Discussion of practically implementing data science models on big data platforms from engineering perspective. An eye opener on the engineering factors associated with designing and working solution. We use a simple text mining example on social media analytics for brand marketing. At the first while, it seems simple solution however if you go deeply and think on implementation aspects of even a simple analytics model, you can discover the degree of complexity at each part of the solution. An Abstraction of the Big Data key advantages would be very helpful to select appropriate Big Data technology components out of very large landscape. Two examples with reference are given for using Lambda Architecture and unusual way of image processing using Big Data abstraction provided.
Webinar: An Enterprise Architect’s View of MongoDBMongoDB
In the world of big data, legacy modernization, siloed organizations, empowered customers, and mobile devices, making informed choices about your enterprise infrastructure has become more important than ever. The alternatives are abundant, and the successful Enterprise Architect must constantly discern which new technology is just a shiny object and which will add true business value.
MongoDB is more than just a great application database for developers; it gives Enterprise Architects new capabilities to solve previously difficult architectural requirements much more easily. Take for example the challenge of many siloed systems at MetLife – with MongoDB, the Metlife team was able to successfully provide a single view into those 70 systems, in only 3 months.
In this webinar, we will:
Explore real life challenges enterprises face with case studies of their solutions
Consider how best to introduce MongoDB in the enterprise
Give an overview of how to optimize the use of MongoDB
Agile Big Data Analytics Development: An Architecture-Centric ApproachSoftServe
Presented at The Hawaii International Conference on System Sciences by Hong-Mei Chen and Rick Kazman (University of Hawaii), Serge Haziyev (SoftServe).
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
Webinar: An Enterprise Architect’s View of MongoDBMongoDB
In the world of big data, legacy modernization, siloed organizations, empowered customers, and mobile devices, making informed choices about your enterprise infrastructure has become more important than ever. The alternatives are abundant, and the successful Enterprise Architect must constantly discern which new technology is just a shiny object and which will add true business value.
MongoDB is more than just a great application database for developers; it gives Enterprise Architects new capabilities to solve previously difficult architectural requirements much more easily. Take for example the challenge of many siloed systems at MetLife – with MongoDB, the Metlife team was able to successfully provide a single view into those 70 systems, in only 3 months.
In this webinar, we will:
Explore real life challenges enterprises face with case studies of their solutions
Consider how best to introduce MongoDB in the enterprise
Give an overview of how to optimize the use of MongoDB
Agile Big Data Analytics Development: An Architecture-Centric ApproachSoftServe
Presented at The Hawaii International Conference on System Sciences by Hong-Mei Chen and Rick Kazman (University of Hawaii), Serge Haziyev (SoftServe).
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
MongoDB and RDBMS: Using Polyglot Persistence at Equifax MongoDB
MongoDB and RDBMS: Using Polyglot Persistence at Equifax. Presented by Michael Lawrence, Pariveda Solutions on behalf of Equifax at MongoDB Evenings Atlanta on September 24, 2015.
Everyone is awash in the new buzzword, Big Data, and it seems as if you can’t escape it wherever you go. But there are real companies with real use cases creating real value for their businesses by using big data. This talk will discuss some of the more compelling current or recent projects, their architecture & systems used, and successful outcomes.
Recently, there's been discussion, even some confusion, around the relationship between Hadoop and Spark. Although they're both big data frameworks with many similarities, they are not one in the same - and are in fact complimentary in an enterprise environment.
View the webinar replay here: http://info.zaloni.com/spark-hadoops-friend-or-foe
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop : Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products such as Oracle Big Data SQL on the Oracle Big Data Appliance along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we'll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete "data fabric" solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Social Network Analysis using Oracle Big Data Spatial & Graph (incl. why I di...Mark Rittman
As presented at OGh SQL Celebration Day 2016 - including new content on why NoSQL and Hadoop is a better solution for social network analysis than the Oracle Database (for now...)
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!
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Presented at the UKOUG Business Analytics SIG Meeting in April 2016, addresses the question as to whether enterprise BI tools such as OBIEE12c are relevant in the world of Gartner BiModal Mode 1 + Mode 2 analytics, and Hybrid cloud/on-premise deployments
The importance of efficient data management for Digital TransformationMongoDB
Digital Transformation has developed from hype into a “standard” tool for businesses that need to modernise and compete. Experiencing pressure from new market entrants, incumbents are challenged on a daily basis to redefine their ways of doing business. This doesn’t only include people and processes, but of course also the underlying technology. With data being the force behind the most successful transformation stories in the past years, we are explored some of the challenges of legacy Information Management Systems, and look at new ways of managing Data in Motion, Data at Rest, and Data in Use to drive a successful Digital Transformation programme to gain a competitive advantage.
