In this session, we'll look at the role of the data engineer in designing, provisioning, and enabling an Oracle Cloud data lake using Oracle Analytics Cloud Data Lake Edition. We’ll also examine the use of data flow and data pipeline authoring tools and how machine learning and AI can be applied to this task. Furthermore, we’ll explore connecting to database and SaaS sources along with sources of external data via Oracle Data-as-a-Service. Finally we’ll delve into how traditional Oracle Analytics developers can transition their skills into this role and start working as data engineers on Oracle Public Cloud data lake projects.
This presentation shows all the posible options to move Oracle BI on-premise system to Oracle Analytics Cloud. We are going to see all the steps to perform this migration as well as the issues that we have seen and how to troubleshoot them. In addition we will review the most common administration tasks.
How to Capitalize on Big Data with Oracle Analytics CloudPerficient, Inc.
The average age of a company listed on the S&P 500 has fallen from almost 60 years old in the 1950s to less than 20 years old today. Innovative companies that are willing to embrace transformative technologies make the list today, while businesses that are hesitant to embrace change risk becoming obsolete.
Innovators use big data solutions as a competitive advantage to increase revenue, reduce cost, and improve cash flow. Turn big data into actionable insights with Oracle Analytics Cloud.
We identified the big data opportunities in front of you and how to take advantage of them:
-Big data and its architecture
-Why a big data strategy is imperative to remaining relevant
-How Oracle Analytics Cloud can help you connect people, places, data, and systems to fundamentally change how you analyze, understand, and act on information
Oracle Autonomous Data Warehouse Cloud and Data VisualizationEdelweiss Kammermann
With the release of the Oracle Autonomous Datawarehouse Cloud service Oracle offers a simple way to create a DW in the cloud with fast query performance and fully managed service requiring no human effort for database tuning
In this session we will see how easily we can create an Autonomous Data Warehouse Cloud instance and start loading data with SQL Developer 18. We will see the details to connect from DV to analyze your data in a very intuitive way for exploration and finding patterns.
Oracle Analytics Cloud: connect; prepare; explore; share. Liberate all data and connect to more than 50 different data sources. Powerful tools for auditable and traceable data blending, wrangling, cleansing, & modeling. Intuitive and rich exploration with self-service data visualization. Build collective intelligence by collaborating with peers and socialize insights across the organization or the world.
In this session, you will see a demo of Oracle Business Intelligence Visual Analyzer, taking a real-world business use case from end to end, to learn how straightforward it is to tell a compelling story with data and prototype with greater speed, while gaining insights into information with this new cutting-edge data visualization access.
This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases
This presentation shows all the posible options to move Oracle BI on-premise system to Oracle Analytics Cloud. We are going to see all the steps to perform this migration as well as the issues that we have seen and how to troubleshoot them. In addition we will review the most common administration tasks.
How to Capitalize on Big Data with Oracle Analytics CloudPerficient, Inc.
The average age of a company listed on the S&P 500 has fallen from almost 60 years old in the 1950s to less than 20 years old today. Innovative companies that are willing to embrace transformative technologies make the list today, while businesses that are hesitant to embrace change risk becoming obsolete.
Innovators use big data solutions as a competitive advantage to increase revenue, reduce cost, and improve cash flow. Turn big data into actionable insights with Oracle Analytics Cloud.
We identified the big data opportunities in front of you and how to take advantage of them:
-Big data and its architecture
-Why a big data strategy is imperative to remaining relevant
-How Oracle Analytics Cloud can help you connect people, places, data, and systems to fundamentally change how you analyze, understand, and act on information
Oracle Autonomous Data Warehouse Cloud and Data VisualizationEdelweiss Kammermann
With the release of the Oracle Autonomous Datawarehouse Cloud service Oracle offers a simple way to create a DW in the cloud with fast query performance and fully managed service requiring no human effort for database tuning
In this session we will see how easily we can create an Autonomous Data Warehouse Cloud instance and start loading data with SQL Developer 18. We will see the details to connect from DV to analyze your data in a very intuitive way for exploration and finding patterns.
Oracle Analytics Cloud: connect; prepare; explore; share. Liberate all data and connect to more than 50 different data sources. Powerful tools for auditable and traceable data blending, wrangling, cleansing, & modeling. Intuitive and rich exploration with self-service data visualization. Build collective intelligence by collaborating with peers and socialize insights across the organization or the world.
