This document summarizes a webinar presented by Talend and Caserta Concepts on the big data ecosystem. The webinar discussed how Talend provides an open source integration platform that scales to handle large data volumes and complex processes. It also overviewed Caserta Concepts' expertise in data management, big data analytics, and industries like financial services. The webinar covered topics like traditional vs big data, Hadoop and NoSQL technologies, and common integration patterns between traditional data warehouses and big data platforms.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
The document discusses Oracle's cloud-based data lake and analytics platform. It provides an overview of the key technologies and services available, including Spark, Kafka, Hive, object storage, notebooks and data visualization tools. It then outlines a scenario for setting up storage and big data services in Oracle Cloud to create a new data lake for batch, real-time and external data sources. The goal is to provide an agile and scalable environment for data scientists, developers and business users.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
This document discusses top big data analytics tools and emerging trends in big data analytics. It defines big data analytics as examining large data sets to find patterns and business insights. The document then covers several open source and commercial big data analytics tools, including Jaspersoft and Talend for reporting, Skytree for machine learning, Tableau for visualization, and Pentaho and Splunk for reporting. It emphasizes that tool selection is just one part of a big data project and that evaluating business value is also important.
MapReduce allows distributed processing of large datasets across clusters of computers. It works by splitting the input data into independent chunks which are processed by the map function in parallel. The map function produces intermediate key-value pairs which are grouped by the reduce function to form the output data. Fault tolerance is achieved through replication of data across nodes and re-executing failed tasks. This makes MapReduce suitable for efficiently processing very large datasets in a distributed environment.
The document outlines an agenda for a presentation on big data. It discusses key topics like the state of big data adoption, a holistic approach to big data, five high value use cases, technical components, and the future of big data and cloud. The presentation aims to provide an overview of big data and how organizations can take a comprehensive approach to leveraging their data assets.
This report examines the rise of big data and analytics used to analyze large volumes of data. It is based on a survey of 302 BI professionals and interviews. Most organizations have implemented analytical platforms to help analyze growing amounts of structured data. New technologies also analyze semi-structured data like web logs and machine data. While reports and dashboards serve casual users, more advanced analytics are needed for power users to fully leverage big data.
This document discusses big data and how enterprises are adopting big data solutions. It describes how data has exploded in terms of volume, velocity, and variety. Big data now includes structured, semi-structured, and unstructured data from sources like sensors, social media, and machine logs. The document outlines how Hadoop has become a popular big data platform that provides scalable and cost-effective storage and processing of large, complex datasets. It also discusses how enterprises are using big data for applications like predictive analytics, social intelligence, and mobile analytics to drive insights and decisions.
This document discusses big data, including the large amounts of data being collected daily, challenges with traditional DBMS solutions, the need for new approaches like Hadoop and Aster Data to handle large volumes of structured and unstructured data, techniques for analyzing big data, and case studies of companies like Mobclix and Yahoo using big data solutions.
Data warehouse appliances provide an integrated hardware and software system optimized specifically for data warehousing. They are designed to handle high volumes of data and perform complex analytical operations quickly in a scalable, fault-tolerant, and secure manner. Key advantages include reduced costs, management overhead, and risks compared to traditional data warehouse infrastructure. Major vendors of these appliances include Teradata, Netezza, and Oracle.
This document provides an introduction and overview of Hadoop. It discusses how businesses have been collecting large amounts of data but face challenges in analyzing it due to application complexities, data growth, infrastructure limitations, and economic factors. Hadoop is presented as a solution that can handle high-volume data, perform complex operations at scale, is robust and fault tolerant. Key components of Hadoop like HDFS, MapReduce, and the Hadoop ecosystem are described at a high level.
Polestar we hope to bring the power of data to organizations across industries helping them analyze billions of data points and data sets to provide real-time insights, and enabling them to make critical decisions to grow their business.
