Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
While vendor offerings have exploded in recent years, in due time frameworks will integrate components into what amounts to, for practical purposes, a single offering for multiple workloads, perhaps even for the enterprise.
A multi-model database is a database that can store, manage, and query data in multiple models, such as relational, document-oriented, key-value, graph (triplestore), and column store.
An enterprise will find reduced overhead and other synergies from choosing a single vendor for these workloads.
This session will explore the multi-model option and some criteria that decision makers should evaluate when choosing a multi-model solution.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Building a Modern Analytic Database with Cloudera 5.8Cloudera, Inc.
Analytic workloads and the ability to determine “what happened” are some of the most common use cases across enterprises today - helping you understand and adapt based on changing trends. However, for most businesses today, they are only able to see a piece of the story. Analytics are limited by the amount of data able to be stored and ultimately accessed, it’s time-intensive to bring in new datasets or fit unstructured data into rigid schemas, and user access is constrained to a select few who must already know the questions they’re trying to answer.
It’s no surprise that big data is disrupting this modus operandi for analytics. A modern, Hadoop-based platform is designed to help businesses break free of these analytic limitations, providing a new kind of adaptive, high-performance analytic database. The recent release of Cloudera 5.8 continues to advance Cloudera Enterprise as the foundation for these analytic workloads.
Join Justin Erickson, Senior Director of Product Management at Cloudera, and Andy Frey, Chief Technology Officer at Marketing Associates, as they discuss:
-What technology is needed to build a modern analytic database with Hadoop
-What’s new with Cloudera 5.8
-How to align your teams around agile analytics
-Real world success from Marketing Associates
-What’s next for Cloudera Enterprise’s Analytic Database
The Modern Database for Enterprise ApplicationsQAware GmbH
Gregor Bauer, Couchbase & QAware Meetup, 02.02.23
Couchbase and Kubernetes: a powerful data management duo
Gregor explains how to escape the common struggles of cloud deployments by leveraging cloud portability across platforms and providers. Here, the Couchbase Autonomous Operator for Kubernetes enables cloud portability and automates operational best practices for deploying and managing the Couchbase Data Platform.
The following key features will be presented:
Native integration with Kubernetes Operator which provides a data platform with rich query support, mobile, analytics, and full-text search functionality out of the box.
Easily deploy Couchbase within a managed private cloud or public cloud, which offers maximum flexibility, customizability, and performance.
Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Building a Modern Analytic Database with Cloudera 5.8Cloudera, Inc.
Analytic workloads and the ability to determine “what happened” are some of the most common use cases across enterprises today - helping you understand and adapt based on changing trends. However, for most businesses today, they are only able to see a piece of the story. Analytics are limited by the amount of data able to be stored and ultimately accessed, it’s time-intensive to bring in new datasets or fit unstructured data into rigid schemas, and user access is constrained to a select few who must already know the questions they’re trying to answer.
It’s no surprise that big data is disrupting this modus operandi for analytics. A modern, Hadoop-based platform is designed to help businesses break free of these analytic limitations, providing a new kind of adaptive, high-performance analytic database. The recent release of Cloudera 5.8 continues to advance Cloudera Enterprise as the foundation for these analytic workloads.
Join Justin Erickson, Senior Director of Product Management at Cloudera, and Andy Frey, Chief Technology Officer at Marketing Associates, as they discuss:
-What technology is needed to build a modern analytic database with Hadoop
-What’s new with Cloudera 5.8
-How to align your teams around agile analytics
-Real world success from Marketing Associates
-What’s next for Cloudera Enterprise’s Analytic Database
The Modern Database for Enterprise ApplicationsQAware GmbH
Gregor Bauer, Couchbase & QAware Meetup, 02.02.23
Couchbase and Kubernetes: a powerful data management duo
Gregor explains how to escape the common struggles of cloud deployments by leveraging cloud portability across platforms and providers. Here, the Couchbase Autonomous Operator for Kubernetes enables cloud portability and automates operational best practices for deploying and managing the Couchbase Data Platform.
