Accumulo Summit 2014: Benchmarking Accumulo: How Fast Is Fast?

Speaker: Varun Sharma (Pinterest) Over the past year, HBase has become an integral component of Pinterest's storage stack. HBase has enabled us to quickly launch and iterate on new products and create amazing pinner experiences. This talk briefly describes some of these applications, the underlying schema, and how our HBase setup stays highly available and performant despite billions of requests every week. It will also include some performance tips for running on SSDs. Finally, we will talk about a homegrown serving technology we built from a mashup of HBase components that has gained wide adoption across Pinterest.

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Speakers: Jingcheng Du and Ramkrishna Vasudevan (Intel) As HBase continues to expand in application and enterprise or government deployments, there is a growing demand for storing data across geographically distributed datacenters for improved availability and disaster recovery. The Cross-Site BigTable extends HBase to make it well-suited for such deployments, providing the capabilities of creating and accessing HBase tables that are partitioned and asynchronously backed-up over a number of distributed datacenters. This talk reveals how the Cross-Site BigTable manages data access over multiple datacenters and removes the data center itself as a single point of failure in geographically distributed HBase deployments.

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Speaker: Jean-Daniel Cryans (Cloudera) HBase Replication has come a long way since its inception in HBase 0.89 almost four years ago. Today, master-master and cyclic replication setups are supported; many bug fixes and new features like log compression, per-family peers configuration, and throttling have been added; and a major refactoring has been done. This presentation will recap the work done during the past four years, present a few use cases that are currently in production, and take a look at the roadmap.

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HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.

Meet hbase 2.0

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Introduction to HBase. HBase is a NoSQL databases which experienced a tremendous increase in popularity during the last years. Large companies like Facebook, LinkedIn, Foursquare are using HBase. In this presentation we will address questions like: what is HBase?, and compared to relational databases?, what is the architecture?, how does HBase work?, what about the schema design?, what about the IT ressources?. Questions that should help you consider whether this solution might be suitable in your case.

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Speaker: Adam Warrington (Cloudera) The Kite SDK is a set of libraries and tools focused on making it easier to build systems on top of the Hadoop ecosystem. HBase support has recently been added to the Kite SDK Data Module, which allows a developer to model and access data in HBase consistent with how they would model data in HDFS using Kite. This talk will focus on Kite's HBase support by covering Kite basics and moving through the specifics of working with HBase as a data source. This feature overview will be supplemented by specifics of how that feature is being used in production applications at Cloudera.

HBase Data Modeling and Access Patterns with Kite SDK

Phoenix has evolved to become a full-fledged relational database layer over HBase data. We'll discuss the fundamental principles of how Phoenix pushes the computation to the server and why this leads to performance enabling direct support of low-latency applications, along with some major new features. Next, we'll outline our approach for transaction support in Phoenix, a work in-progress, and discuss the pros and cons of the various approaches. Lastly, we'll examine the current means of integrating Phoenix with the rest of the Hadoop ecosystem.

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SQL queries are often the #1 requested feature of key/value stores. Argyle will present our integration of Accumulo with Facebook’s PrestoDB distributed query engine. We will discuss: · Data locality between PrestoDB and Accumulo · Predicate pushdown for row keys · Leveraging a secondary index for column based queries The talk will include a live demonstration of big data benchmark queries.

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Speaker: Aaron Cordova Most users of Accumulo start developing applications on a single machine and will to scale to up to four orders of magnitude more machines without having to rewrite. In this talk we describe techniques for designing applications for scale, planning a large scale cluster, tuning the cluster for high speed ingest, dealing with a large amount of data over time, and unique features of Accumulo for taking advantage of up to ten thousand nodes in a single instance. We also include the largest public metrics gathered on Accumulo clusters to date and include a discussion of overcoming practical limits to scaling in the future.