IBM InfoSphere BigInsights provides Hadoop on cloud computing platforms so that users can analyze large volumes of data without requiring large upfront investments in hardware, storage, and networking. It allows users to deploy their own Hadoop clusters on public clouds like Amazon or private clouds in under 30 minutes, paying only for the resources used on an hourly basis starting at $0.34 per node per hour. BigInsights can be deployed on IBM SmartCloud Enterprise with hourly charges starting at $0.30 per cluster per hour and a free trial during a fall promotion. It makes evaluating and learning Hadoop easy without needing to configure hardware or install software.
Big data comes from a variety of sources like social networks, sensors, and financial transactions. It is characterized by its volume, velocity, and variety. Hadoop and NoSQL platforms are commonly used to process and analyze big data. There are many opportunities for applications in domains like healthcare, retail, and finance. However, addressing the skills gap for data scientists remains a key challenge for fully realizing the potential of big data.
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
This document discusses big data, including how much data is now being collected, challenges with traditional database management systems, and the need for new approaches like Hadoop and Aster Data. It provides details on characteristics of big data, architectural requirements, techniques for analysis, and solutions from companies like IBM, Teradata, and Aster Data. Hadoop is discussed in depth, covering how it works, the ecosystem, and example users. Aster Data is also summarized, focusing on its massively parallel SQL layer and in-database analytics capabilities.
Presentation: Study: #Big Data in #Austria, Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH & Martin Köhler, Austrian Institute of Technology, AIT (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna.
Great Expectations is an open-source Python library that helps validate, document, and profile data to maintain quality. It allows users to define expectations about data that are used to validate new data and generate documentation. Key features include automated data profiling, predefined and custom validation rules, and scalability. It is used by companies like Vimeo and Heineken in their data pipelines. While helpful for testing data, it is not intended as a data cleaning or versioning tool. A demo shows how to initialize a project, validate sample taxi data, and view results.
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Modernizing the Legacy - How Dish is Adapting its SOA Services for a Cloud Fi...VMware Tanzu
SpringOne Platform 2016
Speakers: Rob Bennett; Director, Development, Dish Networks; Chandra Nemalipuri; Principal Software Engineer, Dish Networks; Lax Rastogi; Senior Manager, Dish Networks
Like many companies, Dish has a large number of SOA services that have been built using previous generations of technology. In this session we will discuss the challenges faced in converting legacy services to cloud native applications and the different approaches we considered for resolving the conflicts. We will then dive deeper into the approach that we chose to modernize our services and put us on a track towards a microservices based architecture running on Cloud Foundry.
Solr At Scale For Time-Oriented Data: Presented by Brett Hoerner, RocanaLucidworks
This document summarizes Brett Hoerner's presentation on optimizing Solr for time-oriented data at large scales. Some key points discussed include sharding indexes by time to optimize queries and indexing, using techniques like MapReduce to rebuild very large Solr indexes, and layering clusters to optimize for different query types like recent vs historical data. The presentation provided many tips for tuning Solr configuration, indexing pipelines, and cluster architectures for datasets containing billions of documents ingested in real-time.
The document discusses the tradeoffs involved in the decision to build a real-time streaming analytics (RTSA) platform in-house versus buying a pre-built solution from a vendor. Building internally provides more customization and control but risks delays and lack of expertise, while buying from a vendor is faster to implement but risks vendor lock-in. The document proposes a third alternative of using a platform like StreamAnalytix that is based on open source technologies but also provides enterprise-level support.
Architecting Security and Governance Across Multi AccountsAmazon Web Services
Whether it is per business unit or per application, many AWS customers use multiple accounts to meet their infrastructure isolation and billing requirements. In this session, we discuss considerations, limitations, and security patterns when building out a multi-account strategy. We explore topics such as identity federation, cross-account roles, consolidated logging, and account governance.
At the end of the session, we present an enterprise-ready, multi-account architecture that you can start leveraging today.
