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
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
The document discusses data mesh vs data fabric architectures. It defines data mesh as a decentralized data processing architecture with microservices and event-driven integration of enterprise data assets across multi-cloud environments. The key aspects of data mesh are that it is decentralized, processes data at the edge, uses immutable event logs and streams for integration, and can move all types of data reliably. The document then provides an overview of how data mesh architectures have evolved from hub-and-spoke models to more distributed designs using techniques like kappa architecture and describes some use cases for event streaming and complex event processing.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
This presentation is based on Lawrence To's Maximum Availability Architecture (MAA) Oracle Open World Presentation talking about the latest updates on high availability (HA) best practices across multiple architectures, features and products in Oracle Database 19c. It considers all workloads, OLTP, DWH and analytics, mixed workload as well as on-premises and cloud-based deployments.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Make Your Application “Oracle RAC Ready” & Test For ItMarkus Michalewicz
This presentation talks about the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can ensure that your application is “Oracle RAC ready.”. It discusses do's and don'ts and how to test your application for "Oracle RAC readiness". This version was first presented in Sangam19.
Collibra Data Citizen '19 - Bridging Data Privacy with Data Governance BigID Inc
This presentation was shown at the 2019 Collibra Data Citizen Event in New York City.
Presented by Nimrod Vax, Chief Product Officer & Co-Founder & Joaquin Sufuentes, Lead Architect, Metadata Managment and Personal Infomation Protection, enterprise Data Managment, Intel IT
Building a Logical Data Fabric using Data Virtualization (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3FF1ubd
In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.
In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets.
KEY TAKEAWAYS:
- How a Logical Data Fabric is the right approach to assist organizations to unify their data.
- The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science.
- How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.
Oracle Database 19c, builds upon key architectural, distributed data and performance innovations established in earlier versions Oracle Database 12c and 18c releases. Oracle 19c has many new features, in this presentation we have covered below areas
Automated Installation, Configuration and Patching
AutoUpgrade and Database Utilities
Oracle RAC 19c: Best Practices and Secret InternalsAnil Nair
Oracle Real Application Clusters 19c provides best practices and new features for upgrading to Oracle 19c. It discusses upgrading Oracle RAC to Linux 7 with minimal downtime using node draining and relocation techniques. Oracle 19c allows for upgrading the Grid Infrastructure management repository and patching faster using a new Oracle home. The presentation also covers new resource modeling for PDBs in Oracle 19c and improved Clusterware diagnostics.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Delta Lake is an open source storage layer that sits on top of data lakes and brings ACID transactions and reliability to Apache Spark. It addresses challenges with data lakes like lack of schema enforcement and transactions. Delta Lake provides features like ACID transactions, scalable metadata handling, schema enforcement and evolution, time travel/data versioning, and unified batch and streaming processing. Delta Lake stores data in Apache Parquet format and uses a transaction log to track changes and ensure consistency even for large datasets. It allows for updates, deletes, and merges while enforcing schemas during writes.
This document summarizes a presentation about Oracle Analytics Cloud (OAC) given by Mike Killeen of Edgewater Ranzal. The presentation provides an overview of OAC and its capabilities, including standard and enterprise editions. It demonstrates OAC's ability to integrate business analytics solutions like EPM, BI and big data technologies to help improve business performance. The document also discusses the growing need for business analytics and how OAC can help organizations better analyze data and gain actionable insights.
Big Query - Utilizing Google Data Warehouse for Media Analyticshafeeznazri
This topic will cover the intermediate understanding of Google Big Query and how Media Prima Digital utilizing Big Query as data warehouse for production.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
CaixaBank is using big data and its partnership with Oracle to develop a new technology platform to improve business and better anticipate customer needs with a 360 degree view of customers. CaixaBank consolidated 17 data marts into one centralized data pool built on Oracle technologies. This has improved customer relationships, employee efficiency, and regulatory reporting. The data pool collects data from various sources to power business use cases like deposits pricing, customized ATM menus, online risk scoring, and online marketing automation.
