Hadoop World Vertica
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This document discusses integrating Apache Hadoop with Vertica, an analytic database with MPP columnar architecture. It describes how Vertica can be used as a data source and target for Hadoop MapReduce jobs, with Vertica input and output formatters allowing data to be moved between the two systems. Examples are provided of using Vertica to serve as a structured data repository for Hadoop and running algorithms like tickstore with map pushdown to optimize queries.
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Here are the key points about projection segmentation in Vertica:
- Projection segmentation splits large projections into multiple segments and distributes those segments across database nodes for improved parallelism and high availability.
- The segmentation process randomly distributes rows of data across all available nodes using a hash function. This random distribution helps optimize query performance.
- Segmentation allows Vertica to parallelize queries by enabling each node to work independently on its portion of the data.
- It also provides high availability because if a node fails, its data segments are available on other nodes, avoiding data loss.
- During recovery, the replacement node can retrieve missing segments from the live segments on other nodes.
- Administrators can control
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The document discusses various database indexing and joining techniques. It provides details on different types of indexes like B+ tree, bitmap indexes and hash indexes. It also explains different join algorithms like nested loops joins, merge joins and hash joins. It describes how these indexes and joins are used by the query optimizer to generate and select the most efficient execution plan for a given SQL query.
Vertica
Vertica is a column-oriented database developed by Michael Stonebraker based on his earlier work on C-Store at MIT. The document discusses Vertica's storage model, which uses projections to store table columns separately in sorted order to enable more efficient scanning and compression compared to row storage. It also covers compression techniques like run-length encoding used in Vertica to further improve storage and performance.
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Hive_p
The document discusses Hive architecture and workflow. It begins by explaining the key components of Hive including the driver, compiler, metastore, and Hadoop client. It then describes how the compiler converts HiveQL queries into logical and physical plans consisting of operators that are executed as MapReduce jobs. The document outlines the steps taken in the compiler including parsing, semantic analysis, logical and physical plan generation, and optimization. It provides examples of logical operators and how the logical optimizer performs transformations like predicate pushdown.
HPE Vertica_7.0.x Administrators Guide
This document describes the functions performed by an HP Vertica database administrator (DBA).
Perform these tasks using only the dedicated database administrator account that was created
when you installed HP Vertica. The examples in this documentation set assume that the
administrative account name is dbadmin.
l To perform certain cluster configuration and administration tasks, the DBA (users of the
administrative account) must be able to supply the root password for those hosts. If this
requirement conflicts with your organization's security policies, these functions must be
performed by your IT staff.
l If you perform administrative functions using a different account from the account provided
during installation, HP Vertica encounters file ownership problems.
l If you share the administrative account password, make sure that only one user runs the
Administration Tools at any time. Otherwise, automatic configuration propagation does not
work correctly.
l The Administration Tools require that the calling user's shell be /bin/bash. Other shells give
unexpected results and are not supported.
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In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."
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Here are the key points about projection segmentation in Vertica:
- Projection segmentation splits large projections into multiple segments and distributes those segments across database nodes for improved parallelism and high availability.
- The segmentation process randomly distributes rows of data across all available nodes using a hash function. This random distribution helps optimize query performance.
- Segmentation allows Vertica to parallelize queries by enabling each node to work independently on its portion of the data.
- It also provides high availability because if a node fails, its data segments are available on other nodes, avoiding data loss.
- During recovery, the replacement node can retrieve missing segments from the live segments on other nodes.
- Administrators can control
DB
The document discusses various database indexing and joining techniques. It provides details on different types of indexes like B+ tree, bitmap indexes and hash indexes. It also explains different join algorithms like nested loops joins, merge joins and hash joins. It describes how these indexes and joins are used by the query optimizer to generate and select the most efficient execution plan for a given SQL query.
Vertica
Vertica is a column-oriented database developed by Michael Stonebraker based on his earlier work on C-Store at MIT. The document discusses Vertica's storage model, which uses projections to store table columns separately in sorted order to enable more efficient scanning and compression compared to row storage. It also covers compression techniques like run-length encoding used in Vertica to further improve storage and performance.
Vertica 7.0 Architecture Overview
Vertica Analytics Platform. Column Orientation. Advanced Compression. Automatic Database Design. Massively Parallel Processing (MPP). Native SQL and Application Integration. Amazon Machine Image (AMI). Flex Zone. Installation Demonstration. What is a Projection? Tables versus Projections. Replication. Segmentation. Vertica Query Execution Basics. Vertica Transaction Model. Hybrid Data Store: WOS and ROS.
