- Cloudera Search provides an overview of using Solr on Hadoop for search capabilities.
- Key projects involved include Lucene, Solr, and Hadoop which can be integrated to allow indexing of data on HDFS and querying via search.
- The presentation discusses architectural details of running Solr on HDFS and integrating other Hadoop projects like HBase, MapReduce, and Hue.
The document discusses deploying Hadoop in the cloud. Some key benefits of using Hadoop in the cloud include scalability, automated failover of replicated data, and cost efficiency through distributed processing and storage. Microsoft's Azure HDInsight offering provides a fully managed Hadoop and Spark service in the cloud that allows clusters to be provisioned in minutes and is optimized for analytics workloads. The Cortana Intelligence Suite integrates big data technologies like HDInsight with machine learning and data processing tools.
This talk takes you on a rollercoaster ride through Hadoop 2 and explains the most significant changes and components.
The talk has been held on the JavaLand conference in Brühl, Germany on 25.03.2014.
Agenda:
- Welcome Office
- YARN Land
- HDFS 2 Land
- YARN App Land
- Enterprise Land
Cloudera Impala - Las Vegas Big Data Meetup Nov 5th 2014cdmaxime
Maxime Dumas gives a presentation on Cloudera Impala, which provides fast SQL query capability for Apache Hadoop. Impala allows for interactive queries on Hadoop data in seconds rather than minutes by using a native MPP query engine instead of MapReduce. It offers benefits like SQL support, improved performance of 3-4x up to 90x faster than MapReduce, and flexibility to query existing Hadoop data without needing to migrate or duplicate it. The latest release of Impala 2.0 includes new features like window functions, subqueries, and spilling joins and aggregations to disk when memory is exhausted.
The document discusses architectural considerations for implementing clickstream analytics using Hadoop. It covers choices for data storage layers like HDFS vs HBase, data modeling including file formats and partitioning, data ingestion methods like Flume and Sqoop, available processing engines like MapReduce, Hive, Spark and Impala, and the need to sessionize clickstream data to analyze metrics like bounce rates and attribution.
http://www.meetup.com/Hive-User-Group-Meeting/events/218628646/
December 2014 Hive User Group meetup at LinkedIn
Presentation of winning 2015 Cloudera Hackathon project, collaboration with Cloudera Kafka team.
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.
This document provides an introduction to Apache Kudu, a storage layer for Apache Hadoop designed for fast analytics on fast data. It discusses Kudu's motivations of filling gaps in HDFS and HBase capabilities, its design goals of high throughput scans and low latency reads/writes, and how its columnar storage and integration with tools like Spark and Impala enable it to meet these goals. Example use cases like time series and real-time analytics are presented. The document also covers Kudu's architecture of tables and tablets, its replication and fault tolerance model using Raft consensus, and performance comparisons that show it outperforming other storage systems.
Bikas saha:the next generation of hadoop– hadoop 2 and yarnhdhappy001
The document discusses Apache YARN, which is the next-generation resource management platform for Apache Hadoop. YARN was designed to address limitations of the original Hadoop 1 architecture by supporting multiple data processing models (e.g. batch, interactive, streaming) and improving cluster utilization. YARN achieves this by separating resource management from application execution, allowing various data processing engines like MapReduce, HBase and Storm to run natively on Hadoop frames. This provides a flexible, efficient and shared platform for distributed applications.
The Search Is Over: Integrating Solr and Hadoop in the Same Cluster to Simpli...lucenerevolution
Presented by M.C. Srivas | MapR. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
This session addresses the biggest issue facing Big Data – Search, Discovery and Analytics need to be integrated. While creating and maintaining separate SOLR and Hadoop clusters is time consuming, error prone and difficult to keep in synch, most Hadoop installations do not integrate with SOLR within the same cluster. Find out how to easily integrate these capabilities into a single cluster. The session will also touch on some of the technical aspects of Big Data Search including how to; protect against silent index corruption that permeates large distributed clusters, overcome the shard distribution problem by leveraging Hadoop to ensure accurate distributed search results, and provide real-time indexing for distributed search including support for streaming data capture. Srivas will also share relevant experiences from his days at Google where he ran one of the major search infrastructure teams where GFS, BigTable and MapReduce were used extensively.