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Talk Abstract Having the ability to diagnose and understand what is happening in distributed systems is essential. Tracing is one mechanism that enables analysis of operations in distributed systems by dividing each operation into a tree of measurable sub-tasks. HDFS, Accumulo, and HBase are now converging on a single tracing system utilizing HTrace, an open source tracing instrumentation library that recently became a new Apache Incubator project. This talk will cover tracing fundamentals, the instrumentation that has been added to HDFS to support tracing, and changes that have been made in Accumulo's tracing. It will also cover options for collecting and visualizing traces, as well as the current status of the HTrace podling. Speaker Billie Rinaldi Sr. Member of Technical Staff, Hortonworks Billie Rinaldi is a Senior Member of Technical Staff at Hortonworks, Inc., currently prototyping new features related to application monitoring and deployment in the Apache Hadoop ecosystem. Prior to August 2012, Billie engaged in big data science and research at the National Security Agency. Since 2008, she has been providing technical leadership regarding the software that is now Apache Accumulo. Billie is the VP of Apache Accumulo, the Accumulo Project Management Committee Chair, and a member of the Apache Software Foundation. She holds a Ph.D. in applied mathematics from Rensselaer Polytechnic Institute.

Accumulo Summit 2015: Tracing in Accumulo and HDFS [Internals]

Many organizations are looking to Hadoop clusters in order to store and manage an ever-increasing amount of data. As the volume and variety of data in these systems grows, administrators are being confronted with more information, from more sources, than they have ever seen concentrated in a single place. The responsibility for securing all this data can be daunting to an administrator, even intimidating. Could the answer lie in Accumulo? Conventional approaches to data security usually do not suffice for this scenario. They are often coarse-grained, applying only at the file or table level. In a world where arbitrary compute tasks can be pushed into the cluster, defining a security perimeter is difficult or impossible. On the other hand, relegating access policy enforcement to the application level instead of the database level ultimately invites a security disaster. This is the world that Chief Security Officers, Chief Information Officers, and Chief Data Officers live in, and the problem of security for big data is the single biggest impediment to delivering a Hadoop-based solution in the enterprise’s production network. Numerous organizations have implemented Hadoop as a pilot, but find themselves blocked by similar considerations when the time to move into production: • How do you implement fine-grained access controls in a Hadoop system? • What about encryption at rest? Encryption in motion? • How will this tie into our identity infrastructure? • How will this fit into our operational workflow? This keynote will explore the ways in which Apache Accumulo is uniquely positioned to mitigate or resolve problems around access control and security for big data, thus enabling Hadoop clusters to move from pilot to production. – Speaker – Russ Weeks Software Architect, PHEMI Systems Russ Weeks is a Software Architect at PHEMI. Prior to joining PHEMI Systems, Russ worked in the network management groups at Ericsson and Cray Supercomputers, where he discovered a passion for distributed data structures and algorithms. PHEMI Systems is a Vancouver, BC-based startup focused on the storage, retention and governance of structured and unstructured data. — More Information — For more information see http://www.accumulosummit.com/

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Accumulo requires its users to trust each Accumulo installation with their data — a malicious server or user could easily compromise critical data or learn secrets they are not authorized to access. One particular threat is a malicious Accumulo server compromising data’s integrity, by tampering with query results and returning forged, modified, or incomplete results to a user. In prior work, we implemented a lightweight client-side tool to protect against this kind of threat. We now present improvements to this tool that handle a wider range of attacks by a malicious server and reduce overhead for the client. In our solution, Accumulo clients use Authenticated Data Structures (ADSs) to verify their range queries’ integrity. ADS metadata is stored in Accumulo, so that after each query, the server must construct a proof that the query has not been tampered with. We use Accumulo iterators to compute these proofs on the server without requiring an unnecessary computational burden from the client. We will present our approach to adding ADSs to Accumulo, our schema for storing the ADS metadata, and opportunities for future work in efficiency and expressiveness. – Speaker – Leo St. Amour Military Fellow, MIT Lincoln Laboratory Leo St. Amour is a master’s student at Northeastern University and a military fellow at MIT Lincoln Laboratory. He graduated from the United States Military Academy in May 2015, where he worked on a TLS library with enhanced usability and security. In addition to his work on TLS and Accumulo, he is currently working on binary analysis, with a focus on discovering and hardening security properties. — More Information — For more information see http://www.accumulosummit.com/

