FreEed - Open Source eDiscovery
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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.
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This document describes building a real-time search suggestions system using open source tools like Elasticsearch, Hadoop, Redis, Flume, and Node.js. Logs are collected using Flume and processed through two MapReduce jobs - the first counts search terms by time bucket, and the second calculates scores and ranks terms. Results are stored in Redis with timeouts. Improvements discussed include using request diffs to update only changed results and adding more signals like click data to improve suggestions.
Deep Dive on Elastic File System - February 2017 AWS Online Tech Talks
Organizations face significant challenges moving their applications to the cloud when they require a standard file system interface for accessing their cloud data. In this technical session, we will explore the world’s first cloud-scale file system and its targeted use cases. Attendees will learn about the Amazon Elastic File System (EFS) features and benefits, how to identify applications that are appropriate for use with Amazon EFS, and details about its performance and security models. We will highlight and demonstrate how to deploy Amazon EFS in one of our most common use cases and will share tips for success throughout.
Learning Objectives:
• Recognize why and when to use Amazon EFS
• Understand key technical/security concepts
• Learn how to leverage EFS’s performance
• See a demo of EFS in action
• Review EFS’s economics
ProjectHub
The document discusses ProjectHub, a software that crawls, indexes, and allows searching of software project data using various tools like Droids, Tika, Solr, and Hadoop. It describes the architecture which gathers information from different sources like JIRA, mailing lists, code repositories, and indexes it using Solr for faceted search, spellchecking, autocomplete and other features. It also discusses performance monitoring, analytics and future plans.
Hands on Hadoop and pig
This document provides an overview and introduction to BigData using Hadoop and Pig. It begins with introducing the speaker and their background working with large datasets. It then outlines what will be covered, including an introduction to BigData, Hadoop, Pig, HBase and Hive. Definitions and examples are provided for each. The remainder of the document demonstrates Hadoop and Pig concepts and commands through code examples and explanations.
Big Data Day LA 2016/ Big Data Track - Fluentd and Embulk: Collect More Data,...
Since Doug Cutting invented Hadoop and Amazon Web Services released S3 ten years ago, we've seen quite a bit of innovation in large-scale data storage and processing. These innovations have enabled engineers to build data infrastructure at scale, many of them fail to fill their scalable systems with useful data, struggling to unify data silos or failing to collect logs from thousands of servers and millions of containers. Fluentd and Embulk are two projects that I've been involved to solve the unsexy yet critical problem of data collection and transport. In this talk, I will give an overview of Fluentd and Embulk and give a survey of how they are used at companies like Microsoft and Atlassian or in projects like Docker and Kubernetes.
Hadoop File system (HDFS)
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hadoop
Apache Hadoop is an open-source software framework that supports large-scale distributed applications and processing of multi-petabyte datasets across thousands of commodity servers. It implements the MapReduce programming model for distributed processing and the Hadoop Distributed File System (HDFS) for reliable data storage. HDFS stores data across commodity servers, provides high aggregate bandwidth, and detects/recovers from failures automatically.
A gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of #BigData and #Hadoop with also a fast view of what you can do in Azure
Big data with HDFS and Mapreduce
This document discusses Hadoop Distributed File System (HDFS) and MapReduce. It begins by explaining HDFS architecture, including the NameNode and DataNodes. It then discusses how HDFS is used to store large files reliably across commodity hardware. The document also provides steps to install Hadoop in single node cluster and describes core Hadoop services like JobTracker and TaskTracker. It concludes by discussing HDFS commands and a quiz about Hadoop components.
Nadhiya lamp
LAMP technology uses Linux as the operating system, Apache as the web server, MySQL as the database management system, and PHP as the server-side scripting language. Some advantages of LAMP include easy coding with PHP and MySQL, low-cost hosting, and the ability to develop applications locally. To install LAMP, one would download and extract the latest version of XAMPP for Linux and start the Apache and MySQL servers.
Hadoop Technology
This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.
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Finding the needles in the haystack. An Overview of Analyzing Big Data with H...
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Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
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Deep Dive on Elastic File System - February 2017 AWS Online Tech Talks
Deep Dive on Elastic File System - February 2017 AWS Online Tech Talks
Big Data Day LA 2016/ Big Data Track - Fluentd and Embulk: Collect More Data,...
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A gentle introduction to the world of BigData and Hadoop
A gentle introduction to the world of BigData and Hadoop
More from Mark Kerzner
IBM Strategy for 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.
Toorcamp 2016
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 streaming
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 Meetup
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.
Hadoop Hadoop & Spark meetup - Altiscale
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.
Oil and gas big data edition
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
Cloudera search
- 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.
Joe Witt presentation on Apache NiFi
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 popcorn overview
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.
FreeEed presentation
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)
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.
Night owl by Boyd Meyer of PROS
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.
SHMcloud vision
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 hdp
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 EC2
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.
Hadoop on ec2
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.
Houston Hadoop Meetup Presentation by Vikram Oberoi of Cloudera
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 Office in Zurich, Switzerland
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.
Fun art with fruit and vegetable
This very short document is written in an unknown language and does not contain any discernible information that could be summarized in 3 sentences or less. The document appears to only contain symbols with no meaningful words that can be understood.
More from Mark Kerzner (20)
Witsml data processing with kafka and spark streaming
Witsml data processing with kafka and spark streaming
Hadoop as a service presented by Ajay Jha at Houston Hadoop Meetup
Hadoop as a service presented by Ajay Jha at Houston Hadoop Meetup
Nutch + Hadoop scaled, for crawling protected web sites (hint: Selenium)
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Houston Hadoop Meetup Presentation by Vikram Oberoi of Cloudera
Houston Hadoop Meetup Presentation by Vikram Oberoi of Cloudera
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In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
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Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
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How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
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Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
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seamless data management.
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"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
What is an RPA CoE? Session 1 – CoE Vision
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Topics covered:
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Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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FREE A4 Cyber Security Awareness Posters-Social Engineering part 3
Free A4 downloadable and printable Cyber Security, Social Engineering Safety and security Training Posters . Promote security awareness in the home or workplace. Lock them Out From training providers datahops.com
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
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Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Programming Foundation Models with DSPy - Meetup Slides
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zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
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FreEed - Open Source eDiscovery
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