The document discusses Oracle's enterprise architecture approach and services. It provides an overview of Oracle's enterprise architecture framework and reference architectures. It also highlights two customer case studies where Oracle helped customers transform their architecture and move to shared services and cloud computing models.
Alok Singh is seeking challenging assignments in Business Intelligence/Data warehousing. He has nearly 7 years of experience in BI/DW, ETL, data integration, and data warehousing solution design. He is proficient in SQL, ETL tools like Informatica and SSIS, and visualization tools like QlikView and Tableau. He has experience designing and developing ETL solutions, requirements gathering, and data analysis. His past roles include positions at Technologia, Subex, and Reliance Communications where he worked on projects involving Teradata, Oracle, billing systems, and fraud detection. He has a bachelor's degree in electronics and telecommunications.
ADV Slides: Data Pipelines in the Enterprise and ComparisonDATAVERSITY
Despite the many, varied, and legitimate data platforms that exist today, data seldom lands once in its perfect spot for the long haul of usage. Data is continually on the move in an enterprise into new platforms, new applications, new algorithms, and new users. The need for data integration in the enterprise is at an all-time high.
Solutions that meet these criteria are often called data pipelines. These are designed to be used by business users, in addition to technology specialists, for rapid turnaround and agile needs. The field is often referred to as self-service data integration.
Although the stepwise Extraction-Transformation-Loading (ETL) remains a valid approach to integration, ELT, which uses the power of the database processes for transformation, is usually the preferred approach. The approach can often be schema-less and is frequently supported by the fast Apache Spark back-end engine, or something similar.
In this session, we look at the major data pipeline platforms. Data pipelines are well worth exploring for any enterprise data integration need, especially where your source and target are supported, and transformations are not required in the pipeline.
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
AGIT 2015 - Hans Viehmann: "Big Data and Smart Cities"jstrobl
- Location data from sources like social media, sensors, and smart devices is increasingly important for improving city services, security, and operations in smart cities (paragraph 1, 2)
- Oracle provides tools for managing and analyzing large volumes of spatial and location data using big data technologies like Hadoop and streaming data platforms to enable use cases like predictive analytics (paragraph 3, 4, 5, 19)
- Oracle's spatial capabilities allow for indexing, visualization, and analysis of geospatial vector and raster data at scale, including tools for data preparation, spatial queries, and analyzing streaming location data (paragraph 10, 13, 14, 20)
DBCS Office Hours - Modernization through MigrationTammy Bednar
Speakers:
Kiran Tailor - Cloud Migration Director, Oracle
Kevin Lief – Partnership and Alliances Manager - (EMEA), Advanced
Modernisation of mainframe and other legacy systems allows organizations to capitalise on existing assets as they move toward more agile, cost-effective and open technology environments. Do you have legacy applications and databases that you could modernise with Oracle, allowing you to apply cutting edge technologies, like machine learning, or BI for deeper insights about customers or products? Come to this webcast to learn about all this and how Advanced can help to get you on the path to modernisation.
AskTOM Office Hours offers free, open Q&A sessions with Oracle Database experts. Join us to get answers to all your questions about Oracle Database Cloud Service.
Oracle communications data model product overviewGreenHamster
This document provides an overview of the Oracle Communications Data Model (OCDM). It begins with an agenda that covers market opportunities and data challenges for communications service providers, an overview of the OCDM, customer success stories, and a question and answer section. The OCDM is described as an enterprise data model for the communications industry with over 1,500 tables, 30,000 columns, and 1,000 key performance indicators. It is designed to help service providers address challenges around large and growing data volumes, data complexity from multiple systems and lines of business, and constant changes in the industry. Customer case studies highlight how the OCDM has helped providers like Etisalat Nigeria improve data consistency, gain a holistic view, and boost
The Changing Role of a DBA in an Autonomous WorldMaria Colgan
The advent of the cloud and the introduction of Oracle Autonomous Database Cloud presents opportunities for every organization, but what's the future role for the DBA? This presentation explores how the role of the DBA will continue to evolve, and provides advice on key skills required to be a successful DBA in the world of the cloud.
