We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Consolidate your data marts for fast, flexible analytics 5.24.18Cloudera, Inc.
In this webinar, Cloudera and AtScale will showcase:
How a company can modernize their analytic architecture to deliver flexibility and agility to more end-users.
How using AtScale’s Universal Semantic layer can end the data chaos and allow business users to use the data in the modern platform.
Highlight the performance of AtScale and Cloudera’s analytic database with newly completed TPC-DS standard benchmarking.
Best practices for migrating from legacy appliances.
Big data journey to the cloud rohit pujari 5.30.18Cloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
The 6th Wave of Automation: Automation of Decisions | Cloudera Analytics & Ma...Cloudera, Inc.
This presentation provides detail on how we are now in the 6th wave of automation, that is based on Machine Learning. In this 6th wave, Cloudera plays a critical role in providing the data platform for Machine Learning and Analytics built for the Cloud.
Making Self-Service BI a Reality in the EnterpriseCloudera, Inc.
For most analysts, the pace of analytics and data science can be frustrating. The common waterfall approach works well for the fixed reports, but it can be a lengthy process to request additional data sets, create new reports, or serve new use cases. So it’s no surprise that organizations are looking to shift towards a self-service model, empowering business users to discover and iterate quickly.
However, it’s not just about opening up this access, but also ensuring the results are accurate and trusted. When there are petabytes of data, how does a user know which tables to use and which are most relevant? How do you strike the balance between discovery and agility, while still meeting enterprise governance standards to truly get more value from your data?
During this webinar, you’ll learn how to empower end-users to make self-service BI a reality within your organization while fostering governance collaboration between all data stakeholders. We’ll discuss and demo:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Collaboration and access outside of just SQL for data science and beyond
In addition, we will walk through best practices and considerations when developing your organizational strategy around self-service analytics, and highlight several real-world success stories from a wide range of industries.
3 things to learn:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
Preparing data for analysis and insights is the foundation of any data-driven exercise. Moving workloads to a PaaS, be it data engineering, analytic database, or data science requires a two step leap of faith - in trusting the public cloud, and then your PaaS vendor. In this webinar we will discuss the architecture of a PaaS solution for data management and understand the nitty gritty details of what exactly this involves with the following:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
3 things to learn:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...Cloudera, Inc.
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
Consolidate your data marts for fast, flexible analytics 5.24.18Cloudera, Inc.
In this webinar, Cloudera and AtScale will showcase:
How a company can modernize their analytic architecture to deliver flexibility and agility to more end-users.
How using AtScale’s Universal Semantic layer can end the data chaos and allow business users to use the data in the modern platform.
Highlight the performance of AtScale and Cloudera’s analytic database with newly completed TPC-DS standard benchmarking.
Best practices for migrating from legacy appliances.
Big data journey to the cloud rohit pujari 5.30.18Cloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
The 6th Wave of Automation: Automation of Decisions | Cloudera Analytics & Ma...Cloudera, Inc.
This presentation provides detail on how we are now in the 6th wave of automation, that is based on Machine Learning. In this 6th wave, Cloudera plays a critical role in providing the data platform for Machine Learning and Analytics built for the Cloud.
Making Self-Service BI a Reality in the EnterpriseCloudera, Inc.
For most analysts, the pace of analytics and data science can be frustrating. The common waterfall approach works well for the fixed reports, but it can be a lengthy process to request additional data sets, create new reports, or serve new use cases. So it’s no surprise that organizations are looking to shift towards a self-service model, empowering business users to discover and iterate quickly.
However, it’s not just about opening up this access, but also ensuring the results are accurate and trusted. When there are petabytes of data, how does a user know which tables to use and which are most relevant? How do you strike the balance between discovery and agility, while still meeting enterprise governance standards to truly get more value from your data?
During this webinar, you’ll learn how to empower end-users to make self-service BI a reality within your organization while fostering governance collaboration between all data stakeholders. We’ll discuss and demo:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Collaboration and access outside of just SQL for data science and beyond
In addition, we will walk through best practices and considerations when developing your organizational strategy around self-service analytics, and highlight several real-world success stories from a wide range of industries.