The document discusses tools and techniques used by Uber's Hadoop team to make their Spark and Hadoop platforms more user-friendly and efficient. It introduces tools like SCBuilder to simplify Spark context creation, Kafka dispersal to distribute RDD results, and SparkPlug to provide templates for common jobs. It also describes a distributed log debugger called SparkChamber to help debug Spark jobs and techniques like building a spatial index to optimize geo-spatial joins. The goal is to abstract out infrastructure complexities and enforce best practices to make the platforms more self-service for users.
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for productionCloudera, Inc.
It’s no secret that Apache Spark is becoming the successor to MapReduce for data processing in Hadoop. With it’s easy development, flexible API, and performance benefits, Spark is a powerful data processing engine that has quickly gained popularity within the community. On the other hand Hive continues to be the most widely used data warehouse/ETL engine with large scale adoption across enterprises. Therefore, it’s imperative to enable Spark as the underlying execution engine for Hive to seamlessly allow existing and future Hive workloads to leverage the advantages of Spark.
With the recent release of Cloudera 5.7, we have delivered on this goal by adding support for Hive-on-Spark. Data engineers and ETL developers can now transition from MR to Spark for their Hive workloads seamlessly thereby benefitting from the advantages of Spark without any disruption on their end.
Join Santosh Kumar, Senior Product Manager at Cloudera, and Rui Li, Apache Hive committer and engineer at Intel, as we discuss:
An Introduction to Spark and its advantages over MR
An introduction of Hive-on-Spark: Goals and Design Principles
Migrating to HoS and a live demo
Configuring and tuning for batch workloads
What’s next for both tools
Impala is an open-source SQL query engine for Apache Hadoop that allows for fast, interactive queries directly against data stored in HDFS and other data storage systems. It provides low-latency queries in seconds by using a custom query engine instead of MapReduce. Impala allows users to interact with data using standard SQL and business intelligence tools while leveraging existing metadata in Hadoop. It is designed to be integrated with the Hadoop ecosystem for distributed, fault-tolerant and scalable data processing and analytics.
This document discusses deep learning using Spark and DL4J. It introduces the speakers, Adam Gibson and Dhruv Kumar, and outlines the topics to be covered: an overview of deep learning, architectures, implementation and libraries for real-life applications, and a demonstration. Deep learning is described as one technique in data science that excels at tasks like image recognition, speech translation, and voice recognition by being loosely inspired by human brain models. The document then discusses using these techniques for enterprise use cases and realizing modern data applications in a Hadoop-centric world.
Securing Spark Applications by Kostas Sakellis and Marcelo VanzinSpark Summit
This document discusses securing Spark applications. It covers encryption, authentication, and authorization. Encryption protects data in transit using SASL or SSL. Authentication uses Kerberos to identify users. Authorization controls data access using Apache Sentry and the Sentry HDFS plugin, which synchronizes HDFS permissions with higher-level abstractions like tables. A future RecordService aims to provide a unified authorization system at the record level for Spark SQL.
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
NYC HUG - Application Architectures with Apache Hadoopmarkgrover
This document summarizes Mark Grover's presentation on application architectures with Apache Hadoop. It discusses processing clickstream data from web logs using techniques like deduplication, filtering, and sessionization in Hadoop. Specifically, it describes how to implement sessionization in MapReduce by using the user's IP address and timestamp to group log lines into sessions in the reducer.
Presented by Mark Miller, Software Developer, Cloudera
Apache Lucene/Solr committer Mark Miller talks about how Solr has been integrated into the Hadoop ecosystem to provide full text search at "Big Data" scale. This talk will give an overview of how Cloudera has tackled integrating Solr into the Hadoop ecosystem and highlights some of the design decisions and future plans. Learn how Solr is getting 'cozy' with Hadoop, which contributions are going to what project, and how you can take advantage of these integrations to use Solr efficiently at "Big Data" scale. Learn how you can run Solr directly on HDFS, build indexes with Map/Reduce, load Solr via Flume in 'Near Realtime' and much more.