The following key features will be presented:
Native integration with Kubernetes Operator which provides a data platform with rich query support, mobile, analytics, and full-text search functionality out of the box.
Easily deploy Couchbase within a managed private cloud or public cloud, which offers maximum flexibility, customizability, and performance.
Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
How Analytics Teams Using SSAS Can Embrace Big Data and the CloudTyler Wishnoff
You’ve been using SQL Server Analytics Services and you love it.
Its multidimensional analysis enables your team to slice and dice your data any way they want and get the results back easily.
The only problem is:
• it doesn’t work very well with the latest technologies
• It wasn’t built to handle Big Data or to serve large analytics teams
• and most frustrating of all, it still isn’t available in the Cloud
The good news is that there’s a way to unburden yourself from the limitations of SSAS, without losing the capabilities you rely on.
If you’re ready to modernize the way your team does analytics, this presentation will provide you the tools and ideas you need to do so.
This presentation will show you how to:
• Efficiently migrate your SSAS-based workload to the Cloud
• Super-charge your SSAS applications, seamlessly, easily and cost-effectively
• Provide unlimited scale of data, concurrency and deliver sub-second response
• Make your SQL/MDX queries scaling and performing beyond your imagination
• Ensure Enterprise-grade Security
• Plus, get actionable examples and stories, from organizations who have successfully overcome these challenges
For more information, visit www.Kyligence.io
Gamma Soft and NuoDB Speed Up Data Consolidation And Cloud MigrationNuoDB
Learn how Gamma Soft, a real-time data migration and synchronization company, can easily transform and integrate your data to a highly-scalable, geo-distributed NuoDB database.
Enabling Self-Service Analytics with Logical Data WarehouseDenodo
Watch full webinar here: https://buff.ly/2GNO8PC
What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses (DW) and data lakes are not easy to deal with for agile data access. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources.
Attend this webinar to learn:
* How easily new data sources can be made available for analytics and data science
* How your organization can successfully migrate to a flexible LDW architecture in a step-by-step fashion
* How LDWs help integrate self-service analytics with classic forms of business intelligence
Power to the People: A Stack to Empower Every User to Make Data-Driven DecisionsLooker
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
When it comes to the cloud, Gartner may have said it best:
“By 2020, a corporate ‘no-cloud’ policy will be as rare as a corporate ‘no-internet’ policy is today.”
If your organization is still skeptical of the cloud, now is the time to take a closer look. Faster implementation timelines and reduced maintenance costs are just two reasons why the cloud is becoming the standard across all industries.
In our webinar, we dispelled common concerns and explored the benefits of operating in the cloud. We also provided real-world examples of companies that have taken the leap and discovered just how much better business works in the cloud.
Modern apps and services are leveraging data to change the way we engage with users in a more personalized way. Skyla Loomis talks big data, analytics, NoSQL, SQL and how IBM Cloud is open for data.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
AI-Powered Analytics: What It Is and How It’s Powering the Next Generation of...Tyler Wishnoff
Learn how to empower your analysts with easier access to all the data they need, exactly when they need it - all while reducing workloads for IT and data engineering.
This presentation will walk you through those challenges, what modern options are available for solving them, and how taking an AI-powered approach to self-service analytics may yield the greatest level of data access along with the best possible performance. Learn more here: https://kyligence.io/
Addressing the systemic shortcomings of cloud analyticsSamanthaBerlant
Learn how existing open source technologies like Apache Kylin, Spark, and Mondrian can be used to increase the value of your analytics investment.
As we enter what some have called The Golden Age of Analytics, there are still some fundamental challenges that plague even the largest and most sophisticated cloud analytics adopters. Chief among these is the challenge of scale, often reflected in limitations of concurrency, multi-tenancy, distributed query performance, and all manner of latencies.
Other less obvious, but equally crucial, challenges of scale and performance have to do with IT and end-user productivity. In other words, there have been few technological advances that enable the quick deployment of big data analytics and the rapid creation of business value from the data being analyzed.
