Analyzing Unstructured Data in Hadoop Webinar
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Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data. This on-demand webinar will highlight and discuss: How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
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---
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Platfora Girl Geek Dinner
This document provides an overview of big data and Hadoop. It discusses the key concepts of big data including volume, velocity and variety. It then explains what Hadoop is, how it works using HDFS for storage and MapReduce for processing. The document outlines some of Hadoop's strengths like scalability and affordability, as well as limitations like requiring advanced technical skills. It then introduces Platfora and how it builds on Hadoop to make big data accessible and useful for business users without separate tools. The presentation ends with encouraging participation in women in STEM programs for a chance to win Hadoop training.
Making Big Data Easy for Everyone
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve this problem.
As Data Manager, you know the challenges ahead:
- Multitudes of technology choices to make
- Building a team and solving the skill-set disconnect
- Data can be deceiving...
- Figuring out what the successful data product must be
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising, and gaming industries, holding various data or CTO roles. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains encountered by data teams all around.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
Conflict in the Cloud – Issues & Solutions for Big Data
Halo BI CEO, Keith Peterson, presents at the 6th Annual Cloud Computing Conference - AITP San Diego: Conflict in the Cloud – Issues & Solutions for Big Data.
Cloud services make money based on the volume of data stored in the cloud – and big data delivers that volume. But companies seeking to use big data are looking for economies of scale from the Cloud.
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This document discusses best practices for using Hadoop as an enterprise data hub. It provides an overview of how big data is driving new analytical workloads and the need for deeper customer insights. It discusses challenges with analyzing new sources of structured, unstructured and multi-structured data. It introduces the concept of a Hadoop enterprise data hub and data refinery to simplify access to new insights from big data. Key components of the data hub include a data reservoir to capture raw data from various sources, a data refinery to cleanse and transform the data, and publishing high value insights to data warehouses and other systems.
DataOps: Nine steps to transform your data science impact Strata London May 18
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
Unlocking data science in the enterprise - with Oracle and Cloudera
This document discusses unlocking data science in the enterprise with Cloudera Data Science Workbench. It introduces Cloudera Data Science Workbench as a tool that accelerates data science from development to production. It allows data scientists to use R, Python, or Scala from a web browser to directly access and analyze data stored in Hadoop clusters. Cloudera Data Science Workbench provides secure, self-service environments for data scientists while also giving IT control over security and compliance. The document includes a demo of Cloudera Data Science Workbench's features.
Analytics Solutions from SAP
SAP provides analytics solutions to help customers run their businesses better by accessing all relevant information, defining plans to align performance goals, and responding instantly to changing conditions. Their solutions provide capabilities for data analysis, business intelligence applications, and collaboration. Customers in various industries and regions have been able to increase financial and operational performance through SAP's analytics offerings.
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Predictive Analytics - Big Data Warehousing Meetup
Back to Square One: Building a Data Science Team from Scratch
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Big Data Everywhere Chicago: Platfora - Practices for Customer Analytics on H...
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DataOps - Big Data and AI World London - March 2020 - Harvinder Atwal
DataOps - Big Data and AI World London - March 2020 - Harvinder Atwal
Presumption of Abundance: Architecting the Future of Success
Presumption of Abundance: Architecting the Future of Success
Modernizing Architecture for a Complete Data Strategy
Modernizing Architecture for a Complete Data Strategy
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
How to Build a Successful Data Team - Florian Douetteau (@Dataiku)
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
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Conflict in the Cloud – Issues & Solutions for Big Data
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DataOps: Nine steps to transform your data science impact Strata London May 18
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Big Data Analytics with Hadoop
Hadoop, flexible and available architecture for large scale computation and data processing on a network of commodity hardware.
Why Use Hadoop for Big Data Analytics?
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
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This document provides an overview of how to prepare unstructured data for business intelligence and data analytics. It discusses structured, semi-structured, and unstructured data types. It then introduces Recognos' platform called ETI, which uses human-assisted machine learning to extract and integrate data from unstructured documents. ETI can extract data from documents that contain classifiable content through predefined field definitions and templates. It also discusses the challenges of extracting tables and derived fields that require semantic analysis. The document concludes with examples of using extracted data for compliance applications and creating data teams to manage the extraction process over time.
Mining Unstructured Data:Practical Applications, from the Strata O'Reilly Mak...
