnings call, management emphasized that the company's focus on long-term platform expansion is beginning to pay off, despite the fact that macroeconomic pressures remain, particularly in large-scale enterprise consumption. Net retention reached 117% in Q1, demonstrating customer trust in Confluent's platform. Confluent generated $260.9 million in subscription revenue during Q1, up 26% year on year (YOY) and accounting for 96% of total revenue. Confluent Cloud, a fully managed, cloud-native product, generated $142.7 million, a 34% increase YOY. Furthermore, demand for hybrid and on-premises deployments enabled the company's self-managed Confluent Platform to generate a healthy $118.2 million in revenue, up 18% from the previous year. Management stated that Confluent Platform had its best first-quarter performance in three years. Confluent's growth strategy is heavily reliant on migrating the estimated 150,000 organizations that still use open-source Kafka. Confluent added 340 net new customers during the period, marking its best quarterly performance in three years. Confluent's balance sheet showed $1.9 billion in cash, cash equivalents, and marketable securities at the quarter's end. The company also generated $4.9 million in positive free cash flow (FCF) during the quarter, a sign of cost discipline. Management reaffirmed its fiscal 2025 guidance with cautious optimism. Subscription revenue could increase by 19% to 20%, reaching $1.1 billion. Likewise, adjusted net income per share could be around $0.36 per share, compared to $0.29 in fiscal 2024. Additionally, analysts predict earnings will rise by 30.7% to $0.47 by fiscal 2026. Despite being a small company, Confluent is rapidly growing. Its inclusion in mission-critical workloads is what distinguishes it. This technology is not experimental. In fact, it provides real-time network solutions to a variety of industries, including telecommunications, retail logistics and supply chain, and financial services fraud detection. On Wall Street, CFLT stock is rated as a 'Moderate Buy.' Of the 31 analysts covering the stock, 20 rate CFLT as a 'Strong Buy,' three recommend it as a 'Moderate Buy,' seven call it a "Hold,' and one suggests that it is a 'Moderate Sell.' The average target price of $28.14 per share suggests an upside of 17.5% above current levels. The Street-high target price of $36 implies that shares could rally 50.4% over the next 12 months. As the demand for real-time data infrastructure increases, particularly in artificial intelligence (AI) and edge computing, so will the demand for the Confluent's services. For long-term investors, Confluent provides an appealing combination of secular tailwinds, a deep technical moat, high gross margins, and a strong cloud growth engine. However, as a high-growth stock, it trades at a premium of 66x forward earnings. Consequently, risk-averse investors may want to wait for a better entry point. On the date of publication, Sushree Mohanty did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. This article was originally published on
Yahoo
11-06-2025
- Business
- Yahoo
3 Under-the-Radar AI Stocks That Could Help Make You a Fortune
Duolingo's AI-powered language learning app is firing on all cylinders. Confluent will stream and process more data as the AI market expands. MongoDB's non-relational database services are processing more data for AI apps. 10 stocks we like better than Duolingo › Many artificial intelligence (AI) stocks skyrocketed in value in recent years as more companies realized they could accelerate and automate their operations with large language models (LLMs) and generative AI applications. The obvious winners include Nvidia, which produces the data center GPUs for processing those AI tasks, and Microsoft, which acquired a big stake in ChatGPT's creator OpenAI and integrated its AI tools into its own cloud services. While Nvidia and Microsoft are still great long-term AI plays, investors shouldn't overlook the other under-the-radar AI plays that could generate big gains over the next few years. Three of those underappreciated AI-oriented stocks are Duolingo (NASDAQ: DUOL), Confluent (NASDAQ: CFLT), and MongoDB (NASDAQ: MDB). Let's look at how these three lesser-known AI stocks could make you a fortune. Duolingo, which owns the world's most downloaded language learning app, might not seem