Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
How do you balance the need for structured and rule-based governance to assure enterprise data quality - with the imperative to innovate in order to stay relevant and competitive in today's business marketplace?
At the recent CDO Summit in NYC, a range of C-Level Executives across a variety of industries came to hear Joe Caserta, president of Caserta Concepts, put it all in perspective.
Joe talked about the challenges of "data sprawl" and the paradigm shift underway in the evolving big data and data-driven world.
For more information or to contact us, visit http://casertaconcepts.com/
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
Joe Caserta presents his vision of the future of Big Data in the Enterprise.
At the recent Harrisburg University Analytics Summit II, Joe Caserta gave this engaging presentation to Summit attendees including fellow academics, strategists, data scientists and analysts.
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented What Data Do You Have and Where is it?
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
How do you balance the need for structured and rule-based governance to assure enterprise data quality - with the imperative to innovate in order to stay relevant and competitive in today's business marketplace?
At the recent CDO Summit in NYC, a range of C-Level Executives across a variety of industries came to hear Joe Caserta, president of Caserta Concepts, put it all in perspective.
Joe talked about the challenges of "data sprawl" and the paradigm shift underway in the evolving big data and data-driven world.
For more information or to contact us, visit http://casertaconcepts.com/
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
Joe Caserta presents his vision of the future of Big Data in the Enterprise.
At the recent Harrisburg University Analytics Summit II, Joe Caserta gave this engaging presentation to Summit attendees including fellow academics, strategists, data scientists and analysts.
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
The 20th annual Enterprise Data World (EDW) Conference took place in San Diego last month April 17-21. It is recognized as the most comprehensive educational conference on data management in the world.
Joe Caserta was a featured presenter. His session “Evolving from the Data Warehouse to Big Data Analytics - the Emerging Role of the Data Lake," highlighted the challenges and steps to needed to becoming a data-driven organization.
Joe also participated in in two panel discussions during the show:
• "Data Lake or Data Warehouse?"
• "Big Data Investments Have Been Made, But What's Next
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
Moving Past Infrastructure Limitations Presented by MediaMath
This presentation was given at a Big Data Warehousing Meetup with Caserta Concepts, MediaMath and Qubole. You can learn more about the event here: http://www.meetup.com/Big-Data-Warehousing/events/228372516/
Event description:
At Caserta Concepts, we are firm believers in big data thriving on the cloud. The instant-on, nearly unlimited storage and computing capabilities of AWS has made it the defacto solution for a full spectrum of organizations needing to process large amounts of data.
What's more, an ecosystem of value-added platforms has emerged to further ease and democratize the implementation of cloud based solutions. Qubole has developed a great platform for easily deploying and managing ephemeral and long-lived Hadoop and Spark clusters on AWS.
Moving Past Infrastructure Limitations: Data Warehousing at MediaMath
Over the past year and a half, MediaMath has undertaken a “data liberation” effort in an attempt to leave their bigbox, monolithic data warehouse behind. In this talk, Rory Sawyer, Software Engineer at MediaMath, will describe how this effort transformed MediaMath’s legacy architecture and legacy mindset, which imposed harsh inefficiencies on data sharing and utilization. The current mindset removes these inefficiencies and allows them to say “yes” to more projects and ideas.
Rory will also demo how MediaMath uses Amazon Web Services and Qubole so that infrastructure is no longer a limiting factor on what and how users query. This combination allows them to scale their resources up and down as needed while bridging different data sources and execution engines. Using and extending MediaMath’s data warehousing is no longer a privileged activity but an ability that every employee and client has.
Joe Caserta's 2016 Data Summit Workshop "Introduction to Data Science with Hadoop" on May 9, expanded on his Intro to Data Science Workshop held at last year's Summit. Again, Joe presented to a standing-room only audience with a focus on the data lake, governance and the role of the data scientist.
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Defining and Applying Data Governance in Today’s Business EnvironmentCaserta
Caserta Concepts President Joe Caserta featured at Data Governance Winter 2014 Conference with a session on the basic and necessary steps needed for data quality and data governance success
For more information on the event and presentation: http://ow.ly/G3N9N
For more information on the services and solutions offered by Caserta Concepts, visit http://casertaconcepts.com/.
Big Data 2.0: YARN Enablement for Distributed ETL & SQL with HadoopCaserta
In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access.
For more information on our services or upcoming events, please visit our website at http://www.casertaconcepts.com/.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteCaserta
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
What is Big Data and why it is required and needed for the organization those who really need and generating huge amount of data and when it will be use
Integrating the CDO Role Into Your Organization; Managing the Disruption (MIT...Caserta
The role of the Chief Data Officer (CDO) has become integral to the evolution needed to turn a wisdom-driven company into an analytics-driven company. With Data Governance at the core of your responsibility, moving the innovation meter is a global challenge among CDOs. Specifically the CDO must:
• Provide a single point of accountability for data initiatives and issues
• Innovate ways to use existing data and evangelize a data vision for the organization
• Support & enforce data governance policies via outreach, training & tools
• Work with IT to develop/maintain an enterprise data repository
• Set standards for analytical reporting and generate data insights through data science
In this session, Joe Caserta addresses real-word CDO challenges, shares techniques to overcome them, manage corporate disruption and achieve success.
