伉失伙正奶丞煦昴扔奈申旦◇凶屯心月◆毛盓尹月詢褫蚚俶失奈平氾弁民乓

Apr 27, 201513 likes15,130 views

The document outlines an architecture for utilizing AWS services including EC2, ELB, and Redshift for both online and batch processing applications. It describes data unloading from Redshift to an S3 bucket and subsequent data loading from S3 into Redshift, detailing the necessary credentials and formatting. The setup spans multiple availability zones for enhanced reliability.

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The document describes the compilation process of Hive. It explains how the Hive compiler takes a Hive query, parses it into an abstract syntax tree (AST), analyzes it semantically to generate a query block (QB), transforms the QB into a logical query plan, performs logical and physical optimizations, and finally generates the physical query plan consisting of tasks. The semantic analyzer maps elements in the AST to metadata, aliases, and constructs query objects.

Azure 辻け𡦀� - 妏蚚Python, R, Spark, CNTK 旮僅𡦀� Herman Wu

The document discusses Microsoft's Cognitive Toolkit (CNTK), an open source deep learning toolkit developed by Microsoft. It provides the following key points: 1. CNTK uses computational graphs to represent machine learning models like DNNs, CNNs, RNNs in a flexible way. 2. It supports CPU and GPU training and works on Windows and Linux. 3. CNTK achieves state-of-the-art accuracy and is efficient, scaling to multi-GPU and multi-server settings.

Db tech show - hivemallMakoto Yui

This document introduces Hivemall, an open-source machine learning library built as a collection of Hive user-defined functions (UDFs). Hivemall allows users to perform scalable machine learning on large datasets stored in Hive/Hadoop. It supports various classification, regression, recommendation, and feature engineering algorithms. Some key algorithms include logistic regression, matrix factorization, random forests, and anomaly detection. Hivemall is designed to perform machine learning efficiently by avoiding intermediate data reads/writes to HDFS. It has been used in industry for applications such as click-through rate prediction, churn detection, and product recommendation.

Apache: Big Data North America 2017 統樓�豢 #streamctjp喊硃堯棗棗!犯矛伕永由奈生永玄伐奈弁

The document is a report from Yamada Takaya about his attendance at the Apache Big Data North America 2017 conference in Miami from May 16-18. It provides an overview of the conference and various sessions, with a focus on stream processing engines and Apache Beam. Key highlights discussed include the growing support for core streaming capabilities across engines and the potential of Beam to further integrate streaming solutions.

綅蚚CNTK �釬旮僅𡦀�昜璃望舑 Deep Learning based Object Detection with Microsoft Cogniti...Herman Wu

The document discusses the Microsoft Cognitive Toolkit (CNTK), a deep learning framework optimized for performance, its benchmarks against other frameworks, and its features such as support for various programming languages and extensibility. CNTK demonstrates significant speed advantages over TensorFlow in various tasks, and it has been successfully utilized in diverse applications from speech recognition to image classification. The document also provides guidance on getting started with CNTK through resources available on GitHub and other platforms.

There and back_again_oracle_and_big_data_16x9Gleb Otochkin

Pivotal Greenplum 棒岍測穴伙民弁仿它玉?犯奈正煦昴皿仿永玄白巧奈丞Masayuki Matsushita

Pivotal Greenplum is a massively parallel processing (MPP) database for analytics. It provides high performance for data warehousing and big data analytics workloads. Key features include its ability to load and query data in parallel across multiple CPUs and disks, support for SQL and analytical functions and libraries like MADlib, and deployment on public clouds or on-premises. Pivotal Greenplum can be used for both structured and unstructured data and integrates with other Pivotal products like GemFire, Data Flow, and the Pivotal Data Suite for analytics workflows.

Azure Data Explorer deep dive - review 04.2020Riccardo Zamana

Modern Data Science Lifecycle with ADX & Azure This document discusses using Azure Data Explorer (ADX) for data science workflows. ADX is a fully managed analytics service for real-time analysis of streaming data. It allows for ad-hoc querying of data using Kusto Query Language (KQL) and integrates with various Azure data ingestion sources. The document provides an overview of the ADX architecture and compares it to other time series databases. It also covers best practices for ingesting data, visualizing results, and automating workflows using tools like Azure Data Factory.

Maintainable cloud architecture_of_hadoopKai Sasaki

Kai Sasaki discusses Treasure Data's architecture for maintaining Hadoop on the cloud. Some key points are using stateless services like Hive metastore and cloud storage. They also manage multiple Hadoop versions by downloading packages from S3. Regression tests on Hive queries and a REST API help ensure changes don't cause issues. An RDBMS-based queue provides persistence and scheduling across tasks. The overall aim is high maintainability through statelessness, mobility of components, and queueing of jobs.