This document discusses data center strategies for fast growing businesses and outlines Oracle's cloud offerings. It begins with an overview of public, private and hybrid cloud models and Oracle's cloud leadership. It then covers trends in enterprise computing like data growth, mobility and the move to the cloud. The document discusses how different decisions need to be made in small and medium businesses compared to larger enterprises. It provides examples of cloud use cases and an overview of Oracle's platform as a service and infrastructure as a service offerings. Key considerations for workload analysis and cloud selection are also outlined.
How Localytics uses metrics to impact outcomes. The key takeaways are:
1. Think about metrics from the start
2. Mine for metrics within your org
3. Be very thoughtful about your key metrics
4. Instrument internal systems
This document compares the features of vSphere with Operations Manager Enterprise Plus and vCloud Suite Standard. vCloud Suite Standard includes additional features over vSphere with Operations Manager such as built-in high availability, customizable dashboards and reports, and monitoring of OS resources for vRealize Operations Manager. vCloud Suite Standard also includes the full versions of vRealize Business Edition and vRealize Log Insight for additional cloud management and log analysis capabilities. Overall, vCloud Suite Standard provides a more comprehensive cloud management platform than vSphere with Operations Manager alone.
Agile Operations Keynote: Redefine the Role of IT Operations With Digital Tra...CA Technologies
The document discusses how digital transformation initiatives are redefining the role of IT operations. As companies adopt new technologies like cloud, analytics, microservices and software-defined networks, IT operations faces greater complexity in monitoring applications and infrastructure. This introduces more monitoring challenges and blind spots. The presentation argues that IT operations must adopt new approaches using predictive analytics to correlate user experiences, applications and infrastructure insights. It provides examples of how CA technologies help organizations achieve this through application performance management and infrastructure monitoring solutions.
A modern, flexible approach to Hadoop implementation incorporating innovation...DataWorks Summit
A modern, flexible approach to Hadoop implementation incorporating innovations from HP Haven
Jeff Veis
Vice President
HP Software Big Data
Gilles Noisette
Master Solution Architect
HP EMEA Big Data CoE
VMware introduced several new features in vSphere 6 including increased scalability limits, usability improvements to the vSphere Web Client, enhanced vMotion capabilities such as cross-vCenter and long distance vMotion, expanded fault tolerance support, and the introduction of vSphere Virtual Volumes and its policy-based management framework. Key networking updates included Network I/O Control version 3 and multiple TCP/IP stacks. Storage features focused on Virtual SAN enhancements, Storage DRS integration, and support for VASA 2.0 storage capabilities.
This document discusses uncertainties in big data. It presents several case studies where deep learning and multi-view learning techniques have been applied to predict things like traffic incident duration, school truancy factors, and event detection in video. However, it notes that all models have uncertainties and discusses the need to better quantify objectives, propagate uncertainties, evaluate robustness, and communicate uncertainties to users. The document advocates for a multidisciplinary approach to tackling uncertainties in big data predictions and outcomes.
This document discusses big data solutions and analytics. It defines big data in terms of volume, velocity, and variety of data. It contrasts big data analytics with traditional business intelligence, noting that big data looks for untapped insights rather than dashboards. It also provides examples of scalable big data platform architectures and advanced analytics capabilities. Finally, it outlines Anexinet's big data offerings including strategy, starter solutions, projects, and partnerships.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
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/.
Are you confused by Big Data? Get in touch with this new "black gold" and familiarize yourself with undiscovered insights through our complimentary introductory lesson on Big Data and Hadoop!
Explores the notion of "Hadoop as a Data Refinery" within an organisation, be it one with an existing Business Intelligence system or none - looks at 'agile data' as a a benefit of using Hadoop as the store for historical, unstructured and very-large-scale datasets.
The final slides look at the challenge of an organisation becoming "data driven"
Hadoop as Data Refinery - Steve LoughranJAX London
1. Steve Loughran presented on using Hadoop as a data refinery to store, clean, and refine large amounts of raw data for business intelligence and analytics.
