OBIEE12c comes with an updated version of Essbase that focuses entirely in this release on the query acceleration use-case. This presentation looks at this new release and explains how the new BI Accelerator Wizard manages the creation of Essbase cubes to accelerate OBIEE query performance
Using Oracle Big Data SQL 3.0 to add Hadoop & NoSQL to your Oracle Data Wareh...Mark Rittman
As presented at OGh SQL Celebration Day in June 2016, NL. Covers new features in Big Data SQL including storage indexes, storage handlers and ability to install + license on commodity hardware
Riga dev day 2016 adding a data reservoir and oracle bdd to extend your ora...Mark Rittman
This talk focus is on what a data reservoir is, how it related to the RDBMS DW, and how Big Data Discovery provides access to it to business and BI users
Deploying OBIEE in the Cloud - Oracle Openworld 2014Mark Rittman
Introduction to Oracle BI Cloud Service (BICS) including administration, data upload, creating the repository and creating dashboards and reports. Also includes a short case-study around Salesforce.com reporting created for the BICS beta program.
Deploying Full Oracle BI Platforms to Oracle Cloud - OOW2015Mark Rittman
- Mark Rittman presented on deploying full OBIEE systems to Oracle Cloud. This involves migrating the data warehouse to Oracle Database Cloud Service, updating the RPD to connect to the cloud database, and uploading the RPD to Oracle BI Cloud Service. Using the wider Oracle PaaS ecosystem allows hosting a full BI platform in the cloud.
Unlock the value in your big data reservoir using oracle big data discovery a...Mark Rittman
The document discusses Oracle Big Data Discovery and how it can be used to analyze and gain insights from data stored in a Hadoop data reservoir. It provides an example scenario where Big Data Discovery is used to analyze website logs, tweets, and website posts and comments to understand popular content and influencers for a company. The data is ingested into the Big Data Discovery tool, which automatically enriches the data. Users can then explore the data, apply additional transformations, and visualize relationships to gain insights.
SQL-on-Hadoop for Analytics + BI: What Are My Options, What's the Future?Mark Rittman
There are many options for providing SQL access over data in a Hadoop cluster, including proprietary vendor products along with open-source technologies such as Apache Hive, Cloudera Impala and Apache Drill; customers are using those to provide reporting over their Hadoop and relational data platforms, and looking to add capabilities such as calculation engines, data integration and federation along with in-memory caching to create complete analytic platforms. In this session we’ll look at the options that are available, compare database vendor solutions with their open-source alternative, and see how emerging vendors are going beyond simple SQL-on-Hadoop products to offer complete “data fabric” solutions that bring together old-world and new-world technologies and allow seamless offloading of archive data and compute work to lower-cost Hadoop platforms.
Social Network Analysis using Oracle Big Data Spatial & Graph (incl. why I di...Mark Rittman
The document discusses using Hadoop and NoSQL technologies like Apache HBase to perform social network analysis on Twitter data related to a company's website and blog. It describes ingesting tweet and website log data into Hadoop HDFS and processing it with tools like Hive. Graph algorithms from Oracle Big Data Spatial & Graph were then used on the property graph stored in HBase to identify influential Twitter users and communities. This approach provided real-time insights at scale compared to using a traditional relational database.
How To Leverage OBIEE Within A Big Data ArchitectureKevin McGinley
If you've invested in OBIEE and want to start exploring the use of Big Data technology, this presentation talks about how and why you might want to use OBIEE as the common visualization layer across both.
Using Oracle Big Data Discovey as a Data Scientist's ToolkitMark Rittman
As delivered at Trivadis Tech Event 2016 - how Big Data Discovery along with Python and pySpark was used to build predictive analytics models against wearables and smart home data
The document discusses creating hybrid OLAP/relational models in Oracle Business Intelligence Enterprise Edition (OBIEE) by combining Essbase and relational data sources. It describes steps for importing an Essbase cube into OBIEE's semantic model layer and then creating federated models that allow drilling from aggregated Essbase data down to more detailed relational data. Special considerations are discussed for dimensions like time that require transformations when joining Essbase and relational sources.
INFORMATICA ONLINE TRAINING BY QUONTRA SOLUTIONS WITH PLACEMENT ASSISTANCE
We offer online IT training with placements, project assistance in different platforms with real time industry consultants to provide quality training for all it professionals, corporate clients and students etc. Special features by Quontra Solutions are Extensive Training will be in both Informatica Online Training and Placement. We help you in resume preparation and conducting Mock Interviews.
Emphasis is given on important topics which are essential and mostly used in real time projects. Quontra Solutions is an Online Training Leader when it comes to high-end effective and efficient I.T Training. We have always been and still are focusing on the key aspects which are providing utmost effective and competent training to both students and professionals who are eager to enrich their technical skills.
Training Features at Quontra Solutions:
We believe that online training has to be measured by three major aspects viz., Quality, Content and Relationship with the Trainer and Student. Not only our online training classes are important but apart from that the material which we provide are in tune with the latest IT training standards, so a student has not to worry at all whether the training imparted is outdated or latest.
Course content:
• Basics of data warehousing concepts
• Power center components
• Informatica concepts and overview
• Sources
• Targets
• Transformations
• Advanced Informatica concepts
Please Visit us for the Demo Classes, we have regular batches and weekend batches.
QUONTRASOLUTIONS
204-226 Imperial Drive,Rayners Lane, Harrow-HA2 7HH
Phone : +44 (0)20 3734 1498 / 99
Email: info@quontrasolutions.co.uk
The Future of Analytics, Data Integration and BI on Big Data PlatformsMark Rittman
The document discusses the future of analytics, data integration, and business intelligence (BI) on big data platforms like Hadoop. It covers how BI has evolved from old-school data warehousing to enterprise BI tools to utilizing big data platforms. New technologies like Impala, Kudu, and dataflow pipelines have made Hadoop fast and suitable for analytics. Machine learning can be used for automatic schema discovery. Emerging open-source BI tools and platforms, along with notebooks, bring new approaches to BI. Hadoop has become the default platform and future for analytics.
