Shares of <strong>NetApp</strong> Inc. shed 1.01% to $84.60 Wednesday, on what proved to be an all-around favorable trading session for the stock market, with the S&amp;P 500...

NetApp Inc. stock outperforms competitors despite losses on the day - MarketWatch

<strong>NetApp</strong> a company that provides on-premises data storage systems and public cloud services, is poised to report its Q3 FY’24 results toward the end of February.

Up 27% Over The Past Year, Will Higher Margins And Cloud Sales Drive NetApp Stock Higher Post Q3 Earnings?

Get the most recent info and news about NetApp on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

NetApp, Inc. provides software, systems, and cloud services to manage and share data on-premises, and private and public clouds worldwide. The company offers cloud storage services, including NetApp Cloud Volumes; cloud control solutions, such as NetApp Cloud Manager and NetApp Virtual Desktop Service; cloud services and analytics comprising NetApp Cloud Insights, NetApp Cloud Sync, NetApp Cloud Compliance, NetApp Cloud Tiering, NetApp SaaS Backup, NetApp Cloud Backup, and NetApp Global File Cache; and Cloud Optimization solutions, such as Spot by NetApp, Spot Cloud Analyzer by NetApp, Spot Eco by NetApp, Spot Ocean by NetApp, Spot Wave by NetApp, Spot Elastigroup by NetApp, and NetApp Virtual Desktop Managed Service. It provides data storage solutions comprising NetApp All-Flash FAS series, NetApp Fabric Attached Storage, NetApp FlexPod, NetApp E/EF series, NetApp StorageGRID, NetApp SolidFire, and NetApp HCI; data protection solutions, such as NetApp SnapCenter Backup Management, NetApp SnapMirror Data Replication, NetApp SnapLock Data Compliance, and NetApp Data Availability Services; and data management solutions, including NetApp ONTAP Storage Management System, NetApp ElementOS software, NetApp SANtricity software, NetApp Active IQ Digital Advisor, OnCommand Insight, and OnCommand Workflow Automation. In addition, the company offers application-aware data management service under the NetApp Astra name; and payment solutions and storage-as-a-service under the NetApp Keystone name. Further, it provides assessment, design, consulting, and implementation services. The company serves the energy, financial service, government, technology, internet, life science, healthcare service, manufacturing, media, entertainment, animation, video postproduction, and telecommunication markets through a direct sales force and an ecosystem of partners. NetApp, Inc. was incorporated in 1992 and is headquartered in San Jose, California.

