Introduction to Big Data

Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail. Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at. Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn. Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada. Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON Databases: Oracle, MS SQL Server, MYSQL, PostreSQL Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1

This session will provide an executive overview of the Apache Hadoop ecosystem, its basic concepts, and its real-world applications. Attendees will learn how organizations worldwide are using the latest tools and strategies to harness their enterprise information to solve business problems and the types of data analysis commonly powered by Hadoop. Learn how various projects make up the Apache Hadoop ecosystem and the role each plays to improve data storage, management, interaction, and analysis. This is a valuable opportunity to gain insights into Hadoop functionality and how it can be applied to address compelling business challenges in your agency.

At Monsanto, emerging technologies such as IoT, advanced imaging and geo-spatial platforms; molecular breeding, ancestry and genomics data sets have made us rethink how we approach developing, deploying, scaling and distributing our software to accelerate predictive and prescriptive decisions. We created a Cloud based Data Science platform for the enterprise to address this need. Our primary goals were to perform analytics@scale and integrate analytics with our core product platforms. As part of this talk, we will be sharing our journey of transformation showing how we enabled: a collaborative discovery analytics environment for data science teams to perform model development, provisioning data through APIs, streams and deploying models to production through our auto-scaling big-data compute in the cloud to perform streaming, cognitive, predictive, prescriptive, historical and batch analytics@scale, integrating analytics with our core product platforms to turn data into actionable insights.

This presentation suggests the top 5 things architects and IT managers need to look for in a big data solution.

Industry thought leaders Gaurav Dhillon and David Linthicum discuss the future of cloud integration and data management in the API economy. Topics from this webinar and the accompanying slides include: key considerations of today's CIOs, approaching the reality of the multi-cloud world and new solutions for managing cloud and on-premise data. To learn more, visit: http://www.snaplogic.com/.

Non-interactive big-data analysis prohibits experimentation and can interrupt the analyst’s train of thoughts but analyzing and drawing insights in real time is no easy task with jobs often taking minutes/hours to complete. What if you want to put a interactive interface in front of that data that allows iterative insights? What if you need that interactive experience to be sub second? Traditional SQL and most MPP/NoSQL databases cannot run complex calculations over large data in a performant manner. Popular distributed systems such as Hadoop or Spark can execute jobs but their job overhead prohibits sub second response times. Learn how an in-memory computing framework enabled us to perform complex analysis jobs on massive data points with sub second response times — allowing us to plug it into a simple, drag-and-drop web 2.0 interface.

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.

Looking to implement Hadoop but haven’t pulled the trigger yet? You are not alone. Many companies have heard the hype about how Hadoop can solve the challenges presented by big data, but few have actually implemented it. What’s preventing them from taking the plunge? Can it be done in small steps to ensure project success? This session will discuss some of the items to consider when getting started with Hadoop and how to go about making the decision to move to the de facto big data platform. Starting small can be a good approach when your company is learning the basics and deciding what direction to take. There is no need to invest large amounts of time and money up front if a proof of concept is all you aim to provide. Using well known data sets on virtual machines can provide a low cost and effort implementation to know if your big data journey will be successful with Hadoop.

This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.

Sean McKeown, Technical Solutions Architect discusses big data architecture and deployment at Cisco Connect Toronto.

If you missed Strata + Hadoop World, you missed quite a bit. This year's event was packed with Big Data practitioners across industries who shared their experiences and how they are driving new innovations like never before. Just because you weren't there, doesn't mean you missed out. In this session, we'll touch on a few of the key highlights from the show, including: Key trends in Big Data adoption The enterprise data hub How the enterprise data hub is used in practice

Cloudera Tech Day Presentation by Eva Andreasson, Director Product Management, Cloudera. Text-based search recently has become a critical part of the Hadoop stack, and has emerged as one of the highest-performing solutions for big data analytics. In this session, attendees will learn about the new analytics capabilities in Apache Solr that integrate full-text search, faceted search, statistics, and grouping to provide a powerful engine for enabling next-generation big data analytics applications.

This document discusses ING NL's efforts to create a data lake architecture using Hadoop to integrate all of the bank's data sources onto a single processing platform. The data lake aims to collect data in a unified format, securely store it to prevent manipulation and unauthorized access, and make it available for analytical applications. Some of the challenges discussed include managing security, aligning with legacy systems, and facilitating interdepartmental cooperation on agile delivery. The presentation focuses on one part of the data lake, the archive, and how a Hadoop cluster can effectively address the goals of collecting, storing, and accessing data for business intelligence and data science purposes.

