The document provides an introduction to Hadoop. It discusses how Google developed its own infrastructure using Google File System (GFS) and MapReduce to power Google Search due to limitations with databases. Hadoop was later developed based on these Google papers to provide an open-source implementation of GFS and MapReduce. The document also provides overviews of the HDFS file system and MapReduce programming model in Hadoop.
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
Cisco connect toronto 2015 big data sean mc keownCisco Canada
The document provides an overview of big data concepts and architectures. It discusses key topics like Hadoop, HDFS, MapReduce, NoSQL databases, and MPP relational databases. It also covers network design considerations for big data, common traffic patterns in Hadoop, and how to optimize performance through techniques like data locality and quality of service policies.
HBaseCon 2013: Full-Text Indexing for Apache HBaseCloudera, Inc.
This document discusses full-text indexing for HBase tables. It describes how Lucene indices are organized based on HBase regions. Index building is implemented using coprocessors to update indices on data changes. Index splitting is optimized to avoid blocking updates during region splits. Search performance of indexing 10 billion records was tested, showing search times of around 1 second.
Speakers: Eric Czech and Alec Zopf (Next Big Sound)
Managing the evolution of data within HBase over time is not easy: Data resulting from Hadoop processing pipelines or otherwise placed in HBase is subject to the same kinds of oversights, bugs, and faulty assumptions inherent to the software that creates it. While the development of this software is often effectively managed through revision control systems, data itself is rarely modeled in a way that affords the same flexibility. In this session, we'll talk about how to build a versioned, time-series data store using HBase that can provide significantly greater adaptability and performance than similar systems.
Efficient in situ processing of various storage types on apache tajoHyunsik Choi
The document discusses Apache Tajo, an open source data warehouse system that supports efficient in-situ processing of various storage types. It describes Tajo's architecture, how it supports different storage backends like HDFS, S3, HBase and data formats. The key points are:
1) Tajo provides a unified interface to integrate and process data from various storage systems and formats like HDFS, S3, HBase, in a single system.
2) It uses a pluggable storage and data format architecture with tablespaces to abstract different physical storage configurations.
3) Operations can be pushed down to underlying storages for optimization during query execution.
4) Current supported storages include HDFS, S
Realtime Analytics with Hadoop and HBaselarsgeorge
The document discusses realtime analytics using Hadoop and HBase. It begins by introducing the speaker and their experience. It then discusses moving from batch processing with Hadoop to more realtime needs, and how systems like HBase can help bridge that gap. Several designs are presented for using HBase and Hadoop together to enable both realtime and batch analytics on large datasets.
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
The document proposes an Agile Big Data model to address perceived issues with traditional Hadoop implementations. It discusses the motivation for change and outlines an Agile model with self-organized roles including data stewards, data scientists, project teams, and an architecture board. Key aspects of the proposed model include independent and self-managed project teams, a domain-driven data model, and emphasis on data quality and governance through the involvement of data stewards across domains.
Things Every Oracle DBA Needs to Know About the Hadoop Ecosystem (c17lv version)Zohar Elkayam
Big data is one of the biggest buzzword in today's market. Terms like Hadoop, HDFS, YARN, Sqoop, and non-structured data has been scaring DBA's since 2010 - but where does the DBA team really fit in?
In this session, we will discuss everything database administrators and database developers needs to know about big data. We will demystify the Hadoop ecosystem and explore the different components. We will learn how HDFS and MapReduce are changing the data world, and where traditional databases fits into the grand scheme of things. We will also talk about why DBAs are the perfect candidates to transition into Big Data and Hadoop professionals and experts.
Learning Objective #1: What is the Big Data challenge
Learning Objective #2: Learn about Hadoop - HDFS, MapReduce and Yarn
Learning Objective #3: Understand where a DBA fits in this world
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It was designed to support large datasets and scale efficiently using low-cost hardware. Hadoop's core components include HDFS for distributed storage and MapReduce for distributed processing. Hadoop saw early adoption by companies like Yahoo and Facebook to support applications like advertisement targeting, searches, and security using large datasets.
Apachecon Europe 2012: Operating HBase - Things you need to knowChristian Gügi
This document provides an overview of important concepts for operating HBase, including:
- HBase stores data in columns families stored as files on disk and writes to memory before flushing to disk.
- Manual and automatic splitting of regions is covered, as well as challenges of improper splitting.