NetApp

Apache Cassandra—the immensely popular open-source, distributed NoSQL database management system—is approaching the general availability of its 5.0 version (the 5.0 beta is already available). Whether you’re an existing Cassandra user or considering a migration to the open-source database with the release of this new version, there’s a lot to be excited about with 5.0. I spoke with Mo Ansari, a Product Manager at Instaclustr by NetApp, about what developers, engineers, DBAs, and other users should know about Cassandra 5.0—from database upgrades to new use cases to open-source community support. Mo knows Cassandra particularly well (Instaclustr has long had a managed platform around the fully open-source version of the database, which Mo works on). Here’s what Mo had to say about Cassandra 5.0: Cassandra 5.0 promises improved database performance and scalability—what are some specific optimizations or architectural changes that have been made to achieve this? Cassandra 5.0 adds in the advantages of trie (prefix tree)-based Memtables and SSTables—meaning the new version of the open source NoSQL database offers significant potential for improved database performance and memory optimization. Cassandra’s performance for reads and modification operations (and its ability to correctly size structures to data) benefit from these storage formats, which make use of tries and byte-comparable representations of database keys. Memory management overhead and garbage collection also face fewer burdens with trie Memtables and trie-indexed SSTables, delivering even more benefits for Cassandra users (especially those using the database at scale). Aside from performance, are there other changes with Cassandra 5.0 that developers should be excited about? Developers will want to get their hands on Cassandra 5.0’s vector support, including Vector Search for locating content in large datasets. They can also utilize new CQL vector functions and a new vector data type designed for saving and retrieving embeddings vectors. Because of these improvements, Cassandra 5.0 is well-positioned as a data layer technology for supporting AI/ML application development. ML models function by comparing similarities among data and putting data connections in context. Embeddings vectors power similarity comparisons by offering arrays of floating-point numbers representing how similar particular objects are to each other. With Cassandra 5.0, developers now have a powerful (and open source) database with that specific functionality that’s so important to AI/ML applications. Developers should also be hyped about storage-attached indexing, which makes secondary indexes far more usable and efficient. On a Cassandra database table, developers can now easily create one or more secondary indexes, each based on a single column they choose. The result is massively scalable and globally distributed indexing with search throughput that can’t be beat (useful with Vector Search), modular extensibility (also demonstrated by Vector Search), and stunning indexing functionality that captures semantics via queries and content (including large documents and images). Last but not least, developers using Cassandra 5.0 will like the ability to build their own user-defined functions, and to utilize the range of useful new native CQL aggregation and math functions available with the new version. How has the developer experience changed—if it has—with Cassandra 5.0? Cassandra is bringing significant enhancements to the developer experience. The new release features open the door for a vast number of use cases and balance the developer experience. The new version focuses on ease of use, performance, and security and includes features such as storage-attached indexes that make queries on non-primary key columns more efficient. This reduces the complexity and overhead associated with secondary indexes. Additionally, the new version is anticipated to support ACID transactions with version 5.1, which will bring SQL-like functionality to Cassandra—making it more approachable for developers familiar with relational databases. Furthermore, the new version has more guardrails and enhanced tooling, including a new virtual table to view system lows, which will aid in the development process. Overall, Cassandra 5.0 is packed with features that will empower developers to work faster, create more efficient queries, and manage data with increased security and control! Do you anticipate the use cases for Cassandra to change or evolve based on what’s included in 5.0? Yes, with a new vector datatype and storage-attached indexes improving the performance of the usage of vectors, Apache Cassandra is positioning itself as a contender in the AI market. Over the years, Cassandra’s exceptional write throughput and ability to handle large volumes of data made it ideal for several use cases and applications, such as scalable web applications, messaging systems, and event logging and monitoring systems. However, Cassandra 5.0 is going to significantly alter the landscape. Apart from all the use cases that Cassandra supported and suited in the past, version 5.0 will extend its applicability to more complex, transactional, and analytical applications, bridging the gap between NoSQL flexibility and the rigorous demands of modern data-intensive applications. I see several new use cases, such as analytics and machine learning, niche financial services requiring ACID transactions, applications requiring complex querying capabilities, etc. As the Apache Cassandra project calls it, “moving towards an AI-driven future.” With increasing adoption of cloud and containerized environments, how has Cassandra 5.0 been designed to support these deployment models? Apache Cassandra has always been designed with distributed and fault-tolerant computing and scaling in mind. It has always been a good choice for cloud-based workloads. While not introducing specific features under the “cloud-native” label, Cassandra 5.0 continues to support and enhance deployments in cloud and containerized environments through its scalable, resilient, and community-driven projects. Features included in the 4.1 version release have already paved the way for a more cloud-native future for Cassandra. The K8ssandra project is another great example of a commitment to a cloud-native future. With any new release, there’s always a learning curve. How do you see the adoption process going for Cassandra 5.0, and what advice would you give to developers who are looking to migrate? The transition to Cassandra 5.0 does bring a learning curve, but it’s designed to be as smooth as possible. I anticipate gradual adoption as developers familiarize themselves with new features and improvements. I expect the adoption will be pretty quick in dev and testing environments; however, it will be gradual in production environments. Given the focus on cost reduction and AI readiness, there are a lot more reasons, use cases, motivations, and other drivers for adoption. Based on its several utilities, I'm guessing that Apache Cassandra will become the preferred choice for AI workloads. For those looking to migrate, my advice is to leverage the extensive resources available on the Apache website, including its documentation and community forums. Planet Cassandra is a fantastic place to look at the use cases. I would also advise the developers to engage in the community for shared insights and best practices through mailing lists, project town halls, and other events such as contributor meetings. Cassandra has a large and active open-source community. How has the community been involved in the development of 5.0? Apache Cassandra has a large and vibrant open-source community, playing a crucial role in the development of 5.0. Members have been actively involved in proposing new features and improving existing ones based on real-world use cases and future needs. Feedback was collected through various channels, such as mailing lists, Jira tickets, and Cassandra Enhancement Proposals (CEPs), and it has been instrumental in shaping the release to meet the evolving needs of users. The community has been actively contributing to many more features that are currently in the draft stage and will bring many more benefits to Cassandra in the future. Apache Cassandra—the immensely popular open-source, distributed NoSQL database management system—is approaching the general availability of its 5.0 version (the 5.0 beta is already available). 5.0 beta Whether you’re an existing Cassandra user or considering a migration to the open-source database with the release of this new version, there’s a lot to be excited about with 5.0. I spoke with Mo Ansari, a Product Manager at Instaclustr by NetApp , about what developers, engineers, DBAs, and other users should know about Cassandra 5.0—from database upgrades to new use cases to open-source community support. Instaclustr by NetApp Mo knows Cassandra particularly well (Instaclustr has long had a managed platform around the fully open-source version of the database, which Mo works on). Here’s what Mo had to say about Cassandra 5.0: Cassandra 5.0 promises improved database performance and scalability—what are some specific optimizations or architectural changes that have been made to achieve this? Cassandra 5.0 adds in the advantages of trie (prefix tree)-based Memtables and SSTables—meaning the new version of the open source NoSQL database offers significant potential for improved database performance and memory optimization. Cassandra’s performance for reads and modification operations (and its ability to correctly size structures to data) benefit from these storage formats, which make use of tries and byte-comparable representations of database keys. Memory management overhead and garbage collection also face fewer burdens with trie Memtables and trie-indexed SSTables, delivering even more benefits for Cassandra users (especially those using the database at scale). Aside from performance, are there other changes with Cassandra 5.0 that developers should be excited about? Developers will want to get their hands on Cassandra 5.0’s vector support, including Vector Search for locating content in large datasets. They can also utilize new CQL vector functions and a new vector data type designed for saving and retrieving embeddings vectors. Because of these improvements, Cassandra 5.0 is well-positioned as a data layer technology for supporting AI/ML application development. ML models function by comparing similarities among data and putting data connections in context. Embeddings vectors power similarity comparisons by offering arrays of floating-point numbers representing how similar particular objects are to each other. With Cassandra 5.0, developers now have a powerful (and open source) database with that specific functionality that’s so important to AI/ML applications. Developers should also be hyped about storage-attached indexing, which makes secondary indexes far more usable and efficient. On a Cassandra database table, developers can now easily create one or more secondary indexes, each based on a single column they choose. The result is massively scalable and globally distributed indexing with search throughput that can’t be beat (useful with Vector Search), modular extensibility (also demonstrated by Vector Search), and stunning indexing functionality that captures semantics via queries and content (including large documents and images). Last but not least, developers using Cassandra 5.0 will like the ability to build their own user-defined functions, and to utilize the range of useful new native CQL aggregation and math functions available with the new version. How has the developer experience changed—if it has—with Cassandra 5.0? Cassandra is bringing significant enhancements to the developer experience. The new release features open the door for a vast number of use cases and balance the developer experience. The new version focuses on ease of use, performance, and security and includes features such as storage-attached indexes that make queries on non-primary key columns more efficient. This reduces the complexity and overhead associated with secondary indexes. Additionally, the new version is anticipated to support ACID transactions with version 5.1, which will bring SQL-like functionality to Cassandra—making it more approachable for developers familiar with relational databases. Furthermore, the new version has more guardrails and enhanced tooling, including a new virtual table to view system lows, which will aid in the development process. Overall, Cassandra 5.0 is packed with features that will empower developers to work faster, create more efficient queries, and manage data with increased security and control! Do you anticipate the use cases for Cassandra to change or evolve based on what’s included in 5.0? Yes, with a new vector datatype and storage-attached indexes improving the performance of the usage of vectors, Apache Cassandra is positioning itself as a contender in the AI market. Over the years, Cassandra’s exceptional write throughput and ability to handle large volumes of data made it ideal for several use cases and applications, such as scalable web applications, messaging systems, and event logging and monitoring systems. However, Cassandra 5.0 is going to significantly alter the landscape. Apart from all the use cases that Cassandra supported and suited in the past, version 5.0 will extend its applicability to more complex, transactional, and analytical applications, bridging the gap between NoSQL flexibility and the rigorous demands of modern data-intensive applications. I see several new use cases, such as analytics and machine learning, niche financial services requiring ACID transactions, applications requiring complex querying capabilities, etc. As the Apache Cassandra project calls it, “ moving towards an AI-driven future .” moving towards an AI-driven future With increasing adoption of cloud and containerized environments, how has Cassandra 5.0 been designed to support these deployment models? Apache Cassandra has always been designed with distributed and fault-tolerant computing and scaling in mind. It has always been a good choice for cloud-based workloads. While not introducing specific features under the “cloud-native” label, Cassandra 5.0 continues to support and enhance deployments in cloud and containerized environments through its scalable, resilient, and community-driven projects. Features included in the 4.1 version release have already paved the way for a more cloud-native future for Cassandra. The K8ssandra project is another great example of a commitment to a cloud-native future. 4.1 version release K8ssandra project With any new release, there’s always a learning curve. How do you see the adoption process going for Cassandra 5.0, and what advice would you give to developers who are looking to migrate? The transition to Cassandra 5.0 does bring a learning curve, but it’s designed to be as smooth as possible. I anticipate gradual adoption as developers familiarize themselves with new features and improvements. I expect the adoption will be pretty quick in dev and testing environments; however, it will be gradual in production environments. Given the focus on cost reduction and AI readiness, there are a lot more reasons, use cases, motivations, and other drivers for adoption. Based on its several utilities, I'm guessing that Apache Cassandra will become the preferred choice for AI workloads. For those looking to migrate, my advice is to leverage the extensive resources available on the Apache website , including its documentation and community forums. Planet Cassandra is a fantastic place to look at the use cases. I would also advise the developers to engage in the community for shared insights and best practices through mailing lists, project town halls, and other events such as contributor meetings. Apache website Planet Cassandra Cassandra has a large and active open-source community. How has the community been involved in the development of 5.0? Apache Cassandra has a large and vibrant open-source community , playing a crucial role in the development of 5.0. Members have been actively involved in proposing new features and improving existing ones based on real-world use cases and future needs. Feedback was collected through various channels, such as mailing lists, Jira tickets, and Cassandra Enhancement Proposals (CEPs), and it has been instrumental in shaping the release to meet the evolving needs of users. open-source community The community has been actively contributing to m any more features that are currently in the draft stage and will bring many more benefits to Cassandra in the future. any more features