Practical guide to architecting data lakes - Avinash Ramineni - Phoenix Data...Avinash Ramineni
Enterprises have been rapidly adopting data lakes as a complement or replacement of data warehouses. Many of the Data lake implementations are ignoring the inherent drawbacks and limitations of Data Lakes and ending up as data swamps with little or no benefit to the businesses. In this session we will go through some of challenges and the key aspects that need to be considered for successful Data lake implementations.
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Graph databases are used to represent graph structures with nodes, edges and properties. Neo4j, an open-source graph database is reliable and fast for managing and querying highly connected data. Will explore how to install and configure, create nodes and relationships, query with the Cypher Query Language, importing data and using Neo4j in concert with SQL Server... Providing answers and insight with visual diagrams about connected data that you have in your SQL Server Databases!
Max Welling (http://www.ics.uci.edu/~welling/) describes the how big data, massive simulation and advanced models go together to help us start solving challenging problems. He also describes his links to other computer science disciplines within the DSRC.
MongoDB and RDBMS: Using Polyglot Persistence at Equifax MongoDB
MongoDB and RDBMS: Using Polyglot Persistence at Equifax. Presented by Michael Lawrence, Pariveda Solutions on behalf of Equifax at MongoDB Evenings Atlanta on September 24, 2015.
Everyone is awash in the new buzzword, Big Data, and it seems as if you can’t escape it wherever you go. But there are real companies with real use cases creating real value for their businesses by using big data. This talk will discuss some of the more compelling current or recent projects, their architecture & systems used, and successful outcomes.
Recently, there's been discussion, even some confusion, around the relationship between Hadoop and Spark. Although they're both big data frameworks with many similarities, they are not one in the same - and are in fact complimentary in an enterprise environment.
View the webinar replay here: http://info.zaloni.com/spark-hadoops-friend-or-foe
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Enkitec E4 Barcelona : SQL and Data Integration Futures on Hadoop : Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products such as Oracle Big Data SQL on the Oracle Big Data Appliance along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we'll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete "data fabric" solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Social Network Analysis using Oracle Big Data Spatial & Graph (incl. why I di...Mark Rittman
As presented at OGh SQL Celebration Day 2016 - including new content on why NoSQL and Hadoop is a better solution for social network analysis than the Oracle Database (for now...)
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!
Oracle BI Hybrid BI : Mode 1 + Mode 2, Cloud + On-Premise Business AnalyticsMark Rittman
Presented at the UKOUG Business Analytics SIG Meeting in April 2016, addresses the question as to whether enterprise BI tools such as OBIEE12c are relevant in the world of Gartner BiModal Mode 1 + Mode 2 analytics, and Hybrid cloud/on-premise deployments
The importance of efficient data management for Digital TransformationMongoDB
Digital Transformation has developed from hype into a “standard” tool for businesses that need to modernise and compete. Experiencing pressure from new market entrants, incumbents are challenged on a daily basis to redefine their ways of doing business. This doesn’t only include people and processes, but of course also the underlying technology. With data being the force behind the most successful transformation stories in the past years, we are explored some of the challenges of legacy Information Management Systems, and look at new ways of managing Data in Motion, Data at Rest, and Data in Use to drive a successful Digital Transformation programme to gain a competitive advantage.
Practical guide to architecting data lakes - Avinash Ramineni - Phoenix Data...Avinash Ramineni
Enterprises have been rapidly adopting data lakes as a complement or replacement of data warehouses. Many of the Data lake implementations are ignoring the inherent drawbacks and limitations of Data Lakes and ending up as data swamps with little or no benefit to the businesses. In this session we will go through some of challenges and the key aspects that need to be considered for successful Data lake implementations.
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Graph databases are used to represent graph structures with nodes, edges and properties. Neo4j, an open-source graph database is reliable and fast for managing and querying highly connected data. Will explore how to install and configure, create nodes and relationships, query with the Cypher Query Language, importing data and using Neo4j in concert with SQL Server... Providing answers and insight with visual diagrams about connected data that you have in your SQL Server Databases!
Max Welling (http://www.ics.uci.edu/~welling/) describes the how big data, massive simulation and advanced models go together to help us start solving challenging problems. He also describes his links to other computer science disciplines within the DSRC.