3 things to learn:
Strategies of consolidating data across silos for fast, flexible access
Enabling easy discovery and exploration, including understanding which data to trust and where to start
New capabilities for intelligent query assistance as well as immediate performance optimizations and recommendations as-you-go
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
Preparing data for analysis and insights is the foundation of any data-driven exercise. Moving workloads to a PaaS, be it data engineering, analytic database, or data science requires a two step leap of faith - in trusting the public cloud, and then your PaaS vendor. In this webinar we will discuss the architecture of a PaaS solution for data management and understand the nitty gritty details of what exactly this involves with the following:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
3 things to learn:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...Cloudera, Inc.
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
Self-service Big Data Analytics on Microsoft AzureCloudera, Inc.
In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications - without onerous administration.
The Vision & Challenge of Applied Machine LearningCloudera, Inc.
Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
Customer Best Practices: Optimizing Cloudera on AWSCloudera, Inc.
Join Cloudera’s Alex Moundalexis, who will discuss time-saving design and best practices for deploying Cloudera Enterprise clusters in AWS. He will also be joined by Josh Hammer, Partner Solutions Architect at Amazon Web Services who will highlight unique advantages of running Cloudera on AWS.
In this interactive webinar, we will hear from Celgene, a global biopharmaceutical company and we will explore best practices of running your Cloudera Enterprise cluster on AWS:
AWS components (EC2, S3, RDS, EBS, VPC, Direct Connect, Service Limits)
Deployment Topology
Roles & Instance Types
Networking, Connectivity and Security
Storage Configuration
Capacity Planning
Provisioning Instances
3 things to learn:
AWS components (EC2, S3, RDS, EBS, VPC, Direct Connect, Service Limits)
Networking, Connectivity and Security
Deployment Topology
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
Machine learning and analytics applications are exploding in the enterprise; driving use cases for preventative maintenance, delivering new desirable product offers to customers at the right time, and combating insider threats to your business.
But each of these high-value use cases rely on a variety of data analysis capabilities working in concert to combine data from different sources into a single coherent picture. Cloudera SDX delivers a “shared data experience” that makes applications easier to develop, less expensive to deploy and more consistently secure.
3 things to learn:
* Why multi-function applications are difficult to build and secure
* How shared catalog, governance, management, and security applied consistently everywhere can deliver a “shared data experience”
* How enterprise customers are building new, high-value applications with SDX
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Big data journey to the cloud 5.30.18 asher bartchCloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
In this webinar, we’ll show you how Cloudera SDX reduces the complexity in your data management environment and lets you deliver diverse analytics with consistent security, governance, and lifecycle management against a shared data catalog.
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
3 Things to Learn:
-How data is driving digital transformation to help businesses innovate rapidly
-How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business
-How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
A Community Approach to Fighting Cyber ThreatsCloudera, Inc.
3 Things to Learn About:
*Infinitely scale data storage, access, and machine learning
*Provide community defined open data models for complete enterprise visibility
*Open up application flexibility while building on a future proofed architecture
Driving Better Products with Customer Intelligence Cloudera, Inc.
In today’s fast moving world, the ability to capture and process massive amounts of data and make valuable insights is key to gaining a competitive advantage. For RingCentral, a leader in Unified Communications, this is very true since they work with over 350,000 organizations worldwide. With such scale, it can be difficult to address quality issues when they appear while supporting additional calls.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Workload Experience Manager (XM) gives you the visibility necessary to efficiently migrate, analyze, optimize, and scale workloads running in a modern data warehouse. In this recorded webinar we discuss common challenges running at scale with modern data warehouse, benefits of end-to-end visibility into workload lifecycles, overview of Workload XM and live demo, real-life customer before/after scenarios, and what's next for Workload XM.
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.
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloads
Self-service Big Data Analytics on Microsoft AzureCloudera, Inc.
In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications - without onerous administration.
The Vision & Challenge of Applied Machine LearningCloudera, Inc.
Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
Customer Best Practices: Optimizing Cloudera on AWSCloudera, Inc.
Join Cloudera’s Alex Moundalexis, who will discuss time-saving design and best practices for deploying Cloudera Enterprise clusters in AWS. He will also be joined by Josh Hammer, Partner Solutions Architect at Amazon Web Services who will highlight unique advantages of running Cloudera on AWS.
In this interactive webinar, we will hear from Celgene, a global biopharmaceutical company and we will explore best practices of running your Cloudera Enterprise cluster on AWS:
AWS components (EC2, S3, RDS, EBS, VPC, Direct Connect, Service Limits)
Deployment Topology
Roles & Instance Types
Networking, Connectivity and Security
Storage Configuration
Capacity Planning
Provisioning Instances
3 things to learn:
AWS components (EC2, S3, RDS, EBS, VPC, Direct Connect, Service Limits)
Networking, Connectivity and Security
Deployment Topology
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
Machine learning and analytics applications are exploding in the enterprise; driving use cases for preventative maintenance, delivering new desirable product offers to customers at the right time, and combating insider threats to your business.
But each of these high-value use cases rely on a variety of data analysis capabilities working in concert to combine data from different sources into a single coherent picture. Cloudera SDX delivers a “shared data experience” that makes applications easier to develop, less expensive to deploy and more consistently secure.
3 things to learn:
* Why multi-function applications are difficult to build and secure
* How shared catalog, governance, management, and security applied consistently everywhere can deliver a “shared data experience”
* How enterprise customers are building new, high-value applications with SDX
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Big data journey to the cloud 5.30.18 asher bartchCloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
In this webinar, we’ll show you how Cloudera SDX reduces the complexity in your data management environment and lets you deliver diverse analytics with consistent security, governance, and lifecycle management against a shared data catalog.
Part 1: Cloudera’s Analytic Database: BI & SQL Analytics in a Hybrid Cloud WorldCloudera, Inc.
3 Things to Learn About:
* On-premises versus the cloud: What’s the same and what’s different?
* Design and benefits of analytics in the cloud
* Best practices and architectural considerations
3 Things to Learn:
-How data is driving digital transformation to help businesses innovate rapidly
-How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business
-How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
A Community Approach to Fighting Cyber ThreatsCloudera, Inc.
3 Things to Learn About:
*Infinitely scale data storage, access, and machine learning
*Provide community defined open data models for complete enterprise visibility
*Open up application flexibility while building on a future proofed architecture
Driving Better Products with Customer Intelligence Cloudera, Inc.
In today’s fast moving world, the ability to capture and process massive amounts of data and make valuable insights is key to gaining a competitive advantage. For RingCentral, a leader in Unified Communications, this is very true since they work with over 350,000 organizations worldwide. With such scale, it can be difficult to address quality issues when they appear while supporting additional calls.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Workload Experience Manager (XM) gives you the visibility necessary to efficiently migrate, analyze, optimize, and scale workloads running in a modern data warehouse. In this recorded webinar we discuss common challenges running at scale with modern data warehouse, benefits of end-to-end visibility into workload lifecycles, overview of Workload XM and live demo, real-life customer before/after scenarios, and what's next for Workload XM.
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.
[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloads
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
8.17.11 big data and hadoop with informatica slideshareJulianna DeLua
This presentation provides a briefing on Big Data and Hadoop and how Informatica's Big Data Integration plays a role to empower the data-centric enterprise.
Enabling Next Gen Analytics with Azure Data Lake and StreamSetsStreamsets Inc.
Big data and the cloud are perfect partners for companies who want to unlock maximum value from all of their unstructured, semi-structured, and structured data. The challenge has been how to create and manage a reliable end-to-end solution that spans data ingestion, storage and analysis in the face of the volume, velocity and variety of big data sources.
In this webinar, we will show you how to achieve big data bliss by combining StreamSets Data Collector, which specializes in creating and running complex any-to-any dataflows, with Microsoft's Azure Data Lake and Azure analytic solutions.