like an AI company. But it's been using generative AI to produce its online courses at a faster rate, replacing a lot of its human contractors with AI-powered services, and expanding its premium Duolingo Max tier with AI-driven conversations. It also uses its own "Birdbrain" LLM to customize its lessons for its users based on their proficiency, pace, and learning styles. Duolingo served 130.2 million monthly active users (MAUs), 46.6 million daily active users (DAUs), and 10.3 million paid subscribers in the first quarter of 2025. That's up from just 40.5 million MAUs, 9.6 million DAUs, and 2.5 million paid subscribers at the end of 2021. From 2024 to 2027, analysts expect Duolingo's revenue and EPS to grow at a CAGR of 29% and 51%, respectively. That growth should be driven by the expansion of Duolingo Max's AI-driven services, more non-language subjects like math and music, new pricing tiers, and stickier gamification features (like leagues and streaks). Duolingo's stock isn't cheap at 115 times next year's earnings, but its rapid growth, its dominance of the language learning market, and its expanding AI ecosystem all justify that higher valuation. Confluent's cloud-based platform processes "data in motion" as it flows between different applications within an organization. Its namesake platform runs on Apache Kafka, an open-source platform for processing streaming data, but it integrates additional analytics services to differentiate itself from other "Kafka-as-a-service" providers. Confluent doesn't build its own large language models or generative AI platforms, but it accelerates the delivery of data for those services. That's why it set an ambitious goal for becoming the "central nervous system for modern digital enterprises" in its IPO filing four years ago. Its total number of customers grew from 3,470 in 2021 to 6,140 in the first quarter of 2025, and the market's demand for its streaming data services should continue rising as the AI market grows. From 2024 to 2027, analysts expect Confluent's revenue to rise at a CAGR of 19% as it narrows its net losses. Its near-term catalysts include its deeper partnerships with Microsoft's Azure, Alphabet's Google Cloud, OpenAI, Databricks, and other cloud and AI leaders, its growth among larger enterprise customers, and the expansion of its ecosystem. Confluent's stock still seems reasonably valued at 6 times next year's sales, and it should still have plenty of room to grow as the rapid expansion of the AI market drives companies to stream more data across its digital pipelines. MongoDB is another company that helps companies organize large amounts of data for AI applications. Its namesake platform allows companies to store large amounts of unstructured data in a non-relational database. That approach differentiates it from older relational databases, which only store their data in rigidly structured tables and rows, and makes it easier for its clients to customize their data for specific tasks. MongoDB's subscription-based cloud-based service, Atlas, allows its clients to analyze all of that data. Its MongoDB Copilot -- a generative AI assistant that was launched last year -- streamlines, optimizes, and accelerates those queries. It also uses AI to detect suspicious and misconfigured patterns within its database. It served 57,100 customers in the first quarter of fiscal 2026 (which ended this April), up from its 33,000 customers at the end of fiscal 2022. From fiscal 2025 to fiscal 2028, analysts expect its revenue to grow at a CAGR of 16%. It's not profitable yet and might not seem cheap at 7 times next year's sales, but its expansion of Atlas, its recent acquisition of Voyage AI (which adds embedding and reranking models to its apps), and new AI partnerships should fuel its long-term growth. Before you buy stock in Duolingo, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Duolingo wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $660,341!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $874,192!* Now, it's worth noting Stock Advisor's total average return is 999% — a market-crushing outperformance compared to 173% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of June 9, 2025 Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Leo Sun has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet, Microsoft, MongoDB, and Nvidia. The Motley Fool recommends Confluent and Duolingo and recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy. 3 Under-the-Radar AI Stocks That Could Help Make You a Fortune was originally published by The Motley Fool Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Techday NZ