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presented at Strata O'Reilly Making Data Work Conference on March 1, 2012
The challenge of unstructured data is a top priority for organizations that are looking for ways to search, sort, analyze and extract knowledge from masses of documents they store and create daily. Text mining uses knowledge-driven algorithms to make sense of documents in a similar way a person would do by reading them. Lately, text mining and analytics tools became available via APIs, meaning that organizations can take immediate advantage these tools. We discuss three examples of how such APIs were utilized to solve key business challenges.
Most organizations dream of paperless office, but still generate and receive millions of print documents. Digitizing these documents and intelligently sharing them is a universal enterprise challenge. Major scanning providers offer solutions that analyze scanned and OCR’d documents and then store detected information in document management systems. This works well with pre-defined forms, but human interaction is required when scanning unstructured text. We describe a prototype build for the legal vertical that scans stacks of paper documents and on the fly categorizes and generates meaningful metadata.
In the area of forensics, intelligence and security, manual monitoring of masses of unstructured data is not feasible. The ability of automatically identify people’s names, addresses, credit card and bank account numbers and other entities is the key. We will briefly describe a case study of how a major international financial institution is taking advantage of text mining APIs in order to comply with a recent legislation act.
In healthcare, although Electronic Health Records (EHRs) have been increasingly becoming available over the past two decades, patient confidentiality and privacy concerns have been acting as obstacles from utilizing the incredibly valuable information they contain to further medical research. Several approaches have been reported in assigning unique encrypted identifiers to patients’ ID but each comes with drawbacks. For a number of medical studies consistent uniform ID mapping is not necessary and automated text sanitization can serve as a solution. We will demonstrate how sanitization has practical use in a medical study.
And read a full interview with Alyona and Anna at http://radar.oreilly.com/2012/02/unstructured-data-analysis-tools.html
Dealing with Unstructured Data: Scaling to Infinity
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Hotsos 2013 - Creating Structure in Unstructured Data
This document discusses creating structure from unstructured XML data and optimizing XML performance in Oracle databases. It provides examples of structuring Wikipedia XML data and indexing it in various ways using XMLType, binary XML, structured and unstructured XML indexes. The key is choosing the right storage and indexing approach depending on the query patterns and data structure. Proper design can significantly outperform default XML handling.
Lecture 11 Unstructured Data and the Data Warehouse
This chapter discusses integrating structured and unstructured data in a data warehouse. It presents methods like using common text to link the two environments, employing a two-tiered structure with separate warehouses for structured and unstructured data, and using techniques like self-organizing maps to visualize unstructured data. The goal is to find ways to relate the different data types while addressing issues like incompatible formats and large unstructured data volumes.
The Analytic System: Finding Patterns in the Data
Dr. Haughom set the stage for this upcoming discussion in his previous webinar, explaining the key components of an effective analytical system that enables self-exploration and learning. In this session Attendees will learn:
How the distinction between random variation and assignable cause variation is critically important to patient care
Creation and application of Statistical Process Control (SPC) charts to:
Monitor process variation over time
Differentiate between assignable cause and random cause variation
Assess effectiveness of change on a given process
Achieve and maintain process stability
How implementing inlier management and creating a collaborative environment will drive continuous improvement
How to identify patterns in data using a live demonstration of advanced analytical tools.
Israel redefining innovation at International CES 2015
This document provides information on 13 Israeli technology companies that will be exhibiting at CES 2015 in Las Vegas. It includes brief 1-3 sentence descriptions of each company's product or solution and highlights what makes it innovative and impactful in the market. The companies cover a range of technologies including virtual reality cameras, personalized video marketing platforms, mobile entertainment for kids, automated translation for phone calls, and wearable devices for interacting with projected digital content.
Service Cloud für Fortgeschrittene – Die Roadmap für 2012
Auf der Cloudforce 2011 präsentiert Claus Schmälzle, Salesforce, die Service Cloud Roadmap für 2012 mit den Themen Multi-Channel Social Contact Center, Self-Service Communities und Social Customer Service.
Model-Driven Software Development 2.0
Model-Driven Software Development 2.0
Slides of Etienne Juliot at the Atsen Keynote, Istanbul, 2014: http://atsen-conferences.org/
My vision about the need to think generic and infrastructure to create innovations with modeling.
Model Driven Software Development - Data Model Evolution
The document describes research on data model evolution. It discusses how data models change over time through the addition, removal, and modification of entities and properties. The research aims to specify data model evolutions through a language that describes transformations at the level of entities and properties. This includes basic transformations like adding/removing entities/properties as well as more advanced transformations like moving properties. The transformations are applied using program rewriting to migrate data between different versions of a data model.
iPhonical and model-driven software development for the iPhone
These are the slides of my talk "iPhonical and model-driven software development on the iPhone" at the German iPhone Developer Conference 2009 in Cologne. Unfortunately, this version does not include the 25+ minutes demos I presented during the talk.