- Tools for monitoring, debugging, and visualizing HBase operations are discussed.
- Key lessons focus on proper data modeling, extensive monitoring, and understanding the whole Hadoop ecosystem.
Apache Hive is a data warehouse infrastructure tool built on Hadoop that allows users to query and analyze large datasets stored in Hadoop using SQL. It works by translating SQL queries into MapReduce jobs that process the data. Hive provides a metastore to store metadata about the schema and HDFS location of tables, and uses a query language called HiveQL that is similar to SQL. It allows users to run analytics on large datasets without needing to write MapReduce code directly.
HBaseCon 2012 | Building a Large Search Platform on a Shoestring BudgetCloudera, Inc.
This document discusses YapMap, a visual search platform built on Hadoop and HBase. It summarizes how YapMap interfaces with HBase data, uses HBase as a data processing pipeline with checkpoints, and had to adjust schemas and migrate data as the system evolved. It also covers how YapMap constructs search indexes in shards based on HBase regions and stored indexes on HDFS. The document concludes with some lessons learned around optimizing HBase operations.
This document provides an overview of a NoSQL Night event presented by Clarence J M Tauro from Couchbase. The presentation introduces NoSQL databases and discusses some of their advantages over relational databases, including scalability, availability, and partition tolerance. It covers key concepts like the CAP theorem and BASE properties. The document also provides details about Couchbase, a popular document-oriented NoSQL database, including its architecture, data model using JSON documents, and basic operations. Finally, it advertises Couchbase training courses for getting started and administration.
HBaseCon 2012 | Mignify: A Big Data Refinery Built on HBase - Internet Memory...Cloudera, Inc.
Mignify is a platform for collecting, storing and analyzing Big Data harvested from the web. It aims at providing an easy access to focused and structured information extracted from Web data flows. It consists of a distributed crawler, a resource-oriented storage based on HDFS and HBase, and an extraction framework that produces filtered, enriched, and aggregated data from large document collections, including the temporal aspect. The whole system is deployed in an innovative hardware architecture comprising of a high number of small (low-consumption) nodes. This talk will tackle the decisions made along the design and development of the platform, both under a technical and functional perspective. It will introduce the cloud infrastructure, the LTE-like ingestion of the crawler output into HBase/HDFS, and the triggering mechanism of analytics based on a declarative filter/extraction specification. The design choices will be illustrated with a pilot application targeting Daily Web Monitoring in the context of a national domain.
Harmonizing Multi-tenant HBase Clusters for Managing Workload DiversityHBaseCon
Speakers: Dheeraj Kapur, Rajiv Chittajallu & Anish Mathew (Yahoo!)
In early 2013, Yahoo! introduced multi-tenancy to HBase to offer it as a platform service for all Hadoop users. A certain degree of customization per tenant (a user or a project) was achieved through RegionServer groups, namespaces, and customized configs for each tenant. This talk covers how to accommodate diverse needs to individual tenants on the cluster, as well as operational tips and techniques that allow Yahoo! to automate the management of multi-tenant clusters at petabyte scale without errors.
Big data refers to large datasets that are difficult to process using traditional database management tools. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It provides reliable data storage with the Hadoop Distributed File System (HDFS) and high-performance parallel data processing using MapReduce. The Hadoop ecosystem includes components like HDFS, MapReduce, Hive, Pig, and HBase that provide distributed data storage, processing, querying and analysis capabilities at scale.
Apache Tez - A unifying Framework for Hadoop Data ProcessingDataWorks Summit
This document provides an overview of Apache Tez, a framework for building data processing applications on Hadoop YARN. It describes how Tez allows applications to define complex data flows as directed acyclic graphs (DAGs) and handles distributed execution, fault tolerance, and resource management. Tez has improved the performance of Apache Hive and Pig by an order of magnitude by enabling more flexible DAG definitions and runtime optimizations. It also supports integration with other data processing engines like Spark, Storm and interactive SQL queries. The document outlines how Tez works and provides guidance on how developers can contribute to the open source project.
El Centro Agroindustria y Pesquero de la Costa Pacífica está ofreciendo la certificación en operar maquinaria y equipo agrícola según el manual técnico. La oferta está abierta hasta el 15 de julio de 2016 para tractores de empresas palmicultoras y tractores particulares. Los requisitos para inscribirse incluyen una copia de la cédula al 150%, una copia del recibo de energía del año 2016 y un correo electrónico si está disponible.
