Pradeep Varadan, Verizon's Wireline OSS Data Science Lead and Scott Gidley, Zaloni's VP, Product Management discuss the benefits of augmenting your DW with a data lake in this webinar presentation.
Ovum Fireside Chat: Governing the data lake - Understanding what's in thereZaloni
In Ovum’s upcoming Big Data Trends to Watch 2016 report, Tony Baer forecasts that data lake management will become a front-burner issue as early Hadoop adopters get to the point of production implementation.
During this fireside chat, Tony Baer and Scott Gidley, VP of Product Management at Zaloni will assess the state of the industry regarding governance and data management tools, technologies, and practices that should fall into place as part of a data lake strategy.
Watch the webinar here: http://hubs.ly/H03374z0
Webinar - Risky Business: How to Balance Innovation & Risk in Big DataZaloni
Big data is a game-changer for organizations that use it right. However, a dynamic tension always exists between rapid innovation using big data and the high level of production maturity required for an enterprise implementation. Is it possible to find the right mix? Our webinar answers this question.
Webinar - Data Lake Management: Extending Storage and Lifecycle of DataZaloni
Join Gus Horn of NetApp and Scott Gidley of Zaloni as they discuss effective data lake lifecycle management and data architecture modernization. This webinar will address the best ways to achieve new levels of data insight and how to get superior value from your data.
Understanding Metadata: Why it's essential to your big data solution and how ...Zaloni
In this O'Reilly webcast, Ben Sharma (cofounder and CEO of Zaloni) and Vikram Sreekanti (software engineer in the AMPLab at UC Berkeley) discuss the value of collecting and analyzing metadata, and its potential to impact your big data solution and your business.
Watch the replay here: http://oreil.ly/28LO7IW
Recently, there's been discussion, even some confusion, around the relationship between Hadoop and Spark. Although they're both big data frameworks with many similarities, they are not one in the same - and are in fact complimentary in an enterprise environment.
View the webinar replay here: http://info.zaloni.com/spark-hadoops-friend-or-foe
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
Ovum Fireside Chat: Governing the data lake - Understanding what's in thereZaloni
In Ovum’s upcoming Big Data Trends to Watch 2016 report, Tony Baer forecasts that data lake management will become a front-burner issue as early Hadoop adopters get to the point of production implementation.
During this fireside chat, Tony Baer and Scott Gidley, VP of Product Management at Zaloni will assess the state of the industry regarding governance and data management tools, technologies, and practices that should fall into place as part of a data lake strategy.
Watch the webinar here: http://hubs.ly/H03374z0
Webinar - Risky Business: How to Balance Innovation & Risk in Big DataZaloni
Big data is a game-changer for organizations that use it right. However, a dynamic tension always exists between rapid innovation using big data and the high level of production maturity required for an enterprise implementation. Is it possible to find the right mix? Our webinar answers this question.
Webinar - Data Lake Management: Extending Storage and Lifecycle of DataZaloni
Join Gus Horn of NetApp and Scott Gidley of Zaloni as they discuss effective data lake lifecycle management and data architecture modernization. This webinar will address the best ways to achieve new levels of data insight and how to get superior value from your data.
Understanding Metadata: Why it's essential to your big data solution and how ...Zaloni
In this O'Reilly webcast, Ben Sharma (cofounder and CEO of Zaloni) and Vikram Sreekanti (software engineer in the AMPLab at UC Berkeley) discuss the value of collecting and analyzing metadata, and its potential to impact your big data solution and your business.
Watch the replay here: http://oreil.ly/28LO7IW
Recently, there's been discussion, even some confusion, around the relationship between Hadoop and Spark. Although they're both big data frameworks with many similarities, they are not one in the same - and are in fact complimentary in an enterprise environment.
