IBM Cloud Pak for Data is a unified platform that simplifies data collection, organization, and analysis through an integrated cloud-native architecture. It allows enterprises to turn data into insights by unifying various data sources and providing a catalog of microservices for additional functionality. The platform addresses challenges organizations face in leveraging data due to legacy systems, regulatory constraints, and time spent preparing data. It provides a single interface for data teams to collaborate and access over 45 integrated services to more efficiently gain insights from data.
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon RedshiftAmazon Web Services
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos. A customer is joining us to share how they were able to extend their data warehouse to their data lake to encompass multiple data sources and data formats. This modern architecture helps them tie together data sources to get actionable insights across their business units.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
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.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon RedshiftAmazon Web Services
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos. A customer is joining us to share how they were able to extend their data warehouse to their data lake to encompass multiple data sources and data formats. This modern architecture helps them tie together data sources to get actionable insights across their business units.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
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.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
This Red Hot session is designed for customers who are currently using Oracle Cloud applications such as Fusion and EPM, and are interested in gaining a better understanding of the integration options that are available to them.
Here is a high level agenda:
- We will start by discussing the modern data platform on OCI, the Lakehouse architecture and the OCI related services that supports it.
- We will then discuss the data extraction methods available on OCI for Fusion and EPM.
- Last but not least, we will end with a few best practices and possible use cases.
In the interest of time, we will mainly focus on integration patterns that are recommended for Fusion and EPM, but don’t hesitate to reach out if you would to talk to us about other Oracle applications.
Enjoy!
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Getting Started with Databricks SQL AnalyticsDatabricks
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
This is the presentation for the talk I gave at JavaDay Kiev 2015. This is about an evolution of data processing systems from simple ones with single DWH to the complex approaches like Data Lake, Lambda Architecture and Pipeline architecture
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Denodo Data Virtualization Platform: Overview (session 1 from Architect to Ar...Denodo
This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on.
More information and FREE registrations to this webinar: http://goo.gl/fLi2bC
To learn more click to this link: http://go.denodo.com/a2a
Join the conversation at #Architect2Architect
Agenda:
The Denodo Platform
Platform Architecture
Key Modules
Connectors
Data Services and APIs
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Analytics and Lakehouse Integration Options for Oracle ApplicationsRay Février
This Red Hot session is designed for customers who are currently using Oracle Cloud applications such as Fusion and EPM, and are interested in gaining a better understanding of the integration options that are available to them.
Here is a high level agenda:
- We will start by discussing the modern data platform on OCI, the Lakehouse architecture and the OCI related services that supports it.
- We will then discuss the data extraction methods available on OCI for Fusion and EPM.
- Last but not least, we will end with a few best practices and possible use cases.
In the interest of time, we will mainly focus on integration patterns that are recommended for Fusion and EPM, but don’t hesitate to reach out if you would to talk to us about other Oracle applications.
Enjoy!
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Struggling to keep up with an ever-increasing demand for data at your organisation? Do you spend hours tinkering with your streaming data pipelines? Does that one data scientist with direct EDW access keep you up at night? Introducing Snowflake, a brand new SQL data warehouse built for the cloud. We’ve designed and implemented a unique cloud-based architecture that addresses the most common shortcomings of existing data solutions. With Snowflake, you can unlock unlimited concurrency, enable instant scalability, and take advantage of built-in tuning and optimisation. Join us and find out what Netflix, Adobe, and Nike all have in common.
Event: Passcamp, 07.12.2017
Speaker: Stefan Kirner
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel: https://www.inovex.de/blog
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Getting Started with Databricks SQL AnalyticsDatabricks
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...Denodo
Watch full webinar here: https://buff.ly/46pRfV7
This Denodo session explores the power of data virtualization, shedding light on its architecture, customer value, and a diverse range of use cases. Attendees will discover how the Denodo Platform enables seamless connectivity to various data sources while effortlessly combining, cleansing, and delivering data through 5 differentiated use cases.
Architecture: Delve into the core architecture of the Denodo Platform and learn how it empowers organizations to create a unified virtual data layer. Understand how data is accessed, integrated, and delivered in a real-time, agile manner.
Value for the Customer: Explore the tangible benefits that Denodo offers to its customers. From cost savings to improved decision-making, discover how the Denodo Platform helps organizations derive maximum value from their data assets.
Five Different Use Cases: Uncover five real-world use cases where Denodo's data virtualization platform has made a significant impact. From data governance to analytics, Denodo proves its versatility across a variety of domains.
- Logical Data Fabric
- Self Service Analytics
- Data Governance
- 360 degree of Entities
- Hybrid/Multi-Cloud Integration
Watch this illuminating session to gain insights into the transformative capabilities of the Denodo Platform.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/3fpitC3
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
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.
How IBM is Creating a Foundation for Cloud InnovationCCG
IBM is making waves in the Cloud Innovation. At our Data Analytics Meetup, Tom Ericsson, explores the transformation that IBM has taken with its recent announcement of moving from Bluemix to Cloud.