We will walk through an example of how a major bank is using StreamSets to transport their on-premise data to the Azure Cloud Computing Platform and Azure Data Lake to take advantage of analytics tools with unprecedented scale and performance.
DataLakes kan skalere i takt med skyen, nedbryde integrationsbarrierer og data gemt i siloer og bane vejen for nye forretningsmuligheder. Det er alt sammen med til at give et bedre beslutningsgrundlag for ledelse og medarbejdere. Kom og hør hvordan.
David Bojsen, Arkitekt, Microsoft
Apache Hadoop Summit 2016: The Future of Apache Hadoop an Enterprise Architec...PwC
Hadoop Summit is an industry-leading Hadoop community event for business leaders and technology experts (such as architects, data scientists and Hadoop developers) to learn about the technologies and business drivers transforming data. PwC is helping organizations unlock their data possibilities to make data-driven decisions.
Accelerate Cloud Migrations and Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3N46zxX
Cloud migration brings scalability and flexibility, and often reduced cost to organizations. But even after moving to the cloud, more often than not, organizational data can be found to be siloed, hard to access and lacking centralized governance. That leads to delay and often missed opportunities in value creation from enterprise data. Join Amit Mody, Senior Manager at Accenture, in this keynote session to learn why current physical data architectures are hindrance to value creation from data, what is a logical data fabric powered by data virtualization and how a logical data fabric can unlock the value creation potential for enterprises.
Against the backdrop of Big Data, the Chief Data Officer, by any name, is emerging as the central player in the business of data, including cybersecurity. The MITCDOIQ Symposium explored the developing landscape, from local organizational issues to global challenges, through case studies from industry, academic, government and healthcare leaders.
Joe Caserta, president at Caserta Concepts, presented "Big Data's Impact on the Enterprise" at the MITCDOIQ Symposium.
Presentation Abstract: Organizations are challenged with managing an unprecedented volume of structured and unstructured data coming into the enterprise from a variety of verified and unverified sources. With that is the urgency to rapidly maximize value while also maintaining high data quality.
Today we start with some history and the components of data governance and information quality necessary for successful solutions. I then bring it all to life with 2 client success stories, one in healthcare and the other in banking and financial services. These case histories illustrate how accurate, complete, consistent and reliable data results in a competitive advantage and enhanced end-user and customer satisfaction.
To learn more, visit www.casertaconcepts.com
Democratized Data & Analytics for the CloudPrecisely
In an era driven by data, organizations are constantly seeking ways to harness the power of their data assets to make informed decisions, gain competitive advantages, and foster innovation. The cloud has emerged as a game-changer, offering unparalleled scalability and accessibility for data and analytics solutions. However, achieving true democratization of data and analytics in the cloud remains a significant challenge.
In this session we will discuss:
· Why companies are pushing to move workloads to the cloud
· How data silos and a lack of democratized data can impact organizations
· Best practices and expectations for bringing data to the cloud for analytics
· Precisely’s solution for trusted data and analytics for the cloud
Watch our 10-minute webinar and embark on a journey to democratize data and analytics, enabling your organization to thrive in the data-driven age. Whether you are a data professional, IT leader, or business executive, this session will equip you with the knowledge and tools to harness the full potential of your data assets in the cloud.
Data and Application Modernization in the Age of the Cloudredmondpulver
Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.
Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.
Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:
When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?What value does real-time replication play in migrating data and applications to modern cloud data architectures?What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds
Big Data Solutions on Cloud – The Way Forward by Kiththi Perera SLTKiththi Perera
ITU-TRCSL Symposium on Cloud Computing 2015 Colombo
Session 04: Big Data Strategy in the Cloud and Applications
Speaker's PPT by K. A. Kiththi Perera, Chief Enterprise and Wholesale Officer, Sri Lanka Telecom
Similar to Big data journey to the cloud maz chaudhri 5.30.18 (20)
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Session Overview
-------------------------------------------
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4. Value Identification Value Demonstration Value Realization
Exploring big data and
working to identify a
business case
Past the business case
and need to demonstrate
value for broader adoption
Implemented early use
cases with limited value
and lacking traction
5. It takes a long time
We don’t have big data
I’m not sure how to
start
All our data is
structured data
We already have a
data warehouse
We don’t have
business case for it
It is hard to find the
required skillsets
No one using our
Hadoop data
Hadoop ecosystem is
overwhelming
Business users are not
happy
Big data has come long way and the enterprises are at different phase of their journey. However,
broader adoption of the computation ecosystem is still in its early stages.