The business and technology teams within a health insurer must align the company’s central data platform with its data strategy. That requires substantial organizational alignment. Hear the firsthand perspective from Health Care Service Corporation (HCSC), the largest customer-owned health insurance company in the United States. The speaker will cover how they integrated membership information, regulatory compliance, and the general ledger, to improve overall healthcare management. At HCSC, the strong alignment between executive leadership, business portfolio direction, architectural strategy, technology delivery, and program management have helped create leading-edge capabilities which help the company respond nimbly to a quickly evolving healthcare industry.

- Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. - Cloudera's Data Operating System (CDH) is an enterprise-grade distribution of Apache Hadoop that includes additional components for management, security, and integration with existing systems. - CDH enables enterprises to leverage Hadoop for data agility, consolidation of structured and unstructured data sources, complex data processing using various programming languages, and economical storage of data regardless of type or size.

Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics driven by data streams, technology advances, business needs and consumer demands, 3) the growth of in-memory analytics using tools like Alluxio and RocksDB, 4) rapid application development using APIs, notebooks, GUIs and microservices, 5) open sourcing of machine learning systems by tech giants, and 6) hybrid cloud computing models for deploying big data applications both on-premise and in the cloud.

This document discusses big data analytics using Spark. It provides an overview of the history and growth of data from the 1980s to present. It then demonstrates how to perform word count analytics on text data using both traditional MapReduce techniques in Hadoop as well as using Spark. The code examples show how to tokenize text, count word frequencies, and output results.

This document discusses the big data market and winners and losers. It finds that traditional companies like Oracle, Teradata, and SAP are under pressure as newer big data technologies like Hadoop and NoSQL have seen rapid growth. While the big data market is expected to be worth over $50 billion by 2017, practitioners have faced barriers adopting big data within their organizations. Overall, practitioners are seen as the biggest winners from leveraging big data, though the market remains in early stages.

The document discusses big data market trends and provides advice on how organizations can develop a big data strategy and implementation plan. It outlines a 5 step approach for modernizing an organization's data warehouse with new big data technologies: 1) enhancing the data warehouse with unstructured data, 2) extending it with data virtualization, 3) increasing scalability with MPP databases, 4) accelerating analytics with in-database processing, and 5) creating an operational data store with Hadoop. The document also provides tips for selecting big data vendors, such as evaluating a vendor's ability to integrate with existing systems and make analytics accessible to both power users and business users.

This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.

Fully embracing a BI tool can mean the difference between the full payoff of your data analytics and returns that are just so-so. Learn how to avoid BI pitfalls and boost BI adoption to become a truly data driven organisation.

Talk at #BigDataCanarias (June 16, 2014)

Are you lost between web pages and links to big data ? I've collected all about big data and Hadoop togeather.

Title: BigData, AllData, Old Data: Predictive Analytics in a Changing Data Landscape Abstract: The landscape of the platform, access methodologies, shapes, and storage representations has changed dramatically. Much of the assumptions of a structured data world dominated by relational databases have been rendered obsolete. Today’s data analyst faces big challenges and a bewildering environment of technologies and challenges involving semi-structured and unstructured data with access methodologies that have almost no relation to the past. This talk will cover issues and challenges in how to make the benefits of advanced analytics fit within the application environment. The requirement for Real-time data streaming and in situ data mining is stronger than ever. We demonstrate how many of the critical problems remain open with much opportunity for innovative solutions to play a huge enabling role. This opportunity extends equally well to Knowledge Management and several related fields.

This document provides an overview of Hadoop and Big Data. It begins with introducing key concepts like structured, semi-structured, and unstructured data. It then discusses the growth of data and need for Big Data solutions. The core components of Hadoop like HDFS and MapReduce are explained at a high level. The document also covers Hadoop architecture, installation, and developing a basic MapReduce program.

The document discusses the importance of a data-driven culture for businesses. It provides the following key points: 1. Research has shown that companies that emphasize data-driven decision making have 5-6% higher productivity and output than comparable companies. This relationship also appears in other financial metrics like return on equity. 2. Data science draws from various fields like operations research, probability theory, analytics, and computer science. It is used for optimal decision making, handling uncertainties, generating insights from data, and implementing analytical solutions. 3. When adopting a data-driven approach, companies should focus on specific business goals and KPIs rather than just collecting data. Iterative testing is also important to measure impact

This presentation introduces concepts of Big Data in a layman's language. Author does not claim the originality of the content. The presentation is made by compiling from various sources. Author does not claim copyrights or privacy issues. Big data is exponentially rising in today's age of information and digital shrinkage. This presentation potentially clears the concept and revolving hype around it.

