After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
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 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.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
Oracle's BigData solutions consist of a number of new products and solutions to support customers looking to gain maximum business value from data sets such as weblogs, social media feeds, smart meters, sensors and other devices that generate massive volumes of data (commonly defined as ‘Big Data’) that isn’t readily accessible in enterprise data warehouses and business intelligence applications today.
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.
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
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.
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
Oracle Solaris Build and Run Applications Better on 11.3OTN Systems Hub
Build and Run Applications Better on Oracle Solaris 11.3
Tech Day, NYC
Liane Praza, Senior Principal Software Engineer
Ikroop Dhillon, Principal Product Manager
June, 2016
Slides from a presentation I gave at the 5th SOA, Cloud + Service Technology Symposium (September 2012, Imperial College, London). The goal of this presentation was to explore with the audience use cases at the intersection of SOA, Big Data and Fast Data. If you are working with both SOA and Big Data I would would be very interested to hear about your projects.
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.
The document discusses Oracle's big data platform and how it can extend Hortonworks' data platform. It provides an overview of Oracle's enterprise big data architecture and the key components of its big data platform. It also discusses how Oracle's platform provides rich SQL access across different data sources and describes some big data solutions for adaptive marketing and predictive maintenance.
This document discusses strategies for successfully utilizing a data lake. It notes that creating a data lake is just the beginning and that challenges include data governance, metadata management, access, and effective use of the data. The document advocates for data democratization through discovery, accessibility, and usability. It also discusses best practices like self-service BI and automated workload migration from data warehouses to reduce costs and risks. The key is to address the "data lake dilemma" of these challenges to avoid a "data swamp" and slow adoption.
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 provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
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
Tame Big Data with Oracle Data IntegrationMichael Rainey
In this session, Oracle Product Management covers how Oracle Data Integrator and Oracle GoldenGate are vital to big data initiatives across the enterprise, providing the movement, translation, and transformation of information and data not only heterogeneously but also in big data environments. Through a metadata-focused approach for cataloging, defining, and reusing big data technologies such as Hive, Hadoop Distributed File System (HDFS), HBase, Sqoop, Pig, Oracle Loader for Hadoop, Oracle SQL Connector for Hadoop Distributed File System, and additional big data projects, Oracle Data Integrator bridges the gap in the ability to unify data across these systems and helps deliver timely and trusted data to analytic and decision support platforms.
Co-presented with Alex Kotopoulis at Oracle OpenWorld 2014.
Oracle Cloud : Big Data Use Cases and ArchitectureRiccardo Romani
Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer.
Presentation done at Digital Transformation event - February 2017
Spark and Hadoop Perfect Togeher by Arun MurthySpark Summit
Spark and Hadoop are perfectly together. Spark is a key tool in Hadoop's toolbox that provides elegant developer APIs and accelerates data science and machine learning. It can process streaming data in real-time for applications like web analytics and insurance claims processing. The future of Spark and Hadoop includes innovating the core technologies, providing seamless data access across data platforms, and further accelerating data science tools and libraries.
This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
3 CTOs Discuss the Shift to Next-Gen Analytic EcosystemsHortonworks
Wow! When have you ever sat in on a Big Data analytics discussion by three of the most influential CTOs in the industry? What do they talk about among themselves?
Join Teradata's Stephen Brobst, Informatica's Sanjay Krishnamurthi, and Hortonworks' Scott Gnau as they provide a framework and best practices for maximizing value for data assets deployed within a Big Data & Analytics Architecture.
Data issues are deeply rooted and extremely complex. Not only do organizations have trouble choosing a starting point for bad data, but they also have difficulty finding the root cause of the issue. As a result, data audits can be long, tedious initiatives, that offer little insight into the data issues.
Traditional data audits are too narrowly focused on security and compliance. Availability and trust issues need to be uncovered as well.
Organizations are not aware of all their data sources.
Organizations are conducting data audits that are ineffective, only focusing on issues and failing to get to the root of the problem.
Data audits are failing to provide insight into possible solutions and problem resolution.
Critical Insight
A combination of technical profiling and user profiling will help you understand where issues are and why they exist.
An annual data audit initiative will continually revise and fine-tune ongoing practices, processes, and procedures for the management and handling of data within the organization.
You can’t do everything at once. Pick a process, see some early victories, gain momentum, and repeat.
Impact and Result
Prepare for the audit: Prepare in advance to make the audit process smoother and less time-intensive. Identify and create an inventory of all data sources that are within the scope of your data audit. Use these data sources to understand which users would provide a valuable, insightful interview. Schedule interviews and complete technical profiling.
Conduct audit: Interview relevant stakeholders identified in the audit preparation. Use insight from these interviews to complete user profiling. Update the data sources and data inventory with any information that may have been missed.
Analyze and assess results: Get to the root of the problem through conducting a root cause analysis. Find out why the issues are occurring.
Correct plan: You know what the issues are and you now know why they are being caused. Create the corrective plan through prioritizing initiatives and data activities. Use a combination of short-term and long-term initiatives.
Hw09 Rethinking The Data Warehouse With Hadoop And HiveCloudera, Inc.
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
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
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.
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
Oracle Solaris Build and Run Applications Better on 11.3OTN Systems Hub
Build and Run Applications Better on Oracle Solaris 11.3
Tech Day, NYC
Liane Praza, Senior Principal Software Engineer
Ikroop Dhillon, Principal Product Manager
June, 2016
Slides from a presentation I gave at the 5th SOA, Cloud + Service Technology Symposium (September 2012, Imperial College, London). The goal of this presentation was to explore with the audience use cases at the intersection of SOA, Big Data and Fast Data. If you are working with both SOA and Big Data I would would be very interested to hear about your projects.
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.
The document discusses Oracle's big data platform and how it can extend Hortonworks' data platform. It provides an overview of Oracle's enterprise big data architecture and the key components of its big data platform. It also discusses how Oracle's platform provides rich SQL access across different data sources and describes some big data solutions for adaptive marketing and predictive maintenance.
This document discusses strategies for successfully utilizing a data lake. It notes that creating a data lake is just the beginning and that challenges include data governance, metadata management, access, and effective use of the data. The document advocates for data democratization through discovery, accessibility, and usability. It also discusses best practices like self-service BI and automated workload migration from data warehouses to reduce costs and risks. The key is to address the "data lake dilemma" of these challenges to avoid a "data swamp" and slow adoption.
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 provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
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
Tame Big Data with Oracle Data IntegrationMichael Rainey
In this session, Oracle Product Management covers how Oracle Data Integrator and Oracle GoldenGate are vital to big data initiatives across the enterprise, providing the movement, translation, and transformation of information and data not only heterogeneously but also in big data environments. Through a metadata-focused approach for cataloging, defining, and reusing big data technologies such as Hive, Hadoop Distributed File System (HDFS), HBase, Sqoop, Pig, Oracle Loader for Hadoop, Oracle SQL Connector for Hadoop Distributed File System, and additional big data projects, Oracle Data Integrator bridges the gap in the ability to unify data across these systems and helps deliver timely and trusted data to analytic and decision support platforms.
Co-presented with Alex Kotopoulis at Oracle OpenWorld 2014.
Oracle Cloud : Big Data Use Cases and ArchitectureRiccardo Romani
Oracle Itay Systems Presales Team presents : Big Data in any flavor, on-prem, public cloud and cloud at customer.
Presentation done at Digital Transformation event - February 2017
Spark and Hadoop Perfect Togeher by Arun MurthySpark Summit
Spark and Hadoop are perfectly together. Spark is a key tool in Hadoop's toolbox that provides elegant developer APIs and accelerates data science and machine learning. It can process streaming data in real-time for applications like web analytics and insurance claims processing. The future of Spark and Hadoop includes innovating the core technologies, providing seamless data access across data platforms, and further accelerating data science tools and libraries.
This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
3 CTOs Discuss the Shift to Next-Gen Analytic EcosystemsHortonworks
Wow! When have you ever sat in on a Big Data analytics discussion by three of the most influential CTOs in the industry? What do they talk about among themselves?
Join Teradata's Stephen Brobst, Informatica's Sanjay Krishnamurthi, and Hortonworks' Scott Gnau as they provide a framework and best practices for maximizing value for data assets deployed within a Big Data & Analytics Architecture.