This document discusses integrating Apache Solr with Apache Hadoop for big data search capabilities. It provides background on Mark Miller and the history of search on Hadoop. It outlines how Solr, Lucene, Hadoop, and related projects can be integrated to allow full-text search across large datasets in HDFS. Specific integration points discussed include allowing Solr to read and write directly to HDFS, custom directory support in Solr, replication support, and using Morphlines for extraction, transformation, and loading of data into Solr.
Nutch is an open source web crawler built on Hadoop that can be used to crawl websites at scale. It integrates directly with Solr to index crawled content. HDFS provides a scalable storage layer that Nutch and Solr can write to and read from directly. This allows building indexes for Solr using Hadoop's MapReduce framework. Morphlines allow defining ETL pipelines to extract, transform, and load content from various sources into Solr running on HDFS.
Solr + Hadoop: Interactive Search for Hadoopgregchanan
This document discusses Cloudera Search, which integrates Apache Solr with Cloudera's distribution of Apache Hadoop (CDH) to provide interactive search capabilities. It describes the architecture of Cloudera Search, including components like Solr, SolrCloud, and Morphlines for extraction and transformation. Methods for indexing data in real-time using Flume or batch using MapReduce are presented. The document also covers querying, security features like Kerberos authentication and collection-level authorization using Sentry, and concludes by describing how to obtain Cloudera Search.
Cloudera Search provides full-text search capabilities for Hadoop data by integrating Apache Solr. It allows for near real-time and batch indexing from data sources like HDFS, HBase, and Flume. Cloudera Search uses components like SolrCloud, Morphlines, and Sentry to provide distributed, scalable, and secure search across the Hadoop ecosystem.
This document provides an overview and introduction to Hadoop, HDFS, and MapReduce. It covers the basic concepts of HDFS, including how files are stored in blocks across data nodes, and the role of the name node and data nodes. It also explains the MapReduce programming model, including the mapper, reducer, and how jobs are split into parallel tasks. The document discusses using Hadoop from the command line and writing MapReduce jobs in Java. It also mentions some other projects in the Hadoop ecosystem like Pig, Hive, HBase and Zookeeper.
This document summarizes how Solr and Lucidworks Fusion can be used for big data search and analytics. It discusses indexing strategies like using MapReduce, Spark, and Fusion connectors to index structured and unstructured data from HDFS. It also covers topics like Solr on HDFS, auto add replicas, security, cluster sizing, and using the lambda architecture with Spark streaming to enable real-time search over batch-processed historical data. The document promotes Lucidworks Fusion as a search platform that can handle massive scales of data, provide real-time search capabilities, and work with any data source securely.
1. Cloudera Search provides full-text search capabilities for Hadoop ecosystems by integrating Apache Solr. It allows batch, near real-time, and on-demand indexing of data in HDFS, HBase, and other data sources.
2. Indexing can be done through various methods like Flume for near real-time indexing, HBase indexer for indexing HBase data, and MapReduce jobs for scalable batch indexing. Extraction and mapping of data is done through the Cloudera Morphlines framework.
3. Queries can be done through the built-in Solr web UI, custom UIs like Hue, or Solr APIs. Security features include Kerberos authentication and
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of petabytes of data. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. Many large companies use Hadoop for applications such as log analysis, web indexing, and data mining of large datasets.
The document introduces Yann Yu from Lucidworks and provides information about Lucidworks and its products Solr and Hadoop. It discusses how Solr can be used to provide search capabilities for large amounts of both structured and unstructured data stored in Hadoop. Integrating Solr and Hadoop allows for fast search across big data stored in Hadoop along with real-time indexing and querying capabilities. Examples discussed include enabling enterprise-wide search of documents stored in Hadoop and using Flume to index log data from Hadoop into Solr for real-time analytics and search.
Near Real Time Indexing Kafka Messages into Apache Blur: Presented by Dibyend...Lucidworks
This document discusses Pearson's use of Apache Blur for distributed search and indexing of data from Kafka streams into Blur. It provides an overview of Pearson's learning platform and data architecture, describes the benefits of using Blur including its scalability, fault tolerance and query support. It also outlines the challenges of integrating Kafka streams with Blur using Spark and the solution developed to provide a reliable, low-level Kafka consumer within Spark that indexes messages from Kafka into Blur in near real-time.