OAC - From Cloud Entry to Data Engineering to Data ScienceChristian Berg
Everybody has read about all the usual buzzwords endlessly. Yet how do these translate into what’s actually available in the products and how are they really being used? Let’s cut away the marketing nonsense and the empty buzzwords and GO to the cloud, DO data engineering and DO Machine Learning
High Scale Relational Storage at Salesforce Built with Apache HBase and Apach...Salesforce Engineering
Apache HBase is an open source, non-relational, distributed datastore modeled after Google’s Bigtable, that runs on top of the Apache Hadoop Distributed Filesystem and provides low-latency random-access storage for HDFS-based compute platforms like Apache Hadoop and Apache Spark. Apache Phoenix is a high performance relational database layer over HBase optimized for low latency applications. This session will explore how the Data Platform and Services group at Salesforce.com supports teams of application developers accustomed to structured relational data access, while surfacing additional advantages of the underlying flexible scale-out datastore.
Presentación sobre la futura base de datos 18c, en la cual se incorpora todo lo mejor de las tecnologías Oracle, perfilando así una base de datos autónoma.
Big data architectures and the data lakeJames Serra
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Agile Methods and Data Warehousing (2016 update)Kent Graziano
This presentation takes a look at the Agile Manifesto and the 12 Principles of Agile Development and discusses how these apply to Data Warehousing and Business Intelligence projects. Several examples and details from my past experience are included. Includes more details on using Data Vault as well. (I gave this presentation at OUGF14 in Helsinki, Finland and again in 2016 for TDWI Nashville.)
What is Big Data Discovery, and how it complements traditional business anal...Mark Rittman
Data Discovery is an analysis technique that complements traditional business analytics, and enables users to combine, explore and analyse disparate datasets to spot opportunities and patterns that lie hidden within your data. Oracle Big Data discovery takes this idea and applies it to your unstructured and big data datasets, giving users a way to catalogue, join and then analyse all types of data across your organization.
In this session we'll look at Oracle Big Data Discovery and how it provides a "visual face" to your big data initatives, and how it complements and extends the work that you currently do using business analytics tools.
Oracle aims to partner with companies to embed Oracle databases and middleware in their products, noting the benefits of a turnkey solution, lower costs, and leveraging Oracle's brand and ecosystem. Oracle offers embeddable products like databases and Fusion Middleware and an Embedded Software License program. The presentation provides an overview of Oracle's embedded initiative and resources for partners.
Getting the Most Out of EPM: A deep dive into Account Reconciliation Managerfinitsolutions
Since its introduction in version 11.1.2.2 Oracle Hyperion Account Reconciliation Manager has become one of the fastest growing products in the EPM suite. Join us to hear about how clients are realizing the benefits of real-time visibility into the account reconciliation process while ensuring consistency across all reconciliations. We will discuss the standard ARM functionality, some of the customizable features that can greatly enhance your account reconciliation process, and the features that are being planned for future releases.
Beginning Calculation Manager for Essbase and Hyperion Planning Alithya
This presentation will introduce the attendee to Calculation Manager. Calculation Manager is the new tool to create business rules and business rule sets to run against Hyperion Planning and Oracle Essbase. By attending the presentation, the attendee will learn about differences between Calculation Manager and Hyperion Business Rules, as well as see a live demo of the tool to develop and deploy business rules to a Hyperion Planning application.
The document provides steps to extract data from a Hyperion Essbase cube and load it into a relational database using Oracle Data Integrator (ODI). There are three methods for extracting data from Essbase - using a Calc script, Report script, or MDX query. The steps include creating a Calc script using the DATAEXPORT function to extract data to a text file, configuring the Essbase connection in ODI's topology, reversing the Essbase cube, establishing the target database connection, creating an ODI interface using the LKM Hyperion Essbase DATA to SQL knowledge module, and running the interface to load the extracted Essbase data into the relational database tables.
This document provides steps to extract metadata and build hierarchies from Hyperion Planning using Oracle Data Integrator (ODI). It describes how to connect ODI to Hyperion Planning, reverse engineer the Planning metadata into ODI, and load dimension data from flat files into Planning dimensions using ODI interfaces. The document is intended to guide users through setting up the required ODI objects, mappings, and executions to integrate data between flat files and Hyperion Planning dimensions.
The document discusses new features and enhancements in Oracle Business Intelligence 12c, including:
- Improved front-end usability features like an updated home page, enhanced sorting, and view properties access in compound layouts.
- New data visualization options like Oracle Data Visualization for ad-hoc analysis and mashups of spreadsheets with existing subject areas.
- Changes to installation, configuration, and architecture like a simplified installation process and separation of environment metadata from configuration.
- Upgrades are supported from 11g using a new Baseline Validation Tool to test and compare systems before and after upgrades.
18. Madhur Hemnani - Result Orientated Innovation with Oracle HR AnalyticsCedar Consulting
The document discusses Oracle's analytics cloud strategy and Oracle Analytics Cloud (OAC) platform. It covers OAC's features such as self-service report creation, data visualization capabilities, and integration with other Oracle products. The document also summarizes how customers can migrate existing on-premise analytics solutions like OBIEE, BICS, and DVCS to OAC. Finally, it provides an overview of Oracle Analytic Cloud - Essbase for flexible analytic applications and management reporting in the cloud.
Essbase On-Prem to Oracle Analytics Cloud - How, When, and WhyDatavail
Kurt Mayer, an analytics consultant with 15 years of experience working with Oracle products like Essbase, discusses strategies for migrating Essbase implementations from on-premises to Oracle's new Essbase 19c Cloud offering. Key points include that Essbase 19c Cloud provides significant performance improvements over existing on-premises versions. While staying on-premises is still an option, the cloud offers advantages like reduced maintenance costs, access to new functionality, and the ability to leverage the scalability of the cloud. The presentation provides recommendations and a case study of successfully migrating a large insurance company's Essbase environment to the cloud.
Vasu Balla of Pythian presented on best practices for upgrading an Oracle E-Business Suite database to Oracle Database 12c. The typical upgrade process involves installing the 12c Oracle home, upgrading the database using DBUA or CLI, and completing post-upgrade steps. Key best practices include ensuring initialization parameters and patches are configured properly, using AWR to identify performance issues and bad execution plans post-upgrade, and preserving good plans using SQL plan baselines. Regular statistics gathering and tuning of database settings also helps optimize performance.
Con7091 sql tuning for expert db as-oow17_oct2_1507314871265001m0x4asifanw
This document provides an agenda and overview for a presentation on SQL tuning for expert DBAs. The 3-sentence summary is:
The presentation agenda includes discussing SQL tuning challenges and existing solutions, providing tips for a proactive approach and reactive approach to SQL tuning, and presenting a customer case study. The document outlines the program agenda and provides information on SQL performance issues, existing SQL tuning solutions, and introduces Oracle tools that can be used for proactive SQL tuning like the Automatic SQL Tuning Advisor and SQL Performance Analyzer.
This document discusses connecting Oracle Analytics Cloud (OAC) Essbase data to Microsoft Power BI. It provides an overview of Power BI and OAC, describes various methods for connecting the two including using a REST API and exporting data to Excel or CSV files, and demonstrates some visualization capabilities in Power BI including trends over time. Key lessons learned are that data can be accessed across tools through various connections, analytics concepts are often similar between tools, and while partnerships exist between Microsoft and Oracle, integration between specific products like Power BI and OAC is still limited.
Is OLAP Dead?: Can Next Gen Tools Take Over?Senturus
Explores pros and cons of current OLAP technologies, new generation visualization tools, in-memory databases and OLAP for big data. We also discuss real-life client scenarios for a pragmatic perspective. View the video recording and download this deck at: http://www.senturus.com/resources/is-olap-dead/
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Take full benefit of Oracle Analytics on 12c DB offering In-Memory and new Data Visualization tools. Run this Analytics platform on the Oracle Database Appliance to allow for small size data warehouse applications with maximized security, high availability and performance.
The latest versions of OBIEE have been released for on-premise implementation, through SaaS via Oracle BI Cloud Service, and on the desktop with Data Visualization. This session gives OBIEE Architects and Developers exposure and direction on where to best spend their time on investigating new features and enhancements with the newest releases, and how they may apply those to their real-world business use cases. Participants will get a heads-up on upgrades, migrations, regression testing, new features, and lifecycle management. At the end of this session, attendees will have a fresh set of insights on new features for OBIEE developers that they can immediately take advantage of through new releases of OBIEE.
SAP #BOBJ #BI 4.1 Upgrade Webcast Series 3: BI 4.1 Sizing and VirtualizationSAP Analytics
http://spr.ly/BI41_Migration_Webinars - Learn how to develop a good strategy for sizing your SAP BusinessObjects BI 4.1 deployment. Understand why core architectural differences between former BOE XI releases and BI 4.1 mandate new sizing considerations. Find out how to test and tune your BI system before releasing it to your user base. Also, learn about virtualization support and guidelines when deploying to virtual and cloud environments.
• Understand how and where virtualization works well
• Learn how to avoid difficult situations when managing virtualized resources
• Develop a strategy that allows for growth, and talk the same language as your administrators
For more on upgrading to SAP BusinessObjects BI 4.1, visit http://www.sapbusinessobjectsbi.com
Back to the Future - Oracle Essbase - Then and Now Joseph Alaimo Jr
Join Edgewater Ranzal for a whimsical look at the history of Oracle Essbase. Take a walk down memory lane to the days of Arbor Software, where all that was required was a “server under the desk”, and tools such as Application Manager and the Excel Add-In. We will show historical artifacts, and share examples of “alternative” applications from days gone by, in addition to the traditional triad of management reporting, planning and profitability analysis that so many of our customers leverage today. Come for a breakfast treat, take home a few souvenirs, and reminisce with like-minded peers regarding the amazing Essbase!
- Sobre interRel is a leading provider of Oracle EPM and BI consulting, education, and support services.
- It has won awards including Oracle Solution EPM & BI of the Year and has authored over 10 bestselling books on Hyperion and Essbase.
- Founded in 1997, it has the most experience with Oracle EPM/BI solutions worldwide.
What’s New in Amazon RDS for Open-Source and Commercial Databases: Amazon Web Services
This document summarizes Amazon RDS features and roadmap items. It discusses how RDS provides a fully managed database service, supporting multiple open source and commercial database engines. Key features highlighted include high availability, automated backups, cross-region read replicas, encryption, and integration with other AWS services. Upcoming improvements discussed are RDS Performance Insights, larger storage volumes, new database versions, and expanded compliance capabilities. The presentation concludes with an invitation for questions.
During this webinar, we will review best practices and lessons learned from working with large and mid-size companies on their deployment of PostgreSQL. We will explore the practices that helped industry leaders move through these stages quickly, and get as much value out of PostgreSQL as possible without incurring undue risk.
The document discusses Amazon Redshift, a fully managed data warehousing service. It describes how Redshift allows users to analyze vast amounts of data stored in Amazon S3 in a fast, simple, and cost-effective way using SQL. Redshift offers benefits like massively parallel processing, automatic backups and restore, security, and integration with other AWS services and third-party tools.
The document describes Toad for Sybase, a software tool that helps simplify Sybase database development, administration, and performance monitoring. It provides automated tools to manage maintenance tasks, diagnose and resolve performance issues, and mitigate the risks of changes. The document outlines the various editions of Toad for Sybase that are available, which include additional features like SQL optimization, database administration, benchmarking, and data modeling. It also lists the system requirements and supported databases and platforms.
Las nuevas arquitecturas, servicios y micro-servicios web, aplicaciones y apps, Bots, IoT, AI, etc., que demandan las organizaciones, necesitan cada vez más del talento y experiencia de los Administradores de Bases de Datos para dar consejos, sugerencias y respuestas que aporten un valor diferencial a los grupos de desarrollo y usuarios de negocio.
Te mostramos las claves del nuevo rol del DBA, que complementa la “A” de Administrar con: Analizar, Aconsejar, Automatizar y crear Arquitecturas eficientes y Autónomas para la gestión Avanzada de datos, colaborando con los desarrolladores y usuarios desde un conocimiento profundo de las base de datos.