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
Building a New Platform for Customer Analytics Caserta
Caserta Concepts and Databricks partner up to bring you this insightful webinar on how a business can choose from all of the emerging big data technologies to figure out which one best fits their needs.
Enterprise Search: Addressing the First Problem of Big Data & Analytics - Sta...StampedeCon
This session addresses the first problems of Big Data & Analytics–Identifying, Indexing, Connecting and Gaining Insight of Existing Data to Drive Value. HPE’s Chief Field Technologist will give her perspectives on Enterprise Search as a Fundamental Cornerstone of Building a Data Driven Enterprise.
A modern, flexible approach to Hadoop implementation incorporating innovation...DataWorks Summit
A modern, flexible approach to Hadoop implementation incorporating innovations from HP Haven
Jeff Veis
Vice President
HP Software Big Data
Gilles Noisette
Master Solution Architect
HP EMEA Big Data CoE
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Using Machine Learning & Spark to Power Data-Driven MarketingCaserta
Joe Caserta provides a statistically-driven model to understanding the customer path to purchase, which combines online, offline and third-party data sources. He shows how customer data is fed to machine learning, which assigns weighted credit to customer interactions in order to give insight to what marketing activities truly matter. This presentation is from Caserta's February 2018 Big Data Warehousing Meetup co-hosted with Databricks.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
Joe Caserta was a featured speaker, along with MIT Sloan School faculty and other industry thought-leaders. His session 'You're the New CDO, Now What?' discussed how new CDOs can accomplish their strategic objectives and overcome tactical challenges in this emerging executive leadership role.
In its tenth year, the MIT CDOIQ Symposium 2016 continues to explore the developing role of the Chief Data Officer.
For more information, visit http://casertaconcepts.com/
Moving Past Infrastructure Limitations Presented by MediaMath
This presentation was given at a Big Data Warehousing Meetup with Caserta Concepts, MediaMath and Qubole. You can learn more about the event here: http://www.meetup.com/Big-Data-Warehousing/events/228372516/
Event description:
At Caserta Concepts, we are firm believers in big data thriving on the cloud. The instant-on, nearly unlimited storage and computing capabilities of AWS has made it the defacto solution for a full spectrum of organizations needing to process large amounts of data.
What's more, an ecosystem of value-added platforms has emerged to further ease and democratize the implementation of cloud based solutions. Qubole has developed a great platform for easily deploying and managing ephemeral and long-lived Hadoop and Spark clusters on AWS.
Moving Past Infrastructure Limitations: Data Warehousing at MediaMath
Over the past year and a half, MediaMath has undertaken a “data liberation” effort in an attempt to leave their bigbox, monolithic data warehouse behind. In this talk, Rory Sawyer, Software Engineer at MediaMath, will describe how this effort transformed MediaMath’s legacy architecture and legacy mindset, which imposed harsh inefficiencies on data sharing and utilization. The current mindset removes these inefficiencies and allows them to say “yes” to more projects and ideas.
Rory will also demo how MediaMath uses Amazon Web Services and Qubole so that infrastructure is no longer a limiting factor on what and how users query. This combination allows them to scale their resources up and down as needed while bridging different data sources and execution engines. Using and extending MediaMath’s data warehousing is no longer a privileged activity but an ability that every employee and client has.
Joe Caserta's 2016 Data Summit Workshop "Introduction to Data Science with Hadoop" on May 9, expanded on his Intro to Data Science Workshop held at last year's Summit. Again, Joe presented to a standing-room only audience with a focus on the data lake, governance and the role of the data scientist.
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Defining and Applying Data Governance in Today’s Business EnvironmentCaserta
Caserta Concepts President Joe Caserta featured at Data Governance Winter 2014 Conference with a session on the basic and necessary steps needed for data quality and data governance success
For more information on the event and presentation: http://ow.ly/G3N9N
For more information on the services and solutions offered by Caserta Concepts, visit http://casertaconcepts.com/.
Big Data 2.0: YARN Enablement for Distributed ETL & SQL with HadoopCaserta
In our most recent Big Data Warehousing Meetup, we learned about transitioning from Big Data 1.0 with Hadoop 1.x with nascent technologies to the advent of Hadoop 2.x with YARN to enable distributed ETL, SQL and Analytics solutions. Caserta Concepts Chief Architect Elliott Cordo and an Actian Engineer covered the complete data value chain of an Enterprise-ready platform including data connectivity, collection, preparation, optimization and analytics with end user access.