Hive_p
The document discusses Hive architecture and workflow. It begins by explaining the key components of Hive including the driver, compiler, metastore, and Hadoop client. It then describes how the compiler converts HiveQL queries into logical and physical plans consisting of operators that are executed as MapReduce jobs. The document outlines the steps taken in the compiler including parsing, semantic analysis, logical and physical plan generation, and optimization. It provides examples of logical operators and how the logical optimizer performs transformations like predicate pushdown.
HPE Vertica_7.0.x Administrators Guide
This document describes the functions performed by an HP Vertica database administrator (DBA).
Perform these tasks using only the dedicated database administrator account that was created
when you installed HP Vertica. The examples in this documentation set assume that the
administrative account name is dbadmin.
l To perform certain cluster configuration and administration tasks, the DBA (users of the
administrative account) must be able to supply the root password for those hosts. If this
requirement conflicts with your organization's security policies, these functions must be
performed by your IT staff.
l If you perform administrative functions using a different account from the account provided
during installation, HP Vertica encounters file ownership problems.
l If you share the administrative account password, make sure that only one user runs the
Administration Tools at any time. Otherwise, automatic configuration propagation does not
work correctly.
l The Administration Tools require that the calling user's shell be /bin/bash. Other shells give
unexpected results and are not supported.
Big Data Day LA 2015 - Scalable and High-Performance Analytics with Distribut...
"R is the most popular language in the data-science community with 2+ million users and 6000+ R packages. R’s adoption evolved along with its easy-to-use statistical language, graphics, packages, tools and active community. In this session we will introduce Distributed R, a new open-source technology that solves the scalability and performance limitations of vanilla R. Since R is single-threaded and does not scale to accommodate large datasets, Distributed R addresses many of R’s limitations. Distributed R efficiently shares sparse structured data, leverages multi-cores, and dynamically partitions data to mitigate load imbalance.
In this talk, we will show the promise of this approach by demonstrating how important machine learning and graph algorithms can be expressed in a single framework and are substantially faster under Distributed R. Additionally, we will show how Distributed R complements Vertica, a state-of-the-art columnar analytics database, to deliver a full-cycle, fully integrated, data “prep-analyze-deploy” solution."
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Oath has one of the largest footprint of Hadoop, with tens of thousands of jobs run every day. Reliability and consistency is the key here. With 50k+ nodes there will be considerable amount of nodes having disk, memory, network, and slowness issues. If we have any hosts with issues serving/running jobs can increase tight SLA bound jobs’ run times exponentially and frustrate users and support team to debug it.
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The document discusses Vertica, a column-oriented database management system. It explains that Vertica provides 10x to 100x better performance than traditional RDBMS through its columnar storage format, linear scalability, and built-in fault tolerance. The document then provides details on how Vertica works, how to properly use it through configuration of projections and sort orders, and examples of queries and optimizations on a sample dataset.
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Speaker:
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Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
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This document provides information about Vertica 8th Avenue, a residential condominium located in Bonifacio Global City, Taguig. It highlights the development's superior location in central BGC near international schools and hospitals. Details include the building layout with 33 residential floors and 727 units ranging from studios to 4-bedroom lofts. Amenities include pools, a gym, and garden on the 7th floor. Parking is provided on the first 5 podium floors. Sample unit sizes and configurations are shown for different unit types.
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Vertica
Vertica is a column-oriented, distributed database that provides faster performance than Postgres for large datasets. It allows queries to be distributed across multiple servers for horizontal scaling. While it does not have indexes like row-oriented databases, it uses projections similar to materialized views to optimize queries. Based on benchmarks, Vertica was over 100 times faster than Postgres for an aggregate count query on a large transaction table and over 300 times faster for a distinct user count. While replacing Postgres with Vertica required some code and configuration changes, its performance gains make it a suitable replacement for large analytical workloads.
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HPE Vertica Chile Desayuno Oct 2016
Carlos González, Hewlett Packard Enterprise, nos habla acerca en la implicación del mercado de Big Data en su negocio y el papel que una solución como Vertica juega en éste de la mano de Qlik.
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This document summarizes algorithms for large-scale data mining using MapReduce, including:
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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