Spark is a general-purpose cluster computing framework that provides high-level APIs and is faster than Hadoop for iterative jobs and interactive queries. It leverages cached data in cluster memory across nodes for faster performance. Spark supports various higher-level tools including SQL, machine learning, graph processing, and streaming.
This document discusses integrating Hadoop and Solr. Hadoop is useful for storing and processing large amounts of data, while Solr enables fast search across structured and unstructured data. The document outlines how Hadoop can store documents and Solr can index them for search, as well as how technologies like Flume can process streaming data and index it in real-time in Solr.
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.
Solr Distributed Indexing in WalmartLabs: Presented by Shengua Wan, WalmartLabsLucidworks
This document discusses Solr distributed indexing at WalmartLabs. It describes customizing an existing MapReduce indexing tool to index large XML files in a distributed manner across multiple servers. Key points covered include using two custom utilities for index generation and merging, experiments showing indexing is CPU-bound while merging is I/O-bound, and lessons learned around data locality and using n-way merging of shards for best performance. Solutions discussed include dedicating an indexing Hadoop cluster to improve I/O speeds for merging indexes.
P.Maharajothi,II-M.sc(computer science),Bon secours college for women,thanjavur.MaharajothiP
Hadoop is an open-source software framework that supports data-intensive distributed applications. It has a flexible architecture designed for reliable, scalable computing and storage of large datasets across commodity hardware. Hadoop uses a distributed file system and MapReduce programming model, with a master node tracking metadata and worker nodes storing data blocks and performing computation in parallel. It is widely used by large companies to analyze massive amounts of structured and unstructured data.
Your Big Data Stack is Too Big!: Presented by Timothy Potter, LucidworksLucidworks
Timothy Potter presented at a Big Data conference in Boston from October 11-14, 2016. He discussed how Lucidworks Fusion provides an alternative to traditional big data stacks that emphasizes fast access, agility and automation over integration. Fusion allows for common access patterns like fast lookups, ranked retrieval and distributed scans while integrating technologies like Solr, Spark, HDFS and more. It provides tools for data ingestion, time-based partitioning, analytics, machine learning and more to solve business problems rather than focus on infrastructure.
1. Apache Spark is an open source cluster computing framework for large-scale data processing. It is compatible with Hadoop and provides APIs for SQL, streaming, machine learning, and graph processing.
2. Over 3000 companies use Spark, including Microsoft, Uber, Pinterest, and Amazon. It can run on standalone clusters, EC2, YARN, and Mesos.
3. Spark SQL, Streaming, and MLlib allow for SQL queries, streaming analytics, and machine learning at scale using Spark's APIs which are inspired by Python/R data frames and scikit-learn.
http://bit.ly/1BTaXZP – As organizations look for even faster ways to derive value from big data, they are turning to Apache Spark is an in-memory processing framework that offers lightning-fast big data analytics, providing speed, developer productivity, and real-time processing advantages. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Spark Streaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. By the end of the session, you’ll come away with a deeper understanding of how you can unlock deeper insights from your data, faster, with Spark.
This document provides an introduction and overview of Spark:
- Spark is an open-source in-memory data processing engine that can handle large datasets across clusters of computers using an API in Scala, Python, or R.
- IBM is heavily committed to Spark, contributing the most code and fixing the most issues reported by other organizations to continually improve the full analytics stack.
- An example is presented on using Spark to predict hospital readmissions from diabetes patient data, obtaining AUC scores comparable to other published models.
Yosef Kerzner's report on Toorcamp 2016. Presented at Houston Hadoop Meetup in July 2016.
• Your own drone to deliver vegetarian tacos from nearby town (of Seattle)
• Reverse engineering and attacking the .NET applications
• Hacking the North American railways, and more...
Witsml data processing with kafka and spark streamingMark Kerzner
This document summarizes a presentation about using Kafka and Spark Streaming to process real-time well data in WITSML format. It discusses WITSML data standards, using Kafka as a messaging system to ingest WITSML data from rigs and service companies, and Spark Streaming to consume Kafka topics and apply rules to detect anomalies and send alerts. Visualizing the data in real-time using Highcharts javascript is also covered. Lessons learned focus on improving data partitioning and managing producer/consumer services.