For more information on our services or upcoming events, please visit our website at http://www.casertaconcepts.com/.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteCaserta
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
What is Big Data and why it is required and needed for the organization those who really need and generating huge amount of data and when it will be use
Integrating the CDO Role Into Your Organization; Managing the Disruption (MIT...Caserta
The role of the Chief Data Officer (CDO) has become integral to the evolution needed to turn a wisdom-driven company into an analytics-driven company. With Data Governance at the core of your responsibility, moving the innovation meter is a global challenge among CDOs. Specifically the CDO must:
• Provide a single point of accountability for data initiatives and issues
• Innovate ways to use existing data and evangelize a data vision for the organization
• Support & enforce data governance policies via outreach, training & tools
• Work with IT to develop/maintain an enterprise data repository
• Set standards for analytical reporting and generate data insights through data science
In this session, Joe Caserta addresses real-word CDO challenges, shares techniques to overcome them, manage corporate disruption and achieve success.
The Data Lake and Getting Buisnesses the Big Data Insights They NeedDunn Solutions Group
Do terms like "Data Lake" confuse you? You’re not alone. With all of the technology buzzwords flying around today, it can become a task to keep up with and clearly understand each of them. However a data lake is definitely something to dedicate the time to understand. Leveraging data lake technology, companies are finally able to keep all of their disparate information and streams of data in one secure location ready for consumption at any time – this includes structured, unstructured, and semi-structured data. For more information on our Big Data Consulting Services, don’t hesitate to visit us online at: http://bit.ly/2fvV5rR
Building a New Platform for Customer Analytics Caserta
Caserta Concepts and Databricks partner up to bring you this insightful webinar on how a business can choose from all of the emerging big data technologies to figure out which one best fits their needs.
Enterprise Search: Addressing the First Problem of Big Data & Analytics - Sta...StampedeCon
This session addresses the first problems of Big Data & Analytics–Identifying, Indexing, Connecting and Gaining Insight of Existing Data to Drive Value. HPE’s Chief Field Technologist will give her perspectives on Enterprise Search as a Fundamental Cornerstone of Building a Data Driven Enterprise.
A modern, flexible approach to Hadoop implementation incorporating innovation...DataWorks Summit
A modern, flexible approach to Hadoop implementation incorporating innovations from HP Haven
Jeff Veis
Vice President
HP Software Big Data
Gilles Noisette
Master Solution Architect
HP EMEA Big Data CoE
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Using Machine Learning & Spark to Power Data-Driven MarketingCaserta
Joe Caserta provides a statistically-driven model to understanding the customer path to purchase, which combines online, offline and third-party data sources. He shows how customer data is fed to machine learning, which assigns weighted credit to customer interactions in order to give insight to what marketing activities truly matter. This presentation is from Caserta's February 2018 Big Data Warehousing Meetup co-hosted with Databricks.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Building the Enterprise Data Lake: A look at architecturemark madsen
The topic is building an Enterprise Data Lake, discussing high level data and technology architecture. We will describe the architecture of a data warehouse, how a data lake needs to differ, and show a high level functional and data architecture for a data lake. This webinar will cover:
Why dumping data into Hadoop and letting users get it out doesn't work
The difference between a Hadoop application and a Data Lake
Why new ideas about data architecture are a key element
An Enterprise Data Lake reference architecture to frame what must be built
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this webinar, select APN Partners will share their specific methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Best Practices for Building a Data Lake with Amazon S3 - August 2016 Monthly ...Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone. A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data.
Learning Objectives:
• Introduce key architectural concepts to build a Data Lake using Amazon S3 as the storage layer
• Explore storage options and best practices to build your Data Lake on AWS
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some important Data Lake implementation considerations when using Amazon S3 as your Data Lake
Implementing a Data Lake with Enterprise Grade Data GovernanceHortonworks
Hadoop provides a powerful platform for data science and analytics, where data engineers and data scientists can leverage myriad data from external and internal data sources to uncover new insight. Such power is also presenting a few new challenges. On the one hand, the business wants more and more self-service, and on the other hand IT is trying to keep up with the demand for data, while maintaining architecture and data governance standards.
In this webinar, Andrew Ahn, Data Governance Initiative Product Manager at Hortonworks, will address the gaps and offer best practices in providing end-to-end data governance in HDP. Andrew Ahn will be followed by Oliver Claude of Waterline Data, who will share a case study of how Waterline Data Inventory works with HDP in the Modern Data Architecture to automate the discovery of business and compliance metadata, data lineage, as well as data quality metrics.
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
AWS re:Invent 2016: How to Build a Big Data Analytics Data Lake (LFS303)Amazon Web Services
For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.
"Conceptually, a data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created “on demand”, providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we will introduce key concepts for a data lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management. We will also provide insight on how AWS enables a data lake architecture.
A data lake is a flat data store to collect data in its original form, without the need to enforce a predefined schema. Instead, new schemas or views are created ""on demand"", providing a far more agile and flexible architecture while enabling new types of analytical insights. AWS provides many of the building blocks required to help organizations implement a data lake. In this session, we introduce key concepts for a data lake and present aspects related to its implementation. We discuss critical success factors and pitfalls to avoid, as well as operational aspects such as security, governance, search, indexing, and metadata management. We also provide insight on how AWS enables a data lake architecture. Attendees get practical tips and recommendations to get started with their data lake implementations on AWS."
We will introduce key concepts for a data lake and present aspects related to its implementation. Also discussing critical success factors, pitfalls to avoid operational aspects, and insights on how AWS enables a server-less data lake architecture.
Speaker: Sebastien Menant, Solutions Architect, Amazon Web Services
With the increasing adoption of NoSQL data base systems like MongoDB or CouchDB more and more applications store structured data according to a non-relational, document oriented model. Exposing this structured data as Linked Data is currently inhibited by a lack of standards as well as tools and requires the implementation of custom solutions. While recent efforts aim at expressing transformations of such data models into RDF in a standardized manner, there is a lack of approaches which facilitate SPARQL execution over mapped non-relational data sources. With SparqlMap-M we show how dynamic SPARQL access to non-relational data can be achieved. SparqlMap-M is an extension to our SPARQL-to-SQL rewriter SparqlMap that performs a (partial) transformation of SPARQL queries by using a relational abstraction over a document store. Further, duplicate data in the document store is used to reduce the number of joins and custom optimiza-tions are introduced. Our showcase scenario employs the Berlin SPARQL Benchmark (BSBM) with different adap-tions to a document data model. We use this scenario to demonstrate the viability of our approach and compare it to different MongoDB setups and native SQL.
Jörg Unbehauen | AKSW, Universität Leipzig
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Large corporations have to master vast amounts of heterogeneous data in order to stay competitive. While existing approaches have attempted to consolidate and manage the data by forcing it into a single shared data model, data lakes recently emerged that instead provide a central storage point for holding all data sets in their original form.
In this talk, we present eccenca CorporateMemory, which extends the data lake paradigm with a semantic integration layer for managing diverse, but semantically enriched data. eccenca CorporateMemory builds an extensible knowledge graph that employs RDF vocabularies for transforming and linking multiple datasets in order to generate an integrated semantic understanding of the data.
Robert Isele | Head of Data Integration Unit at eccenca GmbH
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification.
Michael Krug | Technische Universität Chemnitz
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Collaboration is one of the most important topics regarding the evolution of the World Wide Web and thus also for the Web of Data. In scenarios of distributed collaboration on datasets it is necessary to provide support for multiple different versions of datasets to exist simultaneously, while also providing support for merging diverged datasets. In this paper we present an approach that uses SPARQL 1.1 in combination with the version control system Git, that creates commits for all changes applied to an RDF dataset containing multiple named graphs. Further the operations provided by Git are used to distribute the commits among collabora-tors and merge diverged versions of the dataset. We show the advantages of (public) Git repositories for RDF datasets and how this represents a way to collaborate on RDF data and consume it. With SPARQL 1.1 and Git in combination, users are given several opportunities to participate in the evolution of RDF data.
Natanael Arndt | AKSW, Universität Leipzig
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Coherent and consistent tracking of provenance data and in particular update history information is a crucial building block for any serious information system architecture.
Marvin Frommhold | AKSW, Universität Leipzig
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Workshop with Joe Caserta, President of Caserta Concepts, at Data Summit 2015 in NYC.
Data science, the ability to sift through massive amounts of data to discover hidden patterns and predict future trends and actions, may be considered the "sexiest" job of the 21st century, but it requires an understanding of many elements of data analytics. This workshop introduced basic concepts, such as SQL and NoSQL, MapReduce, Hadoop, data mining, machine learning, and data visualization.
For notes and exercises from this workshop, click here: https://github.com/Caserta-Concepts/ds-workshop.
For more information, visit our website at www.casertaconcepts.com
Architecting for Big Data: Trends, Tips, and Deployment OptionsCaserta
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
MT101 Dell OCIO: Delivering data and analytics in real timeDell EMC World
Today’s business operations increasingly rely on sophisticated integration of data streaming across the enterprise. This requires an analytics ecosystem that is highly current and highly available. This session explores the infrastructure and methods Dell IT used for keeping the complex flows, integration processes, BI, and analytics operating 24x7.
Data Governance, Compliance and Security in Hadoop with ClouderaCaserta
In our recent Big Data Warehousing Meetup, we discussed Data Governance, Compliance and Security in Hadoop.
As the Big Data paradigm becomes more commonplace, we must apply enterprise-grade governance capabilities for critical data that is highly regulated and adhere to stringent compliance requirements. Caserta and Cloudera shared techniques and tools that enables data governance, compliance and security on Big Data.
For more information, visit www.casertaconcepts.com
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.
Five Attributes to a Successful Big Data StrategyPerficient, Inc.
The veracity, variety and sheer volume of data is increasing exponentially. With Hadoop and NoSQL solutions becoming commonplace, there are many technical options for managing and extracting value from this data. Many companies create labs to experiment with Big Data solutions, only later become IT playgrounds or unstructured dumping grounds.