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The document discusses unstructured data and its importance for business intelligence. It notes that 80% of organizational data is typically unstructured and resides in various documents and sources, both internal and external to the organization. Environmental scanning involves systematically analyzing unstructured external data to produce market forecasts and intelligence reports. Text mining can help untangle unstructured data through content analytics and indexing content from sources like emails, websites and social media. This can provide insights for applications like brand, competitor and organizational intelligence. However, challenges include ensuring accurate content tagging and addressing scalability issues for large volumes of unstructured data.
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Israel redefining innovation at International CES 2015
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Service Cloud für Fortgeschrittene – Die Roadmap für 2012
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Incorporating the Data Lake into Your Analytic Architecture
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
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This document discusses moving from a centralized data architecture to a distributed data mesh architecture. It describes how a data mesh shifts data management responsibilities to individual business domains, with each domain acting as both a provider and consumer of data products. Key aspects of the data mesh approach discussed include domain-driven design, domain zones to organize domains, treating data as products, and using this approach to enable analytics at enterprise scale on platforms like Azure.
Big Data: Setting Up the Big Data Lake
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
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This document discusses balancing data governance and innovation. It describes how traditional data analytics methods can inhibit innovation by requiring lengthy processes to analyze new data. The document advocates adopting a data lake approach using tools like Hadoop and Spark to allow for faster ingestion and analysis of diverse data types. It also discusses challenges around simultaneously enabling innovation through a data lake while still maintaining proper data governance, security, and quality. Achieving this balance is key for organizations to leverage data for competitive advantage.
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Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Architecting for Big Data: Trends, Tips, and Deployment Options
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
Deliveinrg explainable AI
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Keyrus US Information
Keyrus is a data analytics consultancy that helps customers make data-driven decisions. It provides services including big data solutions, data management strategies, data integration, business intelligence dashboards, predictive analytics, and data science consulting. Keyrus has expertise in structured and unstructured data, data discovery visualization tools, and building end-to-end analytics solutions. Sample projects include building Hadoop environments for large telecom data and creating risk monitoring dashboards for investment banks.
Keyrus US Information
Keyrus is a data analytics consultancy that helps customers make data-driven decisions. It provides services including big data solutions, data management strategies, data integration, machine learning, predictive analytics, and data visualization dashboards. Keyrus consultants have skills in databases, data modeling, programming, and business requirements. For example, for a bank, Keyrus built interactive dashboards from multiple databases to provide regulators with risk monitoring dashboards.
The Right Data Warehouse: Automation Now, Business Value Thereafter
The Briefing Room with Dr. Robin Bloor and WhereScape
Live Webcast on April 1, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=7b23b14b532bd7be60a70f6bd5209f03
In the Big Data shuffle, everyone is looking at Hadoop as “the answer” to collect interesting data from a new set of sources. While Hadoop has given organizations the power to gather more information assets than ever before, the question still looms: which data, regardless of source, structure, volume and all the rest, are significant for affecting business value – and how do we harness it? One effective approach is to bolster the data warehouse environment with a solution capable of integrating all the data sources, including Hadoop, and automating delivery of key information into the rights hands.
Register for this episode of The Briefing Room to hear veteran Analyst Robin Bloor as he explains how a rapidly changing information landscape impacts data management. He will be briefed by Mark Budzinski of WhereScape, who will tout his company’s data warehouse automation solutions. Budzinski will discuss how automation can be the cornerstone for closing the gap between those responsible for data management and the people driving business decisions.
Visit InsideAnlaysis.com for more information.
Big Data Evolution
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
Data In Action: Business Value of Data
Lecture on databases for Technology and Operations class at Pepperdine Graziado School of Business. Presented October 2023.
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see the recording: http://youtu.be/qdhF1sfef10
Ofer Medelvitch, Director of Data Science of Hortonworks and Michael Zeller, Founder and CEO of Zementis present key learnings as to what drives successful implementations of big data analytics projects. Their knowledge comes from working with dozens of companies from small cloud-based start-ups to some of the largest companies in the world.
Big data analytics
This document provides an overview of big data and big data analytics. It defines big data as large, complex datasets that grow quickly in volume and variety. Big data analytics involves examining these large datasets to find patterns and useful information. The challenges of big data include increased storage needs and handling diverse data formats. Hadoop is a framework that allows distributed processing of big data across clusters of computers. Common big data analytics tools include MapReduce, Spark, HBase and Hive. The benefits of big data analytics include improved decision making, customer service and efficiency.