Hadoop as a service presented by Ajay Jha at Houston Hadoop MeetupMark Kerzner
Altiscale provides a big data-as-a-service platform based on Apache Hadoop and related technologies like Spark, Hive, and Tez. Interest in big data is growing rapidly but many independent implementations fail. Altiscale aims to help with its experienced team and fully managed platform that offers fast time to value, scalability, security, and lower total cost of ownership. The platform core is built on Apache Hadoop 2.7.1 and related open source projects. Altiscale also provides Hadoop administration services and tools for accessing and running jobs on the cloud platform.
Altiscale provides a big data-as-a-service platform based on Apache Hadoop and related technologies like Spark, Hive, and Tez. Interest in big data is growing rapidly but many independent implementations fail. Altiscale aims to help with its experienced team and fully managed platform that offers fast time to value, scalability, security, and lower total cost of ownership. The platform core is Apache open source components like Hadoop, Spark, Hive and Tez. Altiscale handles administration of the Hadoop cluster including hardware, upgrades, tuning, and addressing failures so customers can focus on their data and jobs.
The document discusses Informatica's data integration platform and its capabilities for big data and analytics projects. Some key points:
- Informatica is a leading data integration vendor with over 5,000 customers including over 70% of the Global 500.
- The Informatica platform provides capabilities across the entire data lifecycle from ingestion to delivery including data quality, master data management, integration, and analytics.
- It supports a variety of data sources including structured, unstructured, cloud, and big data and can run on-premises or in the cloud.
- Customers report the Informatica platform improves agility, scalability, and operational confidence for data integration projects compared to
Apache NiFi is a dataflow system developed at NSA that was donated to the Apache Software Foundation in 2014. It provides real-time data routing, transformation, and system mediation capabilities with an intuitive visual interface. Key features include flow-based programming, provenance tracking, security controls, and clustering support. The system aims to automate dataflows from any source to systems that analyze or store the data.
FreeEed eDiscovery Popcorn is a free and easy to use eDiscovery application that allows lawyers to process client data for lawsuits. It comes pre-installed as a virtual machine kernel that can be downloaded and used to "cook" client data. Each kernel represents a single case, allowing data to be securely separated and processed independently. The kernels can also be archived and reused later as needed. It provides a low-cost alternative to traditional expensive eDiscovery systems that do not allow for such flexibility.
The document discusses FreeEed, an open source Hadoop-based eDiscovery tool. It provides scalable processing and review of electronic documents for legal cases. FreeEed allows preservation, archiving, and production of documents in a way that complies with legal regulations. It uses Hadoop and NoSQL technologies like Lucene, Solr, and HBase to allow fast searching and culling of large document collections in an affordable and scalable manner. FreeEed aims to make eDiscovery more accessible to small law firms and individuals by providing a free and open source option.
Nutch + Hadoop scaled, for crawling protected web sites (hint: Selenium)Mark Kerzner
The document summarizes a presentation on using Nutch with Hadoop for web crawling. It discusses Nutch's architecture and how it can be configured to crawl specific domains. It also describes how Nutch can be scaled using HDFS for storage and MapReduce for crawling. The presentation demonstrates using Burp and Selenium tools with Nutch to perform tasks like password testing and browser interaction during the crawling process.
The document discusses using Elasticsearch and Hadoop to analyze large amounts of log data from multiple servers and applications in a centralized way. It describes setting up Elasticsearch to enable fast querying of the log data, Logstash to ingest logs from various sources into Elasticsearch, and Kibana for visualization. Hadoop is used to handle the large volumes of log data, and Pig scripts are used to do analysis on the data stored in Elasticsearch.
Houston Technology Center presentation by SHMsoft. eDiscovery, data governance, and compliance vision that can be build on Hadoop clusters and public or private clouds.
Porting your hadoop app to horton works hdpMark Kerzner
The document discusses porting a Java-based eDiscovery application from Cloudera on Amazon EC2 to Hortonworks Hadoop Distribution Public Cloud (HDP). It provides details on setting up an HDP cluster on EC2, including choosing services to install, customizing Nagios for monitoring, and troubleshooting an initial HBase installation failure. The author seeks instructions for integrating custom control scripts during cluster startup and management.