This presentation will consider a few of these systemic challenges and suggest some ways that they can be addressed with available open source technology such as Apache Kylin, Apache Spark, and Apache Mondrian.
Presenter:
Kaige Liu is a Senior Solutions Architect at Kyligence, where he works on building the next-generation big data analytics platform. Previously, he worked on the OpenStack and Bluemix team at IBM, focusing on cloud computing and virtualization technology. Kaige loves the open source community and is an active Apache Kylin committer.
During the “Architecting for the Cloud” breakfast seminar we discussed the requirements of modern cloud-based applications and how to overcome the constraints of traditional database infrastructure.
We discussed how the right cloud-based database architecture can:
- Provide easy and fast access across multiple geographies
- Handle rapid user growth by adding new servers on demand
- Provide high performance even in the face of heavy application usage
- Offer around-the-clock resiliency and uptime
- Deliver cloud-enabled apps in public, private, or hybrid cloud environments
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
Turning Data into Business Value with a Modern Data PlatformCloudera, Inc.
3 Things to Learn About:
-Real-time analytics and data in motion
-Self-service access for SQL analysts and data scientists alike
-Public cloud and hybrid infrastructure
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.
Modernizing Integration with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3CMqS0E
Today, businesses have more data and data types combined with more complex ecosystems than they have ever had before. Examples include on-premise data marts, data warehouses, data lakes, applications, spreadsheets, IoT data, sensor data, unstructured, etc. combined with cloud data ecosystems like Snowflake, Big Query, Azure Synapse, Amazon S3, Redshift, Databricks, SaaS apps, such as Salesforce, Oracle, Service Now, Workday, and on and on.
Data, Analytics, Data Science and Architecture teams are struggling to provide the business users with the right data as quickly and efficiently as possible to quickly enable Analytics, Dashboards, BI, Reports, etc. Unfortunately, many enterprises seek to meet this pressing need by utilizing antiquated and legacy 40+ year-old approaches. There is a better way. Proven by thousands of other companies.
As Forrester so astutely reported in their recent Total Economic Impact Study, companies who employed Data Virtualization reported a “65% decrease in data delivery times over ETL” and an “83% reduction in time to new revenue.”
Join us for this very educational webinar to learn firsthand from Denodo Technologies and Fusion Alliance how:
- Data Virtualization helps your company save time and money by eliminating superfluous ETL pipelines and data replication.
- Data Virtualization can become the cornerstone of your modern data approach to deliver data faster and more efficiently than old legacy approaches at enterprise scale.
- How quickly and easily, Data Virtualization can scale, even in the most complex environments, to create a universal abstraction semantic model(s) for all of your cloud, on premise, structured, unstructured and hybrid data
- Data Mesh and Data Fabric architecture patterns for maximum reuse
- Other customers have used, and are using, Data Virtualization to tackle their toughest data integration and data delivery challenges
- Fusion Alliance can help you define a data strategy tailored to your organization’s needs and requirements, and how they can help you achieve success and enable your business with self-service capabilities
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
Webinar - Delivering Enhanced Message Processing at Scale With an Always-on D...DataStax
Managing 3.8 million e-prescriptions daily for more than 1 million healthcare professionals is no small feat. And, with rapid growth in the number of digital transactions and expansion of its network, Surescripts needed to replace its legacy relational database system to address a new set of data management challenges while meeting their customers’ demanding SLAs. Join us for this on-demand webinar to hear from Keith Willard, Chief Architect at Surescripts, to learn how and why Surescripts leverages DataStax Enterprise to deliver enhanced message processing at scale.
View recording: https://youtu.be/1T6V1XAoaJQ
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
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Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
How Analytics Teams Using SSAS Can Embrace Big Data and the CloudTyler Wishnoff
You’ve been using SQL Server Analytics Services and you love it.
Its multidimensional analysis enables your team to slice and dice your data any way they want and get the results back easily.
The only problem is:
• it doesn’t work very well with the latest technologies
• It wasn’t built to handle Big Data or to serve large analytics teams
• and most frustrating of all, it still isn’t available in the Cloud
The good news is that there’s a way to unburden yourself from the limitations of SSAS, without losing the capabilities you rely on.
If you’re ready to modernize the way your team does analytics, this presentation will provide you the tools and ideas you need to do so.
This presentation will show you how to:
• Efficiently migrate your SSAS-based workload to the Cloud
• Super-charge your SSAS applications, seamlessly, easily and cost-effectively
• Provide unlimited scale of data, concurrency and deliver sub-second response
• Make your SQL/MDX queries scaling and performing beyond your imagination
• Ensure Enterprise-grade Security
• Plus, get actionable examples and stories, from organizations who have successfully overcome these challenges
For more information, visit www.Kyligence.io
Gamma Soft and NuoDB Speed Up Data Consolidation And Cloud MigrationNuoDB
Learn how Gamma Soft, a real-time data migration and synchronization company, can easily transform and integrate your data to a highly-scalable, geo-distributed NuoDB database.
Enabling Self-Service Analytics with Logical Data WarehouseDenodo
Watch full webinar here: https://buff.ly/2GNO8PC
What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses (DW) and data lakes are not easy to deal with for agile data access. A more practical solution is the logical data warehouse (LDW), which has shown to be a more agile foundation for delivering and transforming data and makes it easy to quickly plug in new data sources.
Attend this webinar to learn:
* How easily new data sources can be made available for analytics and data science
* How your organization can successfully migrate to a flexible LDW architecture in a step-by-step fashion
* How LDWs help integrate self-service analytics with classic forms of business intelligence
Power to the People: A Stack to Empower Every User to Make Data-Driven DecisionsLooker
Infectious Media runs on data. But, as an ad-tech company that records hundreds of thousands of web events per second, they have have to deal with data at a scale not seen by most companies. You can not make decisions with data when people need to write manual SQL only for queries take 10-20 minutes to return. Infectious Media made the switch to Google BigQuery and Looker and now every member of every team can get the data they need in seconds.
Infectious Media shares:
- Why they chose their current stack
- Why faster data means happier customers
- Advantages and practical implications of storing and processing that much data
Check out the recording at https://info.looker.com/h/i/308848878-power-to-the-people-a-stack-to-empower-every-user-to-make-data-driven-decisions
When it comes to the cloud, Gartner may have said it best:
“By 2020, a corporate ‘no-cloud’ policy will be as rare as a corporate ‘no-internet’ policy is today.”
If your organization is still skeptical of the cloud, now is the time to take a closer look. Faster implementation timelines and reduced maintenance costs are just two reasons why the cloud is becoming the standard across all industries.
In our webinar, we dispelled common concerns and explored the benefits of operating in the cloud. We also provided real-world examples of companies that have taken the leap and discovered just how much better business works in the cloud.
Modern apps and services are leveraging data to change the way we engage with users in a more personalized way. Skyla Loomis talks big data, analytics, NoSQL, SQL and how IBM Cloud is open for data.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
AI-Powered Analytics: What It Is and How It’s Powering the Next Generation of...Tyler Wishnoff
Learn how to empower your analysts with easier access to all the data they need, exactly when they need it - all while reducing workloads for IT and data engineering.
This presentation will walk you through those challenges, what modern options are available for solving them, and how taking an AI-powered approach to self-service analytics may yield the greatest level of data access along with the best possible performance. Learn more here: https://kyligence.io/
Addressing the systemic shortcomings of cloud analyticsSamanthaBerlant
Learn how existing open source technologies like Apache Kylin, Spark, and Mondrian can be used to increase the value of your analytics investment.
As we enter what some have called The Golden Age of Analytics, there are still some fundamental challenges that plague even the largest and most sophisticated cloud analytics adopters. Chief among these is the challenge of scale, often reflected in limitations of concurrency, multi-tenancy, distributed query performance, and all manner of latencies.
