Latest news with #ApacheIceberg
India.com
30-07-2025
- Business
- India.com
Bhargav Kumar Konidena: Effective contributions to the Open Source: Transforming DevOps and Data Workflows with Apache Projects
Open source is not only a technology approach, but is indeed a movement in the global environment of technology-driven industries where engineers, researchers, and creators are united in a common goal to construct mechanism platforms to be the composite of present-day enterprise computing. There are a lot of contributors that make this ecosystem and just one of them that I would highlight is Bhargav Kumar Konidena who is an experienced DevOps and a Java developer and the work he has done with leading Apache Foundation projects showcases not only his technical skills but his collective commitment to innovation. Bhargav has worked in the field of DevOps, cloud automation and enterprise systems more than ten years, and with his practical knowledge in AWS, Kubernetes, Docker and other similar resources, he has engaged with a number of open source projects that are well-used by enterprises. His contributions cut across key areas in data lake systems, data pipeline orchestration systems and systems for containerized application deployment; inputs that are currently affecting software teams and companies in every continent. Enhancing Data Lake supporting Apache Iceberg A significant contribution to Apache Iceberg as a high-performance table format to big analytic data is made by Bhargav. Apache Iceberg is built to support lake-scale data lakes and is now part of data infrastructure stacks built around it, run by companies such as Netflix, Apple, and LinkedIn. As of July 2025, the Iceberg GitHub source code repository had been starred more than 4,300 times, indicating good developer interest and uptake. Bhargav has worked on Iceberg in terms of restructuring the SQL and DataFrame queries supporting the Time Travel feature so that we can query historical snapshots of datasets. This entailed metadata processing and performance improvements, which are the most crucial factors in achieving the lightning fast and stable real-time access to versioned data as analytics workflows evolve. His work has direct impact on the interaction of teams with historical data increasing system reproducibility and auditability of machine learning and BI activities. The Apache Beam Making Data Pipelines leaner The other important field of work by Bhargav is in Apache Beam which is a unified model of defining batch and streaming data-parallel processing pipelines. Beam is an abstraction framework that operates on top of such engines as Apache Flink Google Cloud Dataflow and Apache Spark. The project is widely used in verticals of need of real-time analytics and complex event processing and has more than 6,400 stars on GitHub at the moment. Through the Beam code submission, Bhargav provided a fix to one of the drawbacks in the support of the handling of user-specified providers through command-line arguments, such as custom FileSystem implementations or external transforms. His modification of the code makes sure that each time the users provide certain configurations through flags, such as –filesystem or –transform-providers, Beam dynamically prioritizes and incorporates the same as inputs in the runtime environment. Such an update was vital in the enhancement of Beam flexibility, especially in situations where developers have deployed the platform in a hybrid environment or where the extensions are proprietary. His improvement makes it possible to do even more customization and less friction during the process of adapting Beam to fit a distinct organizational context. Facilitating the DSL configuration understanding in Helm DevOps expertise is demonstrated by Bhargav in his contribution of Helm, a software project used as a package manager in the Kubernetes, and the most commonly used tool so far to facilitate application deployment. Helm has more than 25000 stars on GitHub, making it an essential tool in CI/CD toolchains of teams that deal with the containerized microservices. Bhargav added enhancements regarding the processing of precedence of configuration input options namely, -set, -set-file and -values. These inputs give users the option to override the default deployment-time configuration with different ones at deployment time, but the results were unreliable due to precedence inconsistency and the results of this could mean a badly configured deployment. His update was extra clear that –set and –set-file arguments should take precedence over –values, and this correlates tool behavior with the points in the documentation and the intentions of its users. He also increased malformed inputs error handling, which minimizes the risk of production deployment. Such modifications allow the Helm users to circumvent the configuration drift and guarantee a repeatable and trustworthy application provisioning between the development, staging, and production setting. Effect of the Apache Contributions Donating to Apache is not a technical process only, it is a duty that develops software ecosystems all over the world. Being one of the contributors, Bhargav belongs to an elite group of people who develop and update the tools provided to the companies of the Fortune 500 list as well as to data scientists, cloud architects, the products of DevOps, etc. Projects hosted by Apache Foundation features elementary blocks of platforms by known cloud major providers such as AWS, Azure, and Google Cloud. In addition, every code submitted to it is rigorously subjected to peer, architectural and integration test prior to inclusion into the main codebase. Through the work done in the three varied and technically challenging projects, including its