2. A data refinery uses Hadoop to ingest raw data from various sources, clean it, filter it, and forward it to destinations like data warehouses or new agile data systems. It retains raw data for future analysis and offloads work from core data warehouses.
3. Hadoop allows organizations to become more data-driven by supporting ad-hoc queries, storing more historical data affordably, and serving as a platform for data science applications and machine learning. This helps drive innovative business models and competitive advantages.
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarioskcmallu
What's the origin of Big Data? What are the real life usage scenarios where Hadoop has been successfully adopted? How do you get started within your organizations?
BAR360 open data platform presentation at DAMA, SydneySai Paravastu
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.
Big data is driving transformative changes in traditional data warehousing. Traditional ETL processes and highly structured data schemas are being replaced with schema flexibility to handle all types of data from diverse sources. This allows for real-time experimentation and analysis beyond just operational reporting. Microsoft is applying lessons from its own big data journey to help customers by providing a comprehensive set of Apache big data tools in Azure along with intelligence and analytics services to gain insights from diverse data sources.
DataLakes kan skalere i takt med skyen, nedbryde integrationsbarrierer og data gemt i siloer og bane vejen for nye forretningsmuligheder. Det er alt sammen med til at give et bedre beslutningsgrundlag for ledelse og medarbejdere. Kom og hør hvordan.
David Bojsen, Arkitekt, Microsoft
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
If you’ve spent time investigating Big Data, you quickly realize that the issues surrounding Big Data are often complex to analyze and solve. The sheer volume, velocity and variety changes the way we think about data – including how enterprises approach data architecture.
Significant reduction in costs for processing, managing, and storing data, combined with the need for business agility and analytics, requires CIOs and enterprise architects to rethink their enterprise data architecture and develop a next-generation approach to solve the complexities of Big Data.
Creating the data architecture while integrating Big Data into the heart of the enterprise data architecture is a challenge. This webinar covered:
-Why Big Data capabilities must be strategically integrated into an enterprise’s data architecture
-How a next-generation architecture can be conceptualized
-The key components to a robust next generation architecture
-How to incrementally transition to a next generation data architecture
The document provides an overview of Perficient, a leading information technology consulting firm, and their big data architectural series webinar on creating a next-generation big data architecture. The webinar discusses big data business use cases, the Hadoop ecosystem, realizing a Hadoop-centric architecture through different architectural roles for Hadoop including analytics, data warehousing, stream processing, data integration and transactional data stores. It also covers challenges in moving from potential to reality and provides recommendations for integrating Hadoop into the enterprise.
This document provides an overview of big data fundamentals and considerations for setting up a big data practice. It discusses key big data concepts like the four V's of big data. It also outlines common big data questions around business context, architecture, skills, and presents sample reference architectures. The document recommends starting a big data practice by identifying use cases, gaining management commitment, and setting up a center of excellence. It provides an example use case of retail web log analysis and presents big data architecture patterns.
Apache Hadoop and the Big Data Opportunity in Banking
The document discusses Apache Hadoop and how it can help banks leverage big data opportunities. It provides an overview of what Apache Hadoop is, how it works, and the core projects. It then discusses how Hadoop can help banks create value by detecting fraud, managing risk, improving products based on customer data analysis, and more. The presenters are from Hortonworks, the lead commercial company for Hadoop, and Tresata, a company focused on using Hadoop for banking applications.
5 Things that Make Hadoop a Game Changer
Webinar by Elliott Cordo, Caserta Concepts
There is much hype and mystery surrounding Hadoop's role in analytic architecture. In this webinar, Elliott presented, in detail, the services and concepts that makes Hadoop a truly unique solution - a game changer for the enterprise. He talked about the real benefits of a distributed file system, the multi workload processing capabilities enabled by YARN, and the 3 other important things you need to know about Hadoop.