Here's What to Know About Apache Cassandra 5.0

With the explosive growth of ChatGPT, discussions around how to integrate AI have made their way into corporate boardrooms and product roadmaps at a rapid pace. In fact, AI already plays a vital role in our daily lives. Billions of people spend hours every day glued to apps such as Instagram, TikTok, Facebook, and Twitter, all of which use AI ranking algorithms to personalize your feed. These algorithms analyze thousands of data points per second to predict what a user would like to see most. And the key to getting personalization right is user data. Except, when dealing with user data, it is critical to uphold user privacy laws and protect users from breaches. The mainstream public is still shaken by how used people’s personal data to maneuver politics. Cambridge Analytica According to a report by the Identity Theft Resource Center (ITRC), the US experienced a record number of 1,862 data breaches in 2021. Prominent companies such as Microsoft, LinkedIn, Facebook, Plex, and Cash App experienced significant in just the last two years. data breaches Fortunately, product experts like Sunil Tej Gorantla, who are passionate about building products, are focusing on keeping user data sacrosanct. Having graduated from VIT University, one of the top engineering institutions in India, with a bachelor’s degree in Information Technology, Sunil has a technical background. With more than a decade of experience in product management, Sunil has helped various startups build large-scale web and mobile applications that generate millions in revenue. He is a founder, product expert, growth hacker, and writer who believes in solving what people need, making data-driven decisions, and building products with a strong mission at their core to serve others. During his extensive career, Sunil was fortunate to be mentored by some of the industry's eminent personalities, such as Harishankaran Karunanidhi, Hunter Walk, and Justin Kan. Sunil spent the early years of his career working with leading high-growth early-stage startups. He joined HackerRank as the first Product Evangelist immediately after the company became . To acquire HackerRank's first few thousand users, he spearheaded the campus ambassador program. India’s first YCombinator startup He recruited hundreds of student ambassadors who formed Hacker Clubs at their universities to encourage students to solve programming challenges and participate in weekly programming contests online. He also collaborated with companies such as Airbnb, Adobe, Quora, and NetApp, which hired top programming talent from these contests by being sponsors. However, at the time, retaining users after the contests was a challenge for HackerRank. To improve retention, Sunil implemented gamification and designed HackerRank’s first incentive mechanism to keep users hooked to the product. Working with the team, he launched "Hackos"—a term he coined—which are points users earned by solving programming challenges. Users loved this feature so much that the product's retention rate experienced a significant increase. Sunil's experience scaling HackerRank from scratch to the first 200k users has made his skillset attractive to startups worldwide. His ability to articulate unmet user needs and build product features to drive growth led him to places he least expected. As a Growth Hacker, he helped scale Mobilizr, a consumer tech startup in Nigeria. He also worked as Head of Data for a property tech startup in London. Afterward, he joined Global Savings Group (GSG) in Munich, Germany, as the first Product Owner for CupoNation's mobile web application. In just a few months, he launched CupoNation's first mobile web application in 17+ countries, which tripled the revenue generated from mobile users. Today, GSG is one of the most successful ventures from Rocket Internet, which also spun off Delivery Hero, Zalando, HelloFresh, and Jumia. After leaving GSG, Sunil joined Juniqe, a startup that was ranked as one of the top 3 fastest growing companies in Germany at the time. As an experienced Product Manager, he managed two cross-functional teams to rebuild the entire technical stack, scaling the mobile and web applications to handle millions of users. In addition to leading the Conversion Rate Optimization (CRO) team, he was also the stakeholder manager for Marketing, Search Engine Optimization (SEO), and Customer Relationship Management (CRM). During his time at Juniqe, he played a pivotal role in scaling the product from less than a million to €20 million in annual recurring revenue, turning the startup into one of the largest art e-commerce sites in Europe. While exploring strategies to improve conversion rates at Juniqe, Sunil encountered Dynamic Yield (DY), a personalization solution for e-commerce products. His foray into personalization began as he worked on integrating DY with Juniqe to bring features like “Users who bought this also liked” and “Products you might like.” As one of the first handful of customers to try out DY, he worked closely with DY's team to fix their product issues and share feedback to improve their machine learning (ML) algorithms. This gave Sunil a behind-the-scenes look into how ML was deployed at scale and equipped him to suggest strategies that helped DY become successful. Gartner has named DY a leader in the Magic Quadrant for Personalization Engines for the past five consecutive years. Later, DY was first acquired by McDonald's and then by Mastercard to enhance customer experience using personalization. Armed with the experience transforming user experiences using personalization, Sunil currently leverages his expertise in product development and growth hacking to build Gulpie—a personalized food discovery application. The app helps people discover restaurants based on their unique tastes, while also helping restaurants fight against the manipulation of their reviews and empowering reviewers to monetize their expertise. To create Gulpie, Sunil interviewed hundreds of food lovers, food reviewers, and restaurant owners. Through these interviews, he discovered that a lack of trust in restaurant reviews and user data protection leads to inefficiencies in the current restaurant discovery process, leaving many people perplexed and frustrated. To address inefficiencies in the market, Sunil built a state-of-the-art personalized recommendation engine using an ensemble of cutting-edge machine-learning algorithms. This engine provides users with personalized restaurant recommendations based on their individual tastes, helping them make informed decisions without going through a cumbersome search process. While working on the recommendation engine, Sunil gained key insights into how to deduce unique user tastes. He found that gaining users' trust to share their data requires a promise of user data protection and strict privacy measures. Leaders in the industry like Sriram Krishnan—an investor at a16z who previously built product teams at Twitter, Meta, and Snap—share this sentiment. https://twitter.com/sriramk/status/1631779635376828416?s=46&t=m-tchH2RTFFMPB2qJSBrFQ What Sunil Had to Say Sharing his experience working on personalization at Gulpie, Sunil explains: “Before the invention of blockchains, users were unable to truly own their data as centralized entities held control over it. However, with Gulpie, our aim is to empower users to take control of their data and provide them with better visibility into its usage. We understand that users expect product builders to be cognizant of the skepticism people experience when sharing their data with apps. That’s why we focus on enabling personalization while ensuring that users’ data is absolutely not at risk. We anonymize user data before training our machine-learning models. Additionally, with GDPR and other privacy protection regulations in place, it's important for companies to actively monitor and prevent the misuse of user data to avoid situations like the Cambridge Analytica-Facebook scandal.” In an age where large organizations scavenge user data and abuse privacy without consent, product experts like Sunil lead the way in protecting user privacy. As product managers, builders, and founders strive for personalization without sacrificing privacy, they can rely on experts like Sunil who push the boundaries of what’s possible. With his expertise and vision, we can look forward to a future where user privacy is truly protected, and products can provide tailored experiences that put users first.