It is too expensive
We yet to realize the
benefits it promises
Value Identification Value Demonstration Value Realization
Exploring big data and
working to identify a
business case
Past the business case
and need to demonstrate
value for broader adoption
Implemented early use
cases with limited value
and lacking traction
7. Traditional architectures use rigid data models, costly platforms, resource-intensive ETL and lack
support for new use cases.
Rigid Data Architecture
Early binding to the pre-defined schema makes it inflexible
and costly
Flexible Architecture
Data is ingested and transformed without prior knowledge of target
schema
Costly Infrastructure and Solution
Data duplicated across costly platforms
50-70% spend on acquisition and integration
Simplified Infrastructure and Solution
Flexible on-premise and cloud infrastructure
API-based pipelines automate data ingestion
Lacks Support for “New” Use Cases
Data silo’s impede real-time processing required to support
modern use cases
Best Suited for “New” Use Cases
Centralized hub for heterogeneous data and variety of tools enable
real-time analytics
Declining Talent Pool
The new talent lacks excitement for the traditional
technologies and tools
Growing Talent Pool
Elevated interest in data engineering and data science work
Traditional Modern
8. Requires army of costly professionals to support longer delivery cycles and brittle data processes.
Slower Speed-to-Market
Longer delivery lifecycle involving too many project phases
Accelerated Speed-to-Market
Separation of data management from discovery and analytics
accelerates solution delivery
Heavy Reliance on Costly IT Resources
Point-to-point ETL and early binding data model requires IT
resources for any data changes
Enabled Business Self-Service
Centralized data enables “data wrangling” and analytics by
business users and data scientists
Army of Data Professionals Streamlined Data Roles
Traditional Modern
10. Select outcome-based high impact use case(s) and deliver minimal viable product (MVP) to demonstrate
immediate success.
.
11. Story contact:
C L I E N T S O L U T I O N S I N D U S T R Y
The client had a vision to drive improved customer experience and engagement through
personalized marketing campaigns and needed an on-premise solution that enables the initial use
cases and provides foundation for enterprise-wide analytics. Slalom architected a multi-zone data
lake to harness and analyze internal and external customer and product data, enabling real-time
analytics and a personalized customer experience.
Financial ServicesA financial services company serving over 16 million
customers nationwide. They pride themselves on being
able to provide a personal touch for their customers,
and the size of their customer base meant they needed
a solution that would be able to integrate large amounts
of traditionally siloed customer and product data.
Enterprise data hub provides
foundation for data-driven culture
A L L I A N C E S
Data architecture and
solution design
Data governance
deployment
Multi-zone data lake
design and buildout
Ingestion and
integration using
metadata-based big
data integration tool
Data discovery
enabled and Tableau
dashboard deployed
Financial Services
INDUSTRY
BIG DATA SERVICES
Big Data Startup Planning
Big Data Governance
Big Data Implementation
Enablement and Adoption
STORC
13. We think, most of the organizations lack engineering skills required to fully leverage Hadoop ecosystem
and realize the potential of new technologies.