Building the Artificially Intelligent Enterprise
Mike Ferguson is Managing Director of Intelligent Business Strategies Limited and specializes in business intelligence/analytics and data management. He discusses building the artificially intelligent enterprise and transitioning to a self-learning enterprise. Some key challenges discussed include the siloed and fractured nature of current data and analytics efforts, with many tools and scripts in use without integration. He advocates sorting out the data foundation, implementing DataOps and MLOps, creating a data and analytics marketplace, and integrating analytics into business processes to drive value from AI.
BAR360 open data platform presentation at DAMA, Sydney
Sai Paravastu discusses the benefits of using an open data platform (ODP) for enterprises. The ODP would provide a standardized core of open source Hadoop technologies like HDFS, YARN, and MapReduce. This would allow big data solution providers to build compatible solutions on a common platform, reducing costs and improving interoperability. The ODP would also simplify integration for customers and reduce fragmentation in the industry by coordinating development efforts.
Data Profiling: The First Step to Big Data Quality
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
The Emerging Role of the Data Lake
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Intro big data analytics
This document provides an overview of big data analytics. It defines big data as large, complex datasets that require new techniques and tools to analyze. The key characteristics of big data are described as the 5 V's: volume, velocity, variety, veracity, and value. Hadoop is introduced as an open-source framework for distributed processing of large datasets across clusters of computers using MapReduce. The document also outlines different types of big data analytics including descriptive, predictive, supervised, and unsupervised analytics. It concludes with an overview of the analytics life cycle and some common analytics tools.
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This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
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Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
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In Big Data projects, analysts often spend 80% of their time preparing data for analysis. In addition, users don’t have a good understanding of their data quality.
Today there are multiple tools that assist with integration, data preparation, analysis and visualization. However, data quality continues to be one of the biggest challenges businesses face when deploying big data analytics.
People need to profile their data and ensure data quality to get accurate insights and make informed business decisions. Join Datameer as we address these pain points with visualizations at every step.
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You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
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best practices
Complement Your Existing Data Warehouse with Big Data & Hadoop
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
Lean Production Meets Big Data: A Next Generation Use Case
The document discusses how big data and analytics can help optimize business processes using lean principles. It provides an overview of lean production concepts like identifying and eliminating waste. Big data is presented as a new approach to gain insights from process data that can help pinpoint improvement opportunities. The speaker demonstrates how Datameer's software allows users to easily analyze data from multiple sources and measure key performance indicators to drive continuous process improvements.
The Economics of SQL on Hadoop
Watch the recorded event at: http://info.datameer.com/Slideshare-
Economics-SQL-Hadoop.html
As organizations clamor to utilize their new investments in Hadoop ecosystems AND leverage their existing analytical infrastructures, many rush to integrate SQL as a data access layer to leverage existing skill sets and get started faster.
However, this approach relegates Hadoop to a data management and processing platform rather than the storage and compute engine optimized for analytical workloads it was purpose-built to be.
These slides by EMA and Datameer, will discuss the technical limitations of SQL on Hadoop and propose alternative ways to fully maximize Hadoop investments.
You will understanding:
*how SQL negates the inherent benefits of Hadoop
*why technological paradigm changes can sometimes be good
*use cases when SQL on Hadoop makes sense
Top 3 Considerations for Machine Learning on Big Data
This document discusses considerations for machine learning on big data. It provides background on speakers Karen Hsu and Elliott Cordo. It then covers drivers and challenges of big data, including how companies like Amazon and Netflix have leveraged big data analytics. Alternatives to machine learning on big data like data mining, traditional BI, and visualization are discussed. Example use cases and key criteria around ease of use and quality for algorithms like clustering, column dependencies, and decision trees are presented. Best practices for machine learning on big data are provided for clustering, recommendations, and overall analytics processes. The document concludes with a polling question and call to action.
How to do Data Science Without the Scientist
1. The document discusses making data science and analytics easier and more accessible for non-experts. It outlines challenges with traditional tools like requiring coding skills, long cycle times, and siloed data and applications.
2. The author proposes simplifying data integration and preparation, generating rather than writing code, and moving computation to the data to address these challenges.
3. A demo is presented of Datameer's product which aims to simplify and speed up the analytics process for users without advanced data science skills.