24-04-2025
- Business
- Techday NZ
OVHcloud launches all-in-one data platform for analytics & AI
OVHcloud has announced the general availability of its Data Platform, an all-in-one platform-as-a-service designed for organisations seeking to manage and analyse data while maintaining compliance and cost predictability. The Data Platform, according to OVHcloud, offers end-to-end capabilities for data collection, storage, processing, analysis, and visualisation in a cloud environment that aims to address the challenges of increasing data volume, complexity, and the growing adoption of artificial intelligence tools. Built on open-source technologies, the solution is aimed at helping organisations avoid dependency on major cloud hyperscalers and is pitched as particularly relevant for highly regulated industries and any business concerned with data sovereignty and vendor lock-in. "With the launch of OVHcloud Data Platform we are providing customers with a complete and integrated solution for their data journey. Businesses can leverage their data to find new insights through AI and analytics projects. We are proud to deliver this important milestone having implemented customer feedback throughout the Beta phase," said Alexis Gendronneau, Chief Data Officer OVHcloud. The platform consists of a set of managed services, including data streaming, storage, pipeline orchestration, and advanced visualisation and exploration tools. Users are able to focus on utilising data for value creation rather than managing infrastructure, according to the company. Target use cases span several sectors. For retail and e-commerce, the platform is designed to assist in identifying customer groups and predicting inventory needs. In financial services, it provides tools for portfolio risk assessment, fraud detection, and credit scoring. Healthcare use cases include analysis of clinical trial data to speed up drug development. The platform can also measure key performance indicators for media and entertainment advertising campaigns and perform audience sentiment analysis. In Industry 4.0 settings, it may be used for supply chain optimisation, predictive maintenance, and quality control. The platform supports a broad spectrum of data sources, including Object Storage, Apache Kafka, ClickHouse, MongoDB, MySQL, Oracle, HTTP/FTP, Google Analytics, Google BigQuery, Snowflake, X, the OVHcloud API, and others. Data can be processed, stored, and made available for analytics and sharing via built-in applications or APIs. Listed technical features include compatibility with languages and frameworks such as ANSI SQL, Python, Apache Iceberg, Spark, Pandas, Jupyter notebooks, Trino, SuperSet, Prometheus, and Kubernetes. The solution has been optimised using technology developed by ForePaaS, which OVHcloud acquired previously, and is now integrated within the wider OVHcloud portfolio. OVHcloud presents the Data Platform as suitable for use by data engineers, analytics engineers, data analysts, data scientists, and dataops teams, providing a single user interface aimed at facilitating cross-department data collaboration. The service is available for businesses of varying sizes, including small and mid-size companies requiring advanced analytics services. The workflow of the Data Platform can be further enhanced with OVHcloud's AI Endpoints for tasks such as document data extraction, multi-modal transcription, automated data cleansing, or anomaly detection. The service integrates with serverless GPU-powered OVHcloud AI Training and AI Deploy services, supporting the acceleration of data-to-model lifecycle processes. On data sovereignty and security, the company states that all data is hosted in OVHcloud's European cloud infrastructure, designed to meet high security and compliance standards. "Data is hosted in Europe, providing protection against non-European regulations and giving organisations technical and strategic autonomy. The platform is also based on open-source technologies, providing users with superior data portability, control and freedom of choice," the company stated.