Data issues are deeply rooted and extremely complex. Not only do organizations have trouble choosing a starting point for bad data, but they also have difficulty finding the root cause of the issue. As a result, data audits can be long, tedious initiatives, that offer little insight into the data issues.
Traditional data audits are too narrowly focused on security and compliance. Availability and trust issues need to be uncovered as well.
Organizations are not aware of all their data sources.
Organizations are conducting data audits that are ineffective, only focusing on issues and failing to get to the root of the problem.
Data audits are failing to provide insight into possible solutions and problem resolution.
Critical Insight
A combination of technical profiling and user profiling will help you understand where issues are and why they exist.
An annual data audit initiative will continually revise and fine-tune ongoing practices, processes, and procedures for the management and handling of data within the organization.
You can’t do everything at once. Pick a process, see some early victories, gain momentum, and repeat.
Impact and Result
Prepare for the audit: Prepare in advance to make the audit process smoother and less time-intensive. Identify and create an inventory of all data sources that are within the scope of your data audit. Use these data sources to understand which users would provide a valuable, insightful interview. Schedule interviews and complete technical profiling.
Conduct audit: Interview relevant stakeholders identified in the audit preparation. Use insight from these interviews to complete user profiling. Update the data sources and data inventory with any information that may have been missed.
Analyze and assess results: Get to the root of the problem through conducting a root cause analysis. Find out why the issues are occurring.
Correct plan: You know what the issues are and you now know why they are being caused. Create the corrective plan through prioritizing initiatives and data activities. Use a combination of short-term and long-term initiatives.
Hw09 Rethinking The Data Warehouse With Hadoop And HiveCloudera, Inc.
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
This document discusses various aspects of data marts, including external data, reference data, performance issues, monitoring requirements, and security. External data is stored in the data warehouse to avoid redundancy. Reference data cannot be modified and is copied from the data warehouse. Performance considerations for data marts are different from OLAP environments, with response times ranging from 1 minute to 24 hours. Monitoring helps track data access, users, usage times, and content growth. Security measures like firewalls, login/logout, and encryption are needed to protect sensitive information in data marts.
This document provides an overview of dimensional modeling techniques for data warehouse design, including what a data warehouse is, how dimensional modeling fits into the data presentation area, and some of the key concepts and components of dimensional modeling such as facts, dimensions, and star schemas. It also discusses design concepts like snowflake schemas, slowly changing dimensions, and conformed dimensions.
Using the right data model in a data martDavid Walker
A presentation describing how to choose the right data model design for your data mart. Discusses the pros and benefits of different data models with different rdbms technologies and tools
Dimensional Modeling Basic Concept with ExampleSajjad Zaheer
This document discusses dimensional modeling, which is a process for structuring data to facilitate reporting and analysis. It involves extracting data from operational databases, transforming it according to requirements, and loading it into a data warehouse with a dimensional model. The key aspects of dimensional modeling covered are identifying grains, dimensions, and facts, then designing star schemas with fact and dimension tables. An example of modeling a user points system is provided to illustrate the dimensional modeling process.
1. The document discusses security considerations for deploying big data as a service (BDaaS) across multiple tenants and applications. It focuses on maintaining a single user identity to prevent data duplication and enforce access policies consistently.
2. It describes using Apache Ranger to centrally define and enforce policies across Hadoop services like HDFS, HBase, Hive. Ranger integrates with LDAP/AD for authentication.
3. The key challenge is propagating user identities from the application layer to the data layer. This can be done by connecting HDFS directly via Kerberos or using a "super-user" that impersonates other users when accessing HDFS.
Hybrid Data Warehouse Hadoop ImplementationsDavid Portnoy
The document discusses the evolving relationship between data warehouse (DW) and Hadoop implementations. It notes that DW vendors are incorporating Hadoop capabilities while the Hadoop ecosystem is growing to include more DW-like functions. Major DW vendors will likely continue playing a key role by acquiring successful new entrants or incorporating their technologies. The optimal approach involves a hybrid model that leverages the strengths of DWs and Hadoop, with queries determining where data resides and processing occurs. SQL-on-Hadoop architectures aim to bridge the two worlds by bringing SQL and DW tools to Hadoop.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
The document discusses how big data and analytics can transform businesses. It notes that the volume of data is growing exponentially due to increases in smartphones, sensors, and other data producing devices. It also discusses how businesses can leverage big data by capturing massive data volumes, analyzing the data, and having a unified and secure platform. The document advocates that businesses implement the four pillars of data management: mobility, in-memory technologies, cloud computing, and big data in order to reduce the gap between data production and usage.