To help avoid these pitfalls,companies with successful Big Data projects approach challenges by formulating a strategy that assures real business value is derived from their Big Data investments. In a Perficient poll, 73% of companies stated they are in the early-evaluation stage to find solutions to their Big Data problems and are only beginning to create their strategy.
Join us for a webinar featuring thought-provoking best practices used by successful companies to quickly realize business value from their Big Data investments. You'll learn:
The top five steps to increased business value
What the top companies are doing in Big Data that you need to know
Next steps to lay the ground work for a successful Big Data strategy
Active Governance Across the Delta Lake with AlationDatabricks
Alation provides a single interface to provide users and stewards to provide active and agile data governance across Databricks Delta Lake and Databricks SQL Analytics Service. Understand how Alation can expand adoption in the data lake while providing safe and responsible data consumption.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Presentation at Data Summit 2015 in NYC.
Elliott Cordo shared real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.
For more information, visit www.casertaconcepts.com
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
Understanding, Planning and Achieving
Data Quality in Your Organization
by Joe Caserta, President of Caserta Concepts
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!
Predictive Analytics - Big Data Warehousing MeetupCaserta
Predictive analytics has always been about the future, and the age of big data has made that future an increasingly dynamic place, filled with opportunity and risk.
The evolution of advanced analytics technologies and the continual development of new analytical methodologies can help to optimize financial results, enable systems and services based on machine learning, obviate or mitigate fraud and reduce cybersecurity risks, among many other things.
Caserta Concepts, Zementis, and guest speaker from FICO presented the strategies, technologies and use cases driving predictive analytics in a big data environment.
For more information, visit www.casertaconcepts.com or contact us at info@casertaconcepts.com
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
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
Metadata provides context for the “who, what, when, where, and why” of data, and is of critical interest in today’s data-driven business environment. Since metadata is created and used by both business and IT, architectural and organizational techniques need to encompass a holistic approach across the organization to address all audiences. This webinar provides practical ways to manage metadata in your organization using both technical architecture and business techniques.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
Looker Data Modeling in the Age of Cloud - BDW Meetup May 2, 2017Caserta
Looker Presentation
Caserta Concepts, Blue Apron, and Looker are joining forces to recount the journey of an enterprise-level migration to the cloud with exciting new technologies. This is the analytics platform of the future.
Greg Wells shares Caserta's view on Enterprise Cloud Adoption and how this is impacting BI and the data architecture needed to support big data analytics. Daniel Mintz, Looker’s Chief Data Evangelist, presents a comprehensive introduction to Looker, and Jason Jho, Head of Data Engineering, Blue Apron, gives the audience an in-depth look at the environment that is helping Blue Apron achieve success by using data to become an analytics-driven company.
There is an overwhelming list of expectations – and challenges – in this new, emerging and evolving role. In this presentation, given at the 2016 CDO Summit, Joe Caserta focuses on:
- Defining the CDO title
- Outlining the skills that enhance chances for success
- Listing all the many things the company thinks you are responsible for
- Providing an overview of the core technologies you need to be familiar with and will serve to ultimately support your success
- Presenting a concise list of the most pressing challenges
- Sharing insights and arguments for how best to meet the challenges and succeed in your new role
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
Introducing Kudu, Big Data Warehousing MeetupCaserta
Not just an SQL interface or file system, Kudu - the new, updating column store for Hadoop, is changing the storage landscape. It's easy to operate and makes new data immediately available for analytics or operations.
At the Caserta Concepts Big Data Warehousing Meetup, our guests from Cloudera outlined the functionality of Kudu and talked about why it will become an integral component in big data warehousing on Hadoop.
To learn more about what Caserta Concepts has to offer, visit http://casertaconcepts.com/
During a Big Data Warehousing Meetup in NYC, Elliott Cordo, Chief Architect at Caserta Concepts discussed emerging trends in real time data processing. The presentation included processing frameworks such as Spark and Storm, as well datastore technologies ranging from NoSQL to Hadoop. He also discussed exciting new AWS services such as Lambda, Kenesis, and Kenesis Firehose.
Big Data Warehousing Meetup: Dimensional Modeling Still Matters!!!Caserta
Joe Caserta went over the details inside the big data ecosystem and the Caserta Concepts Data Pyramid, which includes Data Ingestion, Data Lake/Data Science Workbench and the Big Data Warehouse. He then dove into the foundation of dimensional data modeling, which is as important as ever in the top tier of the Data Pyramid. Topics covered:
- The 3 grains of Fact Tables
- Modeling the different types of Slowly Changing Dimensions
- Advanced Modeling techniques like Ragged Hierarchies, Bridge Tables, etc.
- ETL Architecture.
He also talked about ModelStorming, a technique used to quickly convert business requirements into an Event Matrix and Dimensional Data Model.
This was a jam-packed abbreviated version of 4 days of rigorous training of these techniques being taught in September by Joe Caserta (Co-Author, with Ralph Kimball, The Data Warehouse ETL Toolkit) and Lawrence Corr (Author, Agile Data Warehouse Design).