Presentation about how to reach a data driven culture. I gave the presentation at the Data Driven Digital Marketing Event 26 September 2016

Big Data 101 - Originally presented during a seminar when the idea behind big data was just beginning to catch on.

This presentation by Gartner discusses big data industry insights and trends. It provides an overview of organizations' investments in big data technology, the challenges they face in adoption, the types of big data being analyzed now and planned for the future, and examples of how different industries are using big data to address key business problems.

This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.

Datameer 6 is completely re-imagining the user experience for modern BI, helping you deliver new insights faster and more results to your data-hungry business. During this one-hour webinar, we demonstrated all that's new with Datameer 6 and how you can: Discover answers to a new range of business questions using an iterative, exploratory approach Find answers faster and deliver more insights with a new faster analytic workflow Utilize Spark to speed analytic processing time without needing to know technical details Watch this on-demand webinar, with special guest speaker, Sean Anderson, Senior Product Marketing Manager, from Cloudera, who discusses Cloudera's view of the Hadoop data processing stack and how the market place is benefiting from Spark.

The document discusses Oracle's approach to enterprise cloud strategy. It notes that most public and private cloud offerings are incompatible and incomplete for enterprise needs. Oracle proposes an integrated cloud solution providing enterprise SaaS, PaaS and IaaS that can span both private and public clouds. This would allow enterprises to run applications and workloads across their on-premises infrastructure and Oracle's public cloud platform. Oracle argues this integrated approach is needed to bring true cloud agility to enterprise applications and IT.

Learn how to get started with Big Data using a platform based on Apache Hadoop, Apache Spark, and IBM BigInsights technologies. The emphasis here is on free or low-cost options that require modest technical skills.

Data integration is just plain hard and there is no magic bullet. That said, three new data integration techniques do ameliorate the misery, making silo-busting possible, if not trivial. The three approaches – data lakes, virtual databases (aka federated databases), and data hubs – are a boon to organizations big enough to have separate systems, separate lines of business, and redundant acquired or COTS data stores. Each approach has its place, but how do you make the right decision about which data silo integration approach to choose and when? This webinar describes how you can use the key concepts of data Movement, Harmonization, and Indexing to determine what you are giving up or investing in, and make the best decision for your project.

This session will describe and demonstrate the longstanding integration between Couchbase Server and Apache Kafka and will include descriptions of both the mechanics of the integration and practical situations when combining these products is appropriate.

Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer. Presentation done at Digital Transformation event - February 2017

The keynote presentation discusses how cloud providers are impacting traditional data centers. It notes that as companies grow from startups to established enterprises, their hosting needs change from fully public cloud to hybrid models. The presentation outlines the tradeoffs of different hosting options like owning your own data center, colocation, managed hosting, and public cloud. It argues that a hybrid multi-cloud approach combining on-premises, dedicated, managed, public and other specialty clouds provides the most flexibility, cost savings, and ability to put the right workload in the right environment. Case studies are presented showing how hybrid cloud delivered major cost reductions and performance gains for Explore.org and enabled critical security and compliance requirements for Samsung. The presentation concludes that

3 Things to Learn: -How data is driving digital transformation to help businesses innovate rapidly -How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business -How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements

Hyper-converged systems offer a great deal of promise and yet come with a set of limitations. While they allow enterprises to re-integrate system components into a single enclosure and reduce the physical complexity, floor space and cost of supporting a workload in the data center, they also often will not support existing storage in local SANs or offered by cloud service providers. There are solutions available to address these challenges and allow hyper-converged systems to realize their promise. During this session you will learn: - What are hyper-converged systems? - What challenges do they pose? - What should the ideal solution to those challenges look like? - About a solution that helps integrate hyper-converged systems with existing SANs

Moving to the cloud can raise more questions than answers. Do I move and improve to Infrastructure as a service, or redesign my business processes on Software as a Service. This presentation covers several cloud migrations to OAC, EBS/Cloud (HCM/Financials) and Hyperion, and outlines what went well and what could have gone better.