Highlights and summary of long-running programmatic research on data science; practices, roles, tools, skills, organization models, workflow, outlook, etc. Profiles and persona definition for data scientist model. Landscape of org models for data science and drivers for capability planning. Secondary research materials.
Open Source Framework for Deploying Data Science Models and Cloud Based Appli...ETCenter
Next generation applications address more sophisticated questions that go beyond 'What happened?' by using Machine Learning/Statistical modelling to answer 'Why?' and 'What will happen next? Data insights can be easily deployed and rapidly delivered to the decision makers via cloud based applications. This framework focuses on technologies available for the entire data workflow from ingestion and modeling to cloud deployment; Hadoop, MADlib, Python, R, CloudFoundry, etc. This presentation will also include examples of how this framework and innovative Data Science techniques have been applied across diverse business units within Media, including pricing analyses for ad optimization and predicting viewership.
Becoming Data-Driven Through Cultural ChangeCloudera, Inc.
We've arrived at a crossroads. Big data is an initiative every business knows they should take on in order to evolve their business, but no one knows how to tackle the project.
This is the first in a series of webinars that describe how to break down the challenge into three major pieces: People, Process, and Technology. We'll discuss the industry trends around big data projects, the pitfalls with adopting a modern data strategy, and how to avoid them by building a culture of data-driven teams.
H2O World - Top 10 Data Science Pitfalls - Mark LandrySri Ambati
H2O World 2015 - Mark Landry
Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Machine Learning with Apache Flink at Stockholm Machine Learning GroupTill Rohrmann
This presentation presents Apache Flink's approach to scalable machine learning: Composable machine learning pipelines, consisting of transformers and learners, and distributed linear algebra.
The presentation was held at the Machine Learning Stockholm group on the 23rd of March 2015.
Pivotal Cloud Foundry: A Technical OverviewVMware Tanzu
"Do your teams release software to production weekly, daily or every hour ? Do you practice software development with tools, process and culture that can respond to the speed of market and customer changes? Agility allows you to experiment with new business models, learn from your mistakes and identify patterns that work. Deliver faster, look for feedback, gain knowledge. In every market, speed wins.
Cloud Native describes the patterns of high performing organizations delivering software faster, consistently and reliably at scale. Continuous delivery, DevOps, and microservices label the why, how and what of the cloud natives, the true digital enterprises."
Speaker: Vijay Rajagopal, Advisory Platform Architect, Pivotal
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
A look at the evolution of analytics and its revolutionary potential to transform ordinary businesses, power new business models, enable innovation, and deliver greater value. http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends.html
The Challenges of Bringing Machine Learning to the MassesAlice Zheng
Why is it hard to build ML software, and why it is like designing a database. Jointly created with Sethu Raman (Dato/GraphLab). Talk at NIPS 2014 workshop on Software Engineering for Machine Learning (https://sites.google.com/site/software4ml/).
L'architettura di classe enterprise di nuova generazione - Massimo BrignoliData Driven Innovation
La nascita dei data lake - La aziende, ormai, sono sommerse dai dati e il classico datawarehouse fa fatica a macinare questi dati per numerosità e varietà. In molti hanno iniziato a guardare a delle architetture chiamate Data Lakes con Hadoop come tecnologia di riferimento. Ma questa soluzione va bene per tutto? Vieni a capire come operazionalizzare i data lakes per creare delle moderne architetture di gestione dati.
A talk given by Ted Dunning on February 2013 on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Big Data is the reality of modern business: from big companies to small ones, everybody is trying to find their own benefit. Big Data technologies are not meant to replace traditional ones, but to be complementary to them. In this presentation you will hear what is Big Data and Data Lake and what are the most popular technologies used in Big Data world. We will also speak about Hadoop and Spark, and how they integrate with traditional systems and their benefits.
Big Data Analytics 2: Leveraging Customer Behavior to Enhance Relevancy in Pe...MongoDB
This session covers how to capture and analyize customer behavior to create more relevent contexts for customers. We will cover how to use your current BI features, and more importantly, how newer technologies approach the challenge. You will walk away with a good idea on how to build and drive even more contextually relevant experiences to customers for even more successful engagements.
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Big Data Open Source Tools and Trends: Enable Real-Time Business Intelligence...Perficient, Inc.
Most organizations still rely on batch and offline processing of data streams to gain meaningful analysis and insight into their business. However, in our instant gratification world, real-time computation and analysis of streaming data is crucial in gaining insight into patterns and threats. A trend is emerging for real-time and instant analysis from live data streams, promoting the value of logs and a move toward functional programming.