Other less obvious, but equally crucial, challenges of scale and performance have to do with IT and end-user productivity. In other words, there have been few technological advances that enable the quick deployment of big data analytics and the rapid creation of business value from the data being analyzed.
This presentation will consider a few of these systemic challenges and suggest some ways that they can be addressed with available open source technology such as Apache Kylin, Apache Spark, and Apache Mondrian.
Presenter:
Kaige Liu is a Senior Solutions Architect at Kyligence, where he works on building the next-generation big data analytics platform. Previously, he worked on the OpenStack and Bluemix team at IBM, focusing on cloud computing and virtualization technology. Kaige loves the open source community and is an active Apache Kylin committer.
During the “Architecting for the Cloud” breakfast seminar we discussed the requirements of modern cloud-based applications and how to overcome the constraints of traditional database infrastructure.
We discussed how the right cloud-based database architecture can:
- Provide easy and fast access across multiple geographies
- Handle rapid user growth by adding new servers on demand
- Provide high performance even in the face of heavy application usage
- Offer around-the-clock resiliency and uptime
- Deliver cloud-enabled apps in public, private, or hybrid cloud environments
Watch this webinar in full here: https://buff.ly/2MVTKqL
Self-Service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy.
Attend this session to learn why data virtualization:
• Is a must for implementing the right self-service BI
• Makes self-service BI useful for every business user
• Accelerates any self-service BI initiative
Turning Data into Business Value with a Modern Data PlatformCloudera, Inc.
3 Things to Learn About:
-Real-time analytics and data in motion
-Self-service access for SQL analysts and data scientists alike
-Public cloud and hybrid infrastructure
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.
Modernizing Integration with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3CMqS0E
Today, businesses have more data and data types combined with more complex ecosystems than they have ever had before. Examples include on-premise data marts, data warehouses, data lakes, applications, spreadsheets, IoT data, sensor data, unstructured, etc. combined with cloud data ecosystems like Snowflake, Big Query, Azure Synapse, Amazon S3, Redshift, Databricks, SaaS apps, such as Salesforce, Oracle, Service Now, Workday, and on and on.
Data, Analytics, Data Science and Architecture teams are struggling to provide the business users with the right data as quickly and efficiently as possible to quickly enable Analytics, Dashboards, BI, Reports, etc. Unfortunately, many enterprises seek to meet this pressing need by utilizing antiquated and legacy 40+ year-old approaches. There is a better way. Proven by thousands of other companies.
As Forrester so astutely reported in their recent Total Economic Impact Study, companies who employed Data Virtualization reported a “65% decrease in data delivery times over ETL” and an “83% reduction in time to new revenue.”
Join us for this very educational webinar to learn firsthand from Denodo Technologies and Fusion Alliance how:
- Data Virtualization helps your company save time and money by eliminating superfluous ETL pipelines and data replication.
- Data Virtualization can become the cornerstone of your modern data approach to deliver data faster and more efficiently than old legacy approaches at enterprise scale.
- How quickly and easily, Data Virtualization can scale, even in the most complex environments, to create a universal abstraction semantic model(s) for all of your cloud, on premise, structured, unstructured and hybrid data
- Data Mesh and Data Fabric architecture patterns for maximum reuse
- Other customers have used, and are using, Data Virtualization to tackle their toughest data integration and data delivery challenges
- Fusion Alliance can help you define a data strategy tailored to your organization’s needs and requirements, and how they can help you achieve success and enable your business with self-service capabilities
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
Webinar - Delivering Enhanced Message Processing at Scale With an Always-on D...DataStax
Managing 3.8 million e-prescriptions daily for more than 1 million healthcare professionals is no small feat. And, with rapid growth in the number of digital transactions and expansion of its network, Surescripts needed to replace its legacy relational database system to address a new set of data management challenges while meeting their customers’ demanding SLAs. Join us for this on-demand webinar to hear from Keith Willard, Chief Architect at Surescripts, to learn how and why Surescripts leverages DataStax Enterprise to deliver enhanced message processing at scale.