To access the recorded webinar, visit the event site: https://www.brighttalk.com/webcast/9061/131029
For more information the services and solutions that Caserta Concepts offers, please visit http://casertaconcepts.com/
Bill Hayduk is the founder and CEO of QuerySurge, a software division that provides data integration and analytics solutions, with headquarters in New York; QuerySurge was founded in 1996 and has grown to serve Fortune 1000 customers through partnerships with technology companies and consulting firms. The document discusses the data and analytics marketplace and provides an overview of concepts like data warehousing, ETL, BI, data quality, data testing, big data, Hadoop, and NoSQL.
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Analytic Platforms in the Real World with 451Research and Calpont_July 2012Calpont Corporation
Matt Aslett of 451 Research discussed the rise of analytic platforms and their role in enabling exploratory analytics on large datasets. Bob Wilkinson from Calpont then presented on InfiniDB, Calpont's columnar analytic platform that provides scalable and fast performance for complex queries. InfiniDB was shown to accelerate analytics for telecommunications customer experience data and online advertising attribution. The discussion highlighted how InfiniDB supports flexible schemas and a spectrum of analytic approaches to enable exploratory analysis on structured data.
Similar to Introducing the Big Data Ecosystem with Caserta Concepts & Talend (20)
Using Machine Learning & Spark to Power Data-Driven MarketingCaserta
Joe Caserta provides a statistically-driven model to understanding the customer path to purchase, which combines online, offline and third-party data sources. He shows how customer data is fed to machine learning, which assigns weighted credit to customer interactions in order to give insight to what marketing activities truly matter. This presentation is from Caserta's February 2018 Big Data Warehousing Meetup co-hosted with Databricks.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Integrating the CDO Role Into Your Organization; Managing the Disruption (MIT...Caserta
The role of the Chief Data Officer (CDO) has become integral to the evolution needed to turn a wisdom-driven company into an analytics-driven company. With Data Governance at the core of your responsibility, moving the innovation meter is a global challenge among CDOs. Specifically the CDO must:
• Provide a single point of accountability for data initiatives and issues
• Innovate ways to use existing data and evangelize a data vision for the organization
• Support & enforce data governance policies via outreach, training & tools
• Work with IT to develop/maintain an enterprise data repository
• Set standards for analytical reporting and generate data insights through data science
In this session, Joe Caserta addresses real-word CDO challenges, shares techniques to overcome them, manage corporate disruption and achieve success.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteCaserta
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.
Introduction to Data Science (Data Summit, 2017)Caserta
This document summarizes an introduction to data science presentation by Joe Caserta and Bill Walrond of Caserta Concepts. Caserta Concepts is an internationally recognized data innovation and engineering consulting firm. The agenda covers why data science is important, challenges of working with big data, governing big data, the data pyramid, what data scientists do, standards for data science, and a demonstration of data analysis. Popular machine learning algorithms like regression, decision trees, k-means clustering and collaborative filtering are also discussed.
Looker Data Modeling in the Age of Cloud - BDW Meetup May 2, 2017Caserta
This document discusses the evolution of data analytics and modeling. It describes three waves: the first with slow hardware and manual entry; the second with faster PCs but tool explosions; and the third wave now with big data, cloud warehouses, and data-driven tools like Looker and BigQuery. It argues that in this current wave, having a flexible yet performant data model built on SQL in a warehouse, and using a language like LookML to define relationships and translate questions, allows gaining reliable answers with agility without worrying about low-level syntax or tools.
There is an overwhelming list of expectations – and challenges – in this new, emerging and evolving role. In this presentation, given at the 2016 CDO Summit, Joe Caserta focuses on:
- Defining the CDO title
- Outlining the skills that enhance chances for success
- Listing all the many things the company thinks you are responsible for
- Providing an overview of the core technologies you need to be familiar with and will serve to ultimately support your success
- Presenting a concise list of the most pressing challenges
- Sharing insights and arguments for how best to meet the challenges and succeed in your new role
Building a New Platform for Customer Analytics Caserta
Caserta Concepts and Databricks partner up to bring you this insightful webinar on how a business can choose from all of the emerging big data technologies to figure out which one best fits their needs.