Forbes
01-04-2025
- Business
- Forbes
Data Engineering: Transforming The Backbone Of Modern Data Solutions
Mukul Garg is the Head of Support Engineering at PubNub , which powers apps for virtual work, play, learning and health. getty In my journey through data engineering, one of the most remarkable shifts I've witnessed occurred during the integration of real-time data pipelines for a fast-growing SaaS platform. Initially, our data team was bogged down with batch processing and delayed analytics, which severely hindered decision-making speed. However, when we implemented a real-time data architecture using technologies like Apache Kafka and cloud-native solutions, we were able to process and analyze data on the fly, dramatically increasing our business agility. This experience solidified my belief in the transformative power of modern data engineering. This year, data engineering will have become the backbone of every digital transformation strategy. With the growing complexity of data sources and increasing demand for real-time analytics, companies are adopting cutting-edge technologies to build robust, scalable and efficient data infrastructures. Data engineering today plays a pivotal role in unlocking the true value of data across industries, allowing businesses to harness their data in real time, improve decision-making and create personalized experiences for customers. Several companies are at the forefront of implementing advanced data engineering solutions that set benchmarks for the industry: • Netflix's Real-Time Data Pipelines: Netflix utilizes a data architecture that combines both batch and stream processing methods to handle massive quantities of data. This approach balances latency, throughput and fault tolerance by using batch processing for comprehensive views and real-time stream processing for immediate data insights. • Uber's Predictive Analytics Engine: Uber has developed a sophisticated predictive analytics engine to optimize route planning and demand forecasting. By using real-time data processing, Uber can anticipate surge pricing and provide drivers with the most efficient routes in real time. • Shopify's Automated Data Warehouse: Shopify recently moved to an automated data warehouse powered by cloud-native solutions like dbt (data build tool). This has allowed them to integrate sales, inventory and customer data more efficiently, resulting in quicker data-driven insights and better decision-making. • Airbnb's Data Mesh Architecture: Airbnb has embraced a data mesh approach to scale its data infrastructure, decoupling data storage and processing across multiple teams. This approach enables each team to take ownership of its own data domain while using shared infrastructure, improving data discoverability and reducing bottlenecks. Benefits Of Modern Data Engineering Modern data engineering offers several key benefits that have become essential for businesses today: • Real-Time Analytics: With the advent of real-time data pipelines, companies can process and analyze data as it comes in. This has allowed businesses like Uber and Netflix to offer more timely, relevant insights and optimize decision-making in real time. • Scalability: Data engineering solutions today can scale horizontally, handling increasing volumes of data without a corresponding increase in cost. Cloud data platforms like Snowflake and Google BigQuery are prime examples of scalable solutions that allow organizations to scale operations as they grow. • Data Democratization: The rise of self-service data tools such as dbt and Looker has democratized data access, enabling teams across organizations to leverage data without needing deep technical expertise. This leads to faster decision-making across departments. • Cost Efficiency: Cloud-native data solutions enable companies to optimize storage and compute costs by only paying for what they use, making it easier for small and medium-sized businesses to manage their data infrastructure without heavy upfront investments. Challenges And Considerations In Data Engineering While the benefits are clear, there are also challenges in integrating modern data engineering solutions. • Data Quality: Ensuring that the data being ingested into the system is clean, consistent and accurate is one of the most challenging aspects of data engineering. Poor data quality can lead to incorrect insights and missed opportunities. • Data Privacy And Compliance: As data privacy regulations like GDPR continue to evolve, organizations need to ensure that their data pipelines comply with these regulations. This requires robust data governance and regular audits to maintain compliance. • Integration Complexity: Integrating multiple data sources, especially from legacy systems, can be complex. Data engineering teams must ensure seamless integration while maintaining the integrity of the data and ensuring minimal latency. • Maintaining Real-Time Performance: As real-time data processing becomes more prevalent, maintaining low-latency pipelines becomes increasingly difficult. Ensuring high throughput and minimal delays, especially with large datasets, requires careful infrastructure management. Maintaining Data Integrity And Security In an age where data is the new currency, ensuring data integrity and security has never been more important. Implementing secure access controls, encrypted data pipelines and comprehensive monitoring systems can help safeguard data from potential breaches. Companies like Shopify and Airbnb are taking proactive steps to ensure their data infrastructures are both secure and resilient, using advanced data masking and encryption techniques to protect sensitive information. Future Outlook Based on patterns observed in the industry, here are my predictions for data engineering in the next two to three years. • Data Fabric And Data Mesh Expansion: The concept of a data fabric, which integrates disparate data sources into a unified