How to do Predictive Analytics with Limited Data
http://www.datameer.com It is frustrating: even with petabytes of data on a Hadoop cluster, one still encounters situations where there’s a lack of key data for a wide variety of big data analytic use cases. You might have billions of clicks on your web site, but only a few users choose to rate a product. There might be millions of text documents on your cluster, but it is too expensive to have someone categorize more than a tiny fraction of them. In principle, this is where predictive modeling could help. For instance, one could learn a model to predict user ratings so you can better serve product recommendations based on those expected ratings. Or, one could create a model to automatically categorize text documents, saving countless hours and dollars. The main problem is that there is only a limited amount of training material (i.e. user ratings, categorized documents) and it is thus hard to generate good models.
As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.
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Online Fraud Detection Using Big Data Analytics Webinar
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Complement Your Existing Data Warehouse with Big Data & Hadoop
Complement Your Existing Data Warehouse with Big Data & Hadoop
Lean Production Meets Big Data: A Next Generation Use Case
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Top 3 Considerations for Machine Learning on Big Data
Top 3 Considerations for Machine Learning on Big Data
Recently uploaded
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Full-RAG: A modern architecture for hyper-personalization
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
OpenID AuthZEN Interop Read Out - Authorization
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Driving Business Innovation: Latest Generative AI Advancements & Success Story
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.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
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!
Recently uploaded (20)
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Full-RAG: A modern architecture for hyper-personalization
Full-RAG: A modern architecture for hyper-personalization
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Analyzing Unstructured Data in Hadoop Webinar
- 1. © 2014 Datameer, Inc. All rights reserved. Analyzing Unstructured Data in Hadoop!
- 2. View Recording !! You can view the recording of this webinar at: http://info.datameer.com/Online-Slideshare- Analyzing-Unstructured-Data-in-Hadoop- On-Demand.html
- 3. © 2013 Datameer, Inc. All rights reserved. Matt Schumpert @datameer Senior Director, Solutions Engineering Matt has been working in the enterprise infrastructure software space for over 14 years in various capacities, including sales engineering, strategic alliances and consulting. Matt currently runs the pre-sales engineering team at Datameer, supporting all technical aspects of customer engagement from initial contact through roll-out of customers into production. Matt holds a BS in Computer Science from the University of Virginia. #datameer @datameer About Our Speaker!
- 4. Agenda! • Market & Data trends • Tuning into new channels • The good news • The rise of wrangling • Analytics requirements • Bringing order to chaos • Use Cases
- 5. What we learned in 2010… (or before)!
- 6. Market & Data Trends! • Data volumes will grow 800% in 5 years • Unstructured data is growing 62% faster • 80% of all data will be unstructured in 2019 • “Big Unstructured Data” requires new tech. • 85% of the Fortune 500 will be unable to exploit Big Data for competitive advantage through 2015 Source: Gartner
- 7. Market & Data Trends! • ‘Multi-structured’ is the word of the day • Mainstream IT tools broadening the base • Competitive advantage lies outside your firewall! S U
- 8. Tuning Into New Channels!
- 9. Tuning Into New Channels! • Public & social data is available by the firehose • The new discipline: connecting, filtering, switching • Find the right keywords, dictionaries, segments • Learn from, but don’t emulate search engines • Beware of point solutions
- 10. The Good News! • All data has structure • Storage is cheap (Hadoop ~= $300 / TB) • Processing is cheap (“free”) • Unstructured data compresses well • Data APIs abound • Public data blossoming (data.gov, etc.)
- 11. The Rise of Wrangling! • A ‘record’ is no longer a record • Event streams need different angles of attack • Explode, project, align, window, search • New companies/technologies specializing in it Source: Gartner
- 12. Analytics Requirements (1)! • A scalable Big Data foundation (Hadoop) • Schema-on-read • Data profiling & cleansing • Fast, visual iteration over samples Source: Gartner
- 13. Analytics Requirements (2)! • Text mining, without programing • Helper functions for semi/un-structured formats • Data connectors, new visualizations • Patience, and a an culture of data discovery
- 14. Datameer:! End-to-End Big Data Analytics!
- 16. Bringing Order to Chaos! • ‘Big Data Visualization’ is an oxymoron • Rich, detailed summaries are the goal • ‘It’s the analytics, stupid’
- 17. Industry Use Cases! • Retail: Competitive pricing through web scraping • MFG: Product sentiment through Twitter • FSI: Brand preferences from Facebook “likes” • Gov: Nefarious behavior through email seizure!
- 19. For more information! http://www.datameer.com " @datameer " mschumpert@datameer.com Learn more Contact #datameer @datameer