For more information, visit http://casertaconcepts.com/.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
3. @joe_CasertaPhiladelphia
Launched Data Science
Data Interaction and Cloud practices
Awarded for getting data out of SAP
for enterprise data analytics
Top 20 Most Most Powerful
Big Data Companies
Caserta Timeline
Launched Big Data practice
Co-author, with Ralph Kimball, The Data
Warehouse ETL Toolkit (Wiley)
Caserta Concepts founded
Web log analytics solution published in Intelligent
Enterprise
Partnered with Big Data vendors Cloudera,
Hortonworks, IBM, Cisco, Datameer, Basho more…
Launched Training practice, teaching and mentoring
data warehousing concepts world-wide
Laser focus on extending Data Warehouses with Big
Data solutions
2001
2010
2004
2012
2009
2014
Launched Big Data Warehousing (BDW)
Meetup - NYC 3,000+ Members
2013
2015
Established best practices for big data ecosystem
implementation – Healthcare, Finance, Insurance
Dedicated to Data Governance Techniques
on Big Data (Innovation)
America’s Fastest Growing Private
Companies - Ranked #740
1996 – Dedicated to Dimensional Data Warehousing
1986 – 1996 OLTP Data Modeling and Reporting.
4. @joe_CasertaPhiladelphia
About Caserta Concepts
• Consulting firm focused on Data Innovation, Modern Data Engineering to solve
highly complex business data challenges
• Award-winning company
• Internationally recognized work force
• Mentoring, Training, Knowledge Transfer
• Strategy, Architecture, Implementation
• Innovation Partner
• Transformative Data Strategies
• Modern Data Engineering
• Advanced Architecture
• Leader in architecting and implementing enterprise data solutions
• Data Warehousing
• Business Intelligence
• Big Data Analytics
• Data Science
• Data on the Cloud
• Data Interaction & Visualization
• Strategic Consulting
• Technical Design
• Build & Deploy Solutions
8. @joe_CasertaPhiladelphia
The Future of Data is Today
As a Mindful Cyborg, Chris
Dancy utilizes up to
700 sensors, devices,
applications, and services to
track, analyze, and optimize as
many areas of his existence.
Data quantification enables
him to see the connections of
otherwise invisible data,
resulting in dramatic upgrades
to his health, productivity, and
quality of life.
9. @joe_CasertaPhiladelphia
The Progression of Data Analytics
Descriptive
Analytics
Diagnostic
Analytics
Predictive
Analytics
Prescriptive
Analytics
What
happened?
Why did it
happen?
What will
happen?
How can we make
It happen?
Data Analytics Sophistication
BusinessValue
Source: Gartner
Reports Correlations Predictions Recommendations
10. @joe_CasertaPhiladelphia
The Progression of Data Analytics
Source: Gartner
Reports Correlations Predictions Recommendations
Cognitive Computing / Cognitive Data Analytics
11. @joe_CasertaPhiladelphia
Traditional Data Warehousing
• Design – Top Down, Bottom Up
• Customer Interviews and requirements gathering
• Data Profiling
• Create Data Models
• Facts and Dimensions
• Extract Transform Load (ETL)
• Copy data from sources to data warehouse
• Data Governance
• Stewardship, business rules, data quality
• Put a BI Tool on Top
• Design semantic layer
• Develop reports
12. @joe_CasertaPhiladelphia
A Day in the Life
• Onboarding new data is difficult!
• Rigid Structures and Data Governance
• Disconnected/removed from business requirements:
“Hey – I need to analyze some new data”
IT Conforms and profiles the data
Loads it into dimensional models
Builds a semantic layer nobody is going to use
Creates a dashboard we hope someone will notice
..and then you can access your data 3-6 months later to see if it has value!
13. @joe_CasertaPhiladelphia
Houston, we have a Problem: Data Sprawl
• There is one application for every 5-10 employees generating copies of
the same files leading to massive amounts of duplicate idle data strewn all
across the enterprise. - Michael Vizard, ITBusinessEdge.com
• Employees spend 35% of their work time searching for information...
finding what they seek 50% of the time or less.
