This document discusses enterprise data warehousing positioning based on the SAP Real-Time Data Platform. It describes the needs for different types of data marts and analytics in enterprise environments. Complex landscapes often include operational data marts, agile data marts, real-time data marts, and predictive data marts. The document also discusses the role of the enterprise data warehouse as a single point of truth and consolidating data across the enterprise. SAP addresses these needs with offerings like SAP BW, SAP HANA, and Sybase IQ which can be used for packaged or custom-built data warehouse and data mart solutions.
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
These are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
The document provides an overview of data warehousing concepts. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data. It discusses the differences between OLTP and OLAP systems. It also covers data warehouse architectures, components, and processes. Additionally, it explains key concepts like facts and dimensions, star schemas, normalization forms, and metadata.
Traditional Data-warehousing / BI overviewNagaraj Yerram
Business intelligence (BI) refers to technologies that collect, analyze, and present business data to support decision-making. A traditional BI architecture extracts data from source systems, transforms it using ETL processes, and loads it into a data warehouse optimized for analysis (OLAP). Dimensional modeling techniques structure data warehouses into fact and dimension tables arranged in star or snowflake schemas to enable analysis of key business metrics over time and across different dimensions like product or location. This facilitates interactive exploration and reporting on historical, current, and predictive business insights for strategic planning and opportunities.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
- Data warehousing aims to help knowledge workers make better decisions by integrating data from multiple sources and providing historical and aggregated data views. It separates analytical processing from operational processing for improved performance.
- A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data to support analysis. It is maintained separately from operational databases. Common schemas include star schemas and snowflake schemas.
- Online analytical processing (OLAP) supports ad-hoc querying of data warehouses for analysis. It uses multidimensional views of aggregated measures and dimensions. Relational and multidimensional OLAP are common architectures. Measures are metrics like sales, and dimensions provide context like products and time periods.
SAP HANA Architecture Overview | SAP HANA TutorialZaranTech LLC
We are a team of Senior IT consultants with a wide array of knowledge in different domains, methodologies, Tools and platforms.We strive to develop and deliver highly qualified IT consultants to the market.
We differentiate our training and development program by delivering Role-specific traininginstead of Product-based training. Ultimately, our goal is to deliver the best IT consultants to our clients. - http://www.zarantech.com/
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
These are the slides from my talk at Data Day Texas 2016 (#ddtx16).
The world of data warehousing has changed! With the advent of Big Data, Streaming Data, IoT, and The Cloud, what is a modern data management professional to do? It may seem to be a very different world with different concepts, terms, and techniques. Or is it? Lots of people still talk about having a data warehouse or several data marts across their organization. But what does that really mean today in 2016? How about the Corporate Information Factory (CIF), the Data Vault, an Operational Data Store (ODS), or just star schemas? Where do they fit now (or do they)? And now we have the Extended Data Warehouse (XDW) as well. How do all these things help us bring value and data-based decisions to our organizations? Where do Big Data and the Cloud fit? Is there a coherent architecture we can define? This talk will endeavor to cut through the hype and the buzzword bingo to help you figure out what part of this is helpful. I will discuss what I have seen in the real world (working and not working!) and a bit of where I think we are going and need to go in 2016 and beyond.
The document provides an overview of data warehousing concepts. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data. It discusses the differences between OLTP and OLAP systems. It also covers data warehouse architectures, components, and processes. Additionally, it explains key concepts like facts and dimensions, star schemas, normalization forms, and metadata.
Traditional Data-warehousing / BI overviewNagaraj Yerram
Business intelligence (BI) refers to technologies that collect, analyze, and present business data to support decision-making. A traditional BI architecture extracts data from source systems, transforms it using ETL processes, and loads it into a data warehouse optimized for analysis (OLAP). Dimensional modeling techniques structure data warehouses into fact and dimension tables arranged in star or snowflake schemas to enable analysis of key business metrics over time and across different dimensions like product or location. This facilitates interactive exploration and reporting on historical, current, and predictive business insights for strategic planning and opportunities.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
- Data warehousing aims to help knowledge workers make better decisions by integrating data from multiple sources and providing historical and aggregated data views. It separates analytical processing from operational processing for improved performance.
- A data warehouse contains subject-oriented, integrated, time-variant, and non-volatile data to support analysis. It is maintained separately from operational databases. Common schemas include star schemas and snowflake schemas.
- Online analytical processing (OLAP) supports ad-hoc querying of data warehouses for analysis. It uses multidimensional views of aggregated measures and dimensions. Relational and multidimensional OLAP are common architectures. Measures are metrics like sales, and dimensions provide context like products and time periods.