This shift in technology is not about what and how to store the data, but what we can do with it to see emerging patterns and trends across multiple resources, applications, services and environments. Log data represents a wealth of information, yet is often sporadic, unstructured, scattered across the enterprise and difficult to track.
These slides provide insights into some of the most helpful Big Data tools used by the largest social media and data-centric organizations for competitive trends, instant analysis and feedback from large volume data streams. We show how how using Big Data tools Storm, ElasticSearch and an elastic UI can turn application logs into real-time analytical views.
You will also learn how Big Data:
Contains data that is elastic, minimally structured, flexible and scalable
Helps process live streams into meaningful data
Promotes a move toward functional programming
Effects the enterprise data architecture
Works with real-time CEP tools like Storm for functional programming
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
Erik Baardse and Ajit Gadge from EDB Postgres presented on how to transform your DBMS in order to drive digital business. How Postgres enables you to support a wider range of workloads with your relational database which opens the Big Data doors. They also cover EnterpriseDB’s Strategy around Big Data which focuses on 3 areas and finally last but not the last how to find money in IT with Big Data and digital transformation
What exactly is big data? The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources.
Engineering Machine Learning Data Pipelines Series: Streaming New Data as It ...Precisely
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back – and the roadblock that stops many promising machine learning projects. After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Syncsort software helps the data engineer every step of the way.
Building on the process of finding and matching duplicates to resolve entities, the next step is to set up a continuous streaming flow of data from data sources so that as the sources change, new data automatically gets pushed through the same transformation and cleansing data flow – into the arms of machine learning models.
Some of your sources may already be streaming, but the rest are sitting in transactional databases that change hundreds or thousands of times a day. The challenge is that you can’t affect performance of data sources that run key applications, so putting something like database triggers in place is not the best idea. Using Apache Kafka or similar technologies as the backbone to moving data around doesn’t solve the problem of needing to grab changes from the source pushing them into Kafka and consuming the data from Kafka to be processed. If something unexpected happens – like connectivity is lost on either the source or the target side, you don’t want to have to fix it or start over because the data is out of sync.
View this 15-minute webcast on-demand to learn how to tackle these challenges in large scale production implementations.
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
With so much talk of how Big Data is revolutionizing the world and how a data lake with Hadoop and/or Spark will solve all your data problems, it is hard to tell what is hype, reality, or somewhere in-between.
In working with dozens of enterprises in varying stages of their enterprise data management (EDM) strategy, MongoDB enterprise architect, Matt Kalan, sees the same challenges and misunderstandings arise again and again.
In this session, he will explain common challenges in data management, what capabilities are necessary, and what the future state of architecture looks like. MongoDB is uniquely capable of filling common gaps in the data lake strategy.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
Engineering patterns for implementing data science models on big data platforms
1. Data Science Models on Big
Data Platforms
Engineering Patterns for Implementing
Hisham Arafat
Digital Transformation Lead Consultant
Solutions Architect, Technology Strategist & Researcher
Riyadh, KSA – 31 January 2017
4. Social Marketing…Looks Simple!
Ingest Social
Feeds
Build Corpus
Metrics
Design Text
Mining Model
Deploy All to
a Big Data
Platform
Application
for Marketing
Users
What people are saying about our new brand “LemaTea”?