Expert Product Manager Sunil Tej Gorantla on Personalization Without User Data at Risk

Oracle vs. Google (2014) Court Filing, retrieved on May 9, 2014, is part of . You can jump to any part in this filing . This is part 1 of 16. HackerNoon’s Legal PDF Series here Introduction E. JOSHUA ROSENKRANZ, Orrick, Herrington & Sutcliffe LLP, of New York, New York, argued for plaintiffappellant. With him on the brief were MARK S. DAVIES, ANDREW D. SILVERMAN, KELLY M. DALEY; and ANNETTE L. HURST, GABRIEL M. RAMSEY, and ELIZABETH C. MCBRIDE, of San Francisco, California. Of counsel on the brief were DORIAN E. DALEY, DEBORAH K. MILLER, MATTHEW SARBORARIA, and ANDREW C. TEMKIN, Oracle America, Inc., of Redwood Shores, California; and DALE M. CENDALI, DIANA M. TORRES, SEAN B. FERNANDES, and JOSHUA L. SIMMONS, Kirkland & Ellis LLP, of New York, New York. Of counsel were SUSAN M. DAVIES, Kirkland & Ellis LLP, of New York, New York; MICHAEL A. JACOBS, Morrison & Foerster LLP, of San Francisco, California; and KENNETH A. KUWAYTI, of Palo Alto, California. ROBERT A. VAN NEST, Keker & Van Nest LLP, of San Francisco, California, argued for defendant-crossappellant. With him on the brief were CHRISTA M. ANDERSON, STEVEN A. HIRSCH, MICHAEL S. KWUN, and DANIEL E. JACKSON. Of counsel on the brief were IAN C. BALLON and HEATHER MEEKER, Greenberg Traurig, LLP, of East Palo Alto, California; RENNY HWANG, Google Inc., of Mountain View, California; and DARYL L. JOSEFFER and BRUCE W. BABER, King & Spalding LLP, of Washington, DC. MARCIA B. PAUL, Davis Wright Tremaine LLP, of New York, New York, for amicus curiae Ralph Oman. With her on the brief were LACY H. KOONCE, III and DEBORAH A. ADLER. WILLIAM A. RUDY, Lathrop & Gage LLP, of Kansas City, Missouri, for amici curiae Picture Archive Council of America, Inc., et al. With him on the brief were CAROLE E. HANDLER and BRIANNA E. DAHLBERG, of Los Angeles, California. GREGORY G. GARRE, Latham & Watkins, LLP, of Washington, DC, for amici curiae Microsoft Corporation, et al. With him on the brief was LORI ALVINO MCGILL. Of counsel on the brief were PAUL T. DACIER, KRISHNENDU GUPTA, EMC Corporation, of Hopkinton, Massachusetts; and DOUGLAS LUFTMAN, NETAPP, Inc., of Sunnyvale, California. JARED BOBROW, Weil, Gotshal & Manges LLP, of Redwood Shores, California, for amici curiae Eugene H. Spafford, Ph.D., et al. With him on the brief was AARON Y. HUANG. MATTHEW S. HELLMAN, Jenner & Block LLP, of Washington, DC, for amicus curiae BSA/The Software Alliance. With him on the brief was PAUL M. SMITH. STEVEN T. COTTREAU, Clifford Chance US LLP, of Washington, DC, for amici curiae, Scott McNealy, et al. MEREDITH JACOB, Program on Information Justice and Intellectual Property, American University, Washington College of Law, of Washington, DC, for amici curiae Intellectual Property Law Professors. JULIE P. SAMUELS, Electronic Frontier Foundation, of San Francisco, California, for amici curiae Computer Scientists. With her on the brief was MICHAEL BARCLAY. Of counsel on the brief was JASON M. SCHULTZ, NYU Technology Law and Policy Clinic, NYU School of Law, of New York, New York. JONATHAN BAND, Jonathan Band PLLC, of Washington, DC, filed a brief for amicus curiae Computer & Communications Industry Association. With him on the brief was MATTHEW SCHRUERS, Computer & Communications Industry Association, of Washington, DC. CHAD RUBACK, The Ruback Law Firm, of Dallas, Texas, filed a brief for amici curiae Rackspace US, Inc., et al. JENNIFER M. URBAN, Samuelson Law, Technology and Public Policy Clinic, U.C. Berkeley School of Law, of Berkeley, California for amici curiae Software Innovators, et al. Before O’MALLEY, PLAGER, and TARANTO, Circuit Judges. O’MALLEY, Circuit Judge. This copyright dispute involves 37 packages of computer source code. The parties have often referred to these groups of computer programs, individually or collectively, as “application programming interfaces,” or API packages, but it is their content, not their name, that matters. The predecessor of Oracle America, Inc. (“Oracle”) wrote these and other API packages in the Java programming language, and Oracle licenses them on various terms for others to use. Many software developers use the Java language, as well as Oracle’s API packages, to write applications (commonly referred to as “apps”) for desktop and laptop computers, tablets, smartphones, and other devices. Oracle filed suit against Google Inc. (“Google”) in the United States District Court for the Northern District of California, alleging that Google’s Android mobile operating system infringed Oracle’s patents and copyrights. The jury found no patent infringement, and the patent claims are not at issue in this appeal. As to the copyright claims, the parties agreed that the jury would decide infringement, fair use, and whether any copying was de minimis, while the district judge would decide copyrightability and Google’s equitable defenses. The jury found that Google infringed Oracle’s copyrights in the 37 Java packages and a specific computer routine called “rangeCheck,” but returned a noninfringement verdict as to eight decompiled security files. The jury deadlocked on Google’s fair use defense. After the jury verdict, the district court denied Oracle’s motion for judgment as a matter of law (“JMOL”) regarding fair use as well as Google’s motion for JMOL with respect to the rangeCheck files. Order on Motions for Judgment as a Matter of Law, Oracle Am., Inc. v. Google Inc., No. 3:10-cv-3561 (N.D. Cal. May 10, 2012), ECF No. 1119. Oracle also moved for JMOL of infringe-ment with respect to the eight decompiled security files. In granting that motion, the court found that: (1) Google admitted to copying the eight files; and (2) no reasonable jury could find that the copying was de minimis. Oracle Am., Inc. v. Google Inc., No. C 10-3561, 2012 U.S. Dist. LEXIS 66417 (N.D. Cal. May 11, 2012) (“Order Granting JMOL on Decompiled Files”). Shortly thereafter, the district court issued its decision on copyrightability, finding that the replicated elements of the 37 API packages—including the declaring code and the structure, sequence, and organization—were not subject to copyright protection. Oracle Am., Inc. v. Google Inc., 872 F. Supp. 2d 974 (N.D. Cal. 2012) (“Copyrightability Decision”). Accordingly, the district court entered final judgment in favor of Google on Oracle’s copyright infringement claims, except with respect to the rangeCheck code and the eight decompiled files. Final Judgment, Oracle Am., Inc. v. Google Inc., No. 3:10-cv3561 (N.D. Cal. June 20, 2012), ECF No. 1211. Oracle appeals from the portion of the final judgment entered against it, and Google cross-appeals from the portion of that same judgment entered in favor of Oracle as to the rangeCheck code and eight decompiled files. Because we conclude that the declaring code and the structure, sequence, and organization of the API packages are entitled to copyright protection, we reverse the district court’s copyrightability determination with instructions to reinstate the jury’s infringement finding as to the 37 Java packages. Because the jury deadlocked on fair use, we remand for further consideration of Google’s fair use defense in light of this decision. With respect to Google’s cross-appeal, we affirm the district court’s decisions: (1) granting Oracle’s motion for JMOL as to the eight decompiled Java files that Google copied into Android; and (2) denying Google’s motion for JMOL with respect to the rangeCheck function. Accordingly, we affirm-in-part, reverse-in-part, and remand for further proceedings. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 10-CV-3561 retrieved on September 22, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. law.justia.com