Recent advances in Postgres have propelled the database forward to meet today’s data challenges. At some of the world’s largest companies, Postgres plays a major role in controlling costs and reducing dependence on traditional providers.
This presentation addresses:
* What workloads are best suited for introducing Postgres into your environment
* The success milestones for evaluating the ‘when and how’ of expanding Postgres deployments
* Key advances in recent Postgres releases that support new data types and evolving data challenges
This presentation is intended for strategic IT and Business Decision-Makers involved in data infrastructure decisions and cost-savings.
Slides presented at Great Indian Developer Summit 2016 at the session MySQL: What's new on April 29 2016.
Contains information about the new MySQL Document Store released in April 2016.
MySQL in Oracle environment : Quick start guide for Oracle DBA (Part 1)OracleMySQL
You are an IT manager or Oracle DBA, comfortable and successful with your knowledge of how to keep an Oracle database up and running. One day, you find out you’ll now be supporting a popular MySQL database application. No one in your team has MySQL expertise and you have no budget to hire.
This slides covers the different use cases for MySQL and Oracle Database, as well as the tools to manage both databases. Additionally, the presentation spotlights top MySQL solutions for high availability, disaster recovery, and high-level security to protect your databases and business. You’ll also see the advantages of managing a MySQL database side by side with an Oracle database in the Oracle Public Cloud with the push-button ease of the MySQL Cloud Service.
IlOUG Tech Days 2016 - Big Data for Oracle Developers - Towards Spark, Real-T...Mark Rittman
Mark Rittman, CTO of Rittman Mead, gave a keynote presentation on big data for Oracle developers and DBAs with a focus on Apache Spark, real-time analytics, and predictive analytics. He discussed how Hadoop can provide flexible, cheap storage for logs, feeds, and social data. He also explained several Hadoop processing frameworks like Apache Spark, Apache Tez, Cloudera Impala, and Apache Drill that provide faster alternatives to traditional MapReduce processing.
IlOUG Tech Days 2016 - Unlock the Value in your Data Reservoir using Oracle B...Mark Rittman
Mark Rittman from Rittman Mead presented on Oracle Big Data Discovery. He discussed how many organizations are running big data initiatives involving loading large amounts of raw data into data lakes for analysis. Oracle Big Data Discovery provides a visual interface for exploring, analyzing, and transforming this raw data. It allows users to understand relationships in the data, perform enrichments, and prepare the data for use in tools like Oracle Business Intelligence.
Gluent New World #02 - SQL-on-Hadoop : A bit of History, Current State-of-the...Mark Rittman
Hadoop and NoSQL platforms initially focused on Java developers and slow but massively-scalable MapReduce jobs as an alternative to high-end but limited-scale analytics RDBMS engines. Apache Hive opened-up Hadoop to non-programmers by adding a SQL query engine and relational-style metadata layered over raw HDFS storage, and since then open-source initiatives such as Hive Stinger, Cloudera Impala and Apache Drill along with proprietary solutions from closed-source vendors have extended SQL-on-Hadoop’s capabilities into areas such as low-latency ad-hoc queries, ACID-compliant transactions and schema-less data discovery – at massive scale and with compelling economics.
In this session we’ll focus on technical foundations around SQL-on-Hadoop, first reviewing the basic platform Apache Hive provides and then looking in more detail at how ad-hoc querying, ACID-compliant transactions and data discovery engines work along with more specialised underlying storage that each now work best with – and we’ll take a look to the future to see how SQL querying, data integration and analytics are likely to come together in the next five years to make Hadoop the default platform running mixed old-world/new-world analytics workloads.
Adding a Data Reservoir to your Oracle Data Warehouse for Customer 360-Degree...Mark Rittman
This document summarizes a presentation about adding a Hadoop-based data reservoir to an Oracle data warehouse. The presentation discusses using a data reservoir to store large amounts of raw customer data from various sources to enable 360-degree customer analysis. It describes loading and integrating the data reservoir with the data warehouse using Oracle tools and how organizations can use it for more personalized customer marketing through advanced analytics and machine learning.
Delivering the Data Factory, Data Reservoir and a Scalable Oracle Big Data Ar...Mark Rittman
Presentation from the Rittman Mead BI Forum 2015 masterclass, pt.2 of a two-part session that also covered creating the Discovery Lab. Goes through setting up Flume log + twitter feeds into CDH5 Hadoop using ODI12c Advanced Big Data Option, then looks at the use of OBIEE11g with Hive, Impala and Big Data SQL before finally using Oracle Big Data Discovery for faceted search and data mashup on-top of Hadoop
End to-end hadoop development using OBIEE, ODI, Oracle Big Data SQL and Oracl...Mark Rittman
This document discusses an end-to-end example of using Hadoop, OBIEE, ODI and Oracle Big Data Discovery to analyze big data from various sources. It describes ingesting website log data and Twitter data into a Hadoop cluster, processing and transforming the data using tools like Hive and Spark, and using the results for reporting in OBIEE and data discovery in Oracle Big Data Discovery. ODI is used to automate the data integration process.
OBIEE11g Seminar by Mark Rittman for OU Expert Summit, Dubai 2015Mark Rittman
Slides from a two-day OBIEE11g seminar in Dubai, February 2015, at the Oracle University Expert Summit. Covers the following topics:
1. OBIEE 11g Overview & New Features
2. Adding Exalytics and In-Memory Analytics to OBIEE 11g
3. Source Control and Concurrent Development for OBIEE
4. No Silver Bullets - OBIEE 11g Performance in the Real World
5. Oracle BI Cloud Service Overview, Tips and Techniques
6. Moving to Oracle BI Applications 11g + ODI
7. Oracle Essbase and Oracle BI EE 11g Integration Tips and Techniques
8. OBIEE 11g and Predictive Analytics, Hadoop & Big Data
BIWA2015 - Bringing Oracle Big Data SQL to OBIEE and ODIMark Rittman
The document discusses Oracle's Big Data SQL, which brings Oracle SQL capabilities to Hadoop data stored in Hive tables. It allows querying Hive data using standard SQL from Oracle Database and viewing Hive metadata in Oracle data dictionary tables. Big Data SQL leverages the Hive metastore and uses direct reads and SmartScan to optimize queries against HDFS and Hive data. This provides a unified SQL interface and optimized query processing for both Oracle and Hadoop data.