View recording: https://youtu.be/1T6V1XAoaJQ
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Similar to Assessing New Database Capabilities – Multi-Model (20)
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it.
In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen?
This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
In this webinar, Bob will focus on:
-Selecting the appropriate metadata to govern
-The business and technical value of a data catalog
-Building the catalog into people’s routines
-Positioning the data catalog for success
-Questions the data catalog can answer
Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends.
Specific learning objectives include:
- Understanding what types of challenges require data modeling to be part of the solution
- How automation requires standardization on derivable via data modeling techniques
- Why only a working partnership between data and the business can produce useful outcomes
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
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.
Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there.
Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups:
- why it matters,
- what it means,
- what gets in the way,
- who needs it (and how much they need),
- what companies believe it will accomplish.
In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance.
Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance.
In this webinar, Bob will focus on:
- Data Governance’s past, present, and future
- How trials and tribulations evolve to success
- Leveraging lessons learned to improve productivity
- The great Data Governance tool explosion
- The future of Data Governance
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind.
In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving.
Topics that you will hear addressed in this webinar:
- Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends
- Best practices for key management and apply data governance to your day-to-day
- The next wave of Confidential Computing and how to get started, including a demo
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Who Should Own Data Governance – IT or Business?DATAVERSITY
The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question.
Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more.
In this session Bob will share:
- What is meant by “the business” when it comes to owning Data Governance
- Why some people say that Data Governance in IT is destined to fail
- Examples of IT positioned Data Governance success
- Considerations for answering the question in your organization
- The final answer to the question of who should own Data Governance
It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand:
- Their current Data Management practices
- Strengths that should be leveraged
- Remediation opportunities
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
17. William McKnight
President, McKnight Consulting Group
• Frequent keynote speaker and trainer internationally
• Consulted to Pfizer, Scotiabank, Fidelity, TD
Ameritrade, Teva Pharmaceuticals, Verizon, and many
other Global 1000 companies
• Hundreds of articles, blogs and white papers in
publication
• Focused on delivering business value and solving
business problems utilizing proven, streamlined
approaches to information management
• Former Database Engineer, Fortune 50 Information
Technology executive and Ernst&Young Entrepreneur
of Year Finalist
• Owner/consultant: Research, Data Strategy and
Implementation consulting firm
2
William McKnight
The Savvy Manager’s Guide
The
Savvy
Manager’s
Guide
Information
Management
Information Management
Strategies for Gaining a
Competitive Advantage with Data
18. McKnight Consulting Group Offerings
Strategy
Training
Strategy
§ Trusted Advisor
§ Action Plans
§ Roadmaps
§ Tool Selections
§ Program Management
Training
§ Classes
§ Workshops
Implementation
§ Data/Data Warehousing/Business
Intelligence/Analytics
§ Big Data
§ Master Data Management
§ Governance/Quality
Implementation
3
20. Decisions, Decisions, Decisions
• Unprecedented variety of data store choices to meet
the needs of their varied workloads
• Enterprises have many needs for databases, including
cache, operational, data warehouse, master data, ERP,
analytical, graph data, data lake, and time series data
• While vendor offerings have exploded in recent
years, in due time frameworks will integrate
components into what amounts to a single offering
for multiple workloads, perhaps even for the
enterprise
• But what if price-performant offerings for adjacent
workloads in an enterprise have materialized?