Large Scale Accumulo Clusters

Aaron Cordova

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Talk Abstract Bulk ingest enables Accumulo to import externally-prepared data into existing tables. Unlike ingest via batch writers, much of the work of organizing data can be left to external processing frameworks such as MapReduce and scaled independently of the Accumulo cluster itself. This reduces the work required of the tablet servers to support ingest, freeing resources to support other operations. Under the hood, bulk ingest involves a number a moving parts and accounting for a variety of failure scenarios. This talk covers the components of the bulk ingest process in-depth and describes past, current and future implementations of this capability. Attendees will leave this session with an understanding of bulk ingest that will enable troubleshooting, capacity estimation and performance management. Speaker Eric Newton Senior Software Developer, SWComplete Eric Newton has been a programmer for over 30 years, and has worked on Accumulo since 2009. He has been an open-source contributor and consumer since 1988. Through the years, his distributed communications systems work has included Air Traffic Control, Systems Monitoring and Databases. Eric has started 3 of his own companies and helped several other businesses start.

Accumulo Summit 2015: Accumulo In-Depth: Building Bulk Ingest [Sponsored]

Talk Abstract As with all open-source databases, Accumulo developers often compete between building exciting new features and hacking on performance and stability. As the core features solidify and expand, we see many opportunities to improve performance. An effective methodology for performance improvement is scientific in nature, and follows a well-definite modeling and simulation approach, matching theory to experimentation in an iterative fashion. Ingest performance is one of the most differentiating characteristics of Accumulo. However, there is much room for improvement for typical ingest-heavy applications. Accumulo supports two mechanisms to bring data in: streaming ingest and bulk ingest. In bulk ingest, the goal is to maximize throughput without constraining latency. Bulk ingest involves creating a set of files that conform to Accumulo's internal RFile format and then registering those files with Accumulo. MapReduce provides a framework for generating, sorting, and storing key/value pairs, which form the primary elements of preparing RFiles for bulk ingest. MapReduce has been used many times over the years to break sorting records, such as Terasort. We can expect it is a reasonable choice for maximizing bulk ingest throughput. However, the theory often proves challenging to implement as there are many performance pitfalls along the way. In this talk, we dive deep into optimizing MapReduce for Accumulo bulk ingest. We share detailed theoretical and empirical performance models, we discuss techniques for profiling performance, and we suggest reusable techniques for squeezing the maximum performance out of enterprise-grade Accumulo bulk ingest. Speaker Chris McCubbin Director of Data Science, Sqrrl Chris is the Director of Data Science for Sqrrl. He has extensive experience with the Hadoop ecosystem and applying scientific computation algorithms to real-world datasets. Previously, Chris developed Big Data analysis tools for the Intelligence Community and applied artificial intelligence techniques to unmanned vehicle systems. He holds a MS in Computer Science and BS in Computer Science and Mathematics from the University of Maryland.

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Talk Abstract In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time. Speaker Joe Stein Principal Consultant, Big Data Open Source Security, LLC Joe Stein is an Apache Kafka committer and PMC member. Joe is the Founder and Principal Architect of Big Data Open Source Security LLC a professional services and product solutions company. Joe has been a developer, architect and technologist professionally for 15 years now having built back end systems that supported over one hundred million unique devices a day processing trillions of events. He blogs and hosts a podcast about Hadoop and related systems at All Things Hadoop and tweets @allthingshadoop

Accumulo Summit 2015: Real-Time Distributed and Reactive Systems with Apache ...