El documento presenta los nombres y cargos de cinco personas involucradas en el Centro Agroindustrial y Pesquero de la Costa Pacífica, incluyendo un auditor, subdirector, coordinador académico, líder de gestión documental y líder de certificación por competencias.
Инвесткафе — первое в России агентство независимой аналитики. Мы предлагаем профессиональную, легкую для восприятия аналитику по фондовому рынку и рекомендации независимых экспертов по акциям компаний.
5 Tips for Making Charitable Donations CountJohn Partilla
John Partilla shares tips for giving with impact, ensuring that donations and gifts to organizations or individuals help serve their intended purpose. A blog on this topic can be found on John's website: http://johnpartilla.org/5-tips-for-making-your-charitable-donations-count/
SSgt Jesse Reynolds is recommended for any position as he explores new career fields. SSgt Reynolds excelled as an electrician and in aircraft restoration, always looking to improve his knowledge and help the organization. He displayed an ability to interpret technical data, troubleshoot, comprehend policies, and explain details to peers. SSgt Reynolds maintained strong computer skills and created documents to help manage projects. He volunteered on weekends and is always seeking new techniques, enjoying challenges of the career field. SSgt Reynolds has an outstanding work ethic and pleasant demeanor.
- The document discusses developing resilience skills in women who have experienced trauma such as domestic violence, child abuse, or sexual assault.
- It describes how trauma can negatively impact victims' mental health, identity, trust, and problem-solving abilities through feelings of shame, powerlessness, and a loss of self-efficacy. Victims often get trapped in a cycle of abuse.
- Developing resilience involves gaining skills like optimism, self-efficacy, autonomy, and trusting relationships. Programs aim to help victims challenge unhelpful core beliefs formed during trauma and build a new identity through social support and understanding their own capacity for growth.
Child sexual abuse has significant negative impacts beyond just the direct victim. Survivors often develop post-traumatic stress disorder and depression, which influence their relationships and social interactions. They also struggle with emotion regulation and forming secure attachments with others. As a result, survivors typically have more troubled intimate relationships as adults, characterized by less satisfaction, intimacy issues, and higher risk of violence. Their difficulties processing the abuse psychologically and regulating emotions spillover to harming relationships with family, friends, and partners.
This document provides a summary of S. Balaji's qualifications and experience implementing SAP Business One solutions. It outlines his educational background, SAP certifications, skills, and professional experience leading multiple SAP implementation projects for various clients across different industries as a senior consultant. His roles included preparing documentation, conducting training, testing, troubleshooting issues, and providing ongoing support.
Kelvin Esisi has experience in petroleum drilling and a master's degree in gas engineering and management. He has worked as a mudlogging geologist monitoring drilling operations and reporting anomalies. His education included modules focused on natural gas production, distribution, and reducing gas flaring. He is skilled in reservoir modeling software, project management, communication, and working as part of a team.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a master-slave architecture with the NameNode as master and DataNodes as slaves. The NameNode manages file system metadata and the DataNodes store data blocks. Hadoop also includes a MapReduce engine where the JobTracker splits jobs into tasks that are processed by TaskTrackers on each node. Hadoop saw early adoption from companies handling big data like Yahoo!, Facebook and Amazon and is now widely used for applications like advertisement targeting, search, and security analytics.
Big Data Developers Moscow Meetup 1 - sql on hadoopbddmoscow
This document summarizes a meetup about Big Data and SQL on Hadoop. The meetup included discussions on what Hadoop is, why SQL on Hadoop is useful, what Hive is, and introduced IBM's BigInsights software for running SQL on Hadoop with improved performance over other solutions. Key topics included HDFS file storage, MapReduce processing, Hive tables and metadata storage, and how BigInsights provides a massively parallel SQL engine instead of relying on MapReduce.
HadoopDB is a system that combines the performance of parallel database systems with the flexibility and fault tolerance of Hadoop. It uses Hadoop as the communication layer between multiple single-node database instances running on cluster nodes. Benchmark results showed that HadoopDB's performance was close to parallel databases for structured queries and similar to Hadoop for unstructured queries, while also providing Hadoop's ability to operate in heterogeneous environments and tolerate faults.
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
Things Every Oracle DBA Needs to Know about the Hadoop EcosystemZohar Elkayam
Session from BGOUG I presented in June, 2016
Big data is one of the biggest buzzword in today's market. Terms like Hadoop, HDFS, YARN, Sqoop, and non-structured data has been scaring DBA's since 2010 - but where does the DBA team really fit in?