Automated Hadoop Cluster Construction on EC2Mark Kerzner
This document discusses options for running Hadoop clusters on Amazon EC2, including using tools like Whirr to automate cluster setup, limitations of Whirr, using Amazon EMR, manually setting up clusters, and advanced options like monitoring cluster health. It also provides context on Hadoop, clouds, and related technologies like HBase, Cassandra, and different Hadoop distributions from Cloudera, MapR, and others.
The document discusses configuring and running a Hadoop cluster on Amazon EC2 instances using the Cloudera distribution. It provides steps for launching EC2 instances, editing configuration files, starting Hadoop services, and verifying the HDFS and MapReduce functionality. It also demonstrates how to start and stop an HBase cluster on the same EC2 nodes.
The document discusses open source eDiscovery software called FreeEed. It provides an overview of FreeEed's current capabilities including text extraction, flexible search, and scalability across Windows, Mac, Linux and Hadoop clusters. The document also outlines FreeEed's processing stages and screens. Future plans for FreeEed include Amazon cloud processing, enhanced capabilities using Big Data technology, and iPad/tablet review interfaces. The creator of FreeEed sees an exciting future applying Big Data technology to advanced review tasks like predictive coding and automated privilege review.
FreeEed is an open source eDiscovery software that uses big data technologies like Hadoop for processing electronic documents during legal cases. It can currently perform text and metadata extraction and culling during discovery. It will soon add review, analysis, production and presentation capabilities. FreeEed can also do preservation and collection. It leverages modern technologies from open source tools like Tika for extraction and Lucene for searching. It has advantages like easy use, integration with other tools, and community support. FreeEed can run standalone, on Linux clusters, or on Amazon cloud from a laptop. It uses a staging, extraction, culling and output workflow.
Houston Hadoop Meetup Presentation by Vikram Oberoi of ClouderaMark Kerzner
The document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It describes Hadoop's core components - the Hadoop Distributed File System (HDFS) for scalable data storage, and MapReduce for distributed processing of large datasets in parallel. Typical problems suited for Hadoop involve complex data from multiple sources that need to be consolidated, stored inexpensively at scale, and processed in parallel across the cluster.
Google's Zurich office aims to reimagine how work could be by focusing on employee well-being, flexibility, and purpose over traditional metrics. However, the document suggests that while new visions for work are inspiring, practical realities must still be faced in implementing meaningful changes to traditional work structures and cultures. The high-level ideas presented require further refinement and consideration of challenges to become established models.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
Redefining Cybersecurity with AI CapabilitiesPriyanka Aash
In this comprehensive overview of Cisco's latest innovations in cybersecurity, the focus is squarely on resilience and adaptation in the face of evolving threats. The discussion covers the imperative of tackling Mal information, the increasing sophistication of insider attacks, and the expanding attack surfaces in a hybrid work environment. Emphasizing a shift towards integrated platforms over fragmented tools, Cisco introduces its Security Cloud, designed to provide end-to-end visibility and robust protection across user interactions, cloud environments, and breaches. AI emerges as a pivotal tool, from enhancing user experiences to predicting and defending against cyber threats. The blog underscores Cisco's commitment to simplifying security stacks while ensuring efficacy and economic feasibility, making a compelling case for their platform approach in safeguarding digital landscapes.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
2. Who Am I?
Apache Accumulo PMC
Apache Curator PMC
Hobbyist contributor
- Various Apache Projects
- Junit, Jcommander, JLine2
Volunteer with FIRST LEGO League (FLL)
Search/Solr is my ${DayJob}
3. Agenda
We will cover:
- Overview of projects involved
- Architectural discussion of Solr on Hadoop
We will not cover:
- Performance, Tuning, or Optimizations
- Writing custom applications
- Tutorials (kind of)
4. Why Search?
Hadoop for Everyone!
Typical case:
Ingest data to storage engine (HDFS, HBase, etc...)
Process data (MR, Hive, Impala)
Experts know MapReduce
Savvy users know SQL
Everyone knows Search!
10. Strengthen the Family Bonds
•No need to build something radically new - we
have the pieces we need.
•Focus on integration points.
•Create high quality, first class integrations and
contribute the work to the projects involved.