UKOUG Tech'14 Super Sunday : Deep-Dive into Big Data ETL with ODI12cMark Rittman
This document discusses using Hadoop and Hive for ETL work. It provides an overview of using Hadoop for distributed processing and storage of large datasets. It describes how Hive provides a SQL interface for querying data stored in Hadoop and how various Apache tools can be used to load, transform and store data in Hadoop. Examples of using Hive to view table metadata and run queries are also presented.
Part 1 - Introduction to Hadoop and Big Data Technologies for Oracle BI & DW ...Mark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014
In this presentation we cover some key Hadoop concepts including HDFS, MapReduce, Hive and NoSQL/HBase, with the focus on Oracle Big Data Appliance and Cloudera Distribution including Hadoop. We explain how data is stored on a Hadoop system and the high-level ways it is accessed and analysed, and outline Oracle’s products in this area including the Big Data Connectors, Oracle Big Data SQL, and Oracle Business Intelligence (OBI) and Oracle Data Integrator (ODI).
Part 4 - Hadoop Data Output and Reporting using OBIEE11gMark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014.
Once insights and analysis have been produced within your Hadoop cluster by analysts and technical staff, it’s usually the case that you want to share the output with a wider audience in the organisation. Oracle Business Intelligence has connectivity to Hadoop through Apache Hive compatibility, and other Oracle tools such as Oracle Big Data Discovery and Big Data SQL can be used to visualise and publish Hadoop data. In this final session we’ll look at what’s involved in connecting these tools to your Hadoop environment, and also consider where data is optimally located when large amounts of Hadoop data need to be analysed alongside more traditional data warehouse datasets
Part 2 - Hadoop Data Loading using Hadoop Tools and ODI12cMark Rittman
Delivered as a one-day seminar at the SIOUG and HROUG Oracle User Group Conferences, October 2014.
There are many ways to ingest (load) data into a Hadoop cluster, from file copying using the Hadoop Filesystem (FS) shell through to real-time streaming using technologies such as Flume and Hadoop streaming. In this session we’ll take a high-level look at the data ingestion options for Hadoop, and then show how Oracle Data Integrator and Oracle GoldenGate leverage these technologies to load and process data within your Hadoop cluster. We’ll also consider the updated Oracle Information Management Reference Architecture and look at the best places to land and process your enterprise data, using Hadoop’s schema-on-read approach to hold low-value, low-density raw data, and then use the concept of a “data factory” to load and process your data into more traditional Oracle relational storage, where we hold high-density, high-value data.
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/
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataSamuel Jackson
We present our work to improve data accessibility and performance for data-intensive tasks within the fusion research community. Our primary goal is to develop services that facilitate efficient access for data-intensive applications while ensuring compliance with FAIR principles [1], as well as adoption of interoperable tools, methods and standards.
The major outcome of our work is the successful creation and deployment of a data service for the MAST (Mega Ampere Spherical Tokamak) experiment [2], leading to substantial enhancements in data discoverability, accessibility, and overall data retrieval performance, particularly in scenarios involving large-scale data access. Our work follows the principles of Analysis-Ready, Cloud Optimised (ARCO) data [3] by using cloud optimised data formats for fusion data.
Our system consists of a query-able metadata catalogue, complemented with an object storage system for publicly serving data from the MAST experiment. We will show how our solution integrates with the Pandata stack [4] to enable data analysis and processing at scales that would have previously been intractable, paving the way for data-intensive workflows running routinely with minimal pre-processing on the part of the researcher. By using a cloud-optimised file format such as zarr [5] we can enable interactive data analysis and visualisation while avoiding large data transfers. Our solution integrates with common python data analysis libraries for large, complex scientific data such as xarray [6] for complex data structures and dask [7] for parallel computation and lazily working with larger that memory datasets.
The incorporation of these technologies is vital for advancing simulation, design, and enabling emerging technologies like machine learning and foundation models, all of which rely on efficient access to extensive repositories of high-quality data. Relying on the FAIR guiding principles for data stewardship not only enhances data findability, accessibility, and reusability, but also fosters international cooperation on the interoperability of data and tools, driving fusion research into new realms and ensuring its relevance in an era characterised by advanced technologies in data science.
[1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016) https://doi.org/10.1038/sdata.2016.18
[2] M Cox, The Mega Amp Spherical Tokamak, Fusion Engineering and Design, Volume 46, Issues 2–4, 1999, Pages 397-404, ISSN 0920-3796, https://doi.org/10.1016/S0920-3796(99)00031-9
[3] Stern, Charles, et al. "Pangeo forge: crowdsourcing analysis-ready, cloud optimized data production." Frontiers in Climate 3 (2022): 782909.
[4] Bednar, James A., and Martin Durant. "The Pandata Scalable Open-Source Analysis Stack." (2023).
[5] Alistair Miles (2024) ‘zarr-developers/zarr-python: v2.17.1’. Zenodo. doi: 10.5281/zenodo.10790679
[6] Hoyer, S. & Hamman, J., (20
Combined supervised and unsupervised neural networks for pulse shape discrimi...Samuel Jackson
Our methodology for pulse shape discrimination is split into two steps. Firstly, we learn a model to discriminate between pulses using "clean" low-rate examples by removing pile-up & saturated events. In addition to traditional tail sum discrimination, we investigate three different choices for discrimination between γ-pulses, fast, thermal neutrons. We consider clustering the pulses directly using Gaussian Mixture Modelling (GMM), using variational autoencoders to learn a representation of the pulses and then clustering the learned representation (VAE+GMM) and using density ratio estimation to discriminate between a mixed (γ + neutron) and pure (γ only) sources using a multi-layer perceptron (MLP) as a supervised learning problem.