In this session, we will discuss everything database administrators and database developers needs to know about big data. We will demystify the Hadoop ecosystem and explore the different components. We will learn how HDFS and MapReduce are changing the data world, and where traditional databases fits into the grand scheme of things. We will also talk about why DBAs are the perfect candidates to transition into Big Data and Hadoop professionals and experts.
We Provide Hadoop training institute in Hyderabad and Bangalore with corporate training by 12+ Experience faculty.
Real-time industry experts from MNCs
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Scaling Storage and Computation with Hadoopyaevents
Hadoop provides a distributed storage and a framework for the analysis and transformation of very large data sets using the MapReduce paradigm. Hadoop is partitioning data and computation across thousands of hosts, and executes application computations in parallel close to their data. A Hadoop cluster scales computation capacity, storage capacity and IO bandwidth by simply adding commodity servers. Hadoop is an Apache Software Foundation project; it unites hundreds of developers, and hundreds of organizations worldwide report using Hadoop. This presentation will give an overview of the Hadoop family projects with a focus on its distributed storage solutions
- Hadoop was created to allow processing of large datasets in a distributed, fault-tolerant manner. It was originally developed by Doug Cutting and Mike Cafarella at Nutch in response to the growing amounts of data and computational needs at Google and other companies.
- The core of Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for distributed processing. It also includes utilities like Hadoop Common for file system access and other basic functionality.
- Hadoop's goals were to process multi-petabyte datasets across commodity hardware in a reliable, flexible and open source way. It assumes failures are expected and handles them to provide fault tolerance.
Microsoft's Big Play for Big Data- Visual Studio Live! NY 2012Andrew Brust
This document discusses Microsoft's efforts to make big data technologies like Hadoop more accessible through its products. It describes Hadoop, MapReduce, HDFS, and other big data concepts. It then outlines Microsoft's project to create a Hadoop distribution that runs on Windows Server and Windows Azure, including building an ODBC driver to allow tools like Excel to query Hadoop. This will help bring big data to more business users and integrate it with Microsoft's existing BI technologies.
http://www.learntek.org/product/big-data-and-hadoop/
http://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
NBITS is a best hadoop training institute providing customer project-based Training and Placements in Big Data Hadoop. NBITS provides Hadoop Training in Hyderabad by Real time experts faculty with 10+ yrs Experience.
New World Hadoop Architectures (& What Problems They Really Solve) for Oracle...Rittman Analytics
Most DBAs are aware something interesting is going on with big data and the Hadoop product ecosystem that underpins it, but aren't so clear about what each component in the stack does, what problem each part solves and why those problems couldn't be solved using the old approach. We'll look at where it's all going with the advent of Spark and machine learning, what's happening with ETL, metadata and analytics on this platform ... why IaaS and datawarehousing-as-a-service will have such a big impact, sooner than you think
This document discusses scaling ETL processes with Hadoop. It describes using Hadoop for extracting data from various structured and unstructured sources, transforming data using MapReduce and other tools, and loading data into data warehouses or other targets. Specific techniques covered include using Sqoop and Flume for extraction, partitioning and tuning data structures for transformation, and loading data in parallel for scaling. Workflow management with Oozie and monitoring with Cloudera Manager are also discussed.
Rapid Cluster Computing with Apache Spark 2016Zohar Elkayam
This is the presentation I used for Oracle Week 2016 session about Apache Spark.
In the agenda:
- The Big Data problem and possible solutions
- Basic Spark Core
- Working with RDDs
- Working with Spark Cluster and Parallel programming
- Spark modules: Spark SQL and Spark Streaming
- Performance and Troubleshooting
This document discusses loading data from Hadoop into Oracle databases using Oracle connectors. It describes how the Oracle Loader for Hadoop and Oracle SQL Connector for HDFS can load data from HDFS into Oracle tables much faster than traditional methods like Sqoop by leveraging parallel processing in Hadoop. The connectors optimize the loading process by automatically partitioning, sorting, and formatting the data into Oracle blocks to achieve high performance loads. Measuring the CPU time needed per gigabyte loaded allows estimating how long full loads will take based on available resources.