In this session, you will see a demo of Oracle Business Intelligence Visual Analyzer, taking a real-world business use case from end to end, to learn how straightforward it is to tell a compelling story with data and prototype with greater speed, while gaining insights into information with this new cutting-edge data visualization access.
This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases
Understanding Your Crown Jewels: Finding, Organizing, and Profiling Sensitive...DataWorks Summit
Emerging regulations such as GDPR and increasing incidence of data breaches such as those at Equifax are bringing a firm’s handling and processing of sensitive data such as personal data of its customers and employees into focus. Enterprises need to now be able to discover and manage sensitive data usage to answer compliance and regulatory reporting requirements and to prevent any reputational damage in the event of a data breach. In this talk, we will outline how using the foundation of open source technologies such as Apache Ranger, Apache Atlas and the recently announced Hortonworks DataPlane Service platform components data stewards, analysts, and data engineers can better understand their sensitive data assets across multiple data lakes at scale. We will demonstrate how enterprises can get a comprehensive 360-degree view of their sensitive data including where such data is located, who is accessing what data and how frequently, when was such data accessed, deleted, moved, how is the data protected, and where did this data come from. In addition we will show how such data can be discovered and profiled to understand their characteristics. We will also demonstrate organization and classification use cases for such sensitive data to facilitate their curation into collections for various business purposes and how such collections can be aggregated and summarized to provide a single view of sensitive data footprint in an enterprise from risk management and audit/compliance/forensics perspectives.
Speakers
Srikanth Venkat, Senior Director, Product Management, Hortonworks
Ashwin Rajeeva, Founder, Vidyash OU
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Site | https://www.infoq.com/qconai2018/
Youtube | https://www.youtube.com/watch?v=2h0biIli2F4&t=19s
At PayPal, data engineers, analysts and data scientists work with a variety of datasources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive).
Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed "Gimel", a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that are powered by a centralized data catalog.
In this session, we will introduce you to the various components of Gimel - Compute Platform, Data API, PCatalog, GSQL and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Offload, Transform, and Present - the New World of Data IntegrationMichael Rainey
How much time and effort (and budget) do organizations spend moving data around the enterprise? Unfortunately, quite a lot. These days, ETL developers are tasked with performing the Extract (E) and Load (L), and spending less time on their craft, building Transformations (T). This changes in the new world of data integration. By offloading data from the RDBMS to Hadoop, with the ability to present it back to the relational database, data can be seamlessly integrated between different source and target systems. Transformations occur on data offloaded to Hadoop, using the latest ETL technologies, or in the target database, with a standard ETL-on-RDBMS tool. In this session, we’ll discuss how the new world of data integration will provide focus on transforming data into insightful information by simplifying the data movement process.
Presented at Enkitec E4 2017.
Deep-dive into Microservices Patterns with Replication and Stream Analytics
Target Audience: Microservices and Data Architects
This is an informational presentation about microservices event patterns, GoldenGate event replication, and event stream processing with Oracle Stream Analytics. This session will discuss some of the challenges of working with data in a microservices architecture (MA), and how the emerging concept of a “Data Mesh” can go hand-in-hand to improve microservices-based data management patterns. You may have already heard about common microservices patterns like CQRS, Saga, Event Sourcing and Transaction Outbox; we’ll share how GoldenGate can simplify these patterns while also bringing stronger data consistency to your microservice integrations. We will also discuss how complex event processing (CEP) and stream processing can be used with event-driven MA for operational and analytical use cases.
Business pressures for modernization and digital transformation drive demand for rapid, flexible DevOps, which microservices address, but also for data-driven Analytics, Machine Learning and Data Lakes which is where data management tech really shines. Join us for this presentation where we take a deep look at the intersection of microservice design patterns and modern data integration tech.
Presentation to discuss major shift in enterprise data management. Describes movement away from older hub and spoke data architecture and towards newer, more modern Kappa data architecture
Start today on a relevant and incremental MDM journey.
A turnkey MDM solution allows you to collaborate on, maintain and provision accurate and reliable data across the enterprise; however, extended implementation times can delay time to value. Many successful MDM projects start small and grow over time. Open source provides a vehicle to start your MDM journey and deliver value - today.