While many enterprises consider cloud computing the savior of their data strategy, there is a process they should be following when looking to leveraging database-as-a-service. This includes understanding their own data requirements, selecting the right cloud computing candidate, and then planning for the migration and operations. A huge number of issues and obstacles will inevitably arise, but fortunately best practices are emerging. This presentation will take you through the process of moving data to cloud computing providers.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
10 Best Data Integration Software Platforms.pdfXoxoday Compass
Data integration software platforms are on the rise; inculcating our best data integration platforms gives you an edge over the competition. Learn more.
https://blog.getcompass.ai/data-integration-software/
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
The affect of service quality and online reviews on customer loyalty in the E...
IBM Cloud pak for data brochure
1. Forrester Wave Leader
Enterprise Insight Platform Q1 2019
Cloud Pak for Data
Let’s simplify your information architecture
and put your data to work
2. Don’t be burdened by
your data.
Rely on your data.
IBM Cloud Pak for Data is a single unified platform which helps to unify
and simplify the collection, organization and analysis of data. Enterprises
can turn data into insights through an integrated cloud-native
architecture. IBM Cloud Pak for Data is extensible, easily customized to
unique client data and AI landscapes through an integrated catalog of
IBM, open source and third-party microservices add-ons.
2Cloud Pak for Data
3. It’s becoming more challenging and complex
to establish data driven practices – and data
driven practices are necessary.
The Challenge
Every company must be data driven in
today’s ecosystem.
In the data explosion era the globe is now
creating 2.5 quintillion bytes of data every
day (link).
When it comes to leveraging data though, on
average roughly 70% of data produced
within an enterprise goes completely
unused (link).
Leading 79.4% of executives fearful of
disruption by data-driven startups or
companies and only 7.3% confident in their
future data strategy (link).
It makes sense why most
enterprises are challenged to
be truly data driven.
1. A majority of enterprises have some presence
of now legacy systems making it difficult to
connect and utilize all data sources.
2. Industries are dealing with stronger regulatory
constraints to protect data.
3. Digital transformation has forced enterprises
to strategically think differently than they have in
the past.
4. Highly paid data teams are spending 50-80%
of their time (multiple sources) simply finding,
preparing, and governing data sets before any
business insights work can begin. Growth in team
working silos and work complexity also contribute
to inefficiency.
5. A primary reason why 97.2% of executives say
they’re building or launching Big Data and AI
initiatives is that within the past decade nearly
three-quarters of Fortune 1,000 companies have
been replaced (link). Many are being replaced by
companies like Facebook and Amazon who have
reached Expert levels when it comes to operating
as a data driven company; rather than using data
and AI to cut costs or grow their business they are
reshaping entire industries.
The Factors
We aren’t leveraging the
data we create.
there is
no AI
without IA
Let’s start by
understanding the
situation
3Cloud Pak for Data
4. A cohesive modern data strategy is necessary to
achieve modern AI and analytics results.
Our approach is based on three key take-aways: data fuels digital transformation, AI
unlocks the value of data, and hybrid cloud democratizes data. These guide our over-
arching strategy for achieving real business value from data and AI.
The Journey to AI
Infuse
Operationalize AI with trust and transparency
Analyze
Scale insights with AI everywhere
Organize
Create a trusted analytics foundation
Collect
Make data simple and accessible
The Future of AI is Flexible
It all starts with a hybrid multi-cloud approach.
Keep your head
above the clouds
4Cloud Pak for Data
Our Approach
5. “Simplify your information architecture
and put your data to work”
Find, connect to, govern, and leverage your data
across multiple sources without needing to move
or replicate.
Automate many mundane and repeatable tasks
like cleaning, matching, and metadata creation to
reduce data prep time by 80%.
Leverage deployment flexibility amongst any
Cloud, Hybrid Cloud or Private Cloud environment
and provider with Red Hat OpenShift.
Eliminate working silos with a single unified
experience allowing all data users to collaborate
and connect to multiple analytics applications and
models.
Centralize your teams’ workflow and operations
management with an ecosystem of 45+ integrated
services.
Enable your highly skilled and paid data teams to
spend more of their time on business value
generating innovations in big data and analytics.
What we mean by
How to simplify your
information architecture “IA”
How to get data
working for you
5Cloud Pak for Data
6. INFRASTRUCTURE
LAYER
KUBERNETES
LAYER
PLATFORM
INTERFACE
LAYER
SERVICES
LAYER
On
Premise
Avoid lock-in and leverage all
cloud infrastructure with our
Any Cloud mentality.
Leverage the leading hybrid
cloud, enterprise container
platform for an innovative
and fast deployment strategy.
5. ANY CLOUD
4.
At a click, access and deploy
an ecosystem of 45+
analytics services and
templates from IBM and third
parties. More on page 8
1. SERVICES ECOSYSTEM
Query across multiple data
sources fast and easy without
moving your data.
More on page 7
2. DATA VIRTUALIZATION
Complete yet simple.
Speed time to value with a
single platform that
integrates data management,
data governance and
analysis for greater efficiency
and improved use of
resources.