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Fluttercon 2024: Showing that you care about security - OpenSSF Scorecards fo...Chris Swan
Have you noticed the OpenSSF Scorecard badges on the official Dart and Flutter repos? It's Google's way of showing that they care about security. Practices such as pinning dependencies, branch protection, required reviews, continuous integration tests etc. are measured to provide a score and accompanying badge.
You can do the same for your projects, and this presentation will show you how, with an emphasis on the unique challenges that come up when working with Dart and Flutter.
The session will provide a walkthrough of the steps involved in securing a first repository, and then what it takes to repeat that process across an organization with multiple repos. It will also look at the ongoing maintenance involved once scorecards have been implemented, and how aspects of that maintenance can be better automated to minimize toil.
Coordinate Systems in FME 101 - Webinar SlidesSafe Software
If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights.
During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to:
- Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value
- Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems
- Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors
- Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported
- Look Ahead: Gain insights into where FME is headed with coordinate systems in the future
Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
Performance Budgets for the Real World by Tammy EvertsScyllaDB
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, Tammy revisits old assumptions about performance budgets and offers some new best practices. Topics include:
• Understanding performance budgets vs. performance goals
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• How to stay on top of your budgets to fight regressions
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
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Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
6. 6
Indexing the Web
• Web is huge
• Hundreds of millions of pages in 1999
• How do you index it?
• Crawl all the pages
• Rank pages based on relevance metrics
• Build search index of keywords to pages
• Do it in real-time!
8. 8
Databases in 1999
1. Buy a really big machine
2. Install an expensive DBMS on it
3. Point your workload at it
4. Hope it doesn’t fail
5. Ambitious: buy another really big machine as
a backup
9. 9
Database Limitations
• Didn’t scale horizontally
• High marginal cost ($$$)
• No real fault-tolerance story
• Vendor lock in ($$$)
• SQL unsuited for search ranking
• Complex analysis (PageRank)
• Unstructured data
10. 10
Google Does Something Different
• Designed their own storage and processing
infrastructure
• Google File System and MapReduce
• Goals:
• Cheap
• Scalable
• Reliable
11. 11
Google Does Something Different
• It worked!
• Powered Google Search for many years
• General framework for large-scale batch
computation tasks
• Still used internally at Google to this day
15. 15
Google’s messages from the future
• Google was benevolent enough to publish
• 2003: Google File System (GFS) paper
• 2004: MapReduce paper
• Already mature technologies at this point
16. 16
Google’s messages from the future
• Community didn’t get it immediately
• DB people thought it was silly
• Non-Google weren’t at the same scale yet
• Google had little interest in releasing GFS and
MapReduce
• Business was ads, not infrastructure
17. 17
Birth of Hadoop
• Doug Cutting and Mike Cafarella
• Nutch
• Open-source search platform
• Ran into scaling issues
• 4 nodes
• Hard to program
• Hard to manage
• Immediate application for GFS and MR
18. 18
Birth of Hadoop
• 2004-2006:
Implemented GFS/MR
and ported Nutch to it
• 2006: Spun out into
Apache Hadoop
• Name of Doug’s son’s
stuffed elephant
20. 20
Summary
• The web is huge and unstructured
• Databases didn’t fit the problem
• Didn’t scale, expensive, SQL limitations
• Google did their own thing: GFS + MR
• Hadoop is based on the Google papers
22. 22
HDFS
• Based on GFS
• Distributed, fault-tolerant filesystem
• Primarily designed for cost and scale
• Works on commodity hardware
• 20PB / 4000 node cluster at Facebook
23. 23
HDFS design assumptions
• Failures are common
• Massive scale means more failures
• Disks, network, node
• Files are append-only
• Files are large (GBs to TBs)
• Accesses are large and sequential
25. 25
Quick filesystem primer
• Same concepts as the FS on your laptop
• Directory tree
• Create, read, write, delete files
• Filesystems store metadata and data
• Metadata: filename, size, permissions, …
• Data: contents of a file
• Other concerns
• Data integrity, durability, management
26. 26
Quick disk primer
• Disk does a seek for each I/O operation
• Seeks are expensive (~10ms)
• Throughput / IOPS tradeoff
• 100 MB/s and 10 IOPS
• 10MB/s and 100 IOPS
• Big I/Os mean better throughput
31. 31
HDFS Write Path
• Talk to NameNode
• Store metadata for new file
• Get topology-aware list of DataNodes
• Setup the write pipeline
• Stream data to pipeline
• Tell NameNode when done
32. 32
HDFS Fault-tolerance
• Many different failure modes
• Disk corruption, node failure, switch failure
• Primary concern
• Data is safe!!!