This slideshow will show you:
* How an integrated solution for data integration, data quality and master data management can speed up and simplify implementation
* Why an active data model allows you to quickly reflect unique data requirements
* The importance of a dynamic MDM interface that enables immediate collaboration and stewardship
To view the entire webinar with the demonstration, click on : http://nxy.in/bhl3z
If you wish to see other webinars, click on: http://nxy.in/hkidj
For Live Webinars, click here: http://nxy.in/pjeph
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
From BI Developer to Data Engineer with Oracle Analytics Cloud Data Lake EditionRittman Analytics
Presentation at ODTUG KScope'18 on the data engineering and advanced analytics capabilities in Oracle Analytics Cloud Data Lake Edition, Oracle Big Data Cloud and Oracle Event Hub Cloud Service
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...Deepak Chandramouli
PayPal Data Lake Journey | 2017-Oct | San Diego | Teradata Edge of Next
Gimel [http://www.gimel.io] is a Big Data Processing Library, open sourced by PayPal.
https://www.youtube.com/watch?v=52PdNno_9cU&t=3s
Gimel empowers analysts, scientists, data engineers alike to access a variety of Big Data / Traditional Data Stores - with just SQL or a single line of code (Unified Data API).
This is possible via the Catalog of Technical properties abstracted from users, along with a rich collection of Data Store Connectors available in Gimel Library.
A Catalog provider can be Hive or User Supplied (runtime) or UDC.
In addition, PayPal recently open sourced UDC [Unified Data Catalog], which can host and serve the Technical Metatada of the Data Stores & Objects. Visit http://www.unifieddatacatalog.io to experience first hand.
https://dataworkssummit.com/san-jose-2018/expo-theatre/gimel-paypals-analytics-data-platform/
At PayPal, data engineers, analysts, and data scientists work with a variety of data sources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive). Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed “Gimel”, a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that is powered by a centralized data catalog. In this session, we will introduce you to the various components of Gimel – Compute Platform, Data API, PCatalog, GSQL, and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Unified Data Catalog - Recommendations powered by Apache Spark & Neo4jDeepak Chandramouli
Youtube | https://youtu.be/zGX0fRLdd6s?list=PLPaGQXwz_-RaoHicnGhL5SyOAp3_lUTQ2&t=1
This is a talk from PayPal at Nodes Online Summit, organized by Neo4j.
For more session details and video - please visit this link.
https://neo4j.com/online-summit/session/recommendations-unified-data-catalog-spark-neo4j
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
From BI Developer to Data Engineer with Oracle Analytics Cloud, Data LakeRittman Analytics
In this session look at the role of a data engineer in designing, provisioning, and enabling an Oracle Cloud data lake using Oracle Analytics Cloud, Data Lake. Attendees learn how to use data flow and data pipeline authoring tools and how machine learning and AI can be applied to this task, as well as how to connect to database and SaaS sources along with sources of external data via Oracle Data as a Service. Discover how traditional Oracle Analytics developers can transition their skills into this role and start working as a data engineer on Oracle Public Cloud data lake projects.
Understanding Your Crown Jewels: Finding, Organizing, and Profiling Sensitive...DataWorks Summit
Emerging regulations such as GDPR and increasing incidence of data breaches such as those at Equifax are bringing a firm’s handling and processing of sensitive data such as personal data of its customers and employees into focus. Enterprises need to now be able to discover and manage sensitive data usage to answer compliance and regulatory reporting requirements and to prevent any reputational damage in the event of a data breach. In this talk, we will outline how using the foundation of open source technologies such as Apache Ranger, Apache Atlas and the recently announced Hortonworks DataPlane Service platform components data stewards, analysts, and data engineers can better understand their sensitive data assets across multiple data lakes at scale. We will demonstrate how enterprises can get a comprehensive 360-degree view of their sensitive data including where such data is located, who is accessing what data and how frequently, when was such data accessed, deleted, moved, how is the data protected, and where did this data come from. In addition we will show how such data can be discovered and profiled to understand their characteristics. We will also demonstrate organization and classification use cases for such sensitive data to facilitate their curation into collections for various business purposes and how such collections can be aggregated and summarized to provide a single view of sensitive data footprint in an enterprise from risk management and audit/compliance/forensics perspectives.
