Oracle Essbase is a leading online analytical processing (OLAP) server that supports forecasting, scenario planning, and interactive analysis of large datasets for thousands of users. It integrates with multiple data sources and provides reporting and visualization options. Oracle Essbase is designed for scalability, security, and rapid response to enable business users to gain insights from data.
Oracle Essbase is a leading online analytical processing (OLAP) server that supports forecasting, variance analysis, scenario planning, and other advanced analytics functions. It integrates with multiple data sources and delivers information through a variety of reporting options. Oracle Essbase is designed to scale for large user communities and data sets while providing rapid analysis, calculation times, and a rich user experience through intuitive interfaces.
Learn more about ER/Studio Data Architect and try it free at: http://embt.co/ERStudioDA
With round-trip database support, data architects using ER/Studio Data Architect have the power to easily reverse-engineer, compare and merge, and visually document data assets residing in diverse locations from data centers to mobile platforms. Enterprise data can be more effectively leveraged as a corporate asset, while compliance is supported for business standards and mandatory regulations -- essential factors in an organizational data governance program. A range of data sources are supported ranging from those residing on the cloud to data sources residing on mobile phones. A variety of database platforms, including traditional RDBMS and big data technologies such as MongoDB and Hadoop Hive, can be imported and integrated into shared models and metadata definitions.
This lesson covers creating a DQS knowledge base named "Suppliers" to be used for cleansing and matching supplier data. The following key tasks are covered:
1. Creating the Suppliers knowledge base and domains for fields to be cleansed and matched like "SupplierID".
2. Adding values to domains manually, by importing from Excel, or through knowledge discovery on sample data.
3. Setting domain rules to validate, correct, and standardize values.
4. Setting term relationships to standardize values like treating "Inc." as "Incorporated".
5. Specifying synonym values where one is the leading value used for cleansing.
6. Creating a composite "AddressValidation"
Business objects data services in an sap landscapePradeep Ketoli
The document discusses SAP BusinessObjects Data Services and its role in an SAP landscape. It provides an overview of SAP's enterprise information management solutions including data integration, data quality management, master data management and enterprise data warehousing. It then discusses how Data Services can be used for data integration, data quality, loading SAP BW, extracting from BW, and supporting business processes like data migration and master data management.
The document discusses Master Data Services (MDS) in SQL Server 2012. It provides an overview of what MDS is, common problems it addresses, and its architecture. MDS allows organizations to map, manage, cleanse and organize master data across multiple applications and versions. The presentation then describes MDS capabilities and architecture in more detail, showing how it integrates with SQL Server and other tools to provide consistent, organized master data to various users and systems.
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Hyperion Essbase is a multidimensional database management system (MDBMS) that provides a multidimensional database platform for building analytic applications. It is optimized for processing human queries rather than transactions. The typical lifecycle of an Essbase database involves creating the database, building dimensions, loading data, performing calculations, and generating reports. Essbase uses a multidimensional data model with dimensions, members, and hierarchies to allow for multidimensional viewing and analysis of data.
Oracle Essbase is a leading online analytical processing (OLAP) server that supports forecasting, variance analysis, scenario planning, and other advanced analytics functions. It integrates with multiple data sources and delivers information through a variety of reporting options. Oracle Essbase is designed to scale for large user communities and data sets while providing rapid analysis, calculation times, and a rich user experience through intuitive interfaces.
Learn more about ER/Studio Data Architect and try it free at: http://embt.co/ERStudioDA
With round-trip database support, data architects using ER/Studio Data Architect have the power to easily reverse-engineer, compare and merge, and visually document data assets residing in diverse locations from data centers to mobile platforms. Enterprise data can be more effectively leveraged as a corporate asset, while compliance is supported for business standards and mandatory regulations -- essential factors in an organizational data governance program. A range of data sources are supported ranging from those residing on the cloud to data sources residing on mobile phones. A variety of database platforms, including traditional RDBMS and big data technologies such as MongoDB and Hadoop Hive, can be imported and integrated into shared models and metadata definitions.
This lesson covers creating a DQS knowledge base named "Suppliers" to be used for cleansing and matching supplier data. The following key tasks are covered:
1. Creating the Suppliers knowledge base and domains for fields to be cleansed and matched like "SupplierID".
2. Adding values to domains manually, by importing from Excel, or through knowledge discovery on sample data.
3. Setting domain rules to validate, correct, and standardize values.
4. Setting term relationships to standardize values like treating "Inc." as "Incorporated".
5. Specifying synonym values where one is the leading value used for cleansing.
6. Creating a composite "AddressValidation"
Business objects data services in an sap landscapePradeep Ketoli
The document discusses SAP BusinessObjects Data Services and its role in an SAP landscape. It provides an overview of SAP's enterprise information management solutions including data integration, data quality management, master data management and enterprise data warehousing. It then discusses how Data Services can be used for data integration, data quality, loading SAP BW, extracting from BW, and supporting business processes like data migration and master data management.