White paper making an-operational_data_store_(ods)_the_center_of_your_data_...Eric Javier Espino Man
The document discusses implementing an operational data store (ODS) to centralize data from multiple source systems. An ODS integrates disparate data for reporting and analytics while insulating operational systems. The document recommends selling an ODS internally by highlighting benefits like reduced workload for ETL developers and improved access to real-time data for business users. It also provides best practices like using automation tools that simplify ODS creation and maintenance.
SAP Data Services is a data integration and transformation software application. It also supports changed-data capture (CDC), which is an important capability for providing input data to both data-warehousing and stream-processing systems.
It is an ETL tool which gives a single enterprises level solution for data integration, Transformation, Data quality, Data profiling and text data processing from the heterogeneous source into a target database or data warehouse.
sap hana|sap hana database| Introduction to sap hanaJames L. Lee
SAP HANA, sap hana implementation scenarios, sap hana deployment scenarios, SAP HANA Implementations, sap hana implementation and modeling, sap hana implementation cost, sap hana implementation partners, Applications based on SAP HANA, SAP HANA Databases.
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
SAP HANA is an in-memory database that allows for real-time processing of large quantities of data. It consists of the HANA database, modeling tools, and hardware/software. HANA uses columnar storage and massively parallel processing to perform complex queries and aggregations very quickly. Traditional databases store data in rows, while HANA stores each column together to allow for better compression. HANA is suited for analytical workloads that require aggregations over large datasets.
The document discusses the modern data warehouse and key trends driving changes from traditional data warehouses. It describes how modern data warehouses incorporate Hadoop, traditional data warehouses, and other data stores from multiple locations including cloud, mobile, sensors and IoT. Modern data warehouses use multiple parallel processing (MPP) architecture and the Apache Hadoop ecosystem including Hadoop Distributed File System, YARN, Hive, Spark and other tools. It also discusses the top Hadoop vendors and Oracle's technical innovations on Hadoop for data discovery, transformation, discovery and sharing. Finally, it covers the components of big data value assessment including descriptive, predictive and prescriptive analytics.
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
OLTP systems are used for operational tasks like processing transactions, while OLAP systems are used for analysis of historical data extracted from OLTP systems. OLAP systems allow for complex queries and reporting on aggregated and multidimensional views of the data. Both systems are complementary, with OLTP housing and processing the source transactional data and OLAP leveraging that data for planning, problem solving, and decision making.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Prague data management meetup 2017-02-28Martin Bém
The document discusses an operational data store (ODS) that was implemented to integrate data from two banks, Velká česká banka and Nová česká banka, after a transaction integration, using APIs, ETL workflows, and data transformations to populate the ODS with consolidated customer, account, and transaction data from both banks for operational reporting. It also provides details on the types of data domains integrated into the ODS and growth in API usage over time as more systems accessed the shared ODS.
This document summarizes the key aspects of an enterprise data warehouse project for the Oregon Department of Education called KIDS Phase II. It discusses what a data warehouse is and why it is needed to integrate data from multiple sources. It outlines the current issues with the state's data environment and recommends building a centralized data warehouse and operational data store to integrate student performance and other education data for improved decision making. The document also covers planning the project, developing the data model, extracting and loading data, and delivering reports and business intelligence.
This document discusses building a data lake for a restaurant to optimize performance and enable complex queries. It outlines converting the restaurant's data into a big data and columnar format, accessing the data lake, extracting data to S3 for storage, reporting on the data lake, and using big data techniques for complex queries. The SEE team's requirements and a cross-platform analysis strategy are mentioned for inspiration in building the data lake.
Partner Enablement: Key Differentiators of Denodo Platform 6.0 for the FieldDenodo
If you’re a Denodo Partner, this presentation is for you. Learn how to gain a competitive edge in the marketplace with Denodo Platform 6.0, and leverage off the new features and functionality.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Qh8MeX.
Vertica Analytics Database general overviewStratebi
Vertica is an advanced analytics platform that combines high-performance query processing with advanced analytics and machine learning capabilities. It bridges the gap between high-cost legacy data warehouses and less powerful Hadoop data lakes. Vertica uses a massively parallel processing architecture to deliver fast analytics on large datasets regardless of where the data resides. It has been implemented by various companies across industries to drive customer experience management, operational analytics, and fraud detection through applications like predictive maintenance, customer churn analysis, and network optimization.
- Accel proposes implementing a data warehouse and business intelligence solution using Business Objects software to provide consolidated access to organizational data and generate reports for improved decision making.