5
21. Many Data Types
• Web Crawlers
• Open Linked Data
• JSON
• XML
• Documents
• Binary
• Graph
• Log Files
6
22. Why NoSQL for Operational Big Data
More data model flexibility
– Web Services as a data model
– No !schema first" requirement; load first
Faster time to insight from data acquisition
Relaxed ACID
– Eventual consistency
– Willing to trade consistency for availability
– ACID would crush things like storing clicks on Google
Low upfront software and development costs
Programmers love the freedoms
Fault-tolerant redundancy
Linear Scaling to “webscale”
7
23. • Placement policy:
A copy is written to the node creating the file (write affinity)
A second copy is written to a data node within the same rack (to
minimize cross-rack network traffic)
A third copy is written to a data node in a different rack (to tolerate
switch failures)
Node 5
Node 4
Node 3
Node 2
Node 1
Block
1
Block
3
Block
2
Block
1
Block
3
Block
2
Block
3
Block
2
Block
1
Objectives: load balancing, fast access, fault tolerance
DFS Block Placement
8
24. CAR
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Property Graph Model Components
Nodes
• The objects in the graph
• Can have name-value
properties
• Can be labeled
friends
friends
LIVES WITH
O
W
N
S
PERSON PERSON
Relationships
• Relate nodes by type and
direction
• Can have name-value
properties
9
25. Semantic Graph
• RDF Triple Store
– Semantic databases only work with RDF
• Target market is users of third-party
data in RDF (all Linked open data)
– Working across data sets
10
27. Data Types and NoSQL Data Models
Data Type Data Model
CSV, TSV or web logs Column, Document
Documents Document
JSON Document
Metadata catalog Column, Document
Keyed images and documents Key-Value
RDF, Linked data Graph
12
28. Key-Value Stores
What are they?
• NoSQL’s OLTP equivalent
• Extremely simple
• Key-”blob pairs”, that’s it
• Associative array data model
• Retrieve value given a key
– All access is by a key
(key,value)
13
30. Key-Value Stores
Good for:
• Any single object of unstructured data
• Storing BLOBs
• Fast writes
• Web app cache
• Session Information – get all session information in a
single put/get
• User profile data
• Massive multi-player on-line gaming
• Shopping carts (up until the payment transaction)
• Geo-localized processing
• Speed when you can’t be down
(key,value)
15
31. A multi-model database is a single, integrated
database that can store, manage and query data i
multiple models such as relational, document,
graph, key-value, column-store, cache. It is the
opposite approach to Polyglot Persistence – the
use of multiple databases in a workload.
16
32. Document-oriented Databases
What are they?
• Key-Value Stores with added capabilities
– Ability to nest sub-documents
• JSON/XML data models
• With Tree-Like Structure
• Encapsulated document objects
• Groups data together more naturally and
logically
17
33. Document-oriented Databases
Technical Characteristics:
• Store all data together
– Example: Order document contains all line items
• Documents are self-describing hierarchical tree
structures
• Unlike Key-Value Stores, the value part of the field
can be queried
18
34. Document-oriented Databases
Good for:
• Semi-structured data
• Web pages
• Web traffic/E-Commerce
• Web analytics
• Log files
• User actions/behaviors
• Content Management Systems
• Full text
• Uncertain data
• Extending object-oriented approaches
• Event logging
• JSON/XML data
19
36. Multiple NoSQL Solutions Working Together
You could use
• Key-Value Store for Shopping Cart and
Session Data
• Document or Column Store for Consuming
Completed Orders
• RDBMS for inventory (small, not served real-
time), financials
• Graph Store for Customer Relationships for
Marketing
21
37. Column Stores
What are they?
• Data model:
– A big table, with column families
– Map-reduce for querying/processing
• Schema-lite
• No single point of failure
• Operational simplicity
• Closest NoSQL implementation to RDBMS
22
38. Column Stores
Good for:
• Large amounts of data
• Data that needs compression
• Event logging
• Content Management Systems
• Data model supports semi-structured
data
• Naturally indexed (columns)
• Good at scaling out horizontally
• Time Series data
– Weather data
– Location data
– Sensor data
23
40. What to Look for in Multi-Model 1/2
• Excellent implementation of multiple
models
• Single copy of data
• Model change propagation
• Works in microservices world
• Submillisecond response time
25
41. What to Look for in Multi-Model 2/2
• Globally distributed multi-region
deployments
• Cross-model data processing language
and optimizer
• Edge-capable database
• JSON flattening without data explosion
• Universal indices
26
42. Emerging Technologies
• Use of artificial
intelligence (AI)
• Integration with data
catalog platforms
• Robust user
experience
• Multi-cloud/native
application
27