3. PLATFORM INTERFACE
High level view
of the…
Check out details at
page numbers
referenced above
Explore how we’ve constructed the Forrester Wave’s Leading
enterprise insights platform.
6Cloud Pak for Data
The Platform
7. Integrate your data and teams without needing to
overhaul existing infrastructure.
READ THE REPORT
Data warehouses
and data marts
Relational
databases No SQL
Spreadsheets and
text files
Big data; Hadoop
Ecosystem
Data Virtualization
and Caching Layer
A unified data asset catalog,
lineage and provenance
Access control and security
policies
Data silos are very good at holding potential insights from data tightly within their barriers. Leaving the tedious task of
searching through, moving, and governing those data resources to highly paid and skilled data teams. Often that work
takes 80% of the time dedicated to a single initiative.
Data virtualization connects those data silos to make them appear as if they were a single data set on your desktop. It
also leverages servers where data does sit by performing analytics queries and then simply returning the results to the
original application.
No data is copied. It exists only at the source.
7Cloud Pak for Data
Removing Data Silos
8. Collect
Premium Add-ons,
Accelerators or Existing
License Trade-ups
Cloud Pak for Data
Base Capabilities
à la carte
Let the integrated end-to-end analytics services grow
and scale with you on your journey to AI. Deployment
is easy and premium add-ons have flexible licensing
models. Explore the capabilities that are ready at your
fingertips.
Additional licensing
Data Virtualization
•Query Anything, Anywhere (virtualized data across
multiple sources) •Auto-discovery of data source &
metadata with built-in governance •Distributed Parallel
Processing
Db2 Warehouse
•In-memory optimized columnar engine •SQL, Spatial,
XML, and JSON support •Scales to peta-bytes, portable
and compatible with multiple DBs
Db2 AESE
Db2 Advanced Enterprise Server Edition is suitable for
transactional, warehouse, and mixed workloads
PostgreSQL
Open source object-relational database designed for
developers
Streams
Develop and run applications that process in-flight data
with the IBM Streams add-on. IBM Streams enables
continuous and fast analysis of massive volumes of
moving data to help improve the speed of business
insight and decision making.
Db2 Event Store
Memory-optimized database designed to rapidly ingest
and analyze streamed data for event-driven applications
MongoDB
A cross-platform document-oriented database program.
Analyze & Infuse on
next page
Organize
Data Discovery
Includes services from Information Analyzer
•Default Quality Rules •Quality Score •Ability to Sample
Data •Create Connections •Assign Terms, Rules •Auto
Term Assignment •Review, Approval Process
Data Integration
Includes services from DFD & Datastage
•Create, Update, Delete Jobs •Create, Update, Delete
Connections •Compile Jobs •Job Logs
Data Catalog
Includes services from Information Governance Catalog
& Watson Knowledge Catalog
•Import Business Terms (UI) •Create Policies & Rules
•Import Policies & Rules (UI) •Asset Explorer •Search
Assets •Graph Explorer •Comments, Ratings
Infosphere Information Regulatory
Accelerator
Designed to reduce costs and complexity by:
Extracting selected key terms, available
definitions, policy and controls from the
regulatory taxonomy using Machine Learning,
thereby reducing the manual effort involved
in this process.
Infosphere DataStage Edition
An ETL tool and part of the IBM Information
Platforms Solutions suite and IBM InfoSphere
Watson Knowledge Catalog Pro
A data catalog that is tightly integrated with an enterprise
data governance platform.
8Cloud Pak for Data
Integrated Services Menu
9. Analyze
Infuse
Watson Studio
•Environments (Jupyter, RStudio, Zeppelin, etc.)
•Scripting & Job Automation •Machine Learning
Frameworks & Spark •Image & Packet Management
•Model Management & Deployment •Git Version Control
Cognos Dashboard
Integrates reporting, modeling, analysis, exploration,
dashboards, stories, and event management so you can
understand your organization's data, and make effective
business decisions.
Watson API Kit
•Watson Knowledge Studio •Natural Language
Understanding •Speech to Text •Text to Speech
Watson Assistant
Building conversational interfaces into any
application, device, or channel
Watson OpenScale
Open platform to operate and automate AI across its
lifecycle
Cognos Analytics
Business intelligence and analytics solution that
makes it easy to visualize, analyze and share insights
about your business
Watson Discovery
•Watson Knowledge Studio •Watson Explorer
•Watson Discovery
Watson Studio Premium
•SPSS Modeler, Data Refinery •Decision
Optimization •Model Builder (AutoML)
•Hadoop Services for SPSS / Notebook,
SPSS SQL Pushback •WML Advanced
Training (batch training, HPO, Distributed
Deep Learning) •Continuous Learning
Services
Ask your IBM representative about ways to get
started with Cloud Pak for Data today.
Enterprise
Edition
Supported on any
cloud provider.
Cloud Native
Edition
Cloud Pak for Data
System
Supported on any
cloud provider.
Software + Hardware;
optimized and tested
hyper-converged
system.
9Cloud Pak for Data