One of the challenges that comes from deploying multi-tiered distributed systems, or microservices, atop a dynamic scheduler is the introduction of new problems surrounding load balancing. There are some inherent challenges in building a load balancer that's meant to operate in a highly available way, without any single points of failure. In this talk, Sargun Dhillon will walk through the distributed load balancing mechanism that he built for Mesos. This service discovery mechanism is meant to have the same kinds of features, api, and availability that existed in legacy, statically partitioned environments. The purpose of this is to ease the transition, and remove some of the largest road blocks in moving applications over to modern datacenters. In addition, he will speak to why he built it as opposed to other alternatives for service discovery and load balancing such as using Zookeeper, and the challenges that came from it. We built a library called Lashup that has a membership protocol, a multicast layer, failure detector, and CRDT key/value store. This has allowed us to build applications that orchestrate Mesos clusters with great ease.

We all know that consumer behavior has changed dramatically. How consumers engage with companies, do research and even purchase leaves a deluge of data that companies have never had. Those companies that can parse that data drive business results like never before. This session presentation at Dog Food Con 2016 helps you to learn how Big Data technology can drive business outcomes from data ingestion to cloud and talks about one company’s journey to Customer 360 and their decision process when moving to the cloud.

- Deep learning (Tensorflow) and microservices (Kubernetes, Docker, Kafka) are emerging trends. - While batch processing is still dominant, stream processing is gaining traction with technologies like Kafka, Flink, and Beam. - Python has surpassed Java as the most popular language for data analytics, according to Stack Overflow trends.

This document discusses the evolution of data center networking from 2007 to present day. It describes how earlier networks were static with clear divisions between teams, while modern networks are more dynamic with blurred lines between developers and operations. It outlines projects within DC/OS like Mesos-DNS, Minuteman, and Lashup that provide service discovery, load balancing, and a distributed control plane to manage today's complex networks and microservices applications. Future plans include improved security, quality of service, and potential rewriting of operating systems to enable zero-overhead network functions virtualization.

This document discusses the history and development of container networking and service discovery solutions. It describes how Mesosphere developed DC/OS to provide networking features like load balancing and service discovery using Erlang microservices including Spartan, Minuteman, and Lashup. Spartan provides high availability DNS, Minuteman provides distributed load balancing, and Lashup uses HyParView to maintain global network state across the cluster. The document outlines how these services were developed to enable dynamic container networking and service discovery.

Uptake is the industrial analytics platform that delivers products to major industries to increase productivity, security, safety and reliability. About the Event Launching a successful startup takes more than building on the most flexible, reliable, and scalable infrastructure available today. Startup Day is an opportunity to hear from successful startups about how they've tackled the unique technical challenges in their industry. It's also an opportunity to meet other startup leaders in your community to share ideas, help each other grow, and inspire each other while tackling problems that affect your organization. Who Should Attend? This event is built for early stage (pre-seed & bootstrapped) technical leads and entrepreneurs. Attendees will learn from a technical perspective what did and didn't work well from other startups across a diverse range of industries. Hear what funded and late stage startups wish they knew before they began building. Learn how companies are leveraging SageMaker for optimizing Machine Learning training and deployment, deploying ECS for container orchestration, and using Lambda to build companies that are entirely serverless.

The document discusses how legacy customer data stored in organizations can provide a competitive advantage for training AI/machine learning models and powering personalized customer experiences while ensuring privacy protection. It explains that legacy data is needed to train accurate predictive models, enable cross-channel personalization, and allow for strong governance and control over sensitive customer information. Finally, it states that without access to legacy customer data stores, organizations cannot fully leverage AI/ML to drive predictive marketing, deliver personalized experiences, or comprehensively protect customer privacy.

The document discusses emerging cloud computing technologies including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Database as a Service. It notes that IaaS is currently the fastest growing cloud service, with Gartner reporting 42.4% growth in 2012. Popular IaaS providers include Amazon Web Services, CloudStack, and OpenStack. PaaS offerings from Google App Engine, Heroku, and Amazon Elastic Beanstalk are analyzed in terms of their approaches and limitations. Best practices for adopting PaaS include considering application requirements, resources, data needs, and interactions beyond the platform.

A high level overview of 'The Cloud', Microsoft Windows Azure and experiences building a cloud platform

This document provides an introduction to integration platform as a service (iPaaS) and SnapLogic. It discusses the drivers for iPaaS adoption including big data, hybrid cloud environments, and the need for faster integration. Ten requirements for modern integration are outlined. The document then introduces SnapLogic and its unified platform for connecting applications, data and APIs anywhere through a library of pre-built connectors. Four primary iPaaS use cases are described: hybrid application integration, cloud data warehousing/analytics, big data ingestion/transformation/delivery, and replacing legacy integration platforms.