Contexti / Oracle - Big Data : From Pilot to ProductionContexti
The document discusses challenges in moving big data projects from pilots to production. It highlights that pilots have loose SLAs and focus on a few use cases and demonstrated insights, while production requires enforced SLAs, supporting many use cases and delivering actionable insights. Key challenges in the transition include establishing governance, skills, funding models and integrating insights into operations. The document also provides examples of technology considerations and common operating models for big data analytics.
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
SplunkLive! London - Splunk App for Stream & MINT BreakoutSplunk
The document discusses new features in Splunk's App for Stream and Splunk MINT. It introduces the Splunk App for Stream, which enables real-time insights into private, public and hybrid cloud infrastructures through efficient wire data capture. It also discusses Splunk for Mobile Intelligence (MINT), which provides mobile analytics capabilities. The document promotes these products as enhancing operational intelligence through efficient and cloud-ready wire data collection.
How a Logical Data Fabric Enhances the Customer 360 ViewDenodo
Watch full webinar here: https://bit.ly/3GI802M
Organisations have struggled for years in understanding their customers, this has mainly been due to not having the right data available at the right point in time. In this session we will discuss the role of Data Virtualization in providing customer 360 degree view and look at some of the success stories our customers have told us about.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
Oracle provides an integrated business intelligence solution that includes embedded analytics, applications, platforms, and data integration. The solution aims to deliver operational excellence through management excellence by providing insight, collaboration, and decision making supported by action. Key components include the Oracle BI server for relational and multidimensional analysis, Essbase for specialized multidimensional applications, and scorecards for tracking key performance indicators. The unified platform and applications are designed to provide a complete picture of data from transaction to reporting and analysis.
Analyze billions of records on Salesforce App Cloud with BigObjectSalesforce Developers
Salesforce hosts billions of customer records on Salesforce App Cloud. Making timely decisions on this invaluable data demands a new set of capabilities. From interacting with data in real-time to leveraging a fluid integration with Salesforce Analytics, these capabilities are just around the corner. Join us in this roadmap session to see what the near-future of Big Data on Salesforce App Cloud looks like and how you can benefit from it.
Key Takeaways
- Learn what 100 billion+ records on the Salesforce App Cloud could actually mean to you.
- Understand new services such as AsyncSOQL that can can deliver reliable, resilient query capabilities over your sObjects and BigObjects.
-Gain insights for large scale federated data filtering and aggregation.
-Transform data movement so all your customer records are available across their life cycle.
Intended Audience
This session is for Salesforce Administrators, Developers, Architects and just about anyone who wants to learn more about BigObjects!
Jamal Ouazzani has over 25 years of experience in information technology as a data architect, database administrator, and project manager. He has extensive expertise in areas such as data management, business intelligence, data modeling, ETL, and Oracle database administration. His experience includes roles at PennMutual Life Insurance, Tangoe, GMAC, Centocor, Stonepath Group, Bank One, Araccel Corp, Alliance Consulting, Oracle Corporation, Phoenix International, and Canadian National Railways.
Similar to Oracle Stream Analytics - Developer Introduction (20)
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
Webinar future dataintegration-datamesh-and-goldengatekafkaJeffrey T. Pollock
The Future of Data Integration: Data Mesh, and a Special Deep Dive into Stream Processing with GoldenGate, Apache Kafka and Apache Spark. This video is a replay of a Live Webinar hosted on 03/19/2020.
Join us for a timely 45min webinar to see our take on the future of Data Integration. As the global industry shift towards the “Fourth Industrial Revolution” continues, outmoded styles of centralized batch processing and ETL tooling continue to be replaced by realtime, streaming, microservices and distributed data architecture patterns.