Approach Culture
Organizations are using
traditional source-to-target
approach of acquiring and
integrating data for known
use cases
Mindset has to change from
hoarding and protecting
information to making it
easy to access and use
data as an enterprise asset
Architecture Skills
Usually considered an IT
infrastructure project,
Hadoop is used as a large
file system to dump data
files with limited use and
marginal business value
Majority of the data
professionals (ETL
developers, data analysts)
lack engineering skills
required to fully leverage
Hadoop technologies
14. Smart data lake should be....
Enterpise Scale Auditable
Governed
SupportedSecured
Multi-Use Support
Extensible
Open SourceStandardized
Designed
Right
Governed for
Adoption
Economical
to Use
15. Data Lake
DATA SOURCES DATA MANAGEMENT
Data Lake
RAW
Persistence of
source data
Streaming
Files
Databases
EgressAPIs
Standardized,
reconciled, and
quality checked
ENRICHED
Discovery/
Sandbox
DISCOVERY
DATA STORAGE OPTIONS
HDFS, S3
MODELED
Data Governance & Master Data Management
DATA DELIVERY &
CONSUMPTION
BI & REPORTING
MOBILE &
WEB APPS
EDW On Premise
Relational NoSQL
EDW in Cloud
Relational NoSQL
EXTERNAL BUSINESS
PARTNERS
Multi-zone, self-governed data lake to provide secure and flexible data architecture to harness enterprise
data for accelerated speed to insight.
16. Data Lake
DATA SOURCES HADOOP SOLUTION COMPONENTS
Streaming
Files
Databases
Batch
Streaming
Acquisition and
Ingestion
Transformation
Discovery and
Modeling
RAW ENRICHED DISCOVERY
The architecture implements data pipelines using our purpose-built open source integration APIs
accelerating implementation by 9-12 weeks.
17. The accelerator enables self-service by allowing data analysts and data SMEs to ingest new data
sources and promote data through the lake with limited to no IT dependencies.
DATA SOURCES
Streaming
Files
Databases
RAW
Business/
Data SME
METADATA MANAGEMENT & CONTROL API
Files
TARGETS
18. Foundation Migration Optimization
Assess and Prioritize
Applications
Application Analysis
System
Backlog
Optimize SystemsImplement
Security,
Networking, and
Operating Models
Security & Operations
Assessment
Develop Security and
Operating Model
Application Migration Factory
Sprint 1 - n
Workload 1
Workload 2
Workload n
Workload 1
Workload 2
Workload n
Strategy Definition
Outline Desired
Outcomes
Build & Transition Organization
Process Service Model
Org Structure Capabilities
Governance Metrics
Communications, Training, Change Mgmt
Improvement
System Prioritization
& Roadmap
Design, Migrate, Integrate and Validate
Applications
Value
Realization
Continuous Feedback
Transition to
Operations
On-Premise Cloud
Cloud presents an opportunity to transform on premise workloads into purpose driven scalable solutions
19. Story contact:
P R O J E C T
PEM delivery
methodology was
used to deliver a cost
effective and scalable
solution
Client is exploring
opportunities to
monetize the solution
as an analytics
workbench
The data science
team can leverage
both SAS and R
integration with the
platform for advanced
analytics
Sunset existing
platforms, reducing
licensing and support
maintenance costs
R E S U L T S
Slalom partnered with a Fortune 500 healthcare company to deliver a next generation data platform.
The client’s existing platform could not support increasing data volumes and a growing need for
advanced analytics workloads. The new platform not only addressed these scalability concerns but
also allowed the client to host both structured and unstructured data in near-real time. Most
importantly, this data platform opened doors for new monetization opportunities
Slalom built a next generation Hadoop data platform to
meet the client’s needs. Leveraging the cloud enabled a
quick turnaround time as well as security features ideal for
storing PII and PHI data. Slalom team migrated and
optimized existing data to leverage Hadoop high-
performance features. Slalom also built a near-real time
platform that can ingest HL7 messages from several
hospitals and provide event-driven alerting. Maz Chaudhri
Next Generation Data Platform for
Healthcare Analytics
T E C H N O L O G Y
B A C K G R O U N D
Healthcare
INDUSTRY
BIG DATA SERVICES
Agile Delivery Approach
Big Data Implementation
20. Story contact:
P R O J E C T
Agile Delivery
Methodology
Real-time data
platform
Self-service
enablement
Up-to-the-minute view
into the operations of
over 6,000 restaurant
locations nation-wide.
Ability to monitor KPIs
and react with
targeted efforts to
boost sales exactly
where it is needed.