- “The High Cost of Not Finding Information,” IDC
16. @joe_CasertaPhiladelphia
OLD WAY:
• Structure Ingest Analyze
• Fixed Capacity
• Monolithic
NEW WAY:
• Ingest Analyze Structure
• Dynamic Capacity
• Ecosystem
RECIPE:
• Cloud
• Data Lake
• Polyglot Warehouse
The Paradigm Shift
Big Data is not the problem
It’s the Change Agent
17. @joe_CasertaPhiladelphia
Enrollments
Claims
Finance
ETL
Ad-Hoc Query
Horizontally Scalable Environment - Optimized for Analytics
Data Lake
Canned Reporting
Big Data Analytics
NoSQL
DatabasesETL
Ad-Hoc/Canned
Reporting
Traditional BI
Spark MapReduce Pig/Hive
N1 N2 N4N3 N5
Hadoop Distributed File System (HDFS)
Traditional
EDW
Others…
The Evolution of Modern Data Engineering
Data Science
22. @joe_CasertaPhiladelphia
•This is the ‘people’ part. Establishing Enterprise Data Council, Data Stewards, etc.Organization
•Definitions, lineage (where does this data come from), business definitions, technical
metadataMetadata
•Identify and control sensitive data, regulatory compliancePrivacy/Security
•Data must be complete and correct. Measure, improve, certifyData Quality and Monitoring
•Policies around data frequency, source availability, etc.Business Process Integration
•Ensure consistent business critical data i.e. Members, Providers, Agents, etc.Master Data Management
•Data retention, purge schedule, storage/archiving
Information Lifecycle
Management (ILM)
Data Governance for the Data Lake
23. @joe_CasertaPhiladelphia
•This is the ‘people’ part. Establishing Enterprise Data Council, Data Stewards, etc.Organization
•Definitions, lineage (where does this data come from), business definitions, technical
metadataMetadata
•Identify and control sensitive data, regulatory compliancePrivacy/Security
•Data must be complete and correct. Measure, improve, certifyData Quality and Monitoring
•Policies around data frequency, source availability, etc.Business Process Integration
•Ensure consistent business critical data i.e. Members, Providers, Agents, etc.Master Data Management
•Data retention, purge schedule, storage/archiving
Information Lifecycle
Management (ILM)
Data Governance
• Add Big Data to overall framework and assign responsibility
• Add data scientists to the Stewardship program
• Assign stewards to new data sets (twitter, call center logs, etc.)
• Graph databases are more flexible than relational
• Lower latency service required
• Distributed data quality and matching algorithms
• Data Quality and Monitoring (probably home grown, drools?)
• Quality checks not only SQL: machine learning, Pig and Map Reduce
• Acting on large dataset quality checks may require distribution
• Larger scale
• New datatypes
• Integrate with Hive Metastore, HCatalog, home grown tables
• Secure and mask multiple data types (not just tabular)
• Deletes are more uncommon (unless there is regulatory requirement)
• Take advantage of compression and archiving (like AWS Glacier)
• Data detection and masking on unstructured data upon ingest
• Near-zero latency, DevOps, Core component of business operations
for the Data Lake
24. @joe_CasertaPhiladelphia
The Big Data Pyramid
Ingest Raw
Data
Organize, Define,
Complete
Munging, Blending
Machine Learning
Data Quality and Monitoring
Metadata, ILM , Security
Data Catalog
Data Integration
Fully Governed ( trusted)
Arbitrary/Ad-hoc Queries and
Reporting
Usage Pattern Data Governance
Metadata, ILM,
Security
25. @joe_CasertaPhiladelphia
Peeling back the layers… The Landing Area
• Source data in it’s full fidelity
• Programmatically Loaded
• Partitioned for data processing
• No governance other than catalog and ILM (Security and Retention)
Consumers: ETL Processes, Applications
26. @joe_CasertaPhiladelphia
Data Lake
• Enriched, lightly integrated
• Data has been is accessible in the Hive Metastore
• Either processed into tabular relations
• Or via Hive Serdes directly upon Raw Data
• Partitioned for data access
• Governance additionally includes a guarantee of
completeness
Consumers: Data Scientists, ETL Processes,
Applications, Data Analysts
27. @joe_CasertaPhiladelphia
Data Science Workspace
• No barrier for onboarding and analysis of new data
• Blending of new data with entire Data Lake, including the Big Data
Warehouse
• Data Scientists enrich data with insight
Consumers: Data Scientists
28. @joe_CasertaPhiladelphia
Big Data Warehouse
• Data is Fully Governed
• Data is Structured
• Partitioned/tuned for data access
• Governance includes a guarantee of completeness and
accuracy
Big
Data
Warehouse
Consumers: Data Scientists, ETL Processes, Applications,
Data Analysts, and Business Users (the masses)
29. @joe_CasertaPhiladelphia
The Refinery
BDW
Data Science
Workspace
Data Lake
Landing Area
Cool
new
data
New
Insights
• The feedback loop between Data Science and Data Warehouse is critical
• Successful work products of science must Graduate into the appropriate
layers of the Data Lake
30. @joe_CasertaPhiladelphia
Polyglot Warehouse
We promote the concept that the Big Data Warehouse may live in one or
more platforms
• Full Hadoop Solutions
• Hadoop plus MPP or Relational
Supplemental technologies:
• NoSQL: Columnar, Key value, Timeseries, Graph
• Search Technologies
31. @joe_CasertaPhiladelphia
Hadoop is the Data Warehouse?