This webinar will start with a brief look at the macro-trends happening around distributed data management and how that affects Data Integration. Next, we’ll discuss the event-driven integrations provided by GoldenGate Big Data, and continue with a deep-dive into some essential patterns we see when replicating Database change events into Apache Kafka. In this deep-dive we will explain how to effectively deal with issues like Transaction Consistency, Table/Topic Mappings, managing the DB Change Stream, and various Deployment Topologies to consider. Finally, we’ll wrap up with a brief look into how Stream Processing will help to empower modern Data Integration by supplying realtime data transformations, time-series analytics, and embedded Machine Learning from within data pipelines.
GoldenGate: https://www.oracle.com/middleware/tec...
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Brief training targeted to middle school aged students who are participating in First Lego League robotics and planning to use a version control tool such as EV3Hub
GoldenGate and Stream Processing with Special Guest RakutenJeffrey T. Pollock
Oracle OpenWorld roadmap presentation for GoldenGate, stream processing, analytics and big data use cases with special guest presenters from Rakuten Travel.
A modern approach to streaming data integration, event processing with a big data (kappa style) data architecture. Key patterns are discussed with pros/cons of newer approaches and open source technologies. Focus on Oracle and GoldenGate technology. OpenWorld 2018 presentation.
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
The document discusses Oracle's data integration products and big data solutions. It outlines five core capabilities of Oracle's data integration platform, including data availability, data movement, data transformation, data governance, and streaming data. It then describes eight core products that address real-time and streaming integration, ELT integration, data preparation, streaming analytics, dataflow ML, metadata management, data quality, and more. The document also outlines five cloud solutions for data integration including data migrations, data warehouse integration, development and test environments, high availability, and heterogeneous cloud. Finally, it discusses pragmatic big data solutions for data ingestion, transformations, governance, connectors, and streaming big data.
Oracle Data Integration overview, vision and roadmap. Covers GoldenGate, Data Integrator (ODI), Data Quality (EDQ), Metadata Management (MM) and Big Data Preparation (BDP)
The document discusses the growing role of the Chief Data Officer (CDO) position. It notes that by 2017, half of banking/insurance firms and a third of Fortune 100 companies will have a CDO. CDOs face challenges around ensuring executive support, building data management frameworks, and monetizing data assets. The document outlines strategies CDOs can employ, such as accelerating analytics, adopting open source technologies, and governing data through metadata and quality processes. It positions Oracle as providing a complete data solution to help CDOs address these challenges.
Strata 2015 presentation from Oracle for Big Data - we are announcing several new big data products including GoldenGate for Big Data, Big Data Discovery, Oracle Big Data SQL and Oracle NoSQL
One Slide Overview: ORCL Big Data Integration and GovernanceJeffrey T. Pollock
This document discusses Oracle's approach to big data integration and governance. It describes Oracle tools like GoldenGate for real-time data capture and movement, Data Integrator for data transformation both on and off the Hadoop cluster, and governance tools for data preparation, profiling, cleansing, and metadata management. It positions Oracle as a leader in big data integration through capabilities like non-invasive data capture, low-latency data movement, and pushdown processing techniques pioneered by Oracle to optimize distributed queries.
This document discusses Oracle's data integration and governance solutions for big data. It describes how Oracle uses data integration to load and transform data from various sources into a data reservoir. It also emphasizes the importance of data governance when managing big data and describes Oracle's metadata management, data profiling, and data cleansing tools to help govern data in the reservoir.
Unlocking Big Data Silos in the Enterprise or the Cloud (Con7877)Jeffrey T. Pollock
The document discusses Oracle Data Integration solutions for unifying big data silos in enterprises and the cloud. The key points covered include:
- Oracle Data Integration provides data integration and governance capabilities for real-time data movement, transformation, federation, quality and verification, and metadata management.