Secondly, we aim to classify and recover pile-up events in the < 150 ns regime by training a single unified multi-label MLP. To frame the problem as a multi-label supervised learning method, we first simulate pile-up events with known components. Then, using the simulated data and combining it with single event data, we train a final multi-label MLP to output a binary code indicating both how many and which type of events are present within an event window.
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B...rightmanforbloodline
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
Solution Manual for First Course in Abstract Algebra A, 8th Edition by John B. Fraleigh, Verified Chapters 1 - 56,.pdf
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.
2. info@rittmanmead.com www.rittmanmead.com @rittmanmead 2
•Oracle BI and DW Gold partner
•Winner of five UKOUG Partner of the Year awards in 2013 - including BI
•World leading specialist partner for technical excellence,
solutions delivery and innovation in Oracle BI
•Approximately 80 consultants worldwide
•Offices in US (Atlanta), Europe
•Skills in broad range of supporting Oracle tools:
‣Essbase, Oracle OLAP
‣GoldenGate
‣Endeca
‣OBIEE, OBIA, ODIEE
‣Big Data, Hadoop, NoSQL & Big Data Discovery
About Rittman Mead
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•Latest release of Oracle Business Intelligence Enterprise Edition
‣Visual Analyzer - key new end-user feature
‣Data mashups - allow users to add data to their own subject area
‣Updated look and feel for Answers, Dashboards etc
‣Layered RPD customizations
•Simpler configuration + cloning etc
•Installer includes option, as per 11g,
to install Essbase alongside OBIEE
‣So what does Essbase look like in 12c?
OBIEE 12.2.1 - Initial Release of OBIEE 12c
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•Essbase has been supported a datasource for OBIEE since OBIEE10g
‣BI Server translates Logical SQL queries against RPD into MDX
‣Specific MDX function calls can be passed through (EVALUATE etc)
‣Incremental improvements over years to better support Essbase specifics)
•Hyperion Planning & HFM also supported as sources
•SmartView ships with OBIEE with
dual Essbase/OBIEE connectivity
Essbase and OBIEE Integration Since OBIEE 10g
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•Use OBIEE as the Management Reporting platform for both EPM and Essbase
•Essbase and Hyperion Planning as datasources
‣HFM via EAL
•Replace EPM reporting/dashboard tools
‣WebAnalysis
‣Visual Explorer
•Tighter integrations with Financial Reports
•Integrate BI into EPM Workspace
•Extend SmartView to OBIEE
Integration Between The Product Planned from Day 1
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•Access data in Essbase cube to add as measures / attributes in report
•Combine (federate) with other datasets (relational, Hadoop etc)
•Include planning data with actuals in BI dashboard
•Make use of Essbase calculations and forecasts for BI data
Oracle Essbase as a Data Source for OBIEE
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•OBIEE from version 10g connects to Essbase Server as a data source
‣OBIEE11g min Essbase release 11.1.1.3
‣OBIEE12c min Essbase release 11.1.2.+
•Optional pass-through of Essbase username/password
‣Leverage Essbase data filters and meta filters for row-level security
•Separate OBIEE and Essbase security models
‣SSO possible but one-way only
‣Uses CSS Token generated by BI Server
•Minimum integration but max flexibility
‣Standard approach for most use-cases
Connecting OBIEE (BI Server) to an Essbase Server
Oracle BI Server
Component
Physical
SQL, MDX
BI Presentation Server
Component
Oracle BI Repository
Logical Business
Model
Read model
Logical SQL Results
HTTP(S)
User requests
from dashboard,
ad-hoc queries etc
Results
Standalone
Essbase Server
Shared Services
for User / Roles
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•As well as being sold separately and as part of Oracle EPM Suite, it can also be licensed as
part of a complete Oracle BI Foundation platform
‣Sold as a tightly-integrated OLAP server well-suited to sales reporting-type scenarios
•Co-installed with OBIEE and Oracle RTD
•Omits features such as Shared Services
and applications such as Planning, to
focus solely on BI-type scenarios
•Most of as assumed this was just
packaging and cross-selling from Oracle,
Essbase would still just be Essbase…
Essbase Server Packaged with OBIEE - BI Foundation
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•Essbase in this scenario is all about BI - making OBIEE faster and more fully-featured
•Adds an enterprise-class OLAP server to the stack, deeply integrated
‣Goes one-better than Tableau, Qlikview etc - ASO in-mem OLAP vs. simple query cache
•All integration work in this context is about making BI run better,
not replacing Essbase-specific tools or removing EPM Suite
•And the primary use case is query acceleration
‣Faster Analysis and Calculations
‣Business Agility
‣Immediate Visibility
‣Adapt to changes
‣Accurate models
Essbase As Query Accelerator for OBIEE
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•Runs the BI layer on a high-performance, multi-core, 1-2TB server
•In-memory cache used to accelerate the BI part of the stack
•OBIEE Summary Advisor identifies aggregates based on
previous queries, creates in TT or DB12c In-Mem
•Uses Oracle TimesTen for Exalytics, or Oracle 12c DB In-Mem Option
‣Consistent response times for queries
‣In-memory caching of aggregates
‣40 cores for high concurrency
‣Re-engineered BI and OLAP software
that assumes 40 cores and 1TB RAM
•Works well, and can be great solution for IT-led projects
But … Isn’t This What Exalytics + TimesTen Was For?
ERP/Apps DW
Oracle BI
In-Memory DB/Cache
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•Selectively creates aggregations that would have made historic queries run faster
‣Approach driven by limits on TimesTen memory on An Exalytics server
•Does not pre-compute all aggregations, and needs refining and updating over time
•Coupled with TimesTen immaturity, in hindsight not a great solution
Limitations with the Summary Advisor / TT Approach
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•Use Essbase ASO engine to provide the aggregation / calculation features for OBIEE
•Potential to aggregate and load the entire RPD dataset into ASO - very space-efficient
•No need to continuously identify aggregate candidates - aggregate everything
•Fast ASO aggregation times compared to TT - quicker to refresh
•Sounds great - why not try this?
Another Option : Essbase As Query Accelerator
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•OBIEE 11.1.1.6.2 BP1 introduced Aggregate Persistence into Essbase
‣OBIEE’s Administration tool had the
ability to define aggregates, and then
persist them in an RDBMS
‣BI Server then uses these aggregates
to speed up dashboard queries
•This OBIEE bundle patch enabled
storage of these aggregates in an
Essbase ASO database, using
headless Essbase Studio
OBIEE 11.1.1.6.2 BP1 : Aggregate Persistence in Essbase
Oracle BI Server
Component
Physical
SQL, MDX
Physical
MDX
BI Presentation Server
Component
Oracle BI Repository
Logical Business
Model
Read model
Logical SQL Results
HTTP(S)
User requests
from dashboard,
ad-hoc queries etc
Pre-computed aggregated
data, stored in an Essbase
ASO database
Detail-level, and dynamically-
calculated
aggregate data
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1. Developer/Administrator selects slice of business model to aggregate
2. Aggregate Persistence Wizard then creates a logical SQL script
3. Script is processed by BI Server using nqcmd and the BI Server ODBC client
4. BI Server uses Essbase Studio dmaservlet to create ASO outline, and rules file
5. Detail-level data sourced via BI Server logical model through to source databases
How Did Aggregate Persistence in OBIEE11g Work?
Oracle BI Repository
Logical Business
Model
Oracle BI Server
Component
nqcmd script
containing logical
DDL and DML for
creating and populating
aggregate tables
Logical SQL
via BI Server
ODBC interface
Physical
SQL, MDX
Create outline
Read model Add aggregates
Essbase Studio dmaservlet
XML request SQL queries
Data load via
Essbase rules file
Essbase Server
Developer uses
BI Administrator
tool to select
aggregates for
creation, with an
output of a logical
SQL script for DDL
and DML
1
2
3
4
5
16. info@rittmanmead.com www.rittmanmead.com @rittmanmead
•OBIEE 11.1.1.6, and the 11.1.1.6.2 BP1 bundle patch, included Essbase + administration
tools as part of the install bundle, but these are not by default enabled
•Needs to be patched to 11.1.1.6.2 BP1, then installed using options in a reponse file
•Configures Essbase as part of the OBIEE domain, with EAS and Studio also available
•Narrow use-case : only for Aggregate Persistence, cannot scale-up, repurpose etc
Combined Install of OBIEE and Essbase with 11.1.1.6.2 BP1
[DATAMART_AUTOMATION]
ESSBASE_STUDIO_URL = "http://localhost:9080/dma/dmaservlet";
ESSBASE_SERVER = "localhost";
DMA_DATABASE = "DMA_DB";
18. info@rittmanmead.com www.rittmanmead.com @rittmanmead 18
•From OBIEE 11.1.1.7, Essbase was now an install option
within the OBIEE product installer
‣Has to be licensed separately, or as part of BI Foundation
•Installs Essbase Server, Essbase Studio,
Financial Reporting and other BI-related/complementary
tools alongside OBIEE
•Management of Essbase Server, Security, start/stop etc
all from single Enterprise Manager farm
OBIEE 11.1.1.7 : Integration of Essbase into OBIEE Tech Stack
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•Essbase can be stopped, started, restarted from EM via OPMN
•Essbase metrics reported on from EM
•Integrated security tools via OPSS
application roles and policies
Essbase within Oracle BI Domain - EM Management
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•EPM Workspace integration returns with OBIEE 11.1.1.7!
•SSO via shared OPSS (FMW) security
•Launch BI content from within Workspace
•Store FR reports in Catalog (must launch from
EPM Workspace though,
not from OBIEE dashboard)
The Return of EPM Workspace Integration
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•From OBIEE 11.1.1.7, SmartView can now be used with OBIEE for Office integration
•Ability to create new reports as well as analyze Catalog content via Excel, Word etc
•Replaces BI Office (and original SmartView from OBIEE 10g)
SmartView Compatibility across Essbase and OBIEE
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•Also introduced new Logical SQL features for
Essbase cube spin-off from RPD
‣Command-line only in this release
‣Created an Essbase cube sourced from RPD
logical SQL query
‣Wired Essbase cube back into RPD
‣But didn’t deal with any of the Essbase
member naming incompatibilities
OBIEE 11.1.1.7 - Introduction of (Beta) Cube Spin-off
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23
•Technical Enhancements driven by product direction
Essbase Product Drivers 2013-2015
• Calc language enhancements
• Facilitate goal seeking, tgt setting
• Incremental financial logic
• Data in varying time granularities
• Financial aggregation logic
• Transparent introduction into the
semantic model
• Query performance
• MDX enhancements
• BI query acceleration
• Rapid scenario modeling
• Smaller footprint
• Reduce downtime
• Self-tuning
• Auto-recovery
BI CloudEPM
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•Essbase12c release out soon, will be delivered initially as part of Essbase Cloud Service
‣Part of Oracle PaaS, alongside DBaaS, BICS, PBCS etc
‣New Essbase database web based administration tool
‣Quick database creation by uploading an Excel spreadsheet
‣Scenario management - Lightweight workflow
•Sandboxing - Create a personal slice of the database
•New Java based architecture optimised for Oracle Cloud
‣Designed to support higher concurrency
‣Elimination of SEC file
‣Managed within Weblogic console
‣Part of either EPM or BI Domain
Essbase 12c - Re-Engineered for the Cloud
CDS
Scenario
Management
Thin Client
Editor
Catalog
Java
Agent
Dynamic
Filters
DBX
Drill
Through
Scripting: R,
Groovy,
JACL
Data
Source Grid UI
APS
Java API, REST and Web
Services
Unified Engine
(ASO/BSO)
Background Write
In-Place Write
…
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•Combines the flexibility of BSO models with ASO performance
•BSO traditionally used for r/w planning-type applications, typically now in-memory
•ASO used for rack-and-stack BI-type applications, level 0-writes only
•Hybrid takes ASO engine and layers ASO aggs over BSO level-0 blocks
•100% backward compatible with existing BSO databases
•Translates BSO calc scripts to MDX functions
•Can run in-memory
•Part of a move towards a single Essbase engine (over time…)
BSO/ASO Hybrid Aggregation Mode
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•A selected member which will collect the data from rejected records of the dimension
•Ensures that totals presented by Essbase after data load match source system totals