• Secondary concerns
• Keep accepting reads and writes
• Do it transparently to clients
33. 33
MapReduce – Map
• Records from the data source (lines out of files, rows of a database, etc)
are fed into the map function as key*value pairs: e.g., (filename, line).
• map() produces one or more intermediate values along with an output
key from the input.
Map
Task
(key 1,
values)
(key 2,
values)
(key 3,
values)
Shuffle
Phase
(key 1, int.
values)
(key 1, int.
values)
(key 1, int.
values)
Reduce
Task
Final (key,
values)
34. 34
MapReduce – Reduce
• After the map phase is over, all the intermediate values for a
given output key are combined together into a list
• reduce() combines those intermediate values into one or
more final values for that same output key
Map
Task
(key 1,
values)
(key 2,
values)
(key 3,
values)
Shuffle
Phase
(key 1, int.
values)
(key 1, int.
values)
(key 1, int.
values)
Reduce
Task
Final (key,
values)
36. 36
Word Count Example
The cat sat on the mat
The aardvark sat on the sofa
The, 1
cat, 1
sat, 1
on, 1
the, 1
mat, 1
The, 1
aardvark, 1
sat, 1
on, 1
the, 1
sofa, 1
Mapper Input
Mapping
aardvark, 1
cat, 1
mat, 1
on, 2
sat, 2
sofa, 1
the, 4
aardvark, 1
cat, 1
mat, 1
on, 2
sat, 2
sofa, 1
the, 4
aardvark, 1
cat, 1
mat, 1
on [1, 1]
sat [1, 1]
sofa, 1
the [1, 1, 1, 1]
Shuffling Reducing
Final Result
37. 37
Summary
• GFS and MR co-design
• Cheap, simple, effective at scale
• Fault-tolerance baked in
• Replicate data 3x
• Incrementally re-execute computation
• Avoid single points of failure
41. 41
Hive
SELECT
s.word, s.freq, k.freq
FROM shakespeare
JOIN ON (s.word=
k.word)
WHERE s.freq >= 5;
• Relational database
abstraction using a SQL
like dialect called HiveQL
• Statements are executed
as one or more
MapReduce Jobs
42. 42
Pig
• High-level scripting language
for for executing one or more
MapReduce jobs
• Created to simplify authoring
of MapReduce jobs
• Can be extended with user
defined functions
emps = LOAD 'people.txt’ AS
(id,name,salary);
rich = FILTER emps BY salary >
200000;
sorted_rich = ORDER rich BY
salary DESC;
STORE sorted_rich INTO
’rich_people.txt';
45. 45
Hue
• Hue is an open source web-
based application for making it
easier to use Apache Hadoop.
• Hue features
• File Browser for HDFS
• Job Designer/Browser for MapReduce
• Query editors for Hive, Pig and
Cloudera Impala
• Oozie
49. 49
Cloudera Impala
Cost-effective, ad hoc query environment that offloads the
data warehouse for:
• Interactive BI/analytics on more data
• Asking new questions
• Data processing with tight SLAs
• Query-able archive w/full fidelity
50. 50
Cloudera Impala
Interactive SQL for Hadoop
• Responses in seconds
• Nearly ANSI-92 standard SQL with Hive SQL
Native MPP Query Engine
• Purpose-built for low-latency queries
• Separate runtime from MapReduce
• Designed as part of the Hadoop ecosystem
Open Source
• Apache-licensed
51. 51
Impala Key Features
Fast Flexible Secure
Easy to Implement Easy to Use Simple to Manage
• In-memory data transfers
• Partitioned joins
• Fully distributed aggregations
• Query data in HDFS & HBase
• Supports multiple file formats &
compression algorithms
• Integrated with Hadoop security
• Kerberos authentication
• Authorization (Sentry)
• Leverages Hive’s ODBC/JDBC
connectors, metastore & SQL syntax
• Open source
• Interact with data via SQL
• Certified with leading BI tools
• Deploy, configure & monitor with
Cloudera Manager
• Integrated with Hadoop resource
management
53. 53
Cloudera Search
Offer easy access to non-technical resources
Explore data prior to processing and modeling
Gain immediate access and find correlations in
mission-critical data
Powerful, proven search capabilities
that let organizations:
54. 54
Cloudera Search
Interactive Search for All Data
• Full-text and faceted navigation
• Batch, near real-time, and on-demand indexing
Apache Solr Integrated with CDH
• Established, mature search with vibrant community
• Separate runtime like MapReduce, Impala
• Incorporated as part of the Hadoop ecosystem
Open Source
• 100% Apache, 100% Solr
• Standard Solr APIs
55. 55
Search Key Features
Scalable Flexible Timely
Mature Simple to Use Easy to Manage
• Index storage & retrieval on HDFS
• Indexing with MapReduce and