Speakers
Srikanth Venkat, Senior Director, Product Management, Hortonworks
Ashwin Rajeeva, Founder, Vidyash OU
CON6619 - OpenWorld Presentation. Oracle data integration, big data, data governance, and cloud integration. Replication, ETL, Data Quality, Streaming Big Data, and Data Preparation
Site | https://www.infoq.com/qconai2018/
Youtube | https://www.youtube.com/watch?v=2h0biIli2F4&t=19s
At PayPal, data engineers, analysts and data scientists work with a variety of datasources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive).
Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed "Gimel", a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that are powered by a centralized data catalog.
In this session, we will introduce you to the various components of Gimel - Compute Platform, Data API, PCatalog, GSQL and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Offload, Transform, and Present - the New World of Data IntegrationMichael Rainey
How much time and effort (and budget) do organizations spend moving data around the enterprise? Unfortunately, quite a lot. These days, ETL developers are tasked with performing the Extract (E) and Load (L), and spending less time on their craft, building Transformations (T). This changes in the new world of data integration. By offloading data from the RDBMS to Hadoop, with the ability to present it back to the relational database, data can be seamlessly integrated between different source and target systems. Transformations occur on data offloaded to Hadoop, using the latest ETL technologies, or in the target database, with a standard ETL-on-RDBMS tool. In this session, we’ll discuss how the new world of data integration will provide focus on transforming data into insightful information by simplifying the data movement process.
Presented at Enkitec E4 2017.
Deep-dive into Microservices Patterns with Replication and Stream Analytics
Target Audience: Microservices and Data Architects
This is an informational presentation about microservices event patterns, GoldenGate event replication, and event stream processing with Oracle Stream Analytics. This session will discuss some of the challenges of working with data in a microservices architecture (MA), and how the emerging concept of a “Data Mesh” can go hand-in-hand to improve microservices-based data management patterns. You may have already heard about common microservices patterns like CQRS, Saga, Event Sourcing and Transaction Outbox; we’ll share how GoldenGate can simplify these patterns while also bringing stronger data consistency to your microservice integrations. We will also discuss how complex event processing (CEP) and stream processing can be used with event-driven MA for operational and analytical use cases.
Business pressures for modernization and digital transformation drive demand for rapid, flexible DevOps, which microservices address, but also for data-driven Analytics, Machine Learning and Data Lakes which is where data management tech really shines. Join us for this presentation where we take a deep look at the intersection of microservice design patterns and modern data integration tech.
Presentation to discuss major shift in enterprise data management. Describes movement away from older hub and spoke data architecture and towards newer, more modern Kappa data architecture
Start today on a relevant and incremental MDM journey.
A turnkey MDM solution allows you to collaborate on, maintain and provision accurate and reliable data across the enterprise; however, extended implementation times can delay time to value. Many successful MDM projects start small and grow over time. Open source provides a vehicle to start your MDM journey and deliver value - today.
This slideshow will show you:
* How an integrated solution for data integration, data quality and master data management can speed up and simplify implementation
* Why an active data model allows you to quickly reflect unique data requirements
* The importance of a dynamic MDM interface that enables immediate collaboration and stewardship
To view the entire webinar with the demonstration, click on : http://nxy.in/bhl3z
If you wish to see other webinars, click on: http://nxy.in/hkidj
For Live Webinars, click here: http://nxy.in/pjeph
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
From BI Developer to Data Engineer with Oracle Analytics Cloud Data Lake EditionRittman Analytics
Presentation at ODTUG KScope'18 on the data engineering and advanced analytics capabilities in Oracle Analytics Cloud Data Lake Edition, Oracle Big Data Cloud and Oracle Event Hub Cloud Service
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...Deepak Chandramouli
PayPal Data Lake Journey | 2017-Oct | San Diego | Teradata Edge of Next
Gimel [http://www.gimel.io] is a Big Data Processing Library, open sourced by PayPal.
https://www.youtube.com/watch?v=52PdNno_9cU&t=3s
Gimel empowers analysts, scientists, data engineers alike to access a variety of Big Data / Traditional Data Stores - with just SQL or a single line of code (Unified Data API).
This is possible via the Catalog of Technical properties abstracted from users, along with a rich collection of Data Store Connectors available in Gimel Library.