Google Admits to Copying Eight Decompiled Security Files Developed by Oracle as Part of API Package

Enterprise data solutions can quickly become expensive. reports that organizations see 30% data growth every 12 months and risk seeing their solutions fail if this growth isn’t managed. NetApp echoes the concerns, stating that data solutions costs tend to be an afterthought, not addressed until they’re already a problem. Gartner Being a data-driven organization, you already know this and understand the critical need for cost optimization in your data solutions. So what can you do? In this article, I’ll look at the factors that increase costs, what you can do to contain them, and how stream processing can be a major source of savings. Through it all, I’ll help you create an effective plan that leads to operational efficiencies in data solutions. Why Are Data Solutions Expensive? First, let’s look at the factors that contribute to the increasing costs of data solutions. You probably already face many, if not all, of these situations: : Data is being generated at an accelerated rate. And the cost of storing and managing that data has also increased. Increasing volume of data : Disparate data sources make data integration a herculean task that leads to increased data quality checks and management costs. A myriad of data sources and their associated complexity : Inevitable technological advancements lead to frequent upgrades in hardware and software, resulting in higher costs. Frequently updated technology : Not only do we have to worry about how to store and manage data, but also how to protect it from bad actors. Data privacy is critical, demanding hefty investments in encryption, security, and maintenance. Data security and privacy concerns : Many organizations hoard a tremendous amount of data before consulting their data practitioners and experts. This leads to unnecessary storage costs, incompatible systems, and inefficient and unscalable solutions. Data consolidation : According to , the big data market will grow to $229.4 billion by 2025. With that growth comes the need for specialized—and expensive—skills, such as Apache Kafka, Apache Flink, Hadoop, Docker, containerization, DevOps, and others. Lack of skilled data professionals Bloomberg How Can Enterprises Reduce Their Data Solution Costs? What can enterprises do to keep these costs down? There are many strategies, including: Data compression and archiving (reduce data size and archive unused data) Data partitioning (make it easier and faster to process data) Data caching (keep frequently used data in high-speed storage) On-demand autoscaling Data governance measures (ensure accurate, complete, and well-managed data) Efficient data movement (improve routing from cloud and on-prem in order to speed insights) Cloud-agnostic deployment (reduce vendor lock-in, optimize costs) Using a multi-cloud approach Let’s add one more to this list, the solution that we’ll look at in more detail in this article: . stream processing What Is Stream Processing? Stream processing, as the name suggests, is a data management strategy that involves ingesting (and acting on) continuous data streams—such as the user’s clickstream journey, sensor data, or sentiment from a social media feed—in real time. By using stream processing, organizations can and see an over common frameworks for managing data, such as Splunk, Apache Spark, and others. greatly reduce their data solutions costs increased ROI Stream processing solves many of the issues encountered with other data solutions, including rising costs, by giving you: : You handle the data in real time. Reduced storage costs : Stream processing systems can handle large-scale data using a distributed computing architecture. Real-time scalability : Stream processing enables real-time data validation, transformation, and filtering at the source. Preventing the downstream flow of bad data : Real-time analysis gives you real-time analytics—and real-time insights. Faster decision-making to identify high-impact opportunities : Automated workflows in stream processing platforms reduce complexity by abstracting the low-level details of data processing. This enables your data team to focus on data analytics, visualization, and reporting. Team efficiency through automation Manage Stream Processing Effectively Stream processing is powerful and opens many use cases, but managing the implementation is no easy task! Let’s look at some challenges associated with implementing stream processing from scratch. Storage and Computing Resources Anomalies in streaming data can be difficult and time-consuming to detect and address without specialized tools. Continuous Delivery Deploying updates to streaming applications can be challenging without disrupting the production version of the application. Database Integration Integrating data from different databases can be complex and time-consuming for developers. Handling schema management and scalability frequently becomes cumbersome for developers when integrating databases. Migrating data from one database to another can be a costly and time-consuming exercise, requiring additional infrastructure. Developer Productivity Developing streaming applications requires your developer to invest a significant amount of time in managing the infrastructure and configuration. Such efforts increase with the complex data processing logic, taking more time away from actual application development. We can solve these issues by using a stream processing framework, such as . A stream processing framework connects to an upstream resource to stream data in real-time, receive the data, process it, then send it on to a destination. Meroxa’s Turbine Meroxa has a on what this looks like at a high level. If we were to look more closely at how we might use Go to interact with Turbine, we could look at : good write-up code from this example application func (a App) Run(v turbine.Turbine) error {
	source, err := v.Resources("demopg")
	if err != nil {
		return err
	}
	// a collection of records, which can't be inspected directly
	records, err := source.Records("user_activity", nil)
	if err != nil {
		return err
	}
	// second return is dead-letter queue
	result := v.Process(records, Anonymize{})

	dest, err := v.Resources("s3")
	if err != nil {
		return err
	}
	err = dest.Write(result, "data-app-archive")
	if err != nil {
		return err
	}

	return nil
}

func (f Anonymize) Process(records []turbine.Record) []turbine.Record {
	for i, r := range records {
		hashedEmail := consistentHash(r.Payload.Get("email").(string))
		err := r.Payload.Set("email", hashedEmail)
		if err != nil {
			log.Println("error setting value: ", err)
			break
		}
		records[i] = r
	}
	return records
}

func consistentHash(s string) string {
	h := md5.Sum([]byte(s))
	return hex.EncodeToString(h[:])
} In the above code, we see the following steps in action: Create an upstream source (named ), from a PostgreSQL database (named ). source demopg Fetch records (from the table) from that upstream source. user_activity Call , which performs the stream processing. This process iterates through the list of records and overwrites the email of each record with an encoded hash. v.Process Create a downstream destination (named ), using AWS S3. dest Write the resulting stream-processed records to the destination. As we can see, you only need a little bit of code for Turbine to process new records and stream them to the destination. Using a framework such as Turbine for stream processing brings several benefits, including: It reduces the need for storage and computing resources by using stream processing instead of batch processing. It provides a powerful mechanism to from large volumes of streaming data, assisting with a quick fix using Novelty Detector, a highly specialized real-time anomaly detection tool. identify anomalous behavior Because for streaming applications, Turbine streaming data apps offer Feature Branch Deploys, allowing you to deploy the branch whenever it is ready—without impacting the production version of the application. continuous delivery is a big challenge It integrates any source database to any destination database by leveraging that receives real-time streams and publishes them downstream. The Meroxa platform handles data transformation or processing logic and orchestration through the Turbine app, sparing the developers from worrisome schema management or scalability issues. Change Data Capture (CDC) Ultimately, this allows developers to focus their time and effort on your core business needs. It also reduces the cost of migration associated with additional infrastructure. Its code-first integration is aimed at helping developers to focus on building applications, leading to faster development while also reducing the infrastructure typically needed to support stream processing. Conclusion Data-driven organizations are betting high on data assets to fuel their transformation journeys. But they need better—and more cost-efficient—ways to manage the fast-growing datasets. Stream processing provides the necessary benefits: improved data quality, faster decision-making, and more. But it requires the right plan and the right stream processing platform to handle the data efficiently. By following the tips in this article, you should be well on your way!