Flume
• Shard management with Zookeeper
• Indexing and query of any data in
HDFS and HBase
• Support for multiple file formats
• Field mapping and matching with
Morphlines
• Indexing in batch, on-demand, and
in near real-time
• Scalable extraction and mapping
with built-in Solr sink for Flume
• Proven, enterprise-ready
technology
• Rich ecosystem and knowledge
within community
• Familiar full-text search and faceted
navigation
• Out-of-the-box Search GUI
• Known, readily available standard
Solr APIs
• Integrated with Cloudera Manager
and Apache Sentry
• Integrated coordination and
execution of jobs
• GoLive for incremental changes
57. 57
Sentry
Unlocks Key RBAC Requirements
Secure, fine-grained, role-based authorization
Multi-tenant administration
Open Source
Submitted to ASF
Open Source authorization module for Impala & Hive
58. 58
Defining Security Functions
Perimeter
Guarding access to the cluster
itself
Technical Concepts:
Authentication
Network isolation
Data
Protecting data in the cluster
from unauthorized visibility
Technical Concepts:
Encryption, Tokenization
Data masking
Access
Defining what users and
applications can do with data
Technical Concepts:
Permissions
Authorization
Visibility
Reporting on where data
came from and how it’s being
used
Technical Concepts:
Auditing
Lineage
59. 59
Enabling Enterprise Security
Perimeter
Guarding access to the cluster
itself
Technical Concepts:
Authentication
Network isolation
Data
Protecting data in the cluster
from unauthorized visibility
Technical Concepts:
Encryption, Tokenization
Data masking
Access
Defining what users and
applications can do with data
Technical Concepts:
Permissions
Authorization
Visibility
Reporting on where data
came from and how it’s being
used
Technical Concepts:
Auditing
Lineage
Apache SentryKerberos, AD/LDAP Cloudera NavigatorCertified Partners
60. 60
Authorization Requirements
Secure Authorization
Ability to control access to data and/or privileges on data for authenticated users
Fine-Grained Authorization
Ability to give users access to a subset of data (e.g. column) in a database
Role-Based Authorization
Ability to create/apply templatized privileges based on functional roles
Multitenant Administration
Ability for central admin group to empower lower-level admins to manage
security for each database/schema
61. 61
Key Capabilities of Sentry
Fine-Grained Authorization
Specify security for SERVERS, DATABASES, TABLES & VIEWS
Role-Based Authorization
SELECT privilege on views & tables
INSERT privilege on tables
TRANSFORM privilege on servers
ALL privilege on the server, databases, tables & views
ALL privilege is needed to create/modify schema
Multitenant Administration
Separate policies for each database/schema
Can be maintained by separate admins
62. 62
Challenges with Hadoop without Management
Hadoop is more than a dozen services running across many machines
• Hundreds of hardware components
• Thousands of settings
• Limitless permutations
Complexity
Hadoop is a system, not just a collection of parts
• Everything is interrelated
• Raw data about individual pieces is not enough
• Must extract what’s important
Context
Managing Hadoop with multiple tools and manual process takes longer
• Complicated, error-prone workflows
• Longer issue resolution
• Lack of consistent and repeatable processes
Efficiency
63. 63
Cloudera Manager
End-to-End Administration for Your Enterprise Data Hub
Manage
Easily deploy, configure & optimize clusters1
Monitor
Maintain a central view of all activity2
Diagnose
Easily identify and resolve issues3
Integrate
Use Cloudera Manager with existing tools4
64. 64
One Tool For Everything
Managing Complexity
+
DEPLOYMENT &
CONFIGURATION
MONITORING WORKFLOWS EVENTS & ALERTS LOG SEARCH DIAGNOSTICS REPORTING ACTIVITY MONITORING
DO-IT-YOURSELF
WITH CLOUDERA
66. 66
Why Cloudera Manager
End-to-end administration for the Enterprise Data Hub in a single tool
Simple
Manages Hadoop at a system level – Cloudera’s experience realized in software
Intelligent
Simplifies complex workflows and makes administrators more productive
Efficient
The only enterprise-grade Hadoop management application available
Best-in-Class
67. 67
Why Backup and Disaster Recovery?