Apache Accumulo, originally developed by the National Security Agency and now an Apache Software Foundation project, builds upon Google's Bigtable design to provide a scalable, lightly-structured database capability complementing the ubiquitous Hadoop environment. The core capabilities of Accumulo include cell-level security, flexible schemas, real-time analytics, bulk I/O, and linear scalability beyond trillions of entries and petabytes of data. These new capabilities lead to techniques that unlock the power of Big Data, but don't fit into traditional database design patterns. Learn about the advantages of Apache Accumulo and how it fits into the Hadoop and NoSQL ecosystem. Presenter: Adam Fuchs, CTO, sqrrl

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LocationTech GeoMesa is a project that builds on open-source, distributed databases like Accumulo, HBase, and Cassandra to scale up indexing, querying, and analyzing billions of spatio-temporal data points. GeoMesa uses space-filling curves to index multi-dimensional data in Accumulo, and we'll discuss recent improvements for non-point geometries. Over the two and a half years GeoMesa has been an open-source project, GeoMesa's Accumulo schemas have evolved and our team has had a chance to work through creating and optimizing custom Accumulo iterators. These custom iterators allow for better query performance and interesting aggregations. GeoMesa provides support for distributed processing in Spark via MapReduce input and output formats that extend their Accumulo counterparts. We will discuss the performance benefit gained by reducing the number of default map/Spark tasks created for complex query patterns. The talk will conclude with updates about GeoMesa's integration with Jupyter notebook and improvements to GeoMesa's Spark integration. – Speaker – Dr. James Hughes Mathematician, Commonwealth Computer Research, Inc (CCRi) Dr. James Hughes is a mathematician at Commonwealth Computer Research, Inc. in Charlottesville, Virginia. He is a core committer for GeoMesa which leverages Accumulo and other distributed database systems to provide distributed computation and query engines. He is a LocationTech committer for GeoMesa, SFCurve, and GeoBench. He serves on the LocationTech Project Management Committee and Steering Committee. Through work with LocationTech and OSGeo projects like GeoTools and GeoServer, he works to build end-to-end solutions for big spatio-temporal problems. He holds a PhD in algebraic topology from the University of Virginia. — More Information — For more information see http://www.accumulosummit.com/

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Erik Krogen of LinkedIn presents regarding Dynamometer, a system open sourced by LinkedIn for scale- and performance-testing HDFS. He discusses one major use case for Dynamometer, tuning NameNode GC, and discusses characteristics of NameNode GC such as why it is important, and how it interacts with various current and future GC algorithms. This is taken from the Apache Hadoop Contributors Meetup on January 30, hosted by LinkedIn in Mountain View.

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on-Volatile-Memory express (NVMe) standard promises and order of magnitude faster storage than regular SSDs, while at the same time being more economical than regular RAM on TB/$. This talk evaluates the use cases and benefits of NVMe drives for its use in Big Data clusters with HBase and Hadoop HDFS. First, we benchmark the different drives using system level tools (FIO) to get maximum expected values for each different device type and set expectations. Second, we explore the different options and use cases of HBase storage and benchmark the different setups. And finally, we evaluate the speedups obtained by the NVMe technology for the different Big Data use cases from the YCSB benchmark. In summary, while the NVMe drives show up to 8x speedup in best case scenarios, testing the cost-efficiency of new device technologies is not straightforward in Big Data, where we need to overcome system level caching to measure the maximum benefits.

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

apidays

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Elevate Developer Efficiency & build GenAI Application with Amazon Q

Bhuvaneswari Subramani

DBX First Quarter 2024 Investor Presentation

Dropbox

Discover the innovative features and strategic vision that keep WSO2 an industry leader. Explore the exciting 2024 roadmap of WSO2 API management, showcasing innovations, unified APIM/APK control plane, natural language API interaction, and cloud native agility. Discover how open source solutions, microservices architecture, and cloud native technologies unlock seamless API management in today's dynamic landscapes. Leave with a clear blueprint to revolutionize your API journey and achieve industry success!

WSO2's API Vision: Unifying Control, Empowering Developers

WSO2

Vector Search -An Introduction in Oracle Database 23ai.pptx

Remote DBA Services

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

presentation ICT roal in 21st century education

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"I see eyes in my soup": How Delivery Hero implemented the safety system for ...