•Focus on integration and quality first - then
performance and scale.
11. Very fast and feature rich ‘core’ search engine
library.
Compact and powerful, Lucene is an extremely
popular full-text search library.
Provides low level APIs for analyzing, indexing, and
searching text, along with a myriad of related
features.
Just the core - either you write the ‘glue’ or use a
higher level search engine built with Lucene.
12. Solr (pronounced "solar") is an open source
enterprise search platform from the Apache Lucene
project. Its major features include full-text search,
hit highlighting, faceted search, dynamic clustering,
database integration, and rich document (e.g.,
Word, PDF) handling. Providing distributed search
and index replication, Solr is highly scalable. Solr is
the most popular enterprise search engine.
- Wikipedia
15. Solr Integration
•Read and Write directly to HDFS
•First Class Custom Directory Support in Solr
•Support Solr Replication on HDFS
•Other improvements around usability and
configuration
16. Putting the Index in HDFS
•Extend Lucene's Directory & DirectoryFactory to
abstract HDFS implementation
•Solr relies on the FS cache to operate at full speed,
while HDFS not known for it’s random access speed.
•Apache Blur has already solved this with an
HdfsDirectory that works on top of a BlockDirectory.
•The “block cache” caches the hot blocks of the index
off heap (direct byte array) and takes the place of the
FS cache.
17. Putting TransactionLog in HDFS
•TransactionLog is a basic WAL
•HdfsUpdateLog added - extends UpdateLog
•Triggered by setting the UpdateLog dataDir to a path
starting with hdfs:/
•Benefits from same extensive testing as used on
UpdateLog
18. Running Solr on HDFS
•Cloudera Manager can do all of this for you.
•Set DirectoryFactory to HdfsDirectoryFactory and set the dataDir to a
location in hdfs.
•Set LockType to ‘hdfs’
•Use an UpdateLog dataDir location that begins with ‘hdfs:/’
•i.e. java -Dsolr.directoryFactory=HdfsDirectoryFactory
-Dsolr.lockType=solr.HdfsLockFactory
-Dsolr.updatelog=hdfs://host:port/path -jar start.jar
19. Solr Replication on HDFS
•Take advantage of “distributed filesystem” and allow
for something similar to HBase regions.
•If a node goes down, the data is still available in
HDFS - allow for that index to be automatically
served by a node that is still up if it has the capacity.
Solr
Node
Solr
Node
Solr
Node
HDFS
20. MR Index Building
•Scalable index creation via map-reduce
•Many initial ‘homegrown’ implementations sent documents from
reducer to SolrCloud over http
•To really scale, you want the reducers to create the indexes in
HDFS and then load them up with Solr
•The ideal impl will allow using as many reducers as are available
in your hadoop cluster, and then merge the indexes down to the
correct number of ‘shards’
21. MR Index Building
Mapper:
Parse input into
indexable
document
Mapper:
Parse input into
indexable
document
Mapper:
Parse input into
indexable
document
Index
shard 1
Index
shard 2
Arbitrary reducing steps of indexing and merging
End-Reducer (shard 1):
Index document
End-Reducer (shard 2):
Index document
22. SolrCloud Aware
•Can ‘inspect’ ZooKeeper to learn about Solr cluster.
•What URLs to GoLive to.
•The Schema to use when building indexes.
•Match hash -> shard assignments of a Solr cluster.
23. GoLive
•After building your indexes with map-reduce, how do
you deploy them to your Solr cluster?
•We want it to be easy - so we built the GoLive
option.
•GoLive allows you to easily merge the indexes you
have created atomically into a live running Solr
cluster.
•Paired with the ZooKeeper Aware ability, this allows
you to simply point your map-reduce job to your Solr
cluster and it will automatically discover how many
shards to build and what locations to deliver the final
indexes to in HDFS.
24. HBase Integration
•Collaboration between NGData & Cloudera
•NGData created the Lily data management platform
•Lily HBase Indexer
•Service which acts as a HBase replication listener
•HBase replication features, such as filtering, supported
•Replication updates trigger indexing of updates (rows)
•Integrates Morphlines library for ETL of rows
•AL2 licensed on github https://github.com/ngdata