Cloudera Enterprise is a Mission-Critical Part
of the Data Management Infrastructure
• Stores valuable data and runs important workloads
• Business continuity is a MUST HAVE
1
Managing Business Continuity for Hadoop is
Complex
• Different services that store data – HDFS, HBase, Hive
• Backup and disaster recovery is configured separately for each
• Processes are manual
2
68. 68
BDR in Cloudera Enterprise
Simplified Management of Backup & DR Policies
Central Configuration
• HDFS - Select files & directories to replicate
• Hive - Select tables to replicate
• Schedule replication jobs for optimal times
HDFS
HIVE
NODES
Monitoring & Alerting
• Track progress of replication jobs
• Get notified when data is out of sync
Performance & Reliability
• High performance replication using MapReduce
• CDH-optimized version of DistCP
SITE A SITE B
HDFS
HIVE
NODES
69. 69
Benefits of BDR
Reduce Complexity • Centrally manage backup and DR workflows
• Simple setup via an intuitive user interface
Maximize Efficiency
• Simplify processes to meet or exceed SLAs and
Recovery Time Objectives (RTOs)
• Optimize system performance and network
impact through scheduling
Reduce Risk & Exposure
• Eliminate error-prone manual processes
• Get notified when issues occur
• The only solution for metadata replication (Hive)
70. 70
One Tool For Everything
Managing Complexity
+
DEPLOYMENT &
CONFIGURATION
MONITORING WORKFLOWS EVENTS & ALERTS LOG SEARCH DIAGNOSTICS REPORTING
ACTIVITY
MONITORING
DO-IT-YOURSELF
WITH CLOUDERA
71. 71
1 2 3Find Nodes Install Components Assign Roles
Enter the names of the hosts which will be
included in the Hadoop cluster. Click Continue.
Cloudera Manager automatically installs the CDH
components on the hosts you specified.
Verify the roles of the nodes within your cluster.
Make changes as necessary.
Install a Cluster in Three Simple Steps
Cloudera Manager Key Features
75. 75
Why You Need Cloudera Navigator
Lots of Data Landing in Cloudera Enterprise
• Huge quantities
• Many different sources – structured and unstructured
• Varying levels of sensitivity
1
Many Users Working with the Data
• Administrators and compliance officers
• Analysts and data scientists
• Business users
2
Need to Effectively Control and Consume Data
• Get visibility and control over the environment
• Discover and explore data
3
76. 76
Cloudera Navigator
Data Management Layer for Cloudera Enterprise
Audit & Access Control
Ensuring appropriate permissions and reporting on
data access for compliance
Discovery & Exploration
Finding out what data is available and what it
looks like
Lineage
Tracing data back to its original source
Lifecycle Management
Migration of data based on policies
Enterprise Metadata Repository
• Business metadata
• Lineage metadata
• Operational metadata
Audit &
Access
Control
Discovery &
Exploration
Lineage
Lifecycle
Mgmt.
HDFS HBASE HIVE
CLOUDERA NAVIGATOR
CDH
77. 77
Cloudera Navigator
Data Audit & Access Control
Verify Permissions
View which users and groups have access to files and
directories
Audit Configuration
Configuration of audit tracking for HDFS, HBase
and Hive
Audit Dashboard
Simple, queryable interface to view data access
Information Export
Export audit information for integration with SIEM tools
HDFS
HBASE
HIVE
IAM / LDAP SYSTEM
3rd PARTY SIEM / GRC SYSTEM
ACCESS
SERVICE
AUDIT LOG
SERVICE
VIEW PERMISSIONS AUDIT LOG CONFIG
AUDIT LOG
COLLECTION
CLOUDERA NAVIGATOR 1.0
78. 78
Benefits of Cloudera Navigator
Control • Store sensitive data
• Maintain full audit history
• The first and only centralized audit tool for Hadoop
Visibility
• Verify access permissions to files and directories
• Report on data access by user and type
Integration • View permissions for LDAP/IAM users
• Export audit data for integration with third-party SIEM tools