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
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/
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/
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/
The document provides an introduction and agenda for a presentation on data science and big data. It discusses Joe Caserta's background and experience in data warehousing, business intelligence, and data science. It outlines Caserta Concepts' focus on big data solutions, data warehousing, and industries like ecommerce, financial services, and healthcare. The agenda covers topics like governing big data for data science, introducing the data pyramid, what data scientists do, and standards for data science projects.
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/.
This document discusses appropriate and inappropriate use cases for Apache Spark based on the type of data and workload. It provides examples of good uses, such as batch processing, ETL, and machine learning/data science. It also gives examples of bad uses, such as random access queries, frequent incremental updates, and low latency stream processing. The document recommends using a database instead of Spark for random access, updates, and serving live queries. It suggests using message queues instead of files for low latency stream processing. The goal is to help users understand how to properly leverage Spark for big data workloads.
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
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Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
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So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
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This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
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Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
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Material identification: Identify unknown materials, minerals, and contaminants.
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Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
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Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
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Introducing the Big Data Ecosystem with Caserta Concepts & Talend
1. The Big Data Ecosystem
Talend & Caserta Concepts Webinar
Ciaran Dynes
Director, Product Management & Product Marketing, Talend
Joe Caserta
Founder & President, Caserta Concepts
2. Integration at Any Scale
Talend is the only integration vendor that enables
your business to scale through:
An open source-based solution supported by
a vast community and enterprise-class services
An innovative, unified platform that scales data,
application and business processes of any complexity
A usage-based subscription model delivering
$
a fast return on investment
3. Talend - Integration at Any Scale
Talend offers true
scalability for
• Any integration challenge
• Any data volume
• Any project size
Talend enables
integration
convergence
4. Working with Leading Vendors
Platforms/Hadoop Appliance NoSQL
Data Management Analytics
System Integrators
System Integrators play a vital role in providing expertise
5. The Big Data Ecosystem
Talend & Caserta Concepts Webinar
Joe Caserta
Founder & President, Caserta Concepts
Ciaran Dynes
Director, Product Management & Product Marketing, Talend
6. Joe Caserta Timeline
2012
Partnered with Big Data vendors Laser focus on Big Data solutions for
Cloudera, HortonWorks, Datameer, Financial Sector & eCommerce
more… 2010
Formalized Talend Alliance
2009 Partnership – System Integrators
Launched Big Data practice
2004
Co-author, with Ralph Kimball, The
Launched Training practice, teaching Data Warehouse ETL Toolkit (Wiley)
data concepts world-wide
2001
Web log analytics solution published
Founded Caserta Concepts in NYC
in Intelligent Enterprise
1996
Began consulting career as Dedicated to Data Warehousing,
programmer/data modeler Business Intelligence since 1996
1986
25+ years hands-on experience
building database solutions
7. Caserta Concepts
• Technology services company with expertise in data analysis:
• Data Management
• Big Data & Analytics
• With core focus in the following industries:
• Financial Services
• Insurance / Healthcare
• eCommerce / Higher Education
• Established in 2001:
• Increased growth year-over-year
• Industry recognized work force
• Consulting, Writing, Education
8. Expertise & Offerings
Strategic Roadmap/
Assessment/Consulting
Big Data
Analytics
Data Warehousing/
ETL/Data Integration
BI/Visualization/
Analytics
Master Data Management
10. The Good Old Days: Traditional Data Warehousing
Metadata
Standard Reports
Web Logs
Ad-hoc Query Tools
External Extract
Data Sources Optimized
Load
Transform Data Mining
Data
Warehouse
Relational
Systems/ERP
MDD/OLAP
Closed-loop
Legacy feedback Analytical Applications
Systems applications
Data Marts
(The data warehouse?)