- The proposed solution includes building a data warehouse with an ETL process to integrate data from various sources, deploying Business Objects products for reporting, analysis and dashboards, and sample reports focused on retail business metrics.
- Benefits of the solution include increased access to required information, scalability, improved decision making through analysis, and protection of information access through security controls.
Data mining and data warehousing have evolved since the 1960s due to increases in data collection and storage. Data mining automates the extraction of patterns and knowledge from large databases. It uses predictive and descriptive models like classification, clustering, and association rule mining. The data mining process involves problem definition, data preparation, model building, evaluation, and deployment. Data warehouses integrate data from multiple sources for analysis and decision making. They are large, subject-oriented databases designed for querying and analysis rather than transactions. Data warehousing addresses the need to consolidate organizational data spread across various locations and systems.
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
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 key characteristics of data warehouses including that they contain historical data derived from transactions for querying, reporting, and analysis. It also compares online transaction processing (OLTP) systems to data warehouses. Additionally, it covers data warehouse architectures, design considerations, logical and physical design, and managing large volumes of data through techniques like partitioning and parallelism. Optimizing input/output performance is also highlighted as critical for data warehouses.
Big Data Integration Webinar: Reducing Implementation Efforts of Hadoop, NoSQ...Pentaho
This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
White paper making an-operational_data_store_(ods)_the_center_of_your_data_...Eric Javier Espino Man
The document discusses implementing an operational data store (ODS) to centralize data from multiple source systems. An ODS integrates disparate data for reporting and analytics while insulating operational systems. The document recommends selling an ODS internally by highlighting benefits like reduced workload for ETL developers and improved access to real-time data for business users. It also provides best practices like using automation tools that simplify ODS creation and maintenance.
SAP Data Services is a data integration and transformation software application. It also supports changed-data capture (CDC), which is an important capability for providing input data to both data-warehousing and stream-processing systems.
It is an ETL tool which gives a single enterprises level solution for data integration, Transformation, Data quality, Data profiling and text data processing from the heterogeneous source into a target database or data warehouse.
sap hana|sap hana database| Introduction to sap hanaJames L. Lee
SAP HANA, sap hana implementation scenarios, sap hana deployment scenarios, SAP HANA Implementations, sap hana implementation and modeling, sap hana implementation cost, sap hana implementation partners, Applications based on SAP HANA, SAP HANA Databases.
The document provides information about what a data warehouse is and why it is important. A data warehouse is a relational database designed for querying and analysis that contains historical data from transaction systems and other sources. It allows organizations to access, analyze, and report on integrated information to support business processes and decisions.
SAP HANA is an in-memory database that allows for real-time processing of large quantities of data. It consists of the HANA database, modeling tools, and hardware/software. HANA uses columnar storage and massively parallel processing to perform complex queries and aggregations very quickly. Traditional databases store data in rows, while HANA stores each column together to allow for better compression. HANA is suited for analytical workloads that require aggregations over large datasets.
The document discusses the modern data warehouse and key trends driving changes from traditional data warehouses. It describes how modern data warehouses incorporate Hadoop, traditional data warehouses, and other data stores from multiple locations including cloud, mobile, sensors and IoT. Modern data warehouses use multiple parallel processing (MPP) architecture and the Apache Hadoop ecosystem including Hadoop Distributed File System, YARN, Hive, Spark and other tools. It also discusses the top Hadoop vendors and Oracle's technical innovations on Hadoop for data discovery, transformation, discovery and sharing. Finally, it covers the components of big data value assessment including descriptive, predictive and prescriptive analytics.
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
OLTP systems are used for operational tasks like processing transactions, while OLAP systems are used for analysis of historical data extracted from OLTP systems. OLAP systems allow for complex queries and reporting on aggregated and multidimensional views of the data. Both systems are complementary, with OLTP housing and processing the source transactional data and OLAP leveraging that data for planning, problem solving, and decision making.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Prague data management meetup 2017-02-28Martin Bém
The document discusses an operational data store (ODS) that was implemented to integrate data from two banks, Velká česká banka and Nová česká banka, after a transaction integration, using APIs, ETL workflows, and data transformations to populate the ODS with consolidated customer, account, and transaction data from both banks for operational reporting. It also provides details on the types of data domains integrated into the ODS and growth in API usage over time as more systems accessed the shared ODS.
This document summarizes the key aspects of an enterprise data warehouse project for the Oregon Department of Education called KIDS Phase II. It discusses what a data warehouse is and why it is needed to integrate data from multiple sources. It outlines the current issues with the state's data environment and recommends building a centralized data warehouse and operational data store to integrate student performance and other education data for improved decision making. The document also covers planning the project, developing the data model, extracting and loading data, and delivering reports and business intelligence.
