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More and more organizations are turning to Hadoop and NoSQL to manage big data. In fact, many IT professionals consider each of those terms to be synonymous with big data. At the same time, these two technologies are seen as different beasts that handle different challenges. That means they are often deployed in a rather disjointed way, even when intended to solve the same overarching business problem. The emerging trend of “in-Hadoop databases” promises to narrow the deployment gap between them and enable new enterprise applications. In this talk, Dale will describe that integrated architecture and how customers have deployed it to benefit both the technical and the business teams.

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Deciding the deployment model is critical when enterprises adopt Hadoop. Initially, the bare metal (on-premise cluster with physical servers) model was popular to avoid I/O overhead in the virtualized environments. However, these days, cloud is also a contending option with its compelling cost savings, and ease of operation. To aid in assessing the deployment options, Accenture Technology Labs developed Accenture Data Platform Benchmark suite, a total cost of ownership (TCO) model and has tuned and compared performance of bare metal Hadoop clusters and Hadoop cloud service. Interestingly enough, the study discovered that price/performance ratio is not a critical factor in making a Hadoop deployment decision. Employing empirical and systemic analyses, the study resulted in comparable price/performance ratio from both bare metal Hadoop clusters and Hadoop-as-a-service. Moreover, cheaper purchasing options (e.g., long term contracts) provides better ratio than the bare metal one in many cases. Thus, this result debunks the idea that the cloud is not suitable to Hadoop MapReduce workloads due to their heavy I/O requirements. Furthermore, the study finds that the Hadoop default configuration provides ample headroom for performance tuning, and the cloud infrastructure enables even further performance tuning opportunities.

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This document discusses IBM's Integrated Analytics System (IIAS), which is a next generation hybrid data warehouse appliance. Some key points: - IIAS provides high performance analytics capabilities along with data warehousing and management functions. - It utilizes a common SQL engine to allow workloads and skills to be portable across public/private clouds and on-premises. - The system is designed for flexibility with the ability to independently scale compute and storage capacity. It also supports a variety of workloads including reporting, analytics, and operational analytics. - IBM is positioning IIAS to address top customer requirements around broader workloads, higher concurrency, in-place expansion, and availability solutions.

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SGI has experience designing technical computing hardware and software solutions since 1998. They provide optimized Hadoop solutions using their Rackable and ICE Cube servers and storage systems. SGI's Hadoop solutions offer complete turn-key hardware and software configurations to help customers quickly deploy and architect Apache Hadoop clusters. Their reference architectures are optimized for performance, utilization, power efficiency and lower costs.

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Hadoop has traditionally been an on-premises workload, with very few notable implementations on the cloud. With Organizations either having jumped on the cloud bandwagon or have started planning their expansion into the ecosystem, it is imperative for us to explore how Hadoop conforms to the cloud paradigm. With the coming off age of some very useful cloud paradigms and the nature of Big Data with high seasonality of workloads, this is becoming a very common ask from customers. Robust architectures, elastic scale, open platforms, OSS integrations, and addressing complex pain points will all be part of this lively talk. To be able to implement effective solutions for Big Data in the cloud it is imperative that you understand the core principles and grasp the design principles of how the cloud can enhance the benefits of parallelized analytics. Join this session to understand the nitty-gritties of implementing Big Data in the cloud and the various options therein. Big Data + Cloud is definitely a deadly combination.

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The document discusses machine learning with SQL Server 2016 and R Services. It provides an overview of machine learning, R programming language, and the challenges of using R with SQL databases prior to SQL Server 2016. SQL Server 2016 introduces R Services, which allows running R code directly in the database for high performance, scalable machine learning. R Services integrates R with SQL Server through in-database deployment and parallel processing capabilities. This eliminates data movement and scaling issues while leveraging existing R and SQL skills.