The document discusses Master Data Services (MDS) in SQL Server 2012. It provides an overview of what MDS is, common problems it addresses, and its architecture. MDS allows organizations to map, manage, cleanse and organize master data across multiple applications and versions. The presentation then describes MDS capabilities and architecture in more detail, showing how it integrates with SQL Server and other tools to provide consistent, organized master data to various users and systems.
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Hyperion Essbase is a multidimensional database management system (MDBMS) that provides a multidimensional database platform for building analytic applications. It is optimized for processing human queries rather than transactions. The typical lifecycle of an Essbase database involves creating the database, building dimensions, loading data, performing calculations, and generating reports. Essbase uses a multidimensional data model with dimensions, members, and hierarchies to allow for multidimensional viewing and analysis of data.
Microsoft master data services mds overviewEugene Zozulya
Master data management (MDM) is a technology discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
Master data management tools can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data.
Microsoft Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions.
Other Master Data Services features include hierarchies, granular security, transactions, data versioning, and business rules.
Master Data Services includes the following components and tools:
- Master Data Services Configuration Manager, a tool you use to create and configure Master Data Services databases and web applications.
- Master Data Manager, a web application you use to perform administrative tasks (like creating a model or business rule), and that users access to update data.
- MDSModelDeploy.exe, a tool you use to create packages of your model objects and data so you can deploy them to other environments.
- Master Data Services web service, which developers can use to extend or develop custom solutions for Master Data Services.
DBArtisan XE6 is a database administration tool that helps DBAs manage databases across platforms more efficiently. It streamlines common tasks, reduces errors, and provides comprehensive capabilities for data management. As data volumes grow, the role of the DBA is evolving to handle multiple concurrent responsibilities. DBArtisan facilitates this role by providing performance monitoring, space and data management tools, and security management in a single interface.
Teradata Aster: Big Data Discovery Made Easy
Brad Elo, VP, Aster Data, Teradata
ANALYTICS AND VISUALIZATION FOR THE FINANCIAL ENTERPRISE CONFERENCE
June 25, 2013 The Langham Hotel Boston, MA
This session was about Master Data Services and what it also could be used as - the client wanted an application to validate and submit warehouse inventories.
SAP HANA is a game-changing, real-time platform for analytics and applications. While simplifying the IT stack, it provides powerful features like: significant processing speed, the ability to handle big data, predictive capabilities and text mining capabilities.
20100430 introduction to business objects data servicesJunhyun Song
This document provides an overview and agenda for a presentation on SAP BusinessObjects Data Services XI 3.0. It discusses how data integration and quality tools like Data Services can help address challenges around managing enterprise data by providing a single tool for data integration, quality management, and metadata management. The presentation agenda covers why effective information management is important, an introduction to Data Services, how metadata management impacts data lineage and trustworthiness, use cases for Data Services in SAP environments, and concludes with a wrap-up.
The document discusses the emergence of big data and new data architectures needed to handle large, diverse datasets. It notes that internet companies built their own data systems like Hadoop to process massive amounts of unstructured data across thousands of servers in a fault-tolerant, scalable way. These systems use a map-reduce programming model and distributed file systems like HDFS to store and process data in a parallel, distributed manner.
A whitepaper from qubole about the Tips on how to choose the best SQL Engine for your use case and data workloads
https://www.qubole.com/resources/white-papers/enabling-sql-access-to-data-lakes
This document discusses the benefits of using Spreadsheet Server to provide real-time integration of Excel spreadsheets with SAP data. Key benefits include time savings from reducing dependency on IT for reporting, quicker business decisions through real-time analytics, increased accuracy, and streamlined implementation. Spreadsheet Server allows users to leverage familiar Excel tools to access and report on SAP data in real-time, which improves productivity, decision making, and compliance.
Microsoft SQL Server 2012 Master Data ServicesMark Ginnebaugh
Mark Gschwind, VP of Business Intelligence at DesignMind, gave a presentation on Master Data Services (MDS) in SQL Server 2012. He began with an overview of master data and its importance for central curation, quality management, and ease of access for business users. He then reviewed the key capabilities of MDS, including modeling, validation, stewardship, and integration. Gschwind demonstrated creating an MDS model, using the new Excel interface, business rules, and exposing MDS data to a data warehouse. He concluded with tips for successful MDS implementations such as starting small, engaging business users, and using the development environment.
Predictive Analysis in Microsoft SQL Server 2012 provides powerful predictive analytics capabilities including multiple data mining models, algorithms for tasks like market basket analysis and churn prediction, and integration with Business Intelligence tools. It allows users to gain predictive insights, discover hidden relationships in data, and inform decisions. The predictive capabilities are enterprise-grade, with high performance, scalability, and security integrated across the Microsoft BI stack.
ETL stands for Extraction, Transformation, and Loading. The document describes an example ETL process to load master customer data from an Excel file into an SAP BI platform. First, the data is extracted from the Excel file into the BI data warehouse using a data source and info package. Next, the data in the persistent staging area is transformed by defining the customer ID and name fields as characteristic info objects. Finally, a data transfer process loads the mapped data from the source into the appropriate info objects, completing the ETL process.
The document discusses a unified data architecture that enables any user to access and analyze any data type from data capture through analysis. It describes using a discovery platform to enable interactive data discovery on structured and unstructured data without extensive modeling. It also describes using an integrated data warehouse for cross-functional analysis, shared analytics, and lowest total cost of ownership. Finally, it provides examples of using the architecture for IPTV quality of service analysis, including predictive models using decision trees and naive Bayes.
Enterprise database management systems like Oracle HRIS help organizations more efficiently manage large workforces by automating HR processes. Oracle HRIS captures employee data in a powerful database and uses that data for transaction processing, management reporting, decision support, business intelligence, and other functions. It allows authorized users to securely access and analyze HR data to make better workforce decisions. Oracle HRIS configurations include optimized hardware, software, virtualization, and database technologies to provide scalable, high performance, and secure HR information systems.
Innovation Webinar - Using IFS Applications BI to drive business excellenceIFS
Studies show that best-in-class businesses—those with the best operating margins and turnover growth in their industries—have clearly defined objectives supported by a Business Intelligence solution. In this session, we’ll look at specific features in the IFS Applications Business Intelligence solution. See how easily these features can help you support strategic business initiatives and reach improved operational results.
Introduction to Microsoft SQL Server 2008 R2 Analysis ServiceQuang Nguyễn Bá
The document discusses SQL Server 2008 R2 Analysis Services and provides an overview of its key components including OLAP, multidimensional data analysis using dimensions and hierarchies, and how it utilizes a dimensional data warehouse with fact and dimension tables to store and retrieve data for analysis. It also explains how Analysis Services provides scalable and extensible solutions for analytics and delivers pervasive business insights.
Hexaware analyzed an international mining company's HR data reporting needs and designed a new data warehouse model. They assessed the existing data warehouse system, studied current reporting challenges, and analyzed SAS programs for data extraction. Hexaware then designed a new data warehouse model and modified over 600 SAS programs to work with the new model. This addressed the business users' analytical needs and optimized existing programs for improved data loading and reporting performance.
Helix is a cluster management framework developed by LinkedIn to provide common functionality for distributed data systems (DDSs). It separates coordination and management tasks from the functional components of DDSs. Helix ensures systems satisfy their defined state models while meeting goals for load balancing and throttling state changes. Key features include modeling valid system states and transitions, optimizing resource placement, and responding to failures and elasticity events. The framework has been used to manage several production LinkedIn systems like Espresso, Databus, and a search system.
Human resources informational systems (HRIS) use various subsystems and technologies to manage employee data, automate HR processes, and support decision making. HRIS collects transactional employee data and uses this data for reporting, analytics, and business intelligence to help optimize workforce management and compliance. The data is stored securely and can be accessed by authorized users. HRIS implementations typically involve relational databases, data warehouses, extraction/transformation/loading processes, and online analytical processing to analyze historical HR data and identify trends.
Co 4, session 2, aws analytics servicesm vaishnavi
AWS offers several analytics services to help process and provide insights from data. These include Amazon Athena for interactive querying of data stored in S3 using SQL, Amazon EMR for processing large amounts of data using Hadoop and other open source tools, Amazon CloudSearch for setting up a search solution easily, and Amazon Kinesis for collecting, processing, and analyzing real-time data. Other services are Amazon Redshift for data warehousing, Amazon Quicksight for interactive dashboards, AWS Glue for ETL jobs, and Amazon Lake Formation for securing data lakes.
Discussion post· The proper implementation of a database is es.docxmadlynplamondon
Discussion post
· The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
· Suggest at least two types of databases that would be useful for small businesses, two types for regional level organizations and two types for international companies. Include your rationale for each suggestion.
LP’s post states the following:Top of Form
Question:
The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
Answer:
Planning is the most significant aspect of database design, and here is where most projects for database design will fail because the database does not meet requirements, does not meet expectations, or are just unmanageable. Here you need to be forward-thinking by planning for the future. What information needs to be stored or what things or entities do we need to store information about (Knauff, 2004)? What questions will we need to ask of the database (Knauff, 2004)?