layer, will continue to gain traction. Combined with data mesh architecture, organizations will see greater flexibility and scalability in their data operations, enabling more efficient collaboration across departments. • Serverless Data Platforms: Serverless computing will take on an even greater role in data engineering. Companies will increasingly shift to serverless data architectures, reducing the overhead of managing infrastructure while focusing more on the logic of data processing. • Data Privacy By Design: As privacy concerns grow, companies will build privacy-enhancing technologies into their data pipelines from the outset, ensuring compliance with global regulations without sacrificing performance. Conclusion In 2025, data engineering is not just about building infrastructure—it's about creating agile, scalable and secure systems that can process vast amounts of data in real time. The future of data engineering looks bright as organizations continue to innovate, leveraging modern technologies to unlock new insights, improve decision-making and drive business growth. Companies that invest in the latest data engineering solutions should be well-positioned to gain a competitive edge in an increasingly data-driven world. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
25-03-2025
- Business
- Yahoo
2 Hypergrowth Tech Stocks to Buy in 2025
2025 is proving to be a volatile year for the stock market, as economic uncertainty and unpredictable policies from the Trump administration throw investors for a loop. While anything can happen in the near term, growth investors should focus on investing in fast-growing companies with no shortage of long-term potential. Reddit (NYSE: RDDT) and Confluent (NASDAQ: CFLT) undoubtedly fit the bill. Reddit is a social media network unlike any other. The company's platform, split into subreddits covering specific topics, has emerged as one of the best ways for people to find useful information on the internet. While Alphabet's Google Search is still the king, a focus on serving advertisements and artificial intelligence (AI) overviews of dubious quality can lead to poor results for users. As Reddit works to better monetize its platform, revenue and usage are soaring. Daily active unique users shot up 39% year over year in the fourth quarter of 2024 to 101.7 million, and revenue surged 71% to $427.7 million. Reddit currently makes money through advertising sales and deals with AI companies for data access. Later this year, Reddit is reportedly planning to launch a feature that could lock some content behind paywalls. While this will be tricky to get right without upsetting users, it represents another potential revenue stream. Reddit is already profitable, generating a generally accepted accounting principles (GAAP) operating margin of about 12% in the fourth quarter of 2024. Gross margin was an impressive 93%, allowing the company to spend heavily on research and development while still churning out profits. Reddit runs its platform on third-party cloud computing platforms rather than its own infrastructure, so capital spending is minimal. This asset-light business model helped produce $215.8 million in free cash flow last year. Valued at nearly $21 billion, Reddit is an expensive stock relative to earnings. The stock has also taken a beating lately, tumbling more than 40% from its all-time high. While these two factors may scare some investors away, Reddit is a unique platform that can leverage its status as a source of trusted information to grow revenue and profit rapidly in the coming years. The stock will likely be volatile, but it's a great choice for long-term investors. Large enterprises with complex IT infrastructures need a robust way to connect applications together. Connecting applications directly to each other is fragile, resulting in a web of complexity that could break mission-critical data flows when anything goes wrong. Apache Kafka, an open-source event streaming platform, has become extremely popular among large companies to solve this problem. Major players in manufacturing, banking, insurance, telecom, and other industries rely on Kafka. One problem is that Kafka is a complex piece of software that requires proper configuration and management. Confluent, which was founded by the creators of Kafka, solves this problem by building propriety features on top of Kafka and other open-source software. The company's data streaming platform now has around 5,800 customers, including nearly 1,400 that spend more than $100,000 annually. Confluent's growth has been impressive, particularly for its Confluent Cloud platform. Cloud revenue rose 38% year over year in the fourth quarter, which helped push up total revenue by 23% to $261.2 million. Confluent is just scratching the surface of its total addressable market, which the company pegs at $60 billion. Confluent is still in growth mode, so it's not yet profitable on a GAAP basis. However, the company is producing positive free cash flow, an important step toward profitability. Shares of Confluent have taken an absolute beating since peaking in late 2021, down around 70%. While the stock is tough to value given the lack of profits, strong revenue growth and an attractive value proposition for its enterprise customers makes Confluent a long-term buy for growth investors. Before you buy stock in Reddit, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Reddit wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $721,394!* Now, it's worth noting Stock Advisor's total average return is 839% — a market-crushing outperformance compared to 164% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of March 24, 2025 Timothy Green has no position in any of the stocks mentioned. The Motley Fool recommends Confluent. The Motley Fool has a disclosure policy. 2 Hypergrowth Tech Stocks to Buy in 2025 was originally published by The Motley Fool Sign in to access your portfolio