Intel: How to Use Alluxio to Accelerate BigData Analytics on the Cloud and Ne...Alluxio, Inc.

The document discusses using Alluxio as an acceleration layer for analytics workloads with disaggregated storage on cloud. Key points: - Alluxio provides an in-memory layer that caches frequently accessed data, providing a 2-3x performance boost over using object storage directly. - Workloads like Terasort saw up to 3.25x faster performance when using Alluxio caching compared to the baseline. - For SQL queries, Alluxio caching improved performance for most queries, though the first few queries in a session saw slower performance as the cache was warming up. - Compute nodes saw higher CPU utilization when using Alluxio, indicating it offloads work from storage nodes to take

Apache Spark毛蚚中凶旦弗奈仿皮伙卅媆炵蹈犯奈正及�都𨈘眭乒犯伙悝�末白玄它尼失及嶱逃Ryo 謠 Kawahara 碩埻

The document outlines a method for developing software for scalable anomaly detection in time-series data using Apache Spark. It discusses the implementation of a prediction model that compares observed sensor data with predicted values to identify anomalies, and details the use of lasso regression for training the model. Performance improvements and challenges faced during the process, such as order preservation in RDD operations and the use of alternative APIs, are also highlighted.

Data Science Across Data Sources with Apache ArrowDatabricks

Dremio is presenting its data lake engine at the Spark + AI Summit 2020, highlighting challenges related to data accessibility and growth. The document emphasizes Apache Arrow's role in enhancing performance through columnar storage, which allows for faster data processing and more efficient execution of SQL queries. Dremio's technology offers significant improvements in speed and cost-effectiveness for querying large datasets housed in cloud data lakes.

Keynote Hadoop Summit Dublin 2016: Hadoop Platform Innovations - Pushing The ...Sumeet Singh

The document discusses innovations in the Hadoop platform, emphasizing improvements in cluster utility and heterogeneous computing capabilities across different server configurations. It highlights significant increases in compute capacity and average utilization, alongside advancements in batch and real-time processing, as well as the introduction of new projects like Caffe on Spark and Omid for improved transactional frameworks. The future direction indicates the need for greater intelligence, speed, and efficiency to manage increasing scale demands.

Girish Juneja - Intel Big Data & Cloud Summit 2013IntelAPAC

This document discusses big data trends such as the growth of networked sensors, connected devices, and smartphone users. It then summarizes Intel's investments in big data technologies, including their software, processors, networking, storage and memory products. The document promotes Intel's Distribution for Apache Hadoop software and how it provides security, performance optimizations and support for workloads like data mining, graph analytics and full text search. Real-world customer examples are provided that demonstrate gains in performance, cost savings and new analytics capabilities.

Hive on mesos StrataSzehon Ho

The presentation discusses Criteo's architecture and usage of Apache Hive for data analytics at scale, highlighting key components such as load balancing, monitoring, and the evolution of Hive's architecture. It outlines the company's large scale of operations with significant data handling and performance advertising services. The document also covers challenges faced with Kerberos integration and benchmarks for optimizing Hive performance in a production environment.

Unattended Apache BigTop installer CD using preseedJazz Yao-Tsung Wang

This document discusses an unattended Apache BigTop installer CD using preseeding to automate installation. It includes screenshots and basic information about the CD, which installs Hadoop 2.0 and YARN by default with the option to install Hue. The document also provides links to download ISO files for BigTop versions 0.6-0.7 and the GitHub source for customizing the installer for Ubuntu and Debian. Overall, the document introduces an automated installer for the Apache BigTop Hadoop distribution and related big data technologies.

Time Series Analytics Azure ADXRiccardo Zamana

The document provides a comprehensive overview of Azure Data Explorer (ADX) and its capabilities for time series analytics, covering its architecture, pricing models, usage scenarios, and comparison with other databases. It highlights ADX's features for efficient data ingestion, management, and query performance while identifying suitable applications in fields like IoT and big data analytics. Additionally, it discusses practical configurations, advanced ingestion techniques, and the unique aspects that differentiate ADX from other analytics platforms.

Azure HDInsightAshish Thapliyal

The document discusses HDInsight and provides information on: 1. HDInsight can scale horizontally by adding more nodes to the HDFS cluster. 2. HDInsight clusters on Azure can be used to ingest, transform, and analyze large amounts of data stored in Azure Blob Storage or Azure Data Lake Store. 3. HDInsight supports various query engines like Spark, Hive, and Hadoop for interactive querying and analytics on large datasets.