‣Previously, Essbase totals would reflect just the members that loaded OK
‣Renegate member feature ensures all values are loaded, if only into new “others” bucket
New In Essbase 12c - Renegade Members
Prod (G3,L0)
P1
P2
Geog (G3,L0)
G1
G2
Prod Geog Measure
P1 G1 1
P2 G1 3
P1 G2 5
P2 G3 7
P2 G4 2
P3 G4 1
P3 G2 2
Query RDBMS Essbase Essbase
with RM
Select Prod, SUM(Data)
From Fact, ProdDim
Where Fact.Prod = ProdDim.Prod
Group by Prod
P1, 6
P2, 12
P1, 6
P2, 3
P1, 6
P2, 12
Select Geog, SUM(Data)
From Fact, GeogDim
Where Fact.Geog = GeogDim.Geog
Group by Geog
G1, 4
G2, 7
G1, 4
G2, 5
G1, 4
G2, 7
Geog/Prod P1 P2 _ProdRenegade
G1 1 3 #Missing
G2 5 #Missing 2
_GeogRenegade #Missing 9 1
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•Remove most of the naming restrictions for Essbase
‣Object name length
‣Reserved words
‣Restrictions on characters
•Initial support will be restricted to JAPI and data
loaded from SQL sources
•Significant change in behavior, currently aimed at
BI Query Acceleration use-cases
New in Essbase 12c - Relaxed Naming Restrictions
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•Early access to Essbase 12c, running co-located alongside OBIEE12c in FMW12c
‣Essbase 12c Java agent running in WLS Managed Server
•Aimed solely at BI query acceleration - for other use-cases use standalone Essbase 11g
•Hybrid Agg Mode for faster dynamic calculations
•Improvements to BSO performance
•Ease of integration with 3rd party tools
(Cube Deployment Services)
•Up coming 12c enhancements
‣BI oriented outlines
‣BI specific features
Further Integration of Essbase into OBIEE12c Stack
11
Database: RCU Schemas
Admin Server Managed Server
Node Manager
BI System Components
BI Server BI Scheduler
BI Presentation Server
BI Java Host
Cluster Controller
Oracle Platform
Security Services
End Point Registry
WLSTDMSODL
Identity Store
Essbase Studio
MetadataConfiguration
Service Instance
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•Component that is deployed with Essbase 12c
•Essbase BI acceleration wizard uses its API to create Essbase cubes
‣BI user creates Essbase cube based on BI business model metadata
‣CDS provides integration between the BI repository and Essbase Server
-Creates the Essbase cube definition in XML metadata format.
-Validates the cube definition
-Generates a rules file for each dimension
-Creates the cube outline
-Loads metadata and data
-Wires back metadata mapping information to the BI repository
Cube Deployment Services
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•UI for creating spin-off Essbase12c cubes from RPD business model
•Accessed via http://machine_name:port/cds/view
‣Same port number as /analytics
•Define cube, select dimensions and levels
•Some limited scope for customising cube
•Load, rebuild, drop cubes
•Cubes built using wizard can only be used as
aggregate LTS sources in RPD, not general use
BI Acceleration Wizard
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•Select from BI Business Models that Essbase Server is co-located with
•Either type in a name for the new Essbase application, or select existing one to reload
BI Accelerator Wizard Step 2 : Select BM and Target App
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•Select measures to include in Essbase cube
‣SUM, COUNT, and COUNT DISTINCT
aggregation rules are supported
‣Derived measures are not supported,
i.e. based on logical formula in RPD
BI Accelerator Wizard Step 3 : Select Measures
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•Select levels to include in aggregation
‣Include all, or just subset for skip-level aggregation
‣Just include top-most levels
to accelerate totals
BI Accelerator Wizard Step 5 : Select Levels
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•Review choices made in cube design
•Some limited opportunity to customise - add more logical columns
•See underlying storage settings
•Deploy cube, deploy in background
BI Accelerator Wizard Step 6 : Review and Deploy
BI Accelerator Wizard Step 4 : Select Dimensions
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•As with all OBIEE aggregate persistence utilities, aggregates then wired back into RPD
•BI Server will then use Essbase source for query acceleration as appropriate
•Reload or redeploy from BI Acceleration Wizard
BI Accelerator Wizard Step 7 : Review Deployment
1
2
3
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•MaxL can still be used to create and populate Essbase 12c in this scenario
‣MaxL Shell included with OBIEE12c install
•Supported approach, will work
•But BI Acceleration Wizard is how
you’re supposed to build cubes for now
But … Can We Still Build Essbase Cubes Manually?
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•A separate install of Essbase Administration Services can be connected to this Essbase 12c
•Allows you (in an unsupported, workaround way) to use EAS with co-located Essbase 12c
But … Can We Still Build Essbase Cubes Manually?
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•Yes - and this is the recommended approach for showing Essbase data in OBIEE12c
‣Query accelerator is “behind the scenes” and invisible to end users
•Minimum supported version of Essbase is 11.1.2+
… And Can We Still Access Standalone Essbase?
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•Latest version available as an on-premise, standalone install
‣As shipping with Exalytics, EPM Suite etc
•Recent enhancements to core Essbase server include
‣In-Place write reduces database fragmentation
‣Continue to enrich the calc language
‣Background write deliver high performance for NFS with Exalytics
‣Anonymous data export
‣Controlled shutdown, eliminates most DB corruption issues
‣Smartview Calc Script launcher with context-aware substitution variables
‣Continues to be invested in and available as standalone OBIEE datasource
‣Minimum version supported by OBIEE12c is 11.1.2.x
Core Essbase Server 11.1.2.4 Recent New Features
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•Does not handle more complex, and larger, RPDs - use with caution
•Limited ability to customise storage settings for Essbase cube
•Not a solution for Essbase reporting or custom cubes - it’s all about query acceleration
•Treat as experimental and early access - it’s “supported” by
•In general, we recommend standalone Essbase 11g (11.1.2.+) for general Essbase reporting
•But … a taste of the future
Limitations in Initial Release