A Catalog provider can be Hive or User Supplied (runtime) or UDC.
In addition, PayPal recently open sourced UDC [Unified Data Catalog], which can host and serve the Technical Metatada of the Data Stores & Objects. Visit http://www.unifieddatacatalog.io to experience first hand.
https://dataworkssummit.com/san-jose-2018/expo-theatre/gimel-paypals-analytics-data-platform/
At PayPal, data engineers, analysts, and data scientists work with a variety of data sources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive). Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed “Gimel”, a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that is powered by a centralized data catalog. In this session, we will introduce you to the various components of Gimel – Compute Platform, Data API, PCatalog, GSQL, and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Unified Data Catalog - Recommendations powered by Apache Spark & Neo4jDeepak Chandramouli
Youtube | https://youtu.be/zGX0fRLdd6s?list=PLPaGQXwz_-RaoHicnGhL5SyOAp3_lUTQ2&t=1
This is a talk from PayPal at Nodes Online Summit, organized by Neo4j.
For more session details and video - please visit this link.
https://neo4j.com/online-summit/session/recommendations-unified-data-catalog-spark-neo4j
Data Mesh in Practice: How Europe’s Leading Online Platform for Fashion Goes ...Databricks
The Data Lake paradigm is often considered the scalable successor of the more curated Data Warehouse approach when it comes to democratization of data. However, many who went out to build a centralized Data Lake came out with a data swamp of unclear responsibilities, a lack of data ownership, and sub-par data availability.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
From BI Developer to Data Engineer with Oracle Analytics Cloud, Data LakeRittman Analytics
In this session look at the role of a data engineer in designing, provisioning, and enabling an Oracle Cloud data lake using Oracle Analytics Cloud, Data Lake. Attendees learn how to use data flow and data pipeline authoring tools and how machine learning and AI can be applied to this task, as well as how to connect to database and SaaS sources along with sources of external data via Oracle Data as a Service. Discover how traditional Oracle Analytics developers can transition their skills into this role and start working as a data engineer on Oracle Public Cloud data lake projects.
Data Warehouse Like a Tech Startup with Oracle Autonomous Data WarehouseRittman Analytics
“Tech startups can't afford DBAs, and they don't have time to provision servers and scale them up and down or deal with patches or downtime. They've never heard of indexes and they need data loaded and ready for analysis in days, not months. In this session learn how Oracle Database developers can build data warehouses as a hip startup data engineer would—but using a proper database built on Oracle technology. Oracle Data Visualization Desktop provides analytics and data exploration with techniques explained in this session. Hear real-world development experiences from working on data and analytics projects at a tech startup in the UK.”
Planning a Strategy for Autonomous Analytics and Data WarehousingRittman Analytics
As Oracle Analytics and Data Warehousing becomes self-driving and autonomous, the need for a strategy within your BI function becomes all the more important. How you deliver BI content to your users, the skills your developers now need and the most efficient way to manage your cloud estate are vital components of an autonomous cloud analytics strategy; this session will explain what’s changed, what’s significant and what are the implications of that change.
Risk Analytics Using Knowledge Graphs / FIBO with Deep LearningCambridge Semantics
This EDM Council webinar, sponsored by Cambridge Semantics Inc. and featuring FI Consulting, explores the challenges common to a risk analytics pipeline, application of graph analytics to mortgage loan data and use cases in adjacent areas including customer service, collections, fraud and AML.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Kent Graziano
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
GoldenGate and Oracle Data Integrator - A Perfect Match...Michael Rainey
Oracle Data Integrator and Oracle GoldenGate excel as standalone products, but paired together they are the perfect match for real-time data warehousing. Following Oracle’s Next Generation Reference Data Warehouse Architecture, this discussion will provide best practices on how to configure, implement, and process data in real-time using ODI and GoldenGate. Attendees will see common real-time challenges solved, including parent-child relationships within micro-batch ETL.
Presented at Rittman Mead BI Forum 2013 Masterclass.
How Spark is Enabling the New Wave of Converged ApplicationsMapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market.