The document discusses opportunities for enriching a data warehouse with Hadoop. It outlines challenges with ETL and analyzing large, diverse datasets. The presentation recommends integrating Hadoop and the data warehouse to create a "data reservoir" to store all potentially valuable data. Case studies show companies using this approach to gain insights from more data, improve analytics performance, and offload ETL processing to Hadoop. The document advocates developing skills and prototypes to prove the business value of big data before fully adopting Hadoop solutions.
This document provides an overview and agenda for a presentation on big data landscape and implementation strategies. It defines big data, describes its key characteristics of volume, velocity and variety. It outlines the big data technology landscape including data acquisition, storage, organization and analysis tools. Finally it discusses an integrated big data architecture and considerations for implementation.
The document discusses Oracle's cloud-based data lake and analytics platform. It provides an overview of the key technologies and services available, including Spark, Kafka, Hive, object storage, notebooks and data visualization tools. It then outlines a scenario for setting up storage and big data services in Oracle Cloud to create a new data lake for batch, real-time and external data sources. The goal is to provide an agile and scalable environment for data scientists, developers and business users.
The document discusses how companies are using big data to improve business operations and outcomes. It outlines four main ways that big data is put to work: getting fast answers to new questions, predicting more and more accurately, creating a centralized data reservoir, and accelerating data-driven actions. Case studies of companies like Dell, a large bank and a European bank demonstrate how they have benefited from these big data strategies. The document advocates for tightly integrating big data into business analytics in order to realize its full potential.
The document discusses Oracle's fast data solutions for helping organizations remove event-to-action latency and maximize the value of high-velocity data. It describes how fast data solutions can filter, move, transform, analyze and act on data in real-time to drive better business outcomes. Oracle provides a portfolio of products for fast data including Oracle Event Processing, Oracle Coherence, Oracle Data Integrator and Oracle Real-Time Decisions that work together to capture, filter, enrich, load and analyze streaming data and trigger automated decisions.
NoSQL Databases for Enterprises - NoSQL Now Conference 2013Dave Segleau
Talk delivered at Dataversity NoSQL Now! Conference in San Jose, August 2013. Describes primary NoSQL functionality and the key features and concerns that Enterprises should consider when choosing a NoSQL technology provider.
Cw13 big data and apache hadoop by amr awadallah-clouderainevitablecloud
This document provides an introduction to big data and Apache Hadoop from Cloudera. It discusses how data has changed with 90% now being unstructured, and how Hadoop can address this by allowing storage and analysis of large amounts of diverse data types. It summarizes Cloudera's Hadoop-based platform for batch and real-time processing across industries. Key benefits of Hadoop discussed include flexibility, scalability, and economics for cost-effectively storing and analyzing large amounts of data.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
The document provides an introduction to big data and Hadoop. It describes the concepts of big data, including the four V's of big data: volume, variety, velocity and veracity. It then explains Hadoop and how it addresses big data challenges through its core components. Finally, it describes the various components that make up the Hadoop ecosystem, such as HDFS, HBase, Sqoop, Flume, Spark, MapReduce, Pig and Hive. The key takeaways are that the reader will now be able to describe big data concepts, explain how Hadoop addresses big data challenges, and describe the components of the Hadoop ecosystem.
The document discusses the growth of data and how SAP products can help manage and analyze large amounts of data. It provides the following key details:
- The amount of data in the world has grown dramatically to 1.8 zettabytes in 2011 and 90% of the data today was created in the last two years.
- SAP offers solutions like HANA, BusinessObjects, and big data applications to help organizations capture, store, manage and analyze massive amounts of structured and unstructured data from various sources.
- HANA provides an in-memory database platform for real-time analytics while integrating with Hadoop for infinite storage and processing of large unstructured data sets.