Efficient Enterprise Data Solutions With Stream Processing

This is the story of how a customer made me lose $1000 on moving stuff by 1px. It transformed my career, and I never asked to bet back the money. It also told me something about how web development transforms, and we can adapt to these changes. Back in the day, I was running a social media agency. Aside from running social media accounts with an in-house cutting-edge system, we were also running contests and doing small marketing apps for our clients. Sometimes a client came with no idea, so we were designing an app from the ground up. Only rarely we had clients that were coming with ready designs for a marketing app to be developed. Usually what I did was loosely follow the design to fit the client’s budget. Everyone was happy, the contests were successful and gave our clients a lot of new followers and broad brand awareness. It was the beginning of social media, and it was quite easy compared to how it is now to reach clients with organic activities. So, my life as a web developer was quite comfortable. Until that one client came in. He didn’t want to have a contest. He wanted to have a newsletter subscription page on his social media account. I won’t mention the name of the social media platform, but back in the day, it was able to create a subpage on your social media page with HTML, CSS, and JS. The budget I negotiated for the task was reasonable; the client accepted it. I had enough time to fulfill the order without haste. Did I forget to mention? He came with his own design for the newsletter page. It was beautiful and eye-catching, surpassing in miles what the idea for marketing apps back in the day was. For me, it made the task easier because I just had to compose the page and move to other tasks. After two days mixed with other tasks, I finished the landing page and sent it to the client satisfied with the results. But the client didn’t share my enthusiasm. He told me that the landing page differed from the design. Well, it did. The design was only an inspiration, and I just placed everything more or less in the right place. I thought, ok, I will just adjust it more. Move a button to the left, image upper, and so on. It was quite usual for me, because often, even when making a perfect solution, some clients asked for some changes. It is a natural and important step because it gives the client a feeling of participation and satisfaction in having something he wants. After some more days, I adjusted the newsletter landing page and sent it to the client. After an hour, I got a reply, “It is still skewed.” It was disappointing for me, because the implementation was *almost* a perfect representation of the provided design, and I ran out of budget for this project. It was at that time that I decided to make a screenshot of the implementation and overlay it over the design in the Paint.NET app. What it revealed was that while the implementation was similar to the design, elements were pixels away from the design in various directions. Up until this point in my life, I was pretty happy with not seeing pixel differences. I was frustrated. But I really wanted to finish this task that I took. So I’ve worked on this project further. In total, I’ve exceeded the budget by $300 by working several additional hours. This time, I choose a different approach. I didn’t try to adjust the implementation. I just moved elements, so they fit 100% with the design in Paint.NET. It was my first pixel-perfect implementation. In my head, I was fighting between “it does not make sense” and “let’s try it out.” Finally, I was able to send the new version to the client. Once it was pixel-perfect, the client accepted the result. I was planning to move to the next task right away, but I was curious. What was the difference? I’ve opened two Paint.NET apps side-by-side to compare my first implementation and the pixel-perfect one. What my rather eclectic, engineering eye saw was astonishing. My initial implementation looked ugly! I had never seen it before and thought these 2–3 pixels of a difference didn’t impact the perception. But there I was, looking at the newsletter Mona Lisa with a lousy eye. I initially wanted to charge the client for this nonsense with pixels. However, taking into account I was just learning something extremely important, I just charged him a basic rate. This moment and this client cemented my transformation from a front-end developer to a design developer. Since that time, I have worked with a lot of designers, clients, and front-end developers. Not everyone expects pixel-perfect implementations. It’s not always necessary and not always wise monetarily. But sometimes it is, especially when you want to attract customers. Good designs attract people’s eyes. Good designs hide in that 1px you’ve added to your CSS sheet. To be able to focus on such issues, you have to sort out the technical aspects of your software. So that more effort can be put into the marketing, business, and experience aspects of your e-commerce store or app. That is why I like what offers. It is a company that sponsors the contest I am participating in with this article. They offer all the operational functionalities you need to set up multiple e-commerce sites. Elastic Path As a developer, I love the idea because I rather focus on the business needs than on maintaining and developing product catalogs, payment systems, etc. They offer an easy-to-use API that you can just tap into your new e-commerce venue. If you like the article, please click the heart icon, comment, and share on social media. Cheers!