sound engineering and flawless code, Bhargav has managed to prove both the skill and mastery of coding and his strong mastery of the needs of many user communities. Contributions made are even more comprehensive and publicly viewable in repositories that anyone can view and generate a visible, verifiable record of impact. The popularity of the Iceberg, Beam, and Helm has established a fact that the code developed by Bhargav is not a subject of experimentation only, but it is actually used in production settings in such domains as financial, media, telecommunications, and healthcare spheres. An Approach to Shared Innovation by a Practitioner Open source work by Bhargav is a sign of his wider philosophy in life: to develop scalable, efficient, and modular systems where the user experience, maintainability and long term performance is emphasized. He offers the solutions based on the best enterprise DevOps experience–his experience on DevOps support of leading insurers, telecom, and medical platforms. As an example, his previous work in container orchestration, infrastructure-as-code with the Terraform, and CI/CD automation are directly applied to his efforts in Helm and Beam. Known to him are metadata, schema evolution, and performance bottlenecks on large-scale datasets, which reflect in his efforts at improving Apache Iceberg. This is the synergy between the professional world and open source contribution, which guarantees that the features he is developing should address the real problems experienced by the engineering teams. Conclusion The work of Bhargav Kumar Konidena (Apache Iceberg, Beam, and Helm) can be considered an excellent example of what it is to be a valuable member of the open source community. These are not just small bug fixes or occasional patches, these are architectural advancements and feature improvements that affect thousands of users and are used in mission-critical applications on a worldwide scale. By working with Apache and the Apache ecosystem, Bhargav not only put himself at the center of the global innovation, but is able to collaborate with other first-rate engineers, open-source and peer-review his code and help define the future of data engineering and DevOps.
Business Insider
09-07-2025
- Business
- Business Insider
Read the exclusive pitch deck data startup Ryft used to raise $8 million, led by Index and Bessemer
Data management startup Ryft is emerging from a 10-month stealth period with an $8 million seed round. Index Ventures and Bessemer Venture Partners co-led the round, with participation from members of the founding teams at Wiz and Eon. Ryft cofounder and CEO Yossi Reitblat told Business Insider the company provides a more flexible data product for its customers than cloud giants like Snowflake, Databricks, Microsoft, AWS, and Google. In the age of AI where data is paramount to creating new models, companies "don't want to be locked with a single vendor and then only use their capabilities," he said. "They want to be able to mix and match." Ryft handles data optimization, compliance, disaster recovery, and governance while allowing customers to take ownership of their data, allowing them to access it with different tools — such as those that write code for AI apps, Reitblat said. It works with companies across finance, e-commerce, adtech, gaming, and cybersecurity. The New York-based company was founded by three friends who met in high school and subsequently worked together as software engineers in the Israel Defense Forces, building data software. In addition to Reitblat, there's CTO Yuval Yogev and vice president of research and development Guy Gadon. Ryft has 13 employees. The team built Ryft for Apache Iceberg, an open-source analytic table software developed by Netflix. Ryft makes money by charging a yearly fee based on the amount of data it manages. The seed round will go toward hiring more engineers, scaling sales efforts, and launching new products, Reitblat said. Here's a look at the pitch deck that Ryft used to raise $8 million in seed funding.
Yahoo
26-06-2025
- Business
- Yahoo
Informatica (INFA) Unveils New AI Tools at Snowflake Summit 2025
Informatica Inc. (NYSE:INFA) is one of the . On June 3rd, Informatica Inc. (NYSE:INFA) announced new product innovations at Snowflake Summit, Snowflake's annual user conference. The updates, including expansion of its support for Apache Iceberg, will allow joint customers to use both companies' Generative AI (GenAI) technologies for building reliable and enterprise-level AI applications. The company announced the general availability of new application integration capabilities for Snowflake Cortex AI. This includes new connectors for Cortex AI, Cortex Search, Cortex Analyst, and Cortex Agents, as well as simplified GenAI application creation and no-code development and deployment with Snowflake Cortex AI. A business executive in a modern office looking over reports detailing artificial intelligence. Informatica also announced that it is enhancing its Open Table Connector (Apache Iceberg) to support Apache Polaris. Scheduled for release in July 2025, the enhancement will allow loading data into Snowflake from over 300 sources using the Iceberg table format. The company also announced the launch of its Master Data Management (MDM) SaaS Extension for the Snowflake AI Data Cloud. This will allow customers to consolidate master and transaction data across multiple sources, facilitating analytics and AI use cases. 'Informatica continues to be at the forefront of Generative AI and Apache Iceberg innovation with Snowflake enabling our joint customers to build for the future with a trusted, AI-ready data foundation. Today's announcement underscores our relentless commitment to innovating and leading with Snowflake to deliver greater value for customers through deep product roadmap and partnership alignment.' Informatica is a leader in enterprise AI-powered cloud data management. While we acknowledge the potential of INFA as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: 10 AI Stocks in the Spotlight and Disclosure: None. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Cision Canada
18-06-2025
- Business
- Cision Canada
Starburst Named a Leader & Fast Mover in 2025 GigaOm Radar for Data Lakes & Lakehouses
BOSTON, June 18, 2025 /CNW/ -- Starburst, the data platform for apps and AI, today announced it has been recognized as a Leader and Fast Mover in the newly released 2025 GigaOm Radar for Data Lakes and Lakehouses report. This marks the third consecutive year that Starburst has earned a leadership position in this influential industry benchmark. The GigaOm report highlights Starburst's dominance across key evaluation categories, including: Product capabilities: Recognition for deep integration across modern cloud ecosystems and robust support for data federation and hybrid architectures. Market execution: Acknowledgement of Starburst's momentum and continued adoption as a distributed SQL engine built on open standards. Innovation trajectory: Commended roadmap execution, leadership in the open lakehouse movement, and readiness for future AI-driven analytics workloads. "As organizations seek agile, scalable data platforms that power both BI and AI, Starburst enables query-in-place architectures that eliminate data silos and unlock real-time insights," said Justin Borgman, Co-Founder and CEO of Starburst. "Our recognition as a Leader and Fast Mover by GigaOm validates our mission to deliver uncompromising performance, openness, and innovation." The GigaOm Radar evaluates vendors on a combination of feature richness, usability, performance, market strategy and innovation roadmap. Positioning Starburst in the Leader and Fast Mover affirms its ability to deliver high-performance federated querying and scalability, support for diverse open table and file formats, hybrid and multi-cloud deployments, and a consolidated and governed data stack that supports analytics and AI workloads. "Starburst received a high score in the business criterion of AI readiness. The nature of AI readiness encompasses AI/ML and generative AI and refers to capabilities and frameworks in data lake/lakehouse offerings that equip customers to leverage their data to implement and improve AI across their organization. Starburst's high score in this business criterion reflects a number of its platform's advanced capabilities, such as its strong data cataloging and data management features, which help organizations curate, organize, and improve their data for all their workloads, including AI-related ones," said Andrew Brust, Lead Analyst at GigaOm. Download and explore the report here: About Starburst Starburst is the data platform built for flexibility, delivering fast, secure access to all your data, wherever it lives. Whether on-premises, across clouds, or in hybrid environments, Starburst provides choice and control to your architecture. Built on an open data stack with Trino and Apache Iceberg, it unifies distributed data without complex or costly migrations, unleashing the full power of the data lakehouse for analytics and AI. With our Lakeside AI architecture, enterprises gain federated access, governed collaboration, and full data lineage, laying the foundation for scalable, compliant AI innovation. Starburst empowers data-intensive and security-conscious organizations to unlock the full potential of their data while ensuring performance, governance, and control. Enterprises in 60+ countries, including Comcast, Citigroup, and 4 of the top 5 global banks, trust Starburst to maximize data value. Our strategic partnerships with AWS, Dell Technologies, and top cloud providers ensures seamless interoperability across environments. From insights to action to AI, Starburst fuels innovation at every level. Learn more at
Yahoo
13-06-2025
- Business
- Yahoo
Citi Reiterates Buy on Snowflake (SNOW) as Cortex AI Gains Traction
Snowflake Inc. (NYSE:) is one of the One of the biggest analyst calls on Wednesday, June 11, was for Snowflake. Citi reiterated the stock as 'Buy' with an associated price target of $245.00. The firm said it is sticking with the stock following an investor day. Snowflake is experiencing strong momentum owing to its new products, particularly Cortex AI. Cortex AI is a suite of AI features using large language models (LLMs) to offer intelligent assistance to customers. Customers are particularly excited about the accelerated product development under CEO Sridhar Ramaswamy and the readiness of enterprises to deploy generative artificial intelligence applications. Customers have also resonated well with the company's strategy to avoid vendor lock-in, thereby resulting in widespread adoption of the Cortex AI for tasks such as fraud detection and process automation. This has, in turn, led to reduced operational overheads. The firm also talked about the adoption of Apache Iceberg and the Polaris Catalog across various industries, reflecting on the increasing demand for vendor-neutral data management solutions. Crunchy Data's recent acquisition further strengthens Snowflake's position by enhancing support for Postgres. This, in turn, aligns with its strategy to provide open operational and analytical workloads for AI. 'In general, customer enthusiasm was high around Snowflake's new products.' Snowflake Inc. (NYSE:SNOW) is a cloud-based data storage company providing a data analysis, storage, and sharing platform. While we acknowledge the potential of SNOW as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and Disclosure: None.