A well-designed database promotes consistent data entry and retrieval and reduces the existence of duplication among the database tables. Relational database tables work together to ensure that the correct data is available when you need it.
The first consideration should be what is the database’s intended purpose. Understanding the purpose will help define the need. Some examples might be “to keep a list of customers,” “to manage inventory,” or “to grade students (Filemaker Staff, n.d.).” All stakeholders need to be involved in this process.
Second is Data integrity. Is the data accurate, consistent, and complete? What kind of categories does the data align with? Identifying these categories is critical to designing an efficient database because different types and amounts of data in each category will be stored. Some example categories might be sales that track “customers,” “products,” and “invoices,” or grades that track “students,” “classes,” and “assignments (Filemaker Staff, n.d.).” Once the categories have been defined the relations can be determined. A good exercise to help with this is to write these out in simple sentences:
“customers order products” and “invoices record customers’ orders.”
Now the organization of the data can begin. The categories above can be used as tables so common data can be grouped.
The third is security. Is the database secure? Will the current policy and rules be sufficient going forward? Who should have access? Who should have access to which tables (Nield, 2016)? Read-only access? Write access? Is this database critical to business operations (Nield, 2016)? What are the D&R plans?
Excessive security creates excessive red tape and obstructs agility, but insufficient security will invite catastrophe (Nield, 2016 ...
Microsoft master data services mds overviewEugene Zozulya
Master data management (MDM) is a technology discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
Master data management tools can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data.
Microsoft Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions.
Other Master Data Services features include hierarchies, granular security, transactions, data versioning, and business rules.
Master Data Services includes the following components and tools:
- Master Data Services Configuration Manager, a tool you use to create and configure Master Data Services databases and web applications.
- Master Data Manager, a web application you use to perform administrative tasks (like creating a model or business rule), and that users access to update data.
- MDSModelDeploy.exe, a tool you use to create packages of your model objects and data so you can deploy them to other environments.
- Master Data Services web service, which developers can use to extend or develop custom solutions for Master Data Services.
DBArtisan XE6 is a database administration tool that helps DBAs manage databases across platforms more efficiently. It streamlines common tasks, reduces errors, and provides comprehensive capabilities for data management. As data volumes grow, the role of the DBA is evolving to handle multiple concurrent responsibilities. DBArtisan facilitates this role by providing performance monitoring, space and data management tools, and security management in a single interface.
Teradata Aster: Big Data Discovery Made Easy
Brad Elo, VP, Aster Data, Teradata
ANALYTICS AND VISUALIZATION FOR THE FINANCIAL ENTERPRISE CONFERENCE
June 25, 2013 The Langham Hotel Boston, MA
This session was about Master Data Services and what it also could be used as - the client wanted an application to validate and submit warehouse inventories.
SAP HANA is a game-changing, real-time platform for analytics and applications. While simplifying the IT stack, it provides powerful features like: significant processing speed, the ability to handle big data, predictive capabilities and text mining capabilities.
20100430 introduction to business objects data servicesJunhyun Song
This document provides an overview and agenda for a presentation on SAP BusinessObjects Data Services XI 3.0. It discusses how data integration and quality tools like Data Services can help address challenges around managing enterprise data by providing a single tool for data integration, quality management, and metadata management. The presentation agenda covers why effective information management is important, an introduction to Data Services, how metadata management impacts data lineage and trustworthiness, use cases for Data Services in SAP environments, and concludes with a wrap-up.
The document discusses the emergence of big data and new data architectures needed to handle large, diverse datasets. It notes that internet companies built their own data systems like Hadoop to process massive amounts of unstructured data across thousands of servers in a fault-tolerant, scalable way. These systems use a map-reduce programming model and distributed file systems like HDFS to store and process data in a parallel, distributed manner.
A whitepaper from qubole about the Tips on how to choose the best SQL Engine for your use case and data workloads
https://www.qubole.com/resources/white-papers/enabling-sql-access-to-data-lakes
This document discusses the benefits of using Spreadsheet Server to provide real-time integration of Excel spreadsheets with SAP data. Key benefits include time savings from reducing dependency on IT for reporting, quicker business decisions through real-time analytics, increased accuracy, and streamlined implementation. Spreadsheet Server allows users to leverage familiar Excel tools to access and report on SAP data in real-time, which improves productivity, decision making, and compliance.
Microsoft SQL Server 2012 Master Data ServicesMark Ginnebaugh
Mark Gschwind, VP of Business Intelligence at DesignMind, gave a presentation on Master Data Services (MDS) in SQL Server 2012. He began with an overview of master data and its importance for central curation, quality management, and ease of access for business users. He then reviewed the key capabilities of MDS, including modeling, validation, stewardship, and integration. Gschwind demonstrated creating an MDS model, using the new Excel interface, business rules, and exposing MDS data to a data warehouse. He concluded with tips for successful MDS implementations such as starting small, engaging business users, and using the development environment.
Predictive Analysis in Microsoft SQL Server 2012 provides powerful predictive analytics capabilities including multiple data mining models, algorithms for tasks like market basket analysis and churn prediction, and integration with Business Intelligence tools. It allows users to gain predictive insights, discover hidden relationships in data, and inform decisions. The predictive capabilities are enterprise-grade, with high performance, scalability, and security integrated across the Microsoft BI stack.
ETL stands for Extraction, Transformation, and Loading. The document describes an example ETL process to load master customer data from an Excel file into an SAP BI platform. First, the data is extracted from the Excel file into the BI data warehouse using a data source and info package. Next, the data in the persistent staging area is transformed by defining the customer ID and name fields as characteristic info objects. Finally, a data transfer process loads the mapped data from the source into the appropriate info objects, completing the ETL process.
The document discusses a unified data architecture that enables any user to access and analyze any data type from data capture through analysis. It describes using a discovery platform to enable interactive data discovery on structured and unstructured data without extensive modeling. It also describes using an integrated data warehouse for cross-functional analysis, shared analytics, and lowest total cost of ownership. Finally, it provides examples of using the architecture for IPTV quality of service analysis, including predictive models using decision trees and naive Bayes.
Enterprise database management systems like Oracle HRIS help organizations more efficiently manage large workforces by automating HR processes. Oracle HRIS captures employee data in a powerful database and uses that data for transaction processing, management reporting, decision support, business intelligence, and other functions. It allows authorized users to securely access and analyze HR data to make better workforce decisions. Oracle HRIS configurations include optimized hardware, software, virtualization, and database technologies to provide scalable, high performance, and secure HR information systems.
Innovation Webinar - Using IFS Applications BI to drive business excellenceIFS
Studies show that best-in-class businesses—those with the best operating margins and turnover growth in their industries—have clearly defined objectives supported by a Business Intelligence solution. In this session, we’ll look at specific features in the IFS Applications Business Intelligence solution. See how easily these features can help you support strategic business initiatives and reach improved operational results.
Introduction to Microsoft SQL Server 2008 R2 Analysis ServiceQuang Nguyễn Bá
The document discusses SQL Server 2008 R2 Analysis Services and provides an overview of its key components including OLAP, multidimensional data analysis using dimensions and hierarchies, and how it utilizes a dimensional data warehouse with fact and dimension tables to store and retrieve data for analysis. It also explains how Analysis Services provides scalable and extensible solutions for analytics and delivers pervasive business insights.
Hexaware analyzed an international mining company's HR data reporting needs and designed a new data warehouse model. They assessed the existing data warehouse system, studied current reporting challenges, and analyzed SAS programs for data extraction. Hexaware then designed a new data warehouse model and modified over 600 SAS programs to work with the new model. This addressed the business users' analytical needs and optimized existing programs for improved data loading and reporting performance.
Helix is a cluster management framework developed by LinkedIn to provide common functionality for distributed data systems (DDSs). It separates coordination and management tasks from the functional components of DDSs. Helix ensures systems satisfy their defined state models while meeting goals for load balancing and throttling state changes. Key features include modeling valid system states and transitions, optimizing resource placement, and responding to failures and elasticity events. The framework has been used to manage several production LinkedIn systems like Espresso, Databus, and a search system.
Human resources informational systems (HRIS) use various subsystems and technologies to manage employee data, automate HR processes, and support decision making. HRIS collects transactional employee data and uses this data for reporting, analytics, and business intelligence to help optimize workforce management and compliance. The data is stored securely and can be accessed by authorized users. HRIS implementations typically involve relational databases, data warehouses, extraction/transformation/loading processes, and online analytical processing to analyze historical HR data and identify trends.
Co 4, session 2, aws analytics servicesm vaishnavi
AWS offers several analytics services to help process and provide insights from data. These include Amazon Athena for interactive querying of data stored in S3 using SQL, Amazon EMR for processing large amounts of data using Hadoop and other open source tools, Amazon CloudSearch for setting up a search solution easily, and Amazon Kinesis for collecting, processing, and analyzing real-time data. Other services are Amazon Redshift for data warehousing, Amazon Quicksight for interactive dashboards, AWS Glue for ETL jobs, and Amazon Lake Formation for securing data lakes.
Discussion post· The proper implementation of a database is es.docxmadlynplamondon
Discussion post
· The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
· Suggest at least two types of databases that would be useful for small businesses, two types for regional level organizations and two types for international companies. Include your rationale for each suggestion.
LP’s post states the following:Top of Form
Question:
The proper implementation of a database is essential to the success of the data performance functions of an organization. Identify and evaluate at least three considerations that one must plan for when designing a database.
Answer:
Planning is the most significant aspect of database design, and here is where most projects for database design will fail because the database does not meet requirements, does not meet expectations, or are just unmanageable. Here you need to be forward-thinking by planning for the future. What information needs to be stored or what things or entities do we need to store information about (Knauff, 2004)? What questions will we need to ask of the database (Knauff, 2004)?
A well-designed database promotes consistent data entry and retrieval and reduces the existence of duplication among the database tables. Relational database tables work together to ensure that the correct data is available when you need it.
The first consideration should be what is the database’s intended purpose. Understanding the purpose will help define the need. Some examples might be “to keep a list of customers,” “to manage inventory,” or “to grade students (Filemaker Staff, n.d.).” All stakeholders need to be involved in this process.
Second is Data integrity. Is the data accurate, consistent, and complete? What kind of categories does the data align with? Identifying these categories is critical to designing an efficient database because different types and amounts of data in each category will be stored. Some example categories might be sales that track “customers,” “products,” and “invoices,” or grades that track “students,” “classes,” and “assignments (Filemaker Staff, n.d.).” Once the categories have been defined the relations can be determined. A good exercise to help with this is to write these out in simple sentences:
“customers order products” and “invoices record customers’ orders.”
Now the organization of the data can begin. The categories above can be used as tables so common data can be grouped.
The third is security. Is the database secure? Will the current policy and rules be sufficient going forward? Who should have access? Who should have access to which tables (Nield, 2016)? Read-only access? Write access? Is this database critical to business operations (Nield, 2016)? What are the D&R plans?
Excessive security creates excessive red tape and obstructs agility, but insufficient security will invite catastrophe (Nield, 2016 ...
The document summarizes Oracle Autonomous Data Warehouse, which is an autonomous database in the cloud that fully automates database management tasks like provisioning, tuning, patching, and backup through machine learning. This allows users to build data warehouses and run analytics with just a few clicks without manual administration. Key benefits highlighted are that it reduces costs, frees up DBAs to focus on higher-level work, and provides always-on security through automatic patching and encryption.
Oracle IT Analytics Cloud Service is a software-as-a-service solution that provides 360-degree insight into application and infrastructure performance, availability, and capacity. It enables executives, analysts, and administrators to make critical decisions about IT operations based on comprehensive system and data analysis. Organizations can now become more proactive by identifying issues, analyzing resource usage, and forecasting future demand.
Value of Exalytics for Oracle full stack CustomersMiguel Garcia
The document discusses a study by Nucleus Research on the benefits of Oracle Exalytics In-Memory Machine for business analytics. Key findings include:
1) Customers saw lower total cost of ownership through optimized hardware and software pricing, and needed fewer resources for support.
2) Time to value was accelerated by up to 4 times through a preconfigured engineered system requiring less deployment time.
3) Users experienced increased productivity from accelerated query times and improved visualization tools.
4) Decision making was improved by adding depth, breadth and dimensionality to the data available for analysis.
This document provides an introduction and overview of Azure Data Lake. It describes Azure Data Lake as a single store of all data ranging from raw to processed that can be used for reporting, analytics and machine learning. It discusses key Azure Data Lake components like Data Lake Store, Data Lake Analytics, HDInsight and the U-SQL language. It compares Data Lakes to data warehouses and explains how Azure Data Lake Store, Analytics and U-SQL process and transform data at scale.
Learn more at: http://www.erstudio.com
ER/Studio Team Server provides greater understanding and context to enterprise data through team collaboration on an enterprise glossary of business definitions. This increases the value of enterprise data by giving employees across the organization the ability to use and improve metadata.
SAP Sybase PowerDesigner software provides modeling tools to improve business intelligence and information architecture. It establishes a 360-degree view of key information assets through metadata management. This benefits data governance, business intelligence, data integration, and consolidation efforts. The software performs enterprise-wide impact analysis which helps reduce time, risks, and costs associated with changes to the information architecture. It supports various modeling techniques including conceptual data modeling, logical data modeling, physical data modeling, data warehouse modeling, and XML modeling.
SAP Sybase PowerDesigner software provides modeling tools that help improve business intelligence and information architecture. It establishes a 360-degree view of key information assets through metadata management. Impact analysis functionality reduces the risks and costs of changes. The software supports various modeling techniques including conceptual data modeling, logical data modeling, physical data modeling, and more. It also includes features like an enterprise glossary and repository.
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls.
This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture.
Attend this session to learn about:
- The role of a Data Mesh in the modern cloud architecture.
- How a semantic layer can serve as the binding agent to support decentralization.
- How to drive self service with consistency and control.
Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.
This document summarizes research on optimizing queries for big data analytics. It discusses how organizations use different databases with varied data models to store and query big data. The main focus is improving query performance by having a query framework that can detect optimized data copies created by data engineers and execute queries against these copies. The framework uses the Apache Calcite query optimizer which rewrites queries to use optimized copies when possible based on a cost model. An evaluation on real taxi trip data showed the approach improved query response times.
ER/Studio XE3 is the fastest, easiest and most collaborative way for data modeling professionals to build and maintain enterprise-scale databases and data warehouses. ER/Studio XE3 sets a new standard for data management. ER/Studio XE3 empowers data management professionals to easily share, document, and publish models and metadata to distributed teams.
Learn more at
http://www.embarcadero.com/products/er-studio
Analytical database software solutions are specialized software tools designed to store, manage, and analyze large volumes of data for the purpose of generating insights and supporting data-driven decision-making.
Microsoft® SQL Server® 2012 is a cloud-ready information platform that will help organizations unlock breakthrough insights across the organization and quickly build solutions to extend data across on-premises and public cloud, backed by mission critical confidence.
Today, businesses are forced to maintain two types of analytical systems, data warehouses and data lakes. Data warehouses provide critical insights on business health.
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
The document discusses various options for extracting data from Oracle Fusion and Oracle EPM Cloud applications for analytics purposes. It outlines using the Business Intelligence Cloud Connector (BICC) to extract data to object storage, which can then be loaded into Oracle Analytics Cloud (OAC) or Autonomous Data Warehouse (ADW) for analysis. For EPM Cloud, it notes using the EPM Automate REST API wrapper or Oracle Data Integrator Marketplace connector. The document provides an overview of tools like OAC, ADW, ODI, and OCI Data Integration that can help transform and model the data for analytics and machine learning.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
Enterprise Data Lake:
How to Conquer the Data Deluge and Derive Insights
that Matters
Data can be traced from various consumer sources.
Managing data is one of the most serious challenges faced
by organizations today. Organizations are adopting the data
lake models because lakes provide raw data that users can
use for data experimentation and advanced analytics.
A data lake could be a merging point of new and historic
data, thereby drawing correlations across all data using
advanced analytics. A data lake can support the self-service
data practices. This can tap undiscovered business value
from various new as well as existing data sources.
Furthermore, a data lake can aid data warehousing,
analytics, data integration by modernizing. However, lakes
also face hindrances like immature governance, user skills
and security.
Built on Oracle Analytics Cloud and powered by Oracle Autonomous Data Warehouse Cloud, Fusion Analytics Warehouse
(FAW) provides Oracle ERP and HCM Cloud Application customers with best-practice key performance indicators (KPIs)
and actionable insights driven by advanced analytics
1. ORACLE DATA SHEET
ORACLE ESSBASE
KEY FEATURES AND BENEFITS
Oracle Essbase is the market leading online analytical processing
KEY FEATURES
• Move beyond silos of
(OLAP) server for enterprise performance management (EPM)
business intelligence and applications. Designed specifically for business users, Oracle
disconnected spreadsheets
• Real-time analysis of key Essbase supports forecasting, variance analysis, root cause
customer data, finances and identification, scenario planning and what-if modeling for both
spending, and product
profitability custom and packaged applications. It can be tightly integrated with
• Cost-saving links to existing
systems
multiple data sources and the information generated can be
• Speed-of-thought analysis for delivered through a wide variety of reporting options. Engineered
thousands of concurrent users
• Fast and easy development,
for scalability, security, and rapid-response, Oracle Essbase brings
deployment, and maintenance advanced analytics to the business user to enable greater
• Robust security system
understanding of the business, alignment of resources and improved
KEY BENEFITS business results.
• Supports a large user
community across massive Richest User Experience
data sets
Oracle Essbase brings powerful online analytics processing (OLAP) directly to the
• Uses innovative, visual, easy-
to-understand interfaces business user. Query results can be displayed through interfaces of the user’s choice,
• Facilitates understanding of including Microsoft Office tools, and the variety of intuitive reporting options which
customer segments and
Oracle offers. With the advantage of consistent, sub-second response times, users
behavior patterns
• Enables rapid discovery of
can interact with the data at the speed-of-thought without support from technical
trends and highlights these experts. This ability to “converse” with the data—understanding that an answer to
trends in large data sets
one question leads to another—enables business users to better identify and analyze
• Delivers rapid batch load and
calculation times
the metrics and relationships that influence performance, and to make better, more
• Supports highly dimensional informed decisions. Users can share their saved reports, and modify their
models appearance, or create powerful additional queries as new questions arise.