How I Lost $1000 On One Pixel

কিভাবে আমি এক পিক্সেলে $1000 হারিয়েছি

Wie ich mit einem Pixel 1.000 Dollar verloren habe

Cómo perdí $ 1000 en un píxel

Comment j'ai perdu 1000 $ sur un pixel

मैंने एक पिक्सेल पर $1000 कैसे गंवाए

1 つのピクセルで 1000 ドルを失った方法

One Pixel에서 $1000를 잃은 방법

Como eu perdi $ 1000 em um pixel

Bir Pikselde Nasıl 1000 Dolar Kaybettim

Tôi đã mất 1000 đô la trên một pixel như thế nào

我是如何在一个像素上损失 1000 美元的

CDN stands for Content Delivery Network. It can also be referred to as Content Distribution Network. CDN is a group of spatially distributed servers that increase the rate at which web content is delivered by making it more accessible to users. In plain terms, CDN can be referred to as a group of Interconnected servers that facilitates the swift transfer of digital entities required for loading internet content such as JavaScript files, images, videos, HTML pages, etc. Presently, a significant portion of web traffic is served on CDN, signifying that the usefulness of CDN extends across various aspects. How Does CDN Work? The basic mode by which content delivery networks operate is by temporarily storing digital entities such as web pages, images, audio, and videos on proxy servers that are close to the users' physical locations. As a result, the users can seamlessly utilize these entities by uploading or downloading content,  streaming videos, etc., without delay. Content Delivery Networks utilize cache contents just like other data centers stationed globally. Caching is a process used by digital devices to store file copies temporarily. With the integration of the cache feature into Data centers, users can quickly access internet content from an internet-enabled device via a nearby server. To achieve this, a CDN produces cached versions of its content. These cached versions must be stored on several servers at various geographical locations. These locations are referred to as Points of Presence ( PoP). Each PoP contains multiple servers that temporarily store contents and deliver these contents to users in a close range. This reduces the distance between the users and the web servers. This implies that when a user in a country ( for instance, The United States Of America) intends to access a foreign website ( say The United Kingdom), it will be done via a local USA PoP. This reduces the time it takes to access web content and receive feedback. What Are The Components of CDN? Several technological entities come together to make an effective content delivery network. These are: Media Library A media library is a reservoir that stores contents. The contents from various CDN customers are contained in a media library. Media libraries are usually large file servers, and they are programmed to store contents only. Since media libraries do not deliver content, they support a limited number of simultaneous connections. An example of a media library is NetApp. Media Servers The contents contained in a media library are distributed to users or other caches by media servers. Since media servers perform the delivery functions, they are compatible with many simultaneous users. Caching Servers Caching servers perform a dual function. They can be used to store and, at the same time, share necessary web content with the users. Caching servers are distributed across the content delivery networks hierarchically. Hence, a CDN usually contains 2 or 3 cache tiers. The first tier is the Origin tier. The origin tier entails the cache groups situated close to the media library. After the Origin tier is the Mid-tier, the Mid-tier consists of cache groups deployed at the network's epicenter. A third tier, called the Edge tier, may also be present. The Edge tier serves the users the contents in its cache. Content Routing System A content routing system directs the users to the relevant cache that contains the needed contents. The request initially goes to the content routing system when a user intends to access web content. Afterward, it is directed to the applicable cache in the network Load Monitoring System As the name implies, the load monitoring system regulates the amount of work done by the CDN. From time to time, the load monitoring system analyses the CDN's caches and media servers to monitor their status and service availability. Logging Server Logging servers do log collection, storage, and aggregation in a CDN. CDN networks generate hundreds of log messages per second. These log messages are system logs and service logs. System logs are generated when a notable event occurs in the network. Service logs are generated when there's a transaction on the network. CDN uses customized logging servers and log aggregation servers to relieve the media delivery servers from logging operations. Analytics Server An analytics server in a CDN network provides real-time reports and historical reports. These are used to monitor the CDN network's activities and plan for necessary upgrades. Network Operations Control (NOC) Portal CDN administrators use a network operations control portal to oversee activities on a CDN network. This portal can be used to configure and maintain CDN network tools. Digital Rights Management Server For users to gain authorization into CDN networks, digital rights management servers must step in. DRM servers authorize the users of a CDN network before providing them with the content. Why Do Businesses Need CDN? CDN Improves Marketing Efficiency When Content Distribution Networks are up and running, businesses can monitor the efficiency of their marketing strategies. This implies that businesses can keep a tab on their customers' experience and easily notify them of new deals, products, and discounts. Furthermore, CDN provides businesses with an analysis of customers' data and shopping patterns. Hence, CDN augments a company's marketing strategy and, by implication, improves customer satisfaction. CDN Provides Businesses With Real-Time Updates This is particularly useful for media and publishing companies. One of the ways a media website is ranked is how fast it receives and disseminates information on real-time occurrences. These companies need to deliver prompt and up-to-the-minute information. CDN comes in handy by helping these companies update their website home pages and headlines as they receive real-time updates. CDN Regulates Server Load Since CDN consists of servers that are widespread and these servers deliver cached contents, this implies that the entire contents are not stored on a single primary server. Strategically stationing servers across several distances makes each server less susceptible to overload. This is because CDN frees up additional capacities of the servers and enables it to serve a large number of users simultaneously seamlessly. CDN Augments Website Security By regulating the loads on web servers, content delivery networks effectively combat cyber-attacks that operate by causing traffic spikes on key servers, especially in DDoS(Distributed Denial Of Service) attacks. In these types of attacks, the attackers intend to crash a DNS server by overloading it with a hectic amount of requests. However, CDN mitigates these attacks by distributing these servers and storing a copy of the web contents on each server. All of these point to the fact that the usefulness of CDN in the modern-day business world can not be overemphasized.

What Is a CDN and Why You Should Use One

As the world becomes increasingly digital, nearly every company is looking for to code—no matter the industry. The phenomenon has been great news for software engineers and programmers who can now command high salaries for their hot, in-demand talent. software engineers Perhaps it is no surprise that software engineers in the San Francisco Bay Area, home to Silicon Valley and its many tech giants, can command an even higher premium with their pay packages. According to the more than 5 million verified professionals on the professional social network Blind, these are the top 17 best-paying cities for software engineers in the San Francisco Bay Area. Blind considered the annual base pay and the total compensation package, including the annual salary and any stock-based compensation or bonuses a company might offer. 1. Los Gatos, California n: $500,449 Average Total Compensatio : $475,000 Median Total Compensation : $470,015 Average Salary : $451,000 Median Salary Los Gatos is the best-paying city for software engineers in the U.S., according to Blind. The small town is home to Netflix, which prefers to hire more senior or experienced professionals and pays accordingly. The video-streaming giant typically pays large salaries and gives few employees stock-based compensation. 2. Menlo Park, California : $328,423 Average Total Compensation : $297,000 Median Total Compensation : $167,004 Average Salary : $165,000 Median Salary Menlo Park might be home to only 34,000 people, but its population can swell in size with tech workers during the workweek. Menlo Park is home to the headquarters of Meta, the owner of Facebook and one of the highest-paying technology companies. Google and Roblox were also founded in the city. 3. Cupertino, California n: $279,391 Average Total Compensatio : $255,850 Median Total Compensation : $165,753 Average Salary : $165,000 Median Salary Cupertino is home to Apple and its futuristic Apple Park corporate campus. Software engineers at Apple have enjoyed large paychecks recently, as the iPhone maker briefly topped a jaw-dropping $3 trillion market cap early in 2022. 4. San Bruno, California : $278,368 Average Total Compensation : $225,900 Median Total Compensation : $161,269 Average Salary : $149,500 Median Salary YouTube moved its headquarters to San Bruno in 2007 and has become the city’s largest employer. The Google-owned video streaming service employs more than 2,000 professionals. 5. Mountain View, California : $275,181 Average Total Compensation : $242,000 Median Total Compensation : $160,790 Average Salary : $156,000 Median Salary Mountain View is synonymous with Silicon Valley. Long before startups were birthed in San Francisco Bay Area garages, the city was home to silicon semiconductor producers, which gave the region its name. Of course, the city is now well-known as the headquarters of Google, but other tech companies with prominent presences include 23andMe, Intuit, Mozilla and Synopsys. 6. Oakland, California : $266,869 Average Total Compensation : $197,750 Median Total Compensation : $192,240 Average Salary : $168,750 Median Salary Oakland has become a hub for many financial-technology companies. Credit Karma moved its headquarters from San Francisco to downtown Oakland in 2020. Block and Marqeta also have major offices in “The Town.” 7. San Francisco, California : $265,050 Average Total Compensation : $225,000 Median Total Compensation : $165,042 Average Salary : $160,200 Median Salary San Francisco might be the crown jewel of the technology industry, as the home of too many tech startups and companies to count. The city by the bay is home to Airbnb, DoorDash, Lyft, Twitter, Uber and more, but its top employer is . The cloud giant employs more than 9,000 employees, and its headquarters has made a permanent contribution to the downtown skyline. Salesforce 8. Sunnyvale, California : $260,048 Average Total Compensation : $238,000 Median Total Compensation : $160,499 Average Salary : $160,000 Median Salary Sunnyvale maintains its Silicon Valley roots as home to the chip-maker AMD and advanced technology and networking companies, including Juniper Networks, Microsoft-owned LinkedIn and NetApp. 9. Palo Alto, California : $234,739 Average Total Compensation : $200,000 Median Total Compensation : $157,488 Average Salary : $154,000 Median Salary The tony city of Palo Alto is one of the major cities of Silicon Valley. Cloudera and VMware are some of the largest technology companies based in the city. 10. San Mateo, California : $234,066 Average Total Compensation : $187,875 Median Total Compensation : $158,629 Average Salary : $155,500 Median Salary Companies that call San Mateo home include Coupa, Roblox and Snowflake. Before being acquired by Zendesk, Momentive, which was founded as SurveyMonkey, was headquartered in the city. 11. Pleasanton, California : $225,058 Average Total Compensation : $202,000 Median Total Compensation : $150,887 Average Salary : $150,000 Median Salary Pleasanton is home to many business parks, but the suburb’s top employer is the HR-technology giant Workday. 12. Foster City, California : $220,293 Average Total Compensation : $190,000 Median Total Compensation : $158,319 Average Salary : $148,500 Median Salary Foster City is the headquarters site of the biopharmaceutical giant Gilead Sciences and the enterprise software company Zuora, among other companies. 13. Fremont, California : $218,913 Average Total Compensation : $180,000 Median Total Compensation : $144,638 Average Salary : $142,500 Median Salary Notable companies with headquarters in Fremont include Corsair, Synnex and more, although the city’s No. 1 employer is Tesla. 14. Santa Clara, California : $214,168 Average Total Compensation : $197,000 Median Total Compensation : $153,885 Average Salary : $150,000 Median Salary AMD, Intel, NVIDIA and Palo Alto Networks are some of the most well-known tech companies headquartered in Santa Clara. 15. San Jose, California n: $210,801 Average Total Compensatio : $183,000 Median Total Compensation : $153,390 Average Salary : $145,000 Median Salary The largest city by population in the San Francisco Bay Area, San Jose is home to Adobe, Cisco, eBay, PayPal, Zoom and more. 16. Redwood City, California : $207,955 Average Total Compensation : $190,000 Median Total Compensation : $156,248 Average Salary : $151,000 Median Salary Redwood City is the headquarters location for several global technology companies, such as Box, Carbon, Electronic Arts and Informatica. 17. Milpitas, California : $188,146 Average Total Compensation : $165,750 Median Total Compensation : $154,073 Average Salary : $138,000 Median Salary Some of the top employers in Milpitas are tech employers, including Cisco, FireEye, SanDisk and more. Methodology Blind analyzed the base salaries and total compensation of software engineers in the San Francisco Bay Area on its platform. Cities were ranked by the average total compensation, including annual base salary and, if applicable, stock-based compensation and cash bonuses. All data is self-reported by verified professionals on the professional social network Blind. Also published on Teamblind's blog