R E S U L T S
Our client in the fast-casual food industry was having widespread challenges accurately capturing and measuring key business
metrics. Due to inconsistent data integrity in the nightly batch process, executives and leaders were growing skeptical of the
reliability of reporting and analytics built from the data. Leaders were clamoring for timely visibility to better, cleaner data.
The Slalom team served as Scrum Master, Product
Owner and Analyst during the architecture and delivery
of the AWS-based Cloudera platform. Using a Kafka-
based publish-subscribe architecture, each restaurant
location in addition to the online ordering platform was
set up to stream data feeds to the unified Cloud
platform.
Maz Chaudhri
B A C K G R O U N D
T E C H N O L O G Y
Food Service
INDUSTRY
BIG DATA SERVICES
Agile Delivery Approach
Big Data Startup Planning
Platform Evaluation & Selection
Big Data Implementation
21. Story contact:
P R O J E C T
A scalable and
flexible Big Data
Platform
A universal XML
ingestion framework
HDFS Data lake that
ingests and persists
all data from source
system
Allowed the client to
sunset a reporting
product that saved
over $1MM annually
in support
maintenance cost
Qlik BI & Operational
reports utilizing
Hadoop as the
backend
R E S U L T S
A top 10 Pharmaceutical company, and top 150 Fortune 500, sought to implement a next generation
modern data platform. The platform needed to not only provide end to end supply chain visibility, but
also be flexible and scalable to handle a heavy volume of serialized data. The client also wanted to
establish a data lake so as to be able to predict and prescribe their inventory and shipments to better
serve their customers.
Slalom utilized AWS and Cloudera Hadoop to build this
next generation data platform. The data platform gave
visibility to inventory levels to help drive the
development of inventory optimization strategies and
integrated multiple disparate sources to give end to end
shipment visibility of the client’s supply chain.
Pharmaceuticals
INDUSTRY
Next Generation Data Platform &
Supply Chain visibility
A L L I A N C E S
B A C K G R O U N D
BIG DATA SERVICES
Agile Delivery Approach
Big Data Implementation
22. Time to Value Proven Approach and
Experience
Pre-Built Accelerators
AGILE ENGINEERING
APPROACH
Start small, deliver value and
evolve your Big Data program
BIG DATA STARTUP
PLANNING
Pre-defined epics and stories
for big data startup
DATA GOVERNANCE in
a BOX
Multi-faceted data governance
deployment and tools
READINESS AND ADOPTION
Org readiness and change
strategy and enablement
PLATFORM SELECTION
Best practices-based
evaluation toolset
BIG DATA INTEGRATION
TOOL
Open-source meta-data driven
integration API
1 32
Editor's Notes
Thanks Rohit.
Asher talked about Cloudera as a data platform and Rohit walked us through how you could do more with the data platform in the cloud. In next 30 min, Navendu and I will talk about how to design and build a smart data lake.
In earlier discussion, you heard….however…
…the ecosystem is complex and continue to grow. You need an experienced and knowledgeable implementation partner to do it right.
You could be at different stages of your big data journey…
…based on what we hear from our clients, we define the journey in three stages:
Value Identification
Value Demonstration
Value Realization
At this stage you are educating key stakeholders using various concepts with the goals of identifying impactful use case(s)
Multi-Use Support
Support multiple use cases or services: data analytics, data delivery, reporting,
People: Platform Ownership
Skill and Role Gap Analysis
Adoption Plan & Roadmap
Learning Plan
Sustainability Plan
Process – Operating Model
Onboarding Processes
Support Model
Team Ownership
Monitoring & Measurement
Maintenance & Promotion Processes
Implementation Roadmap
Technology: Platform Utilization
Use case coverage
Tool support
Information: Governance & Controls
Governance Process
Data Sharing
Certification
Accelerate Ingestion: separation of data ingestion from discovery and use of metadata-based API accelerate the most time-consuming and resource-intensive part of any data management projects
Standardize Data: Data is standardized with common transformations ensuring consistent use of data for discovery and modeling
Automate Transformation: As new transformations are identified as “common”, they are applied in Transformation zone