11. What is “Big Data”?
• A collection of data sets so large and complex that
it becomes difficult to process using on-hand
database management tools or traditional data
processing applications.
• Challenges include capture, storage, search,
sharing, transfer, analysis, and visualization.
• Relational databases were designed for
applications, we use only a small fraction of their
capabilities in analytics applications.
• Enforcing a relational structure upon our data is
not always what we want.
12. What’s the Difference?
Traditional Data Big Data
Very accurate transactional data. Lots of data with value that can
Analyzed by humans only be attained by deep analytics
Measured in terabytes Measured in petabytes
Structured data Structured/Unstructured data
Input by human “system users” Created by everybody, plus all of
our machine friends
Oracle, SAP, etc. Open source, Hadoop
HW/SW investment measured in HW/SW investment measured in
$10M $10K
Recording facts Harvesting insights
13. Try to keep up: This slide is already obsolete
14. So where does the data warehouse come in?
• Will Big Data replace the data warehouse?
• Yes – however there is much evolution ahead: real time
integrations, interactive queries
• Data Warehousing principles still apply to Big Data
• Data Quality
• Master Data
• Data architecture
• How do we leverage our existing investment?
15. Enterprise Technical Ecosystem
Traditional BI
ERP
ETL Traditional
EDW
Finance
Ad-Hoc/Canned
ETL Reporting
Legacy
Big Data Cluster Big Data BI
NoSQL
Database Cassandra
Search/Data
Analytics
Mahout MapReduce Pig/Hive
N1 N2 N3 N4 N5
Hadoop Distributed File System (HDFS)
Horizontally Scalable Environment - Optimized for Analytics Canned Reporting
16. Extending EDW with Hadoop
•Eliminate barrier of imposing relational structure on data.
•Storage is fast, durable and cheap: Don’t throw away data that
can be valuable in the future
•Processing power
• Hadoop scales linearly, don’t worry about the data set getting
too big
•Machine learning
•Ad-Hoc reporting by non-technical users requires traditional
methods or additional application
17. Design Pattern #1: Hadoop Staging/Warehouse
feed relational EDW (Composite Warehouse)
• Hadoop serves as the staging ground for all data
- Eliminate barrier of imposing relational structure on data.
- Storage is fast, durable and cheap: Don’t throw away data that can be
valuable in the future
• Data scientists will work in the Hadoop environment to analyze, and mine structured
and unstructured data using Pig, Hive, and Mahout (machine learning)
• Data required for interactive reporting and traditional ad-hoc analysis is sent to
downstream relational EDW
Source Systems
Mahout MapReduce Pig/Hive
Traditional DW
N1 N2 N3 N4 N5
Hadoop Distributed File System (HDFS)
18. Design Pattern #2: NoSQL Enhanced EDW
•Not all structured data lends itself to being stored relationally:
• Relationships: Graph Databases
• Sparse Data: Columnar Databases
•Very Large Datasets:
• NoSQL databases are capable of scaling far beyond relational databases while
maintaining performance
• Ultra-performance key value stores and columnar databases can be very useful in
storing certain types of high volume data for analytic purposes
• Just don’t expect the ad-hoc flexibility of a relational database!
- Web analytics
Mahout MapReduce Pig/Hive Cassandra - Ad Impressions
(columnar)
N1 N2 N3 N4 N5
Hadoop Distributed File System (HDFS) - Networks
Titan
- Recommender
(graph)
- Path optimization
Traditional DW
19. Design Pattern #3: Add analytics to your NoSQL
cluster
• If your application is already based on a NoSQL technology, consider
building analytic site.
• The analytic site is constantly streamed fresh transactions leveraging
Cassandra's native replication
• Aggregates and analytic views are materialized with Pig/Hive map/reduce,
since the work is done on the cluster no load is placed on the applications.