- It supports a highly heterogeneous set of data sources, including various database platforms, big data technologies like Hadoop, cloud applications, and open standards.
- The solutions discussed help improve agility, reduce costs and risk, and provide comprehensive data integration and governance capabilities for enterprises.
This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.
The document provides lessons from iconic product managers throughout history, including Thomas J. Watson Jr., Henry Ford, Steve Jobs, Bill Gates, Ferdinand Porsche, and others. It discusses their philosophies and contributions, such as Watson's belief that good design is good business, Ford's views on quality and market saturation, Jobs' focus on deciding what not to do, and Gates' creation of new markets. Contemporary visionaries like Elon Musk, Larry Ellison, Jeff Bezos, and Larry Page are also examined for their product leadership, vision, and business strategies. Lesser known figures like Marissa Mayer, Jack Dorsey, and Thomas Kurian are highlighted for enforcing vision, identifying opportunities, and using their own products
This document discusses Klarna Tech Talk on managing data. It provides an overview of IBM's data integration, governance, and big data capabilities. IBM states it can help clients turn information into insights, deepen engagement, enable agile business, accelerate innovation, deliver enterprise mobility, optimize infrastructure, and manage risk through technology innovations like big data analytics, security intelligence, cloud computing, and mobile solutions. The document promotes IBM's data fabric and smart data solutions for integrating, governing, and providing access to data across an organization.
The document discusses information management challenges in today's data-intensive world. It highlights how IBM offers a comprehensive vision and single platform to address issues like extreme data growth, complexity, and the need for real-time insights. IBM helps organizations optimize investments, improve customer satisfaction, increase coupon redemption rates, and reduce road congestion through analytics, governance, integration, and other solutions.
Overview of Statistical software such as ODK, surveyCTO,and CSPro
2. Software installation(for computer, and tablet or mobile devices)
3. Create a data entry application
4. Create the data dictionary
5. Create the data entry forms
6. Enter data
7. Add Edits to the Data Entry Application
8. CAPI questions and texts
Big Data and Analytics Shaping the future of PaymentsRuchiRathor2
The payments industry is experiencing a data-driven revolution powered by big data and analytics.
Here's a glimpse into 5 ways this dynamic duo is transforming how we pay.
In essence, big data and analytics are playing a pivotal role in building a future filled with faster, more secure, and convenient payment methods for everyone.
Data analytics is a powerful tool that can transform business decision-making across industries. Contact District 11 Solutions, which specializes in data analytics, to make informed decisions and achieve your business goals.
The Rise of Python in Finance,Automating Trading Strategies: _.pdfRiya Sen
In the dynamic realm of finance, where every second counts, the integration of technology has become indispensable. Aspiring traders and seasoned investors alike are turning to coding as a powerful tool to unlock new avenues of financial success. In this blog, we delve into the world of Python live trading strategies, exploring how coding can be the key to navigating the complexities of the market and securing your path to prosperity.
Introduction to Data Science
1.1 What is Data Science, importance of data science,
1.2 Big data and data Science, the current Scenario,
1.3 Industry Perspective Types of Data: Structured vs. Unstructured Data,
1.4 Quantitative vs. Categorical Data,
1.5 Big Data vs. Little Data, Data science process
1.6 Role of Data Scientist
How AI is Revolutionizing Data Collection.pdfPromptCloud
Artificial Intelligence (AI) is transforming the landscape of data collection, making it more efficient, accurate, and insightful than ever before. With AI, businesses can automate the extraction of vast amounts of data from diverse sources, analyze patterns in real-time, and gain deeper insights with minimal human intervention. This revolution in data collection enables companies to make faster, data-driven decisions, enhance their competitive edge, and unlock new opportunities for growth.
AI-powered tools can handle complex and dynamic web content, adapt to changes in website structures, and even understand the context of data through natural language processing. This means that data collection is not only faster but also more precise, reducing the time and effort required for manual data extraction. Furthermore, AI can process unstructured data, such as social media posts and customer reviews, providing valuable insights into customer sentiment and market trends.