7. Not Only Building Appropriate Model, but
More Into
Designing a Solution…Engineering Factors
8. • Interfacing with sources: REST APIs, source HTML,… (text is assumed)
• Parsing to extract: queries, Regular Expressions,…
• Crawling frequency: every 1 minute, 1 hour, on event,…
• Document structure: post, post + comments, #, Reach, Retweets,…
• Metadata: time, date, source, tags, authoritativeness,…
• Transformations: canonicalization, weights, tokenization,…
- Size: average size of 2 KB / doc
- Initial load: 1.5B doc
- Frequency: every 5 minutes
- Throughput: 2 KB * 60,000 doc
= 120 MB / load
- Grows per day ~ 34 GB
Engineering Factors
9. • Input format: text, encoded text,…
• Document representation: bag of words, ontology…
• Corpus structures: indexes, reverse indexes,…
• Corpus metrics: doc frequency, inverse doc frequency,…
• Preprocessing: annotation, tagging,…
• Files structure: tables, text files, files-day,…
- No of docs: 1.5B + 17M / day
- Processing window: 60K per 3 mins
- Processing rate: 20K doc per min
- Final doc size = 2KB * 5 ~ 10KB
- Scan rate: 20k * 10KB min ~ 200MB/min
- Many overheads need to be added
Engineering Factors
10. • Dimensionality reduction: stemming, lemmatization, noisy words…
• Type of applications: search/retrieval, sentiment analysis…
• Modeling methods: classifiers, topic modeling, relevance…
• Model efficiency: confusion metrics, precision, recall…
• Overheads: intermediate processing, pre-aggregation,…
• Files structure: tables, text files, files-day,…
- No of docs: 1.5B + 17M / day
- Search for “LemaTea sweet taste”
- No of tf to calculate ~ 1.5B * 3 ~ 4.5B
- No of idf to calculate ~ 1.5B
- Total calculations for 1 search ~ 6 B
- Consider daily growth
Engineering Factors
11. • Files structure: tables, text files, files-day,…
• Files formats: HDFS, parquet, avro…
• Platform technology: Hadoop/YARN, Spark, Greenplum, Flink,…
• Model deployment: Java/Scala, Mahoot, Mllib, MADlib, PL/R, FlinkML…
• Data ingestion: Spring XD, Flume, Sqoop, G. Data Flow, Kafka/Streaming…
• Ingestion pattern: real-time, micro batches,…
- Overall Storage
- Processing capacity per node
- No of nodes
- Tables Hive, Hbase, Greenplum
- Individual files Spark, Flink
- Files-day Hadoop HDFS
Engineering Factors
12. • Workload: no of requests, request size,…
• Application performance: response time, concurrent requests…
• Applications interfacing: RESET APIs, native, messaging,…
• Application implementation: integration, model scoring,…
• Security model: application level, platform level,…
- For 3 search terms ~ 6B calculations
- For 5 search terms ~ 9B calculations
- For 10 concurrent requests ~ 75B
- Resource queuing / prioritization
- Search options like date range
- Access control model
Engineering Factors
13. Ongoing Process…Growing Requirements
What if?
• New sources are included
• Wider parsing Criteria
• Advanced modeling: POS, Word Co-
occurrence, Co-referencing, Named
Entity, Relationship Extraction,…
• Better response time is needed
• More frequent ingestion
Dynamic
Platform
Ingestion
Corpus
Processing
Model
Processing
Requests
Processing
• Larger number of docs
• Increased processing requirements
• Platform expansion
• Overall architecture reconsidered
15. What is a Data Science Model?
• Type & format of inputs date
• Data ingestion
• Transformations and feature engineering
• Modeling methods and algorithms
• Model evaluation and scoring
• Applications implantations considerations
• In-Memory vs. In-Database
16. Key Challenges for Data Science Models
Volume
Stationary
Batches
Structured
Insights
Growth
Streams
Real-time
Unstructured
Responsive
Scale out Performance
Data Flow Engines
Event Processing
Complex Formats
Perspective / Deep Models
17. Traditional Data Management Systems
• Shared I/O
• Shared Processing
• Limited Scalability
• Service Bottlenecks
• High Cost Factor
SharedBuffers
Data Files
Database
Cluster
I/O
I/O
I/O
Network
DatabaseService
18. Abstraction of Big Data Platforms Data Nodes
Master Nodes
I/O
Network
Interconnect
• Parallel Processing
• Shared Nothing
• Linear Scalability
• Distributed Services
• Lower Cost Factor
I/O
I/O
I/O
…
Metadata
1
2
3
n
Metadata
User data / Replicas
User data / Replicas
User data / Replicas
User data / Replicas
22. Lambda Architecture…Social Marketing
• Generic, scalable and
fault-tolerant data
processing architecture.
• Keeps a master
immutable dataset
while serving low
latency requests.
• Aims at providing linear
scalability.
Source: http://lambda-architecture.net/
23. Social Marketing…Revisted
Ingest Social
Feeds
Build Corpus
Metrics
Design Text
Mining Model
Deploy All to
a Big Data
Platform
Application
for Marketing
Users
What people are saying about our new brand “LemaTea”?
37. Take Aways
• A Data Science is not just the algorithms but it includes and end-to-end
solution.
• The implementation should consider engineering factors and quantify them
so appropriate components can be selected.
• The Big Data technology land scape is really huge and growing – start with a
solid use case to identify potential components.
• Abstraction of specific technology will enable you to put your hands on the
pros and cons.
• Creativity in solutions design and technology selection case by case.
• Lambda Architecture, Spark, Spark MLlib, Spark Streaming, Spark SQL
Kafka, Hadoop / Yarn, Greenplum, MADLib.