This document discusses building a data lake for a restaurant to optimize performance and enable complex queries. It outlines converting the restaurant's data into a big data and columnar format, accessing the data lake, extracting data to S3 for storage, reporting on the data lake, and using big data techniques for complex queries. The SEE team's requirements and a cross-platform analysis strategy are mentioned for inspiration in building the data lake.
Partner Enablement: Key Differentiators of Denodo Platform 6.0 for the FieldDenodo
If you’re a Denodo Partner, this presentation is for you. Learn how to gain a competitive edge in the marketplace with Denodo Platform 6.0, and leverage off the new features and functionality.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Qh8MeX.
Vertica Analytics Database general overviewStratebi
Vertica is an advanced analytics platform that combines high-performance query processing with advanced analytics and machine learning capabilities. It bridges the gap between high-cost legacy data warehouses and less powerful Hadoop data lakes. Vertica uses a massively parallel processing architecture to deliver fast analytics on large datasets regardless of where the data resides. It has been implemented by various companies across industries to drive customer experience management, operational analytics, and fraud detection through applications like predictive maintenance, customer churn analysis, and network optimization.
- Accel proposes implementing a data warehouse and business intelligence solution using Business Objects software to provide consolidated access to organizational data and generate reports for improved decision making.
- The proposed solution includes building a data warehouse with an ETL process to integrate data from various sources, deploying Business Objects products for reporting, analysis and dashboards, and sample reports focused on retail business metrics.
- Benefits of the solution include increased access to required information, scalability, improved decision making through analysis, and protection of information access through security controls.
Data mining and data warehousing have evolved since the 1960s due to increases in data collection and storage. Data mining automates the extraction of patterns and knowledge from large databases. It uses predictive and descriptive models like classification, clustering, and association rule mining. The data mining process involves problem definition, data preparation, model building, evaluation, and deployment. Data warehouses integrate data from multiple sources for analysis and decision making. They are large, subject-oriented databases designed for querying and analysis rather than transactions. Data warehousing addresses the need to consolidate organizational data spread across various locations and systems.
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
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 key characteristics of data warehouses including that they contain historical data derived from transactions for querying, reporting, and analysis. It also compares online transaction processing (OLTP) systems to data warehouses. Additionally, it covers data warehouse architectures, design considerations, logical and physical design, and managing large volumes of data through techniques like partitioning and parallelism. Optimizing input/output performance is also highlighted as critical for data warehouses.
Big Data Integration Webinar: Reducing Implementation Efforts of Hadoop, NoSQ...Pentaho
This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
This document discusses Data Vault fundamentals and best practices. It introduces Data Vault modeling, which involves modeling hubs, links, and satellites to create an enterprise data warehouse that can integrate data sources, provide traceability and history, and adapt incrementally. The document recommends using data virtualization rather than physical data marts to distribute data from the Data Vault. It also provides recommendations for further reading on Data Vault, Ensemble modeling, data virtualization, and certification programs.
What's New with SAP BusinessObjects Business Intelligence 4.1?SAP Analytics
http://spr.ly/sapbusinessobjectsbi - Learn more about SAP's strategy for an enterprise BI and the new features in SAP BusinessObjects Business Intelligence 4.1.
For more information on “SAP BusinessObjects BI 4.1 Upgrade”, register for our upcoming webcast on October 8, 2013, 8 AM PST: http://bit.ly/1bamzuh
Hadoop in 2015: Keys to Achieving Operational Excellence for the Real-Time En...MapR Technologies
In this webinar, Carl W. Olofson, Research Vice President, Application Development and Deployment for IDC, and Dale Kim, Director of Industry Solutions for MapR, will provide an insightful outlook for Hadoop in 2015, and will outline why enterprises should consider using Hadoop as a "Decision Data Platform" and how it can function as a single platform for both online transaction processing (OLTP) and real-time analytics.
The document discusses tips and strategies for using SAP NetWeaver Business Intelligence 7.0 as an enterprise data warehouse (EDW). It covers differences between evolutionary warehouse architecture and top-down design, compares data mart and EDW approaches, explores real-time data warehousing with SAP, examines common EDW pitfalls, and reviews successes and failures of large-scale SAP BI-EDW implementations. The presentation also explores the SAP NetWeaver BI architecture and Corporate Information Factory framework.