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1. The Hadoop Image Processing (HIP) pipeline acquires vehicle images, identifies updates, generates URLs, crops and resizes images, copies them to asset servers, and removes duplicates. 2. It uses HBase for image storage and archiving, MapReduce for image processing, Kafka for publishing to asset servers, OpenCV for image processing, and Avro for data serialization. 3. Performance testing showed HIP scales linearly and is at least 10x faster than the previous system, and using cascading downloads provided a 20% performance gain.

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Hopsworks in the cloud Berlin Buzzwords 2019 Jim Dowling

Hops is an advanced open-source data platform developed by Logical Clocks, notable for its Hierarchical File System (HopsFS) which provides strong consistency and supports various functionalities including multi-tenancy, GPU management, and a feature store. It has demonstrated significant performance improvements, such as a 16x increase in throughput on specific workloads and achieving over 1.6 million operations per second in tests. Hopsworks facilitates data-intensive AI workflows, enabling efficient training, model serving, and data governance through end-to-end pipelines using familiar tools like Jupyter notebooks and Apache Airflow.

????? ???? ?? ???? - ??? (AWS ????????)Amazon Web Services Korea

1. The document discusses various AWS services that can be used to build serverless applications including AWS Lambda, Amazon API Gateway, Amazon DynamoDB, Amazon S3, and Amazon Cognito. 2. It provides examples of how serverless applications can be built to handle web and mobile backends through code snippets and diagrams showing how different AWS services integrate together. 3. The document also references GitHub repositories containing sample code for building serverless applications using various frameworks and best practices.

Using Alluxio as a Fault Tolerant Pluggable Optimization Component to Compute...Alluxio, Inc.

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The document discusses the challenges of data management and the application of just-in-time (JIT) analytics, particularly using Spark for performance tuning and autonomous database administration. It highlights the benefits of autonomous data management, such as reduced query time and resource computation, allowing analysts to focus on problem-solving rather than data handling. Various tuning strategies in Spark, including indexing and configuration, are emphasized to enhance performance within modern analytics frameworks.

Hadoop Summit Kiosk DeckSumeet Singh

This document discusses Hadoop at Yahoo, including: - Yahoo has built a large multi-tenant Apache Hadoop deployment that powers many of its businesses and use cases. - Over the years, Yahoo has scaled its Hadoop infrastructure significantly, now consisting of over 50,000 servers and 50PB of storage. - Yahoo uses Hadoop for a wide range of use cases across advertising, search, personalization, anti-spam, and more, processing data at massive scales of billions of records daily.

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The document outlines best practices for data infrastructure architecture tailored for medium-sized organizations, focusing on effective data collection, storage, and analysis. It emphasizes the importance of SQL, various querying tools, and the comparison between data formats such as JSON and Parquet for efficient data management. The author, a data architect, provides insights from their experience and suggests using Apache Zeppelin for enhanced visualization and resource management.

Apache Kylin - Balance Between Space and TimeDataWorks Summit

This document provides an overview of Apache Kylin, an open source distributed analytics engine that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop for extremely large datasets. It discusses Kylin's features such as fast OLAP capabilities, ANSI SQL interface, integration with BI tools, and job management. The document also covers Kylin's architecture, cube building process, storage in HBase, and query planning.

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Russia is one of the most aggressive nations when it comes to state coordinated cyberattacks?〞?and Ukraine has been at the center of their crosshairs for 3 years. This report, provided the State Service of Special Communications and Information Protection of Ukraine contains an incredible amount of cybersecurity insights, showcasing the coordinated aggressive cyberwarfare campaigns of Russia against Ukraine. It brings to the forefront that understanding your adversary, especially an aggressive nation state, is important for cyber defense. Knowing their motivations, capabilities, and tactics becomes an advantage when allocating resources for maximum impact. Intelligence shows Russia is on a cyber rampage, leveraging FSB, SVR, and GRU resources to professionally target Ukraine＊s critical infrastructures, military, and international diplomacy support efforts. The number of total incidents against Ukraine, originating from Russia, has steadily increased from 1350 in 2021 to 4315 in 2024, but the number of actual critical incidents has been managed down from a high of 1048 in 2022 to a mere 59 in 2024?〞?showcasing how the rapid detection and response to cyberattacks has been impacted by Ukraine＊s improved cyber resilience. Even against a much larger adversary, Ukraine is showcasing outstanding cybersecurity, enabled by strong strategies and sound tactics. There are lessons to learn for any enterprise that could potentially be targeted by aggressive nation states. Definitely worth the read!

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