• Hadoop can be the entire data pyramid platform including
landing, data lake and the Big Data Warehouse
• Especially serves as the Data Lake and “Refinery”
• Query engines such as Hive, and Impala provide SQL support
32. @joe_CasertaPhiladelphia
Define and Find Your Data
• Data Classification
• Import/Define business taxonomy
• Capture/Automate relationships between data sets
• Integrate metadata with other systems
• Centralized Auditing
• Security access information for every application with data
• Operational information for execution
• Search & Lineage (Browse)
• Predefined navigation paths to explore data
• Text-based search for data elements across data ecosystem
• Browse visualization of data lineage
• Security & Policy Engine
• Rationalize compliance policy at run-time
• Prevent data derivation based on classification (re-classification)
Key Requirements
• Automatic data-
discovery
• Metadata tagging
• Classification
33. @joe_CasertaPhiladelphia
Caution: Assembly Required
Some of the most hopeful tools are brand new or in
incubation!
Enterprise big data implementations typically combine
products with custom built components
Tools
People, Processes and Business commitment is still critical!
Data Integration Data Catalog & Governance Emerging Solutions
34. @joe_CasertaPhiladelphia
Collibra API
Business Glossary
Terms PoliciesWorkflows
API/Exchange ConnectorMDMPower Center Data Quality
Metadata Manager
Active VOS
Systemof
Records
Salesforce
SAP
Workday
Oracle JDE
Analytics
ODS
Data Science
Data Lake
DW
MDM
Domains
Vendor
COA
HR
Customer
Product
Developer Portal
API Management
Security Monitoring & AnalyticsSLA Management
Data Catalog
2
3
Data Sources
1
5
1
4
APILinked/Federated Data Self Service PortalSearch/Visualization
Security &
Entitlements
Publishing Workflows
8
1. Data sources managed
through the MDM
2. Business glossary are mapped
to data sources
3. Business glossary describes
API attributes
4. Data source models used to
develop the APIs
5. All access from the Data
Catalog are through APIs
6. Data catalog utilizes the
business glossary to describe
the data elements
7. Data catalog uses MDM for
lineage
8. Data catalog sources are
defined through and
connected APIs
6
7
Sample Architecture
35. @joe_CasertaPhiladelphia
“…any decent sized enterprise will have a variety of different data
technologies for different kinds of data. There will still be large
amounts of it managed in relational stores, but increasingly
we'll be first asking how we want to manipulate the data
and only then figuring out what technology
is the best bet for it.” - Martin Fowler
Think Ecosystem, Not Tech Stack
36. @joe_CasertaPhiladelphia
Existing On-Premise Solution
• Challenges with operations of Hadoop servers in Data Center
• Increasing infrastructure complexity
• Keeping up with data growth
Cloud Advantages
• Reduced upfront capital investment
• Faster speed to value
• Elasticity
“Those that go out and buy expensive
infrastructure find that the problem scope and
domain shift really quickly. By the time they get
around to answering the original question, the
business has moved on.” - Matt Wood, AWS
Move to the Cloud?
37. @joe_CasertaPhiladelphia
Data Analytics on the Cloud
AWS and other cloud providers present a very powerful design
pattern:
• S3 serves as the storage layer for the Data Lake
• EMR (Elastic Hadoop) provides the Refinery, most clusters can be
ephemeral
• The Active Set is stored into Redshift MPP or Relational Platforms
Eliminate massive on-premise appliance footprint
39. @joe_CasertaPhiladelphia
Come out and Play
CIL - Caserta
Innovations Lab
Experience
Big Data Warehousing Meetup
• Established in 2012 in NYC
• Meet monthly to share data best
practices, experiences
• 3,000+ Members
http://www.meetup.com/Big-Data-Warehousing/
Examples of Previous Topics
• Data Governance, Compliance &
Security in Hadoop w/Cloudera
• Real Time Trade Data Monitoring
with Storm & Cassandra
• Predictive Analytics
• Exploring Big Data Analytics
Techniques w/Datameer
• Using a Graph DB for MDM &
Relationship Mgmt
• Data Science w/Claudia
Perlcih & Revolution Analytics
• Processing 1.4 Trillion Events
in Hadoop
• Building a Relevance Engine
using Hadoop, Mahout & Pig
• Big Data 2.0 – YARN Distributed
ETL & SQL w/Hadoop
• Intro to NoSQL w/10GEN
42. @joe_CasertaPhiladelphia
Electronic Medical Records (EMR) Analytics
Hadoop Data LakeEdge Node
`
100k
files
variant 1..n
…
variant 1..n
HDFS
Put
Netezza DW
Sqoop
Pig EMR
Processor
UDF
Library
Provider table
(parquet)
Member table
(parquet)
Python Wrapper
Provider table
Member table
Forqlift
Sequence
Files
…
variant 1..n
Sequence
Files
…
15 More
Entities
(parquet)
More
Dimensions
And
Facts
• Receive Electronic Medial Records from various providers in various formats
• Address Hadoop ‘small file’ problem
• No barrier for onboarding and analysis of new data
• Blend new data with Data Lake and Big Data Warehouse
• Machine Learning
• Text Analytics
• Natural Language Processing
• Reporting
• Ad-hoc queries
• File ingestion
• Information Lifecycle Mgmt