Exclusive Verizon Employee Webinar: Getting More From Your CDR DataPentaho
This document discusses a project between Pentaho and Verizon to leverage big data analytics. Verizon generates vast amounts of call detail record (CDR) data from mobile networks that is currently stored in a data warehouse for 2 years and then archived to tape. Pentaho's platform will help optimize the data warehouse by using Hadoop to store all CDR data history. This will free up data warehouse capacity for high value data and allow analysis of the full 10 years of CDR data. Pentaho tools will ingest raw CDR data into Hadoop, execute MapReduce jobs to enrich the data, load results into Hive, and enable analyzing the data to understand calling patterns by geography over time.
The Cloudera Impala project is pioneering the next generation of Hadoop capabilities: the convergence of interactive SQL queries with the capacity, scalability, and flexibility of a Hadoop cluster. In this webinar, join Cloudera and MicroStrategy to learn how Impala works, how it is uniquely architected to provide an interactive SQL experience native to Hadoop, and how you can leverage the power of MicroStrategy 9.3.1 to easily tap into more data and make new discoveries.
This document is a presentation on Big Data by Oleksiy Razborshchuk from Oracle Canada. The presentation covers Big Data concepts, Oracle's Big Data solution including its differentiators compared to DIY Hadoop clusters, and use cases and implementation examples. The agenda includes discussing Big Data, Oracle's solution, and use cases. Key points covered are the value of Oracle's Big Data Appliance which provides faster time to value and lower costs compared to building your own Hadoop cluster, and how Oracle provides an integrated Big Data environment and analytics platform. Examples of Big Data solutions for financial services are also presented.
Oracle Unified Information Architeture + Analytics by ExampleHarald Erb
Der Vortrag gibt zunächst einen Architektur-Überblick zu den UIA-Komponenten und deren Zusammenspiel. Anhand eines Use Cases wird vorgestellt, wie im "UIA Data Reservoir" einerseits kostengünstig aktuelle Daten "as is" in einem Hadoop File System (HDFS) und andererseits veredelte Daten in einem Oracle 12c Data Warehouse miteinander kombiniert oder auch per Direktzugriff in Oracle Business Intelligence ausgewertet bzw. mit Endeca Information Discovery auf neue Zusammenhänge untersucht werden.
The document discusses Oracle Database 12c and its capabilities for cloud computing, database as a service, and big data. It highlights features like Oracle Multitenant that allow for more efficient consolidation on clouds and simpler provisioning of database as a service. It also describes Oracle's approach to integrating Hadoop and Oracle Database for big data and analytics.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the enterprise data hub (EDH) as a new approach for data management. The EDH allows organizations to bring applications to data rather than copying data to applications. It provides a full-fidelity active compliance archive, accelerates time to insights through scale, unlocks agility and innovation, consolidates data silos for a 360-degree view, and enables converged analytics. The EDH is implemented using open source, scalable, and cost-effective tools from Cloudera including Hadoop, Impala, and Cloudera Manager.
zData BI & Advanced Analytics Platform + 8 Week Pilot ProgramszData Inc.
This document describes zData's BI/Advanced Analytics Platform and Pilot Programs. The platform provides tools for storing, collaborating on, analyzing, and visualizing large amounts of data. It offers machine learning and predictive analytics. The platform can be deployed on-premise or in the cloud. zData also offers an 8-week pilot program that provides up to 1TB of data storage and full access to the platform's tools and services to test out the Big Data solution.
Special Meetup Edition - TDX Bengaluru Meetup #52.pptxshyamraj55
We’re bringing the TDX energy to our community with 2 power-packed sessions:
🛠️ Workshop: MuleSoft for Agentforce
Explore the new version of our hands-on workshop featuring the latest Topic Center and API Catalog updates.
📄 Talk: Power Up Document Processing
Dive into smart automation with MuleSoft IDP, NLP, and Einstein AI for intelligent document workflows.
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
TrsLabs - Fintech Product & Business ConsultingTrs Labs
Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
Noah Loul Shares 5 Steps to Implement AI Agents for Maximum Business Efficien...Noah Loul
Artificial intelligence is changing how businesses operate. Companies are using AI agents to automate tasks, reduce time spent on repetitive work, and focus more on high-value activities. Noah Loul, an AI strategist and entrepreneur, has helped dozens of companies streamline their operations using smart automation. He believes AI agents aren't just tools—they're workers that take on repeatable tasks so your human team can focus on what matters. If you want to reduce time waste and increase output, AI agents are the next move.