• Leverages investments in
legacy systems Multi-dimensional Representation and Extension
Data is categorized in Oracle Essbase in the form of dimensions, a dimension could
for instance represent a time period or a product or a customer. Thus a query may be
to compare actual sales for a product in a specified state during the month of March
2010 with the corresponding budgeted value for that month. There are often
relationships between members of a dimension and these relationships are
represented by a hierarchy. A hierarchy enables mathematical calculations to be
executed against the data, so for example all the sales for individual states can be
aggregated to create a value for the entire USA. Oracle Essbase allows multiple
hierarchies to be established so data can be speedily calculated or aggregated.
Structures in Oracle Essbase such as dimensions and hierarchies are displayed in the
“Outline”, which is a graphical representation that enables authorized users to easily
review and maintain structures as business requirements change.
Users can extend data by using metrics or drivers to estimate what results will be in
2. ORACLE DATA SHEET
the future. These driver metrics can be based upon history, trends or entered by the
user. These forecasted results can be compared with actual results and the reasons
for variances investigated so that more accurate forecasts may then be produced.
Users can also create further scenarios where they may model for exceptional
changes in business and be prepared for turbulent trading conditions through this
“what-if” analysis. This assists fast resolution of business issues and for risks to be
managed.
Most Highly Advanced Calculation Engine
At its core Oracle Essbase contains a high performance calculation engine with over
350 pre-built, out-of-the-box functions. This comprehensive library enables Oracle
Essbase to scale from simple aggregations to complex, cross-dimensional
allocations. Financial formulas of all types are included to support business model
development. In addition, business rules can be created to manage complex
calculation requirements using a spreadsheet-like syntax.
Reporting Options
A wide variety of users, from many departments, will want to use Oracle Essbase.
Delivering the information to them in a suitable form is of paramount importance.
Data can be delivered to Oracle Business Intelligence Suite Enterprise Edition and in
addition presented through a variety of formats including interactive dashboards,
financial and production reports, Microsoft Office and advanced data visualization
tools. Each of these reporting options suits a particular purpose, but all use the same
data, and common data definitions ensuring consistency across the enterprise.
Best-in-class interfaces provide meaningful insights for business users
Open, Scalable and Secure
Oracle Essbase can be populated through a wide variety of tools that allow it to
access any commonly recognized data source; the data can then be combined into a
single analytic view, so the entire enterprise can be consistently reported upon.
Many organizations have multiple, large data sources from which the data needs to
be extracted. Oracle Essbase performance is unmatched, offering inherent
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3. ORACLE DATA SHEET
capabilities to optimize data load and recalculate data sets so results are speedily
available to the users. Oracle Essbase enables detailed analysis of terabytes of data
for thousands of simultaneous users providing up-to-the-minute, dependable
information. This high-speed analysis provides business users speed-of-thought
responsiveness to manage performance in real time.
User scalability features such as caching, multithreading, partitioning, and cross-
platform support enable IT professionals to use fewer servers to support many
analytic applications and large user communities. Supporting 64-bit architectures,
Oracle Essbase enables larger analytical models with shorter calculation times,
increasing the potential size of analytic applications, and the number of concurrent
users. In addition, its n-tier architecture provides connection pooling, load balancing,
and automatic failover so IT employees meet service-level requirements. With its
High Availability Services feature, Oracle Essbase delivers distribution of
processing across multiple physical servers to increase application availability.
With the potential to support thousands of users accessing significant volumes of
data, security is a priority. Oracle Essbase leverages the Oracle EPM system
foundation’s common authentication system offering both high level and detail cell
level controls and the support of Group or Role based security models. In addition,
Oracle Essbase supports Oracle Fusion Middleware security components like Oracle
Internet Directory, Oracle Identity Management, and a single sign-on for Oracle
Enterprise Performance Management Workspace.
System Maintenance and Deployment
Manageability features within Oracle Essbase drive down IT costs. These features
include packaged business product management applications; J2EE; .NET
development tools; certified enterprise resource planning/customer relationship
management application integration adapters; administrative wizards; automated
maintenance scripts; Unicode support; and reusable dimensions, hierarchies, and
business rules. The open architecture also lowers costs to develop, deploy, and
maintain by leveraging existing IT skill sets. In addition, Oracle Essbase supports
efficient, automated backup and restoration of the database as well as lifecycle
management that provides a consistent way for administrators to migrate
applications and artifacts across product environments.
Oracle Essbase leverages Oracle’s Hyperion Foundation Services and Oracle Fusion
Middleware to provide a common platform of services upon which companies can
create, deploy, and manage EPM applications in one place.
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