This analytic data is in turn replicated throughout the cluster
Site 1
Cassandra
Pig/Hive
Cassandra
MapReduce
Analytics
Site
Site 2 Canned Reporting
Cassandra
Remember, NoSQL
schemas are
Traditional “optimized to a
DW query”, not ad-hoc
20. Emerging Tools
Hive, although an excellent tool for data
analysis is too slow for interactive
queries. Recent projects have increased
speed dramatically 10-100x.
• Google Dremel
• Apache/MapR Drill
• Hortonworks Stinger
• Cloudera Impala
21. Commonly Used Technologies
• Amazon Elastic MapReduce (EMR): Web service to access EC2/S3, pay-as-
you-go hosted Hadoop Infrastructure
• Hadoop Distribution: Cloudera; MapR; Hortonworks
• Apache Projects
• Whirr: Used to launch/kill computing clusters
• Kafka: Publish-subscribe messaging system
• Mahout: Distributed machine learning
• Hive: Map data to structures and use SQL-like queries
• HBase: No-SQL/non-relational database, real-time read/write
• Cassandra: Like HBase, no single point of failure
• Chuckwa/Flume: Large-scale log collection
• Pig: Procedural programming language, from Yahoo
• Sqoop: “SQL-to-Hadoop”, like BCP for Hadoop
• Zookeeper: Used to manage & adminster Hadoop
• Solr: Full-text/Faceted Search
• MongoDB: Document-oriented database
• Languages: Python, SciPy, Java
23. Parting Thought
Polyglot Persistence – “where any decent sized
enterprise will have a variety of different data storage
technologies for different kinds of data. There will still
be large amounts of it managed in relational stores,
but increasingly we'll be first asking how we want to
manipulate the data and only then figuring out what
technology is the best bet for it.”
-- Martin Fowler
Purpose of the slide: Mission / Vision StatementKey themes:Talend’s mission is to enable our customers to innovate faster at a lower cost.We are disrupting the traditional integration market by delivering an: open source-based solution, innovative unified platform, usage-based subscription modelMore from the Talend boilerplate:Talend provides integration that truly scales. From small projects to enterprise-wide implementations, Talend’s highly scalable data, application and business process integration platform maximizes the value of an organization’s information assets and optimizes return on investment through a usage-based subscription model. Ready for big data environments, Talend’s flexible architecture easily adapts to future IT platforms. And a common set of easy-to-use tools implemented across all Talend products enable teams to scale developer skillsets, too.
Purpose of the slide: IntroduceTalend’s solution – Integration At Any ScaleTalking points:Talend is disrupting the integration market to address these integration challenges by providing a differentiated solution that provides “Integration at Any Scale”With Talend, your business can scale to meet any integration challenge, any data volume, or any project size.We will discuss HOW this is done in a moment, but the main point here is what we call “Integration Convergence”Integration Convergence is the ability to address data, application and process integration needs with the same platformThe benefit to you, is that your resources are more efficient and you lower your cost of operationsTalend provides integration that truly scales. From small projects to enterprise-wide implementations, Talend’s highly scalable data, application and business process integration platform maximizes the value of an organization’s information assets and optimizes return on investment through a usage-based subscription model. Ready for big data environments, Talend’s flexible architecture easily adapts to future IT platforms.
Endeca bought by Oracle – “agile information management”SSPS bought by IBMRadian6 bought by SalesforceDataStax – cassandraKarmasphere – data analysis platform for HadoopCouchbase – NoSQL – Membase and CouchbaseClarabridge – text analytics
Alternative NoSQL: Hbase, Cassandra, Druid, VoltDB
Endeca bought by Oracle – “agile information management”SSPS bought by IBMRadian6 bought by SalesforceDataStax – cassandraKarmasphere – data analysis platform for HadoopCouchbase – NoSQL – Membase and CouchbaseClarabridge – text analytics