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GeoWave is an open-source library that connects geospatial software with distributed computing frameworks. GeoWave leverages the scalability of a distributed key-value store for effective storage, retrieval, and analysis of massive geospatial datasets. It uses a space filling curve to preserve locality between multi-dimensional objects and the single dimensional sort order imposed by key-value stores. What this means to a user is that distributed spatial and spatial-temporal retrieval and analysis can be effectively accomplished at a massive scale. At its core, GeoWave solves the problem of multi-dimensional indexing, and particularly extends this capability to spatial/temporal use cases. GeoWave supports raster, vector, and point cloud data, and provides common spatial algorithms that can be extended to create deep analytic capabilities. It also performs fast subsampling via distributed rendering that integrates with GeoServer, so that a user can interactively visualize data at map scale regardless of density. Our goal in presenting GeoWave to the Hadoop Summit is to introduce it to the big data community. We will present GeoWave at a moderate level of detail, to include a short demonstration, and hopefully answer any questions regarding maturity, suitability and implementation details.

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- POWER9 delivers 2x the compute resources per socket through new cores optimized for stronger thread performance and efficiency. - It features direct memory attach with up to 8 DDR4 ports and buffered memory with 8 channels for scale-out and scale-up configurations. - The processor provides leadership hardware acceleration through enhanced on-chip acceleration, NVLink 2.0, CAPI 2.0, and a new open CAPI interface using 25G signaling for high bandwidth and low latency attachment of accelerators.

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Supercharge your software development productivity with our latest webinar! Discover the powerful capabilities of AI tools like GitHub Copilot and ChatGPT 4.X. We'll show you how these tools can automate tedious tasks, generate complete syntax, and enhance code documentation and debugging. In this talk, you'll learn how to: - Efficiently create GitHub Actions scripts - Convert shell scripts - Develop Roslyn Analyzers - Visualize code with Mermaid diagrams And these are just a few examples from a vast universe of possibilities! Packed with practical examples and demos, this presentation offers invaluable insights into optimizing your development process. Don't miss the opportunity to improve your coding efficiency and productivity with AI-driven solutions.

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So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation. In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.

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Developer's time is the most crucial resource in an enterprise IT organization. Too much time is spent on undifferentiated heavy lifting and in the world of APIs and microservices much of that is spent on non-functional, cross-cutting networking requirements like security, observability, and resilience. As organizations reconcile their DevOps practices into Platform Engineering, tools like Istio help alleviate developer pain. In this talk we dig into what that pain looks like, how much it costs, and how Istio has solved these concerns by examining three real-life use cases. As this space continues to emerge, and innovation has not slowed, we will also discuss the recently announced Istio sidecar-less mode which significantly reduces the hurdles to adopt Istio within Kubernetes or outside Kubernetes.

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Tractian, an AI-driven industrial monitoring company, recently discovered that their real-time ML environment needed to handle a tenfold increase in data throughput. In this session, JP Voltani (Head of Engineering at Tractian), details why and how they moved to ScyllaDB to scale their data pipeline for this challenge. JP compares ScyllaDB, MongoDB, and PostgreSQL, evaluating their data models, query languages, sharding and replication, and benchmark results. Attendees will gain practical insights into the MongoDB to ScyllaDB migration process, including challenges, lessons learned, and the impact on product performance.

Corporate Open Source Anti-Patterns: A Decade Later

A little over a decade ago, I gave a talk on corporate open source anti-patterns, vowing that I would return in ten years to give an update. Much has changed in the last decade: open source is pervasive in infrastructure software, with many companies (like our hosts!) having significant open source components from their inception. But just as open source has changed, the corporate anti-patterns around open source have changed too: where the challenges of the previous decade were all around how to open source existing products (and how to engage with existing communities), the challenges now seem to revolve around how to thrive as a business without betraying the community that made it one in the first place. Open source remains one of humanity's most important collective achievements and one that all companies should seek to engage with at some level; in this talk, we will describe the changes that open source has seen in the last decade, and provide updated guidance for corporations for ways not to do it!

Getting Started Using the National Research Platform

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intra-mart Accel series 2024 Spring updates_En

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