Semantic Cultivators : The Critical Future Role to Enable AIartmondano
By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Mobile App Development Company in Saudi ArabiaSteve Jonas
EmizenTech is a globally recognized software development company, proudly serving businesses since 2013. With over 11+ years of industry experience and a team of 200+ skilled professionals, we have successfully delivered 1200+ projects across various sectors. As a leading Mobile App Development Company In Saudi Arabia we offer end-to-end solutions for iOS, Android, and cross-platform applications. Our apps are known for their user-friendly interfaces, scalability, high performance, and strong security features. We tailor each mobile application to meet the unique needs of different industries, ensuring a seamless user experience. EmizenTech is committed to turning your vision into a powerful digital product that drives growth, innovation, and long-term success in the competitive mobile landscape of Saudi Arabia.
AI Changes Everything – Talk at Cardiff Metropolitan University, 29th April 2...Alan Dix
Talk at the final event of Data Fusion Dynamics: A Collaborative UK-Saudi Initiative in Cybersecurity and Artificial Intelligence funded by the British Council UK-Saudi Challenge Fund 2024, Cardiff Metropolitan University, 29th April 2025
https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
#24: THEME: Integrating with Existing EnterpriseBut, Big data is not an island. Oracle customers have a lot invested in their Oracle ecosystems. Their Oracle data warehouses contain valuable data that drive their analyses and insights. Oracle databases are also at the core of transaction systems and enterprise applications. Oracle BI is used to visualize this critical information - using dashboards, answers and mobile - and use these insights to make better decisions.We want to extend these applications to be able to leverage big data. In order to accomplish this, we need blazingly fast connections and simple access to data in Hadoop. Describe Big Data Connectors - 12TB/hour - automatic access to data in Hive. Off-line. On-line. Create queries that combine data in DB w/data in Hadoop.Entire stack is “big data enabled”. Exalytics - access data in Hive. Endeca - information discovery over data in Hadoop.
#25: THEME: Integrating with Existing EnterpriseAnalyze all data in-placeAnalytics is critical - it’s oftentimes the reason you implement big data. And, with the big data platform - you are not limited to analyzing data samples - you can analyze all your data. Simpler algorithms on all your data have been proven to be easier to implement and frankly more effective than more complex algorithms over samples.Rich analytics in both Hadoop and the Oracle database. Explain…We now have ascaleable analytic platform:Database: Oracle Advanced Analytics: SQL & RHadoop: ORCH +
#26: THEME: Integrating with Existing EnterpriseAnalyze all data in-placeAnalytics is critical - it’s oftentimes the reason you implement big data. And, with the big data platform - you are not limited to analyzing data samples - you can analyze all your data. Simpler algorithms on all your data have been proven to be easier to implement and frankly more effective than more complex algorithms over samples.Rich analytics in both Hadoop and the Oracle database. Explain…We now have ascaleable analytic platform:Database: Oracle Advanced Analytics: SQL & RHadoop: ORCH +
#35: Company/BackgroundLeading source of intelligent information for the world’s businesses and professionalsWestlaw & Westlaw Next legal research used by more than 80% of Fortune 500 companies with revenues > $3BUsed by legal professionals to find and share specific points of law and search for topically-related commentaryChallenges/OpportunitiesNeeded better understanding of customer behavior in order to identify cross sell and upsell opportunitiesUnable to justify cost of collecting “low value” data into standard database platformSolutionBDA and NoSQL are being tested to support ingesting up to 50M events/sec to be processed and fed into Exadata EDW. Customer segmentation models drive recommendations. Return legal search results beyond customers’ current subscription. Automatic upsell additional subscriptions.Key ProductsOracle Big Data Appliance, Oracle Exadata, Oracle ExalyticsOracle Big Data Connectors, Oracle Advanced Analytics, Oracle Spatial & Graph, Oracle OLAPWhy OracleBDA price extremely competitive with DIYBDA/Exadata integration and performanceFuture PlansCentralized BDA IaaS for Thomson Reuters divisionsUnderstand/optimize web site usageUnderstand and visualize strength of connections between entities using network graphs