The challenge of computing big data for evolving digital business processes demands variety of computation techniques and engines (SQL, OLAP, time-series, graph, document store), but working in unified framework. A simple architecture of data transformations while ensuring the security, governance, and operational administration are the necessary critical components for enterprise production environments supporting day-to-day business processes. In this session, you will learn about best practices & critical components to ensure business value from latest production deployments. Hear how existing customers are using SAP Vora and the value they have achieved so far with this in-memory engine for distributed data processing. The session provides you with a clear understanding how SAP Vora and open source components like Apache Hadoop and Apache Spark offer an architecture that supports a wide variety of use cases and industries. You will also receive very useful insight where to find development resources, test drive demos, and general documentation.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Lake Acceleration vs. Data Virtualization - What’s the difference?Denodo
Watch full webinar here: https://bit.ly/3hgOSwm
Data Lake technologies have been in constant evolution in recent years, with each iteration primising to fix what previous ones failed to accomplish. Several data lake engines are hitting the market with better ingestion, governance, and acceleration capabilities that aim to create the ultimate data repository. But isn't that the promise of a logical architecture with data virtualization too? So, what’s the difference between the two technologies? Are they friends or foes? This session will explore the details.
Pivotal introduces its new Pivotal HD platform for big data analytics. Pivotal HD integrates Hadoop, HBase, Pig, Hive and other big data tools into an enterprise-grade distribution. It also includes tools like Command Center for job and cluster monitoring and HAWQ for SQL queries on Hadoop. Pivotal positions Pivotal HD as addressing pain points with Hadoop like usability, manageability and performance in order to make big data analytics mission-critical for enterprises.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
This document discusses how organizations can leverage data and analytics to power their business models. It provides examples of Fortune 100 companies that are using Attunity products to build data lakes and ingest data from SAP and other sources into Hadoop, Apache Kafka, and the cloud in order to perform real-time analytics. The document outlines the benefits of Attunity's data replication tools for extracting, transforming, and loading SAP and other enterprise data into data lakes and data warehouses.
This document provides an overview of business intelligence, data warehousing, data marts, and data mining presented by Mr. Manish Tripathi. It defines business intelligence as a process for analyzing data to help business decisions. Data warehousing is described as a centralized repository for storing historical data from various sources to support analysis and reporting. Data marts are subsets of data warehouses focused on specific business units or teams. Common business intelligence tools and the benefits of these systems are also summarized.
Modern data warehouses need to be modernized to handle big data, integrate multiple data silos, reduce costs, and reduce time to market. A modern data warehouse blueprint includes a data lake to land and ingest structured, unstructured, external, social, machine, and streaming data alongside a traditional data warehouse. Key challenges for modernization include making data discoverable and usable for business users, rethinking ETL to allow for data blending, and enabling self-service BI over Hadoop. Common tactics for modernization include using a data lake as a landing zone, offloading infrequently accessed data to Hadoop, and exploring data in Hadoop to discover new insights.
This document defines and describes key concepts related to data warehousing and business intelligence. It defines a data warehouse as a repository of integrated data organized for analysis. Key characteristics of a data warehouse include being subject-oriented, integrated, non-volatile, and summarized. The document also discusses data marts, architectures like three-tier and two-tier, and ETL processes. Risks, best practices, and administration of data warehouses are covered as well.
What’s new in SAP BusinessObject BI 4.1? (part1)tasmc
The document discusses SAP's BusinessObjects Business Intelligence 4.1 software. It focuses on SAP's strategic priorities of core BI capabilities, self-service analytics, mobile BI, big data and social BI. The software suite provides unified and personalized insights through powerful visualizations to enable faster, more accurate decisions. It offers a single standard for enterprise BI with a professional grade platform and broad ecosystem support. New features in version 4.1 include improved deployment tools, multi-tenancy, offline mobile dashboards and support for additional data sources and languages.
The Numberate Rapid Warehouse Solution is a data integration solution that maximises the cost efficiency of data oriented business intelligence environments.
It builds on over a 15 Years of experience delivering these solutions to a range of corporate and government clients who needed to leverage business growth from their organisations underlying data assets.
The document provides an overview of data warehousing concepts including:
1) A data warehouse is a subject-oriented collection of integrated data used to support management decisions. It contains current and historical data.
2) A data warehouse architecture typically includes source systems, a staging area, and presentation layer for querying and reporting.
3) Data marts are focused subsets of a data warehouse tailored for specific business units or departments. There are dependent, independent, and hybrid approaches to building data marts.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
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20. Thank you
Contact information:
Product Management: Lothar Henkes, Courtney Claussen, Brian Wood
Solution Management: Daniel Rutschmann
Consulting: Andreas Scholl
COE: Lars Jakob
CSA: Rudolf Hennecke