View the webinar replay here: http://info.zaloni.com/spark-hadoops-friend-or-foe
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
Strata San Jose 2017 - Ben Sharma PresentationZaloni
Learn about the promise of data lakes:
- Store all types of data in its raw format
- Create refined, standardized, trusted datasets for various use cases
- Store data for longer periods of time to enable historical analysis - Query and Access the data using a variety of methods
- Manage streaming and batch data in a converged platform
- Provide shorter time-to-insight with proper data management and governance
Verizon: Finance Data Lake implementation as a Self Service Discovery Big Dat...DataWorks Summit
Finance Data Lake objective is to create a centralized enterprise data repository for all Finance and Supply Chain data. It serves as the single source of truth. It enables a self-service discovery Analytics platform for business users to answer adhoc business questions and derive critical insights. The data lake is based on open source Hadoop big data platform and a very cost effective solution in breaking the ERP data silos and simplifying the data architecture in the enterprise.
POCs were conducted on in-house Hortonworks Hadoop data platform to validate the cluster performance for Production volumes. Based on business priorities, an initial roadmap was defined using 3 data sources including 2 SAP ERPs and Peoplesoft (OLTP systems). Development environment was established in AWS Cloud for agile delivery. The near real time data ingestion architecture for the data lake was defined using replication tools and custom SQOOP based micro-batching framework and data persisted in Apache Hive DB in ORC format. Data and user security is implemented using Apache Ranger and sensitive data stored at rest in encryption zones. Business data sets were developed in Hive scripts and scheduled using Oozie. Multiple reporting tools connectivity including SQL tools, Excel and Tableau were enabled for Self-service Analytics. Upon successful implementation of the initial phase, a full roadmap is established to extend the Finance data lake to over 25 data sources and enhance data ingestion to scale as well as enable OLAP tools on Hadoop.
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 meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
Hadoop based data Lakes have become increasingly popular within today’s modern data architectures for their ability to scale, handle data variety and low cost. Many organizations start slow with the data lake initiatives but as they grow bigger, they suffer with challenges on data consistency, quality and security, resulting in losing confidence in their data lake initiatives.
This talk will discuss the need for good data governance mechanisms for Hadoop data lakes and it relationship with productivity and how it helps organizations meet regulatory and compliance requirements. The talk advocates carrying a different mindset for designing and implementing flexible governance mechanisms on Hadoop data lakes.
Are You Killing the Benefits of Your Data Lake?Denodo
Watch the full webinar on-demand here: https://goo.gl/RL1ZSa
Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
Traditional data warehouse ETL has become too slow, too complicated, and too expensive to address the torrent of new data sources and new analytic approaches needed for decision making. The new ETL environment is already looking drastically different.
In this webinar, Ralph Kimball, founder of the Kimball Group, and Manish Vipani, Vice President and Chief Architect of Enterprise Architecture at Kaiser Permanente will describe how this new ETL environment is actually implemented at Kaiser Permanente. They will describe the successes, the unsolved challenges, and their visions of the future for data warehouse ETL.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Big Data International Keynote Speaker Mark van Rijmenam shared his vision on Hadoop Data Lakes during a Zaloni Webinar. What are the Hadoop Data Lake trends for 2016, what are the data lake challenges and how can organizations benefit from data lakes.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
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.
Strata San Jose 2017 - Ben Sharma PresentationZaloni
Learn about the promise of data lakes:
- Store all types of data in its raw format
- Create refined, standardized, trusted datasets for various use cases
- Store data for longer periods of time to enable historical analysis - Query and Access the data using a variety of methods
- Manage streaming and batch data in a converged platform
- Provide shorter time-to-insight with proper data management and governance
Verizon: Finance Data Lake implementation as a Self Service Discovery Big Dat...DataWorks Summit
Finance Data Lake objective is to create a centralized enterprise data repository for all Finance and Supply Chain data. It serves as the single source of truth. It enables a self-service discovery Analytics platform for business users to answer adhoc business questions and derive critical insights. The data lake is based on open source Hadoop big data platform and a very cost effective solution in breaking the ERP data silos and simplifying the data architecture in the enterprise.
POCs were conducted on in-house Hortonworks Hadoop data platform to validate the cluster performance for Production volumes. Based on business priorities, an initial roadmap was defined using 3 data sources including 2 SAP ERPs and Peoplesoft (OLTP systems). Development environment was established in AWS Cloud for agile delivery. The near real time data ingestion architecture for the data lake was defined using replication tools and custom SQOOP based micro-batching framework and data persisted in Apache Hive DB in ORC format. Data and user security is implