Android 16 Introduces Wide-Ranging Features and Changes

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MMS Sergio De Simone

Article originally posted on InfoQ. Visit InfoQ

Google has released Android 16, now available for supported Pixel devices. The new release introduces a wide range of new APIs and changes, some of which affect existing apps and require developers to update their code. Material 3 Expressive, the new paradigm for Android user interfaces, is not yet included and will roll out later this year.

Android 16 introduces a wealth of new features and changes across nearly all areas of the OS, from accessibility and connectivity to camera support, privacy, core functionality, security, and more. Notable new behaviors include improved notifications, extended support for full screen apps, advanced protection against USB attacks, desktop-style multitasking, and several enhancements to camera and media capabilities.

Adaptive apps are the default on Android 16 for any “large” device, that is, devices with displays larger than 600dp in both dimensions, such as the inner screens of foldables, tablets, and Chromebooks. The option to restrict an app’s resizability via the manifest is no longer available. For developers, this means adopting good practices to avoid stretched UI components, ensuring the camera works correctly in both orientation, and preserving state across window size changes.

Changes needed to support adaptive apps are also essential for desktop-like multitasking, which is enabled when connecting an Android device to an external monitor. In addition, apps displayed on an external monitor may benefit from more advanced behaviors like allowing multiple instances, support data sharing via drag and drop, support configuration changes, and more.

Android 16 introduces an extended notification system supporting progress-centric notifications, which can be used to denote how a process progresses through multiple states and milestones, such as for ridesharing, delivery, and navigation. The system uses points and segments to visually represent progress. These new features lay out the foundation for the upcoming Live Updates capability.

Google has already scheduled a minor Android 16 update for Q3 and a major feature drop in Q4, which will include the release of Material 3 Expressive. Notably, the current release is the only one that will require updates to existing apps to ensure compatibility with Android 16 devices.

Developers should pay close attention to changes such as new JobScheduler quotas that could stop an app when the developer does not expect it; ART changes potentially crashing apps using reflection, JNI, or accessing Android internals; stronger security against intent redirection attacks; the adoption of 16KB page size; new Bluetooth handling affecting device re-pairing, and more.

As mentioned, Android 16 is currently available exclusively on supported Pixel devices. According to Google, support for third-party models will follow soon. In the meantime, developers can test their apps using the Android emulator in Android Studio to ensure compatibility and expected behavior on Android 16.

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Snowflake Trades Near 52-Week High: Buy, Sell or Hold the Stock? – TradingView

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Snowflake SNOW shares closed at $212.08 on Wednesday, very close to the 52-week high of $214.83, hit on June 4, 2025. SNOW shares have jumped 37.3% year to date (YTD), outperforming the Zacks Internet – Software industry and Zacks Computer and Technology sector gained 12.8% and 1.5%, respectively. 

The upside in Snowflake is driven by strong first-quarter fiscal 2026 results, consistent product innovation and robust customer expansion. Revenues increased 25.7% year over year to $1.04 billion, beating the Zacks consensus mark by 3.74%. Snowflake reported non-GAAP earnings of 24 cents per share, surpassing the consensus estimate of 22 cents and rising from 14 cents reported in the year-ago quarter. The company added 451 net new customers during the quarter, reflecting 18.8% year-over-year growth.

SNOW Stock’s Performance 

Snowflake shares are trading above the 50-day moving average, indicating a bullish trend.

SNOW Shares Trade Above 50-Day SMA

Snowflake Shares Trading at a Premium

Snowflake shares are trading at a premium, as suggested by the Value Score of F.

In terms of forward 12-month P/S, SNOW stock is trading at 14.49X compared with the industry’s 5.67X. The stock is expensive than competitors like Teradata TDC and MongoDB MDB.

Shares of Teradata and MongoDB are currently trading at P/S ratios of 1.28X and 6.91X, respectively.

Price/Sales (F12M)

With shares trading near 52-week high and valuation metrics stretched, the key question is whether SNOW still offers compelling upside at current levels. Let’s take a closer look.

Snowflake’s Strong and Innovative Portfolio Drives Growth

Snowflake’s expanding portfolio has been noteworthy. Products like Generation 2 Warehouses, Adaptive Compute, Openflow and Snowflake Intelligence are helping drive new enterprise adoption.

Building on this momentum, in June 2025, Snowflake launched Generation 2 Warehouses with 2.1x faster analytics and Adaptive Compute to enable automatic resource scaling. The company also introduced Openflow, a managed service built on Apache NiFi, to simplify batch and streaming data ingestion into the AI Data Cloud, supporting faster integration for AI and real-time workloads.

Snowflake’s investments in AI and machine learning, including the introduction of Snowflake Intelligence and enhancements to the Marketplace with agentic native apps and AI-ready datasets, continue to gain traction. These capabilities are helping customers accelerate GenAI deployment across business functions and reduce time to insight.

Strong Clientele and Partnerships Support SNOW’s Momentum

Snowflake’s platform continues to gain adoption among large enterprises across industries. Companies like JPMorgan Chase, AstraZeneca, Siemens, Samsung Ads and Dentsu are leveraging the AI Data Cloud to unify workloads, improve visibility and drive more personalized customer experiences. As of the first quarter, more than 5,200 customers were actively using Snowflake’s AI and ML features weekly.

Snowflake also benefits from a robust partner ecosystem that includes Microsoft MSFT, Amazon, ServiceNow and NVIDIA, along with consulting leaders like EY and S&P Global. In partnership with Microsoft, the company continues to enhance data interoperability and simplify AI development. A recently expanded collaboration with Acxiom enables the unification of identity and audience data within the Snowflake Data Cloud, helping brands launch AI-driven marketing campaigns with improved personalization and reach. Microsoft remains a key technology partner for Snowflake as it pushes co-innovation efforts across industries to drive broader GenAI adoption.

SNOW Offers Positive View for Q2 and FY26

For the second quarter of fiscal 2026, Snowflake expects product revenues in the range of $1.03-$1.04 billion. The projection range indicates year-over-year growth of 25%. For fiscal 2026, Snowflake projects product revenues to grow 25% year over year to reach $4.32 billion.

The Zacks Consensus Estimate for second-quarter fiscal 2026 revenues is currently pegged at $1.08 billion, indicating 24.85% year-over-year growth. The consensus mark for earnings is currently pegged at 26 cents per share, unchanged over the past 30 days. This indicates an increase of 44.44% year over year. 

Snowflake Inc. Price and Consensus

Snowflake Inc. price-consensus-chart | Snowflake Inc. Quote

The Zacks Consensus Estimate for SNOW’s fiscal 2026 revenues is pegged at $4.51 billion, indicating year-over-year growth of 24.50%. The consensus mark for earnings is pegged at $1.06 per share, which has decreased nine cents over the past 30 days. This indicates an increase of 27.71% on a year-over-year basis.

How Should Investors Play SNOW Stock?

Snowflake’s expanding customer footprint, continued platform innovation and strong ecosystem of partners provide a solid foundation for long-term growth. However, intensifying competition from hyperscale cloud providers like AWS, Azure and Google Cloud continues to be a competitive hurdle.

The company also faces increasing pressure from enterprise data cloud and analytics providers such as Teradata and MongoDB, which are enhancing their offerings and capturing market share. In parallel, elevated infrastructure spending, particularly on GPUs to support AI-driven initiatives, is adding to cost pressures. Stretched valuation remains a concern.

SNOW currently carries a Zacks Rank #3 (Hold), suggesting that it may be wise to wait for a more favorable entry point in the stock. You can see the complete list of today’s Zacks #1 Rank (Strong Buy) stocks here.

This article originally published on Zacks Investment Research (zacks.com).

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12 Best Technology Stocks According to Wall Street Analysts – Insider Monkey

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Artificial intelligence is the greatest investment opportunity of our lifetime. The time to invest in groundbreaking AI is now, and this stock is a steal!

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Storage and Data Protection News for the Week of June 20; Updates from Cohesity, Pure …

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Solutions Review Executive Editor Tim King curated this list of notable storage and data protection news for the week of June 20, 2025.

Keeping tabs on all the most relevant storage and data protection news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy storage and data protection news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Top Storage and Data Protection News for the Week of June 20, 2025


Cohesity Boosts Resilience for Mission-Critical MongoDB Workloads

Cohesity has announced a deeper integration with MongoDB, delivering advanced backup and recovery capabilities tailored for large, mission-critical datasets. The new API-based solution enhances performance, scalability, and security—supporting the stringent requirements of global enterprises in banking, finance, and beyond.

Read more: prnewswire.com/apac/news-releases/cohesity-strengthens-resilience-of-large-mission-critical-mongodb-workloads-302485292.html

CTERA First in Hybrid Cloud Storage to Support Model Context Protocol (MCP)

CTERA has become the first hybrid cloud storage provider to support the Model Context Protocol (MCP), enabling seamless integration with next-generation AI and machine learning workflows. This innovation allows enterprises to securely manage and access contextual data across distributed environments, paving the way for more intelligent, scalable AI deployments.

Read more: globenewswire.com/fr/news-release/2025/06/17/3100715/0/en/CTERA-Becomes-First-in-Hybrid-Cloud-Storage-to-Support-the-Model-Context-Protocol-MCP.html

DoControl Launches Dot: The First AI-Powered SaaS Data Security Assistant

DoControl has introduced Dot, the industry’s first AI-powered SaaS Data Security Assistant designed to revolutionize how security teams manage and protect SaaS environments. Leveraging a deeply contextualized data infrastructure, Dot enables natural-language interaction with complex security data, transforming fragmented, manual processes into simple, conversational workflows.

Read more: prnewswire.com/news-releases/docontrol-launches-first-ever-ai-powered-saas-data-security-assistant-dot-302484628.html

Study Finds 72 Percent of Enterprises Plan to Expand AI Use Despite Data Privacy and Ethics Concerns

A recent survey reveals that while 72 percent of enterprises intend to broaden their AI adoption, many remain concerned about data privacy, bias, and ethical risks. Tech leaders prioritize expanding AI capabilities but emphasize the need for robust governance frameworks to mitigate these challenges and ensure responsible AI deployment across industries.

Read more: finance.yahoo.com/news/study-finds-72-enterprises-plan-130000001.html

IBM Introduces Industry-First Software for Unified Agentic Governance and Security

IBM has launched the industry’s first software platform designed to unify agentic governance and security for enterprise AI. The new solution provides organizations with centralized oversight and control of AI agents, ensuring compliance, risk management, and operational transparency as businesses deploy increasingly autonomous AI systems across their operations.

Read more: newsroom.ibm.com/2025-06-18-ibm-introduces-industry-first-software-to-unify-agentic-governance-and-security

NTT DATA Report Reveals C-Suite Divide on GenAI Adoption and Security

A new global report from NTT DATA highlights a significant misalignment within the C-suite regarding generative AI (GenAI) adoption. While 99 percent of executives plan further GenAI investments and 67 percent of CEOs are making significant commitments, nearly half of CISOs express concerns about security gaps and unclear internal guidelines.

Read more: https://www.ndtvprofit.com/technology/c-suite-misalignment-over-gen-ai-adoption-shows-ntt-data-research

Nutanix Report: Public Sector Rapidly Adopting Generative AI, But Faces Security Hurdles

A new Nutanix study reveals that 83 percent of public sector organizations have a generative AI strategy, with most already implementing or preparing to deploy AI solutions. While GenAI is expected to boost productivity and automation, 76 percent of IT leaders say their infrastructure needs significant upgrades to support these advances. Security and privacy remain top concerns, with 92 percent of leaders calling for stronger protections as AI becomes integral to public sector operations.

Read more: nutanix.com/press-releases/2025/nutanix-study-finds-public-sector-embraces-generative-ai

Pure Storage Introduces the Enterprise Data Cloud

Pure Storage has unveiled its Enterprise Data Cloud, a new platform designed to unify data management, storage, and analytics for modern enterprises. The solution promises seamless data mobility, robust security, and simplified operations—helping organizations harness the full value of their data across on-premises and cloud environments.

Read more: purestorage.com/company/newsroom/press-releases/pure-storage-introduces-the-enterprise-data-cloud.html

Qumulo Stratus: Cryptographically Isolated Edge-to-Core-to-Cloud Data Platform

Qumulo has announced Stratus, a multi-tenant data platform that delivers cryptographically isolated data management from edge to core to cloud. Stratus is designed to empower organizations with secure, scalable, and resilient data storage, supporting modern workloads and regulatory requirements across distributed environments.

Read more: businesswire.com/news/home/20250617477912/en/Qumulo-Stratus-Cryptographically-Isolated-Edge-to-CoretoCloud-MultiTenant-Data-Platform

SingleStore Unveils Enterprise AI Platform for Real-Time, Serverless Workflows

SingleStore has launched a major update to its enterprise AI platform, introducing real-time, serverless functions designed to streamline development and deliver ultimate flexibility for AI-powered applications. The new release focuses on enhancing developer productivity and enabling organizations to build, deploy, and scale AI solutions faster and more efficiently.

Read more: aithority.com/machine-learning/singlestore-drops-an-enterprise-ai-glow-up-built-for-real-time-serverless-functions-and-ultimate-dev-flow/

StorONE Launches OneAI to Revolutionize Storage Intelligence

StorONE has introduced OneAI, a new platform that brings advanced artificial intelligence to storage management. OneAI is designed to optimize performance, automate routine tasks, and provide predictive analytics, enabling organizations to maximize the value and efficiency of their storage infrastructure.

Read more: storone.com/news-and-events/storone-unveils-oneai/

Expert Insights

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

Take the Tech Leader Survey – Spring 2025 Now

In partnership with Skiilify Co-Founder and distinguished Northeastern University Professor Paula Caligiuri, PhD, we’ve just launched our latest enterprise tech leader Survey to uncover howthought leaders are thinking about disruption in this AI moment.

Take survey

NEW by SR Expert at Insight Jam Paula Caligiuri, PhD.: How Humility Supercharges Technical Talent

According to our April 2025 report, 81 percent of tech professionals agree that humility—defined as actively seeking and using feedback—is critical for growth. But nearly half (46 percent) say the feedback they receive is too vague or unhelpful and not actionable. Which means even the most open-minded, competent professionals are flying without enough external input to keep growing.

Read on Solutions Review

ICYMI: Mini Jam Q2, 2025 – AI Agents of Change: Watch All Sessions On-Demand

Centered on the theme ‘AI Agent of Change’, this quarterly Insight Jam virtual event explores the emerging role of AI agents in reshaping the Future of  Work through Automation, driving innovation, and facilitating adaptation to the new reality of this AI moment. Be sure to register free for InsightJam.com here to watch all the sessions live or on-demand.

Read on Solutions Review

For consideration in future data protection news roundups, send your announcements to the editor: tking@solutionsreview.com.

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MongoDB, Inc. (NASDAQ:MDB) Stock Position Increased by Fifth Third Bancorp

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Fifth Third Bancorp boosted its position in MongoDB, Inc. (NASDAQ:MDBFree Report) by 15.9% during the 1st quarter, according to the company in its most recent filing with the Securities & Exchange Commission. The fund owned 569 shares of the company’s stock after purchasing an additional 78 shares during the quarter. Fifth Third Bancorp’s holdings in MongoDB were worth $100,000 at the end of the most recent quarter.

Several other hedge funds have also recently bought and sold shares of the company. Assenagon Asset Management S.A. raised its position in shares of MongoDB by 24.4% during the 1st quarter. Assenagon Asset Management S.A. now owns 369,313 shares of the company’s stock worth $64,778,000 after buying an additional 72,424 shares in the last quarter. Handelsbanken Fonder AB raised its position in shares of MongoDB by 0.4% during the 1st quarter. Handelsbanken Fonder AB now owns 14,816 shares of the company’s stock worth $2,599,000 after buying an additional 65 shares in the last quarter. SG Americas Securities LLC raised its position in shares of MongoDB by 1,230.3% during the 1st quarter. SG Americas Securities LLC now owns 24,997 shares of the company’s stock worth $4,384,000 after buying an additional 23,118 shares in the last quarter. Farther Finance Advisors LLC raised its position in shares of MongoDB by 57.2% during the 1st quarter. Farther Finance Advisors LLC now owns 1,242 shares of the company’s stock worth $219,000 after buying an additional 452 shares in the last quarter. Finally, Park Avenue Securities LLC raised its position in shares of MongoDB by 52.6% during the 1st quarter. Park Avenue Securities LLC now owns 2,630 shares of the company’s stock worth $461,000 after buying an additional 907 shares in the last quarter. 89.29% of the stock is currently owned by institutional investors.

Analyst Ratings Changes

A number of analysts have commented on the stock. Rosenblatt Securities dropped their price target on shares of MongoDB from $305.00 to $290.00 and set a “buy” rating on the stock in a research report on Thursday, June 5th. Needham & Company LLC restated a “buy” rating and set a $270.00 price target on shares of MongoDB in a research report on Thursday, June 5th. Macquarie restated a “neutral” rating and set a $230.00 price target (up from $215.00) on shares of MongoDB in a research report on Friday, June 6th. Daiwa Capital Markets assumed coverage on shares of MongoDB in a report on Tuesday, April 1st. They issued an “outperform” rating and a $202.00 target price on the stock. Finally, Citigroup dropped their target price on shares of MongoDB from $430.00 to $330.00 and set a “buy” rating on the stock in a report on Tuesday, April 1st. Eight investment analysts have rated the stock with a hold rating, twenty-four have issued a buy rating and one has issued a strong buy rating to the stock. According to MarketBeat.com, the company has a consensus rating of “Moderate Buy” and an average price target of $282.47.

<!—->

Read Our Latest Analysis on MongoDB

MongoDB Stock Down 0.7%

MDB stock opened at $204.15 on Friday. The company has a market capitalization of $16.68 billion, a price-to-earnings ratio of -179.08 and a beta of 1.39. MongoDB, Inc. has a 12 month low of $140.78 and a 12 month high of $370.00. The firm has a 50 day moving average of $184.32 and a two-hundred day moving average of $223.66.

MongoDB (NASDAQ:MDBGet Free Report) last posted its earnings results on Wednesday, June 4th. The company reported $1.00 EPS for the quarter, topping the consensus estimate of $0.65 by $0.35. The company had revenue of $549.01 million during the quarter, compared to the consensus estimate of $527.49 million. MongoDB had a negative net margin of 4.09% and a negative return on equity of 3.16%. The company’s revenue for the quarter was up 21.8% compared to the same quarter last year. During the same period in the previous year, the firm posted $0.51 earnings per share. On average, research analysts expect that MongoDB, Inc. will post -1.78 EPS for the current fiscal year.

Insider Buying and Selling

In related news, CEO Dev Ittycheria sold 25,005 shares of the company’s stock in a transaction on Thursday, June 5th. The shares were sold at an average price of $234.00, for a total transaction of $5,851,170.00. Following the completion of the sale, the chief executive officer now directly owns 256,974 shares of the company’s stock, valued at approximately $60,131,916. This represents a 8.87% decrease in their position. The transaction was disclosed in a filing with the Securities & Exchange Commission, which is accessible through this hyperlink. Also, insider Cedric Pech sold 1,690 shares of the company’s stock in a transaction on Wednesday, April 2nd. The shares were sold at an average price of $173.26, for a total transaction of $292,809.40. Following the transaction, the insider now owns 57,634 shares of the company’s stock, valued at approximately $9,985,666.84. The trade was a 2.85% decrease in their ownership of the stock. The disclosure for this sale can be found here. In the last three months, insiders sold 49,208 shares of company stock valued at $10,167,739. Company insiders own 3.10% of the company’s stock.

MongoDB Profile

(Free Report)

MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.

Featured Stories

Institutional Ownership by Quarter for MongoDB (NASDAQ:MDB)



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MongoDB Launches an Open Source Real-Time Secret Scanner – It’s FOSS News

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Accidentally exposing secrets like API keys, tokens, or credentials in your code opens the door for threat actors to exploit your systems. Such attackers don’t stop at one breach; they automate their attacks, move fast, and can potentially compromise entire infrastructure within minutes.

To tackle such scenarios, MongoDB has come up with an open source solution called “Kingfisher“.

What’s Happening: Launched as an open source tool for detecting secrets in code, file systems, and Git history, Kingfisher was born out of MongoDB’s need for a fast, reliable way to identify exposed credentials and prevent security risks before they spiral out of control.

The tool doesn’t just stop there; it can also validate any secrets it finds, as long as they are from supported services, so developers know which keys are still active and risky.

MongoDB has been using Kingfisher internally throughout its development and deployment processes, helping them detect and fix exposed secrets early.

What to Expect: As for how it works, Kingfisher scans code, files, and Git history using various techniques like entropy analysis, real-time validation, pattern matching, and source code parsing for or accurate detection of exposed secrets.

It’s written in Rust and has many handy features like multi-language source parsing with Tree-sitter, high-speed regex matching with Hyperscan, extensible rulesets, cross-platform support, and over 700 built-in detection rules that cover a wide range of cloud services and secret types.

All of this runs on the user’s own systems or infrastructure, ensuring no sensitive data is sent to third-party servers, and there’s cross-platform support for Linux, Windows, and macOS. Using Kingfisher also helps security teams stay aligned with SLSA compliance standards.

If you are up for a longer read, then MongoDB has published a detailed blog post explaining how they built Kingfisher.

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Intelligent Cloud Efficiency: How Hari Babu Dama’s AI-Powered Optimization Model … – Outlook India

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As cloud-native digital operations become essential for enterprises, one critical question dominates boardrooms and DevOps pipelines alike: How can we reduce cloud database costs without compromising performance?

In an era of exponential data growth, the financial burden of managing transactional and analytical workloads in the cloud has become unsustainable. Organizations are struggling to manage rising costs from underutilized instances, suboptimal configurations, and lack of Observability into their workload patterns. However, the work of Haribabu, a cloud optimization professional and innovator in data efficiency, is helping to address these challenges.

In his recent study published in the International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), titled “Cloud Cost Optimization for Database Workloads: Real-World Savings Using Utilization Analytics”, Haribabu introduces an intelligent framework that leverages AI-based utilization tracking to reduce database costs by up to 47% in real-world cloud deployments.

“Optimization isn’t just about rightsizing resources,” Haribabu notes. “It’s about aligning computing power with intent, and making cost-efficiency a design principle, not an afterthought.”

From Over provisioning to AI-Led Utilization Intelligence

Haribabu’s framework introduces a data-driven utilization analytics engine that maps real-time database usage patterns across platforms like Amazon RDS, Azure SQL, Google Cloud SQL, and MongoDB Atlas.

Unlike conventional monitoring tools that capture static snapshots, his solution performs continuous workload profiling, capturing IOPS, memory bursts, connection counts, query types, and CPU spikes then correlates these with historical trends and business-critical SLAs.

Key findings from production deployments reveal:

  • 47.2% reduction in monthly cloud database costs

  • 28% improvement in query execution latency due to instance reallocation

  • Zero downtime optimization using phased migration

This approach aim to reduce both over provisioning (paying for unused capacity) and under provisioning (leading to slowdowns and outages). Instead, database instances are dynamically adjusted based on actual workload behavior—ensuring the perfect balance between performance and cost.

Multi-Cloud and Multi-Model Optimization

Today’s enterprises often operate in multi-cloud and multi-database environments—running OLTP workloads on MySQL in AWS, reporting workloads on Azure Synapse, and real-time analytics on NoSQL stores like Cassandra or DynamoDB.

Haribabu’s optimization model supports this diversity with a cloud-agnostic optimization engine. The platform interfaces with all major cloud service providers and supports multiple storage engines—relational, document, graph, time-series, and columnar.

It detects inefficiencies such as:

By recommending low-impact configuration changes like converting standard SSD to magnetic storage for non-critical backups or decommissioning underutilized indexes—The system identified opportunities for significant cost savings while maintaining data integrity.

Predictive Scaling and Cost Forecasting

A key component of Haribabu’s system is its AI-powered predictive analytics engine. Trained on workload patterns and calendar-based trends (quarter-ends, sales spikes, batch ETLs), the engine forecasts capacity needs days or even weeks in advance.

For instance, one use case involved a fintech company that faced high database latency every Monday due to weekly settlement jobs. Haribabu’s solution flagged this spike proactively, scaled up the instance type for just six hours, and then automatically scaled back achieving 100% availability and saving over $120,000 annually.

“Predictive scaling transforms DevOps from a reactive firefighting role into a strategic forecasting engine,” says Haribabu.

This ensures that cost reductions do not come at the expense of security, compliance, or operational risk, a critical requirement for industries like banking, healthcare, and government.

The Road Ahead: Cloud Cost Optimization as a Business Strategy

According to Haribabu that cost optimization is no longer an IT concern it’s a business imperative. As CFOs and CIOs increasingly demand granular visibility into cloud ROI, his solution positions optimization as a core enabler of digital profitability.

He is now working on the next phase autonomous workload optimization agents that will make intelligent decisions on behalf of systems based on evolving business objectives, compliance parameters, and real-time telemetry.

About Hari Babu Dama

Hari Babu Dama is a cloud database architect and optimization expert with over a decade of professional experience spanning Oracle, MySQL, PostgreSQL, MongoDB, and advanced multi-cloud ecosystems. Currently serving as an Application Architect IV at Randstad Digital LLC in Dallas, Texas, he has led high-impact database optimization projects for Fortune 500 clients including Bank of America and Wells Fargo.

His career began in academia, followed by progressive roles in enterprise IT where he specialized in complex database administration, performance tuning, disaster recovery, and cloud migration strategies. Hari Babu’s technical mastery extends across Oracle RAC, Exadata, GoldenGate, Azure, and AWS cloud environments.

He is also adept at automation frameworks using Ansible, Terraform, and Jenkins, integrating FinOps practices into DevOps pipelines for real-time cost and performance optimization. An alumnus of The University of Texas at Dallas with a Master’s in Business Analytics, Hari Babu bridges the gap between infrastructure and intelligence.

Article originally posted on mongodb google news. Visit mongodb google news

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Podcast: Productivity Through Play: Why Messing Around Makes Better Software Engineers

MMS Founder
MMS Holly Cummins

Article originally posted on InfoQ. Visit InfoQ

Transcript

Shane Hastie: Good day folks. This is Shane Hastie for the InfoQ Engineering Culture podcast. Today I have the privilege of sitting down with Holly Cummins. Holly, welcome. Thanks for taking the time to talk to us today.

Holly Cummins: Oh, my pleasure.

Shane Hastie: InfoQ readers, QCon attendees will be familiar with you, but there’s probably four people in the audience who aren’t. Who’s Holly, tell us who you are and what got you where you are today.

Introductions [01:08]

Holly Cummins: So I’m Holly Cummins. I work for Red Hat and before that I was a long time IBMer. I think one of the joys of a career in technology is that I’ve done all sorts of things. So I’m currently part of the Quarkus team. I’m an engineer on the Quarkus team, but before that I worked as a consultant. So I was sort of helping organizations take advantage of the cloud, which was an incredible experience in terms of working with all sorts of different organizations and seeing some organizations that just were so incredibly effective. And then other organizations that maybe needed a little bit of help and needed a nudge in the right direction. And then I’ve been a performance engineer and I’ve worked on WebSphere and yes, all sorts.

Shane Hastie: Most recently at QCon London, you gave a couple of talks that I’d like to dig into. Let’s deal with the first one, productivity is messing around and having fun. Really?

The productivity paradox [02:08]

Holly Cummins: Yes. I mean that was a talk I did with Trisha G, and of course, we chose the title to be a bit provocative. It’s not intuitively obvious that that’s the case, but there is actually a lot of evidence that the way we think about productivity isn’t actually making us more productive. It’s making us less productive. And some of the things that we feel too guilty to do are actually the things that make us more effective. Because what we’re doing in technology, it’s not the same as working on a factory where we have an assembly line and we can just measure ourselves by how many things are coming off the end of the assembly line. We’re problem solving. And that’s fundamentally a creative activity. And one of the challenges we have because it’s a creative activity, because its knowledge work is it’s quite difficult to measure how effective we are.

We can’t just look and say how many widgets are rolling off the assembly line? And if we do it really wrong, we’ll try to and we’ll say, “Well, how many lines of code are running off the assembly line?” And that of course leads to some pretty terrible anti-patterns that I think probably all of us have seen where if you incentivize lines of code, you get lines of code. But lines of code are not an asset. Code is a liability. What you want is the minimum amount of code to solve the problem and the sorts of activities that allow us to solve the problem are not necessarily sitting down and typing as quickly as we can. There are things like thinking about how to solve the problem and where do we think best. We tend to not think best at a desk in front of a computer. We tend to think best doing things that are pretty apparently unproductive.

So a lot of us, for example, will solve problems in the shower. There’s something about being away in that environment where you can’t really do anything. You can’t bring your keyboard into the shower. And so then that just breathes a section of your mind to solve problems.

A lot of people find as well, things like loading the dishwasher or knitting or walking, that’s when you’re actually most productive. And it’s kind of good because I think there’s sort of a double win here because actually all of those things are sort of nice in their own right as well. So we’re in this really fortunate position where the things that make us more productive are actually the things that we kind of would want to do anyway. But the problem happens because they don’t look like productive activities. They look like we’re slacking off, they look like we’re just sort of loafing around. And so then it’s about how do we gather the narrative to give ourselves permission to do these things.

Shane Hastie: Yes, I don’t know whether my boss would be particularly happy with a timesheet entry that said showering, but you’re right, it is in this freer space, freer mental space. So we know this, but our organizations don’t seem to be getting this message. How do we communicate that?

The science behind creative downtime [05:15]

Holly Cummins: With all communication? Really it’s about figuring out the language of the person that you’re speaking to and communicating it to them in their terms. And in general, the business will want to see things in financial terms. So it’s quite useful to be able to actually quantify some of the benefits of this. But it’s also quite useful to be able to present the concrete evidence for it because there is quite a lot of evidence for it. So we can pull, for example, from psychological research because psychological research has actually been able to measure physiologically what’s going on when we have a shower, when we knit, there’s a part of the brain called the default mode network. So there’s a sub-discipline of psychology or what they do is they put people into an MRI machine and then they get them to do some task and they see which areas of the brain become more active.

And what they can see is that if they get people to do nothing at all, there’s one area of the brain or a network in the brain that becomes more active and it’s the doing nothing that enables activity in this area. And it’s called the default mode network. And so what’s the default in the mode network responsible for? It’s responsible for things like creativity, it’s responsible for things like problem solving.

Interestingly as well, it’s also responsible for something called mental time travel, which sounds amazing because it makes it sound like we’re going back and we’re being in medieval England, it’s nothing so exciting. It’s just when you replay what happened yesterday, that’s the default mode network. So we can sort of see at a physiological level what’s going on. We can also model it mathematically, which is kind of interesting. So again, if we need to communicate to someone who doesn’t want the sort of fluffy wuffy, “I feel nice when I do nothing”, who wants the, “Okay, what’s going on?”

Because if we look at queuing theory, that’s modeling how systems do work. And of course our organization is a system. We are a system. And what queuing theory tells us is that if you have a slightly random arrival rate of work, you need to have some period when the people doing the work are actually doing nothing at all.

Otherwise, what ends up happening is because there’s an asymmetry and if work is arriving faster than you can do it will just keep building up and up and up. And then the wait times in the system become well, infinite. You need to have some points in the system where the system appears to be doing nothing in order to keep the latency okay. And it’s exactly the same for us. So we need to have some periods in our day when we’re not doing anything or else any incoming task has potentially infinite latency, which isn’t what our management want.

Shane Hastie: But this is almost antithetical to what we’re seeing and hearing in the industry today. The push for more, do more with less, all of that.

“Do more with less” is counterproductive [08:22]

Holly Cummins: Yes, absolutely. And I think that’s why it’s really important to talk about this because the push to do more and more, more, it’s just wrong and it’s just counterproductive. I saw a social media post recently from someone and they were in the VC space and what they were doing was they had a group for their founders and I think it was WHOOP, maybe it wasn’t a tool that I’ve used, but they were tracking how much sleep their founders were getting. And they wanted their founders to be getting five hours of sleep a night because that shows that they really dedicated.

But again, this is something that there’s enormous amounts of evidence that if you go to work tired, that is equivalent to going to work drunk, you’re not achieving more, you are achieving less, you’re making more errors. And so if you said to people, “Would you like your employees to go to work drunk?” Everybody would be like, “No, that doesn’t seem like a good idea. I think I’d like to be a high quality organization”. But when you said, do you want your employees to go to work exhausted, people are like, “Oh yes, that shows were really..”. And it shows that productivity will be lower and the quality of life will be lower. So why would you want to have that double lose when you could be having the double win instead?

Shane Hastie: Which brings us to what is efficiency.

The efficiency trap [09:41]

Holly Cummins: Yes, and I think mean efficiency is interesting because we have sort of an intuitive definition of efficiency. When we talk about efficiency, what we tend to think of is, “Oh yes, efficiency means doing more”. But actually in a technical sense, efficiency is doing less. And if you want to be specific, efficiency is doing less in achieving the same results, which is obviously an important qualifier. But we tend to think of efficiency as ramming as much in as possible. And that’s not actually going to give us a good outcome. And again, we can see this for people and it’s sort of hard to accept for people because it seems a bit kind of fluffy.

But we see the exact same trade-offs and decisions being made in other fields where it’s more mechanical so it’s easier to accept. So for example, one lesson is that efficiency is always limited. So with an internal combustion engine, there is a top limit on its efficiency of about 36%. No matter how well engineered it is, it is never going to be more than that level of efficiency, which is irritating because you ideally would want more efficiency. Ideally we’d have like 120 efficiency. But then the other thing that’s really interesting is so we have this cap of 30%, but if you look at the efficiency of typical cars on the road, they’re not operating at that 36% level. They’re operating around more like 20%.

And is that because the engineers who designed those cars were sort of lazy and couldn’t be bothered? No, it was a deliberate decision to detune the engines because if the engines operate at that higher efficiency, they wear out faster. So you have this sort of short-term efficiency of how much fuel is going in and how much speed do I get out? And then you have this longer term efficiency of, “Does my engine actually last or do I have to replace my engine more often?” And the analogy with people is pretty obvious as well. If people work too much, it may seem like this short-term gain, but then you get burnout and that doesn’t help anybody.

Shane Hastie: There are times though when you do want the racing car and you’re prepared to throw the engine away.

When to push hard and the importance of recovery periods [11:50]

Holly Cummins: Yes, I think that’s right. And similarly, there’s times in the workplace where for all the sort of nice hand waving of, “Wouldn’t it be nice if we only worked at a sustainable pace? It only works seven and a half hours a day”. There’s times when you have a deadline, there’s times when you are racing against the competition when you need to get to market. So I think keeping that flexibility is important, but just making the conscious trade-off and saying this is going to be a period of high intensity, and then after that period of high intensity, I’m going to have a period of recovery. And again, actually that period of recovery can be very fruitful. So when I used to work in WebSphere, this was 15 years ago, the release cycles were quite different.

So you do a release every two years and it’s such a special occasion and if you miss that release train, you are not getting another chance for two years. So everybody is desperately trying to get into that release. And then of course then you sort of end up with this cycle where, I mean we’ve learned not to do that, right? Because everything gets shoved into the release. The quality is about the quality that you would expect if everything gets shoved in, so then you have to wait for the .1 or the .2 before you actually adopt it. And you think, “Um”. But leaving all that aside, what would happen is we would race to do this release working evenings and getting pizzas brought in and that kind of thing.

And then we would meet the deadline and then it would be like, “Okay”. And then what our management would say is, “Okay, we’re going to have a sprint where we are not going to be project managing this. You just do what you want to do”. Some of our best features came from those time off sprints because people could deal with the technical debt that had been bothering them or there was something that they just knew that would be really good, but they hadn’t managed to persuade project management to put it into the project plan. So they would just implement the feature and then it would go out and people would be like, “Ah, this is amazing”. So even that sort of apparent gift of time to work on what people wanted to work on ended up having quite big productivity benefits.

Shane Hastie: Shifting tac a tiny bit, before we started, I made the slightly tongue in cheek comments about we take the best engineer and we promote them and leave them to be the worst manager. How do we avoid that in our organizations?

Management vs. technical career tracks [14:13]

Holly Cummins: I think often that happens because in an organization, if the management track has higher advancement opportunities than the engineering track, people will be pragmatic and they will move across to that track. And so I think what we need to do is just make sure that we provide an opportunity for engineers who are really good engineers and really bad managers to stay in the engineering track, but be rewarded proportionally to the value that they’re bringing to the organization, which can be really quite significant.

Shane Hastie: And what about those that we want to go into that management track? What’s the advice for the engineer who is interested in going into management?

Holly Cummins: I mean, I have to answer this with caution because I have never been a manager and I have always been lucky enough to be in organization where there was a non-management track that went to quite senior levels. And so I’ve quite happily stayed on that track and never ventured across because I had a suspicion I wouldn’t be very good at it. But I think part of it then again, it’s about the opportunities for professional development, isn’t it? And so we see you have that with all of these things, you need to have that time to invest and then you get a higher reward.

And so that goes back to the efficiency conversation of maybe actually the way to get the highest value isn’t just in the short-term ring every bit of time out of people. We see it in the code base that maybe the way to get the biggest numbers of features isn’t to race through every feature and never put any time into technical debt. And it’s the same for people. Maybe the way to get the best person isn’t to sort of never give them any time for professional development, never give them any time for going off reading, learning. Because if we do that, we win in the short-term, but lose in the long term.

Shane Hastie: And advice for the person who wants to follow your path and stay in that technical… what should they, the more entry-level person who’s listening to this and looks at you and said, “I want to be like that”. What should they be focusing?

Staying technical: the curiosity factor [16:39]

Holly Cummins: I mean, I think so much of it is about the curiosity and keeping that curiosity always alive. I saw a quote recently and it said that creativity is when you do things from a place of curiosity rather than a place of fear. And I think this is such a creative industry, even if we don’t call it creative and it changes every single week. There’s new technologies, there’s new skills. And so we have to have that mindset where we’re embracing that newness and interested in what’s coming along. And also I think always looking to make things better as well.

I think sometimes, especially if we get a bit burned out or we get a bit ground down, you kind of look around and everything’s a bit terrible and there’s all this friction, you kind of go, “I can’t be bothered to change it”. And you have to have that interest in saying, “What’s not helping me? What’s not helping my colleagues? How can I fix it? Let’s make this happen”. And so sort of combining the curiosity and then let’s always be trying to make things better. Let’s be looking for the things that are bad. Let’s be continuously improving.

Shane Hastie: What are the trends that you’re seeing in our industry today that we should be thinking about and jumping onto?

The shift from author to editor role with AI tools and managing industry “fashion” [17:58]

Holly Cummins: I have mixed feelings. Having just said we should be curious and we should be embracing the new. I sometimes have mixed feelings about trends. A colleague of mine said yesterday that ours is a fashion industry and that’s completely true. So we do see some of these things come along where you have to pay attention to it in order to be in with the cool kids, but is it actually adding value? I’m not sure. But I think certainly one of the trends that we’re seeing at the moment is this move for all of us as engineers to be working higher up the stack and to be at the level where we’re managing code and managing code generation rather than writing it ourselves. And I think that’s an interesting transition that we need to figure out, “Well, what does my role look like now if I used to be a coder and now I’m a coder of coders, how does that feel? How do I manage that?”

Shane Hastie: I’ve described it and heard it described as the shift from author to editor.

Holly Cummins: Yes, I like that. And Annie Vella wrote this amazing blog where she was talking about exactly that. And in terms of our identity as software engineers, we came into it to do one thing and now we’re doing something else. And are we okay with that? Did I sign up to be an editor? Yes, it’s a period of change, I think for sure.

Shane Hastie: What are the underlying truths that aren’t changing?

Timeless truths in software engineering [19:37]

Holly Cummins: Oh, I like this. I think so much of it is not changing and we can get caught up in looking at these sizzles of change on the top and then miss all of the stuff that hasn’t changed. In our talk, Trisha and I referred a lot to Fred Brooks because if you read The Mythical Man-Month, so much stuff in there is exactly the same. And even stuff, maybe this has changed a little bit just in the last year, now that we’ve with that shift from author to editor, but even stuff like Fred Brooks wrote that you could expect to write 10 lines of code a day. And you look at that and you think, “That’s ridiculous. I can write 10 lines of code in five minutes. How?”

And then you sort of step back and you look at your output over the whole year, and then you look at how much of it is tests, infrastructure stuff, and then you look at how much of your time is going into meetings and conversations and design discussions. And then you divide that by 365 and you’re like, “Oh, actually that’s about 10 lines of code today”. So that side of it that there’s always overheads, there’s always, always overheads. How do we manage those overheads? Which overheads are the good overheads that we need? Because otherwise we just produce chaos. And which overheads are the overheads that actually we should be pushing back on and saying, “Why am I having to do this reporting? Why am I having to go to these meetings?”

And for both people and machines, that coordination just remains a huge challenge. So at the machine level, at the code level, it’s about the concurrency and how do we get the minimum amount of interlock that allows our code to function while still giving us good throughput and the number of cores is going up. That is a trend that we’re definitely seeing. So concurrency becomes more important. And then at the human level, it’s the exact same. How many meetings do I need to go to in order to ensure that my colleagues and I are pulling in the same direction without losing all of my time to meetings? So you can apply Amdahl’s law to code or you can apply Amdahl’s law to teams.

Shane Hastie: One of the things that we often hear is that the cognitive load on the engineer today is more than it used to be, and it’s getting more constantly. How do we manage that?

Managing cognitive load [21:58]

Holly Cummins: Yes, that one’s really hard. And I have such mixed feelings about it because when you look at a lot of the things that we’re being asked to do now, like shift left. On the one hand, shift left is an absolutely brilliant way of reducing these feedback cycles, improving the efficiency, getting away from that model where we sort of do the bad thing and then we throw it over the wall to the other team who says, you did the bad thing. And then we go back and we redesign it. But on the other hand, shift left for us is bringing it exactly as you say, all this cognitive load.

And there’s all these new skills that we need to know because now I need to be a deployment engineer because I need to understand where stuff is going. And if I want to have mechanical sympathy and write highly tuned code, then I need to have some hardware understanding as well. And I need to be a UX engineer and I need to have the front end skills and I need to do the security. Shift left should definitely apply to security. And then there’s so much. So I think really the only way that you can get rid of cognitive load is to, as more stuff comes in, you have to be getting rid of stuff.

And we’re seeing this a little bit, I think, because it used to be that as a software engineer you were working in such a low level, you were working in assembly, and now we can work in something that’s much more, even a modern high level language is so much more human-readable. So that piece of cognitive load has gone away. And a lot of our tools, we are sort of going up the stack. So in Java for example, we don’t need to be working with a low level concurrency constructs anymore. We can use structured concurrency or something like that. And so hopefully the ideal is for every piece of cognitive load that goes away, another comes in, but that they stay a bit in balance whether we’ve achieved it or not, I don’t know.

Shane Hastie: What are your hopes and dreams for our profession?

The joy of software engineering [23:56]

Holly Cummins: I think ours is such a fantastic profession. I feel so lucky to be a software engineer. So one thing that I do hope is that our industry carries on. There’s been a bit of concern now of will AI make us all disappear? And I don’t think that’s going to happen.

I think Jevons paradox means that our appetite for software is just going to continue growing. But I hope that at the moment, certainly in the right organization, this is such a joyful profession to be in. And we have such a nice combination of the problem solving and the creativity and the creation as well, which is sort of the same as creativity, but sort of not. And so I do hope that that will continue. And one thing I have really loved is the democratization of our profession. So we are seeing more and more people from different walks of life joining it. I really hope that continues because I think that’s so healthy for our industry.

Shane Hastie: Thanks very much. I’ve certainly enjoyed the conversation. If people want to continue the conversation, where can they find you?

Holly Cummins: So I’ve got a website at hollycummins.com, and there I sort of share talks. I blog, but not as often as I would like to, of course, I’ve got the backlog. But yes, I think that’s probably the best place. Or you can find me on Bluesky as well.

Shane Hastie: Wonderful. Thanks so much for taking the time to talk to us today.

Holly Cummins: Oh, thanks very much.

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Multiverse names MongoDB’s Head of EMEA Donn D’Arcy as its new Chief Revenue Officer

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Posted on mongodb google news. Visit mongodb google news

Training platform Multiverse has named Donn D’Arcy as is new Chief Revenue Officer, as the company reports accelerating growth.

D’Arcy joins Multiverse from database provider MongoDB, where he served as Head of EMEA, helping to scale the business to $700 million in Annual Recurring Revenue.

Multiverse says D’Arcy will help scale its goal to build the artificial intelligence (AI) adoption layer for enterprises and solve the workforce skills gap in AI. 

“Enterprise AI adoption won’t happen without fixing the skills gap. Multiverse is the critical partner for any company serious about making AI a reality, and its focus on developing people as the most crucial component of the tech stack is what really drew me to the organisation,” explains Donn D’Arcy, Chief Revenue Officer at Multiverse. “The talent density, and the pathway to hyper growth, means the next chapter here is tremendously exciting.”