Why You Need Manageability Now More than Ever and How to Get ItGustavo Rene Antunez
Whether you are operating in a completely on-premises environment or have some kind of hybrid cloud setup, you need to be able to clearly monitor and manage your entire organization in one single, unified structure. In this session learn how IOUG’s volunteer team decided to review Oracle Management Cloud Services to see if this “single pane of glass” was up to the challenge of providing the information data professionals need to serve their organization. Come and see how to put the pieces together, illustrated with real examples from Oracle Public Cloud services.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Using Data & Analytics To Find Out How Much Daily Mail Readers Hate Me (and W...Rittman Analytics
Fun presentation given at the Brighton Data Forum in April 2018 analyzing Daily Mail reader comments on the article about my WiFi kettle incident back in 2016.
Analytics is Taking over the World (Again) - UKOUG Tech'17Rittman Analytics
In this presentation we'll look at some of the new industries and new technologies that are only possible today with analytics, how employee empowerment and improving your fitness are spin-offs of the same technology used to track boxes around a warehouse and spot fraudulent bank transactions, and how Oracle are embedding these new analytics capabilities in their cloud-based HR.
Petabytes to Personalization - Data Analytics with Qubit and LookerRittman Analytics
How do you turn petabytes of customer data into a personalized retail and e-commerce experience? With Qubit, the customer personalization platform that (with the help of Google Cloud Platform and Looker) gives customers the power of real-time ad-hoc analytics. Because of the scale of data enabled by GCP and the abstraction layer of Looker, Qubit customers are able to use their Live Tap product to to make every visitor experience relevant and engaging.
Budapest Data Forum 2017 - BigQuery, Looker And Big Data Analytics At Petabyt...Rittman Analytics
As big data and data warehousing scale-up and move into the cloud, they’re increasingly likely to be delivered as services using distributed cloud query engines such as Google BigQuery, loaded using streaming data pipelines and queried using BI tools such as Looker. In this session the presenter will walk through how data modelling and query processing works when storing petabytes of customer event-level activity in a distributed data store and query engine like BigQuery, how data ingestion and processing works in an always-on streaming data pipeline, how additional services such as Google Natural Language API can be used to classify for sentiment and extract entity nouns from incoming unstructured data, and how BI tools such as Looker and Google Data Studio bring data discovery and business metadata layers to cloud big data analytics
New World Hadoop Architectures (& What Problems They Really Solve) for Oracle...Rittman Analytics
Most DBAs are aware something interesting is going on with big data and the Hadoop product ecosystem that underpins it, but aren't so clear about what each component in the stack does, what problem each part solves and why those problems couldn't be solved using the old approach. We'll look at where it's all going with the advent of Spark and machine learning, what's happening with ETL, metadata and analytics on this platform ... why IaaS and datawarehousing-as-a-service will have such a big impact, sooner than you think
A series of tweets I posted about my 11hr struggle to make a cup of tea with my WiFi kettle ended-up going viral, got picked-up by the national and then international press, and led to thousands of retweets, comments and references in the media. In this session we’ll take the data I recorded on this Twitter activity over the period and use Oracle Big Data Graph and Spatial to understand what caused the breakout and the tweet going viral, who were the key influencers and connectors, and how the tweet spread over time and over geography from my original series of posts in Hove, England.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Set of product roadmap + capabilities slides from Oracle Data Integration Product Management, and thoughts on data integration on big data implementations by Mark Rittman (Independent Analyst)
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
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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/
3. T: +44 01273 041134 (UK) W: https;//mjr-analytics.com E: info@mjr-analytics.com
Take the Next Step with MJR Analytics
● Specialists in Modern Cloud Analytics
● Founded by Mark Rittman in 2018
● 100% Cloud focus + project delivery
○ Oracle Autonomous Analytics Cloud
○ Oracle Autonomous DW Cloud
○ Oracle Data Integration Cloud
○ Oracle Big Data Cloud
● Speak to us now during OOW 2018
info@mjr-analytics.com
+44 7866 568246
https://www.mjr-analytics.com
MJR Analytics & Red Pill
Analytics Tech’18 Happy Hour
4pm-6pm today, Pump House
13. OAC Data Lake Features for Data Engineers
13
● Explore, catalog and discover data in Oracle Big Data Cloud, Oracle
Database
● Enrich and transform raw data into valuable information and insights
● Analyze at-scale data using Data Visualization
● Combine data from SaaS, social and real-time
● Create predictive and classification models
● Analyze the sentiment in social media feeds
● Data engineering without the hand-coding