Embrace the future of data collection with AI and stay ahead of the curve. Learn more about how PromptCloud’s AI-driven web scraping solutions can transform your data strategy. https://www.promptcloud.com/contact/
Annex K RBF's The World Game pdf documentSteven McGee
Signals & Telemetry Annex K for RBF's The World Game / Trade Federations / USPTO 13/573,002 Heart Beacon Cycle Time - Space Time Chain meters, metrics, standards. Adaptive Procedural template framework structured data derived from DoD / NATO's system of systems engineering tech framework
10. 10
GG Stream Analytics – Key Feature Areas
Interactive
Designer UI
Rich Set of Streaming
Patterns
Predictive Analysis and
Machine Learning
Location and Geospatial
Analysis
Integrated CDC with
Oracle GoldenGate
Robustness, Speed, and
Scalability
20. Oracle Stream Analytics
20Confidential – Oracle Internal/Restricted/Highly Restricted
Data Pipeline
GoldenGate Feeds
Sensor Data
Social Media
Click Stream
Geo Location
Filter
Aggregate
Transform
Correlate/Enrich
Geo-fence
Queries
Time Windows
Data Patterns
Spatial Analytics
Anomalies
Classification
Clustering
Statistical Inference
Regression Models
Business Rules
Policies
Conditional Logic
Notify/Publish
Invoke/Execute
Visualize
Persist
Data Ingestion Pre-processing
Analysis
Prediction
Decisions Actions
Ingest Transform and Correlate Act and Deliver
21. End-to-End Steps to build a Stream Application:
1. Create Connections, Stream, and References for Sources
21Confidential – Oracle Internal/Restricted/Highly Restricted
Kafka, JMS, File,
Database, or REST
Connection Types Message Shape is
detected from
Kafka Topic
22. 2. Create Geographical Areas / Geo Fences
22Confidential – Oracle Internal/Restricted/Highly Restricted
Build Geo Fences
manually or from DB
repository
23. 3. Import Predictive PMML Model
23Confidential – Oracle Internal/Restricted/Highly Restricted
Train and export
PMML models from
common ML tools
such as R, SAS, H2O,
etc.
24. 4. Create New Pipeline
24Confidential – Oracle Internal/Restricted/Highly Restricted
Incoming messages
are displayed
automatically
New Pipeline is
immediately valid
and active
25. 5. Add Joins to Pipeline
25Confidential – Oracle Internal/Restricted/Highly Restricted
Join Stream or
Batch source
Joined events are shown
with color-coded fields
26. 6. Add Patterns to Pipeline
26Confidential – Oracle Internal/Restricted/Highly Restricted
Choose from a library of
vertical patterns
Event locations are shown
on map in real-time
27. 7. Add ML Scoring to Pipeline
27Confidential – Oracle Internal/Restricted/Highly Restricted
Refer to uploaded
PMML model
Map event fields into
PMML model properties
28. 8. Add Target to Pipeline
28Confidential – Oracle Internal/Restricted/Highly Restricted
Send events to Kafka,
JMS, or REST targets
29. 9. Publish Pipeline to Production
29Confidential – Oracle Internal/Restricted/Highly Restricted
One-click deploy into
production Spark cluster
42. Oracle Stream Analytics
42Confidential – Oracle Internal/Restricted/Highly Restricted
Integration with GoldenGate
Kafka Cloud Services
Contextual
Data
ML Models
Real-time
BI
Big Data Lakes
Business
Process
Operational
Dashboards
DB Events
Ingest with
GoldenGate
Actions
Oracle
SQL Server
MySQL
IBM DB2 Z
IBM DB2 i
IBM DB2 LUW
HP NonStop
Informix
Sybase
Messaging
Oracle
GoldenGate
Stage in
Kafka
Stream
Analytics
Capture Trail
Files Delivery
Trail
Files