Prior to joining MongoDB, D’Arcy spent more than 12 years at American company BMC Software, where he helped deliver UK revenue of $500 million, making it the company’s top-performing country worldwide. 

The appointment comes shortly after Jillian Gillespie joined as Chief Financial Officer, also from MongoDB and as Multiverse continues to drive business momentum. The company says its revenue “more than doubled” in the last two years and reports that more than 22,000 learners are now using its technology.

Euan Blair, Founder and CEO of Multiverse, comments: “Truly seizing the AI opportunity requires companies to build a bridge between tech and talent – both within Multiverse and for our customers. Bringing on a world-class leader like Donn, with his incredible track record at MongoDB, is a critical step in our goal to equip every business with the workforce of tomorrow.”

The news comes shortly after Multiverse announced plans to create 15,000 new AI apprenticeships over the next two years, aiming to address the UK’s skills gap.

Article originally posted on mongodb google news. Visit mongodb google news

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Q1 Earnings Outperformers: DigitalOcean (NYSE:DOCN) And The Rest Of The Data Storage Stocks

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Posted on mongodb google news. Visit mongodb google news

DOCN Cover Image

The end of an earnings season can be a great time to discover new stocks and assess how companies are handling the current business environment. Let’s take a look at how DigitalOcean (NYSE: DOCN) and the rest of the data storage stocks fared in Q1.

Data is the lifeblood of the internet and software in general, and the amount of data created is accelerating. As a result, the importance of storing the data in scalable and efficient formats continues to rise, especially as its diversity and associated use cases expand from analyzing simple, structured datasets to high-scale processing of unstructured data such as images, audio, and video.

The 5 data storage stocks we track reported a strong Q1. As a group, revenues beat analysts’ consensus estimates by 3% while next quarter’s revenue guidance was in line.

In light of this news, share prices of the companies have held steady as they are up 4% on average since the latest earnings results.

Weakest Q1: DigitalOcean (NYSE: DOCN)

Started by brothers Ben and Moisey Uretsky, DigitalOcean (NYSE: DOCN) provides a simple, low-cost platform that allows developers and small and medium-sized businesses to host applications and data in the cloud.

DigitalOcean reported revenues of $210.7 million, up 14.1% year on year. This print exceeded analysts’ expectations by 1%. Despite the top-line beat, it was still a mixed quarter for the company with a solid beat of analysts’ EBITDA estimates but EPS guidance for next quarter missing analysts’ expectations significantly.

“The momentum we generated in 2024 in both core cloud and AI continued into Q1, as we grew total revenue 14% year-over-year, our highest quarterly growth rate since Q3 2023, with AI ARR continuing to grow north of 160% year-over-year, and we delivered more than 50 new product features, over 5 times as many as we delivered in Q1 of last year.” said Paddy Srinivasan, CEO of DigitalOcean.

DigitalOcean Total Revenue

DigitalOcean delivered the weakest performance against analyst estimates and weakest full-year guidance update of the whole group. Unsurprisingly, the stock is down 15% since reporting and currently trades at $27.83.

Read our full report on DigitalOcean here, it’s free.

Best Q1: Commvault Systems (NASDAQ: CVLT)

Originally formed in 1988 as part of Bell Labs, Commvault (NASDAQ: CVLT) provides enterprise software used for data backup and recovery, cloud and infrastructure management, retention, and compliance.

Commvault Systems reported revenues of $275 million, up 23.2% year on year, outperforming analysts’ expectations by 4.8%. The business had a very strong quarter with a solid beat of analysts’ billings estimates and an impressive beat of analysts’ EBITDA estimates.

Commvault Systems Total Revenue

Commvault Systems delivered the biggest analyst estimates beat and highest full-year guidance raise among its peers. The market seems happy with the results as the stock is up 8.3% since reporting. It currently trades at $179.45.

Is now the time to buy Commvault Systems? Access our full analysis of the earnings results here, it’s free.

Formed in 2011 with the merger of Membase and CouchOne, Couchbase (NASDAQ: BASE) is a database-as-a-service platform that allows enterprises to store large volumes of semi-structured data.

Couchbase reported revenues of $56.52 million, up 10.1% year on year, exceeding analysts’ expectations by 1.7%. It was a satisfactory quarter as it also posted an impressive beat of analysts’ EBITDA estimates but a significant miss of analysts’ billings estimates.

Couchbase delivered the slowest revenue growth in the group. Interestingly, the stock is up 6.6% since the results and currently trades at $19.79.

Read our full analysis of Couchbase’s results here.

Founded in 2013 by three French engineers who spent decades working for Oracle, Snowflake (NYSE: SNOW) provides a data warehouse-as-a-service in the cloud that allows companies to store large amounts of data and analyze it in real time.

Snowflake reported revenues of $1.04 billion, up 25.7% year on year. This print beat analysts’ expectations by 3.4%. Aside from that, it was a satisfactory quarter as it also recorded an impressive beat of analysts’ EBITDA estimates but a miss of analysts’ billings estimates.

Snowflake delivered the fastest revenue growth among its peers. The company added 26 enterprise customers paying more than $1 million annually to reach a total of 606. The stock is up 18.1% since reporting and currently trades at $211.65.

Read our full, actionable report on Snowflake here, it’s free.

Started in 2007 by the team behind Google’s ad platform, DoubleClick, MongoDB offers database-as-a-service that helps companies store large volumes of semi-structured data.

MongoDB reported revenues of $549 million, up 21.9% year on year. This number surpassed analysts’ expectations by 4.1%. It was a very strong quarter as it also logged EPS guidance for next quarter exceeding analysts’ expectations and a solid beat of analysts’ EBITDA estimates.

The company added 110 enterprise customers paying more than $100,000 annually to reach a total of 2,506. The stock is up 2.1% since reporting and currently trades at $204.

Read our full, actionable report on MongoDB here, it’s free.

Market Update

Thanks to the Fed’s rate hikes in 2022 and 2023, inflation has been on a steady path downward, easing back toward that 2% sweet spot. Fortunately (miraculously to some), all this tightening didn’t send the economy tumbling into a recession, so here we are, cautiously celebrating a soft landing. The cherry on top? Recent rate cuts (half a point in September 2024, a quarter in November) have propped up markets, especially after Trump’s November win lit a fire under major indices and sent them to all-time highs. However, there’s still plenty to ponder — tariffs, corporate tax cuts, and what 2025 might hold for the economy.

Want to invest in winners with rock-solid fundamentals?
Check out our Top 5 Growth Stocks and add them to your watchlist. These companies are poised for growth regardless of the political or macroeconomic climate.

Article originally posted on mongodb google news. Visit mongodb google news

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