17 Highest-Paying Cities in the San Francisco Bay Area for Software Engineers

Las 17 ciudades que mejor pagan en el área de la bahía de San Francisco para ingenieros de software

17 villes les mieux rémunérées de la région de la baie de San Francisco pour les ingénieurs logiciels

सॉफ्टवेयर इंजीनियरों के लिए सैन फ्रांसिस्को खाड़ी क्षेत्र में 17 उच्चतम-भुगतान वाले शहर

ソフトウェア エンジニアにとって、サンフランシスコ ベイエリアで最も給料の高い 17 の都市

17 cidades mais bem pagas na área da baía de São Francisco para engenheiros de software

17 thành phố trả lương cao nhất trong khu vực vịnh San Francisco cho các kỹ sư phần mềm

旧金山湾区软件工程师薪酬最高的 17 个城市

Remember the good ol’ days, when artificial intelligence and machine learning technologies were still in their infancy? Interest was split between tech enthusiasts and skeptics, because many were afraid they might lose their jobs to the robot overlords. As time went on however, it became clear that if anything, it helps people do their jobs better. ** ** In the case of sales automation tech—these solutions are a total godsend. They free up the time of sales reps by taking over the administrative and reporting-related elements, all with greater efficiency than their human counterparts. That gives sales reps the freedom to focus on the more meaningful and impactful tasks to really drive up sales. ** ** According to , early adopters of sales automation tools “...consistently report increases in customer-facing time, higher customer satisfaction, efficiency improvements of 10 to 15 percent, and sales uplift potential of up to 10 percent.” There is a wide array of sales automation tools available, covering most every aspect within the sales realm, from presentations, to lead management, to churn prevention, and more. McKinsey ** ** Here’s a list of the top five sales automation startups that have made headlines in 2022 for their unique take on sales automation, which are certainly worth keeping an eye on. : Interactive and insightful product demos Walnut Upon their launch in mid-2021, Walnut achieved and maintained a degree of virality within many sales circles for being a codeless platform that enables B2B companies to fully customize, manage, and optimize their sales demos—all with zero downtime, or loading time issues. Its early adopters include the likes of Adobe, NetApp, Treasure Data and People AI. The Walnut platform itself also collects deep insights on the team and prospects demo usage, to help improve the sales process. : Sales pipeline management Dooly One-stop-shops in which insights can be viewed in a glance is always a good thing, and it is exactly where Dooly shines. This sales pipeline solution allows sales reps to manage all their deals at once, with one-click updates from a centralized view. This always keeps them ready for their next pipe review, and also saves them from hours of time that would have been spent making manual updates on Salesforce. Some of the heavy-hitters currently using Dooly include Figma, Asana, and BigCommerce, which explains why the solutions raked in a slew of distinctions through the course of 2021. : Sales revenue intelligence Gong Anyone who is anyone in sales has heard of the beast that is Gong. The Gong Revenue Intelligence Platform delivers insights to customer-facing teams that are based on customer interactions. The insights help revenue and go-to-market teams make better informed business decisions to help achieve greater profitability and scalability. Thousands of big brands, such as Slack, Shopify, and LinkedIn use Gong for the intelligence that is gathered from contact to close, and its AI coaching recommendations. : Sales qualifying, routing, and booking software Chili Piper B2B sales and revenue teams worldwide crown Chili Piper as the most advanced scheduling and routing software. They have a range of solutions under their umbrella, which collectively helps teams double their inbound conversion rates, increase customer satisfaction, and reach new levels of productivity. Twilio, AirBnB, and even Gong are amongst the high growth revenue teams that are turning to Chili Piper to qualify and turn leads into meetings. : Sales engagement platform SalesLoft Sure, there may be many sales engagement platforms out there. However, what sets SalesLoft apart from the rest is their Modern Revenue Workspace™. That solution enables sales teams to seamlessly execute their digital selling tasks, in addition to communicating with buyers, all while gaining insights on what needs to be done next to further ensure success. Salesloft is trusted by numerous companies from GrubHub and Square, to legacy brands such as IBM and Cisco, to exponentially increase their revenue. ** ** From consumer trends and expectations, to elements of business processes—there is much that has changed in the sales realm in the post-pandemic world. Thankfully, sales teams can easily rely on their tech counterparts to do a lot of the heavy-lifting to increase efficiency. The solutions featured above do just that, so sales teams can do what tech cannot: adding the human touch to the sales process.

NETAPP

netapp.com

10K+ employees

Since 1992

COMPANY RANKING

NETAPP

EVERGREEN INDEX #416

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NetApp WIKI

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NETAPP

netapp.com

10K+ employees

Since 1992

COMPANY RANKING

NETAPP

EVERGREEN INDEX #416

RELATED COMPANIESRANK

STORIES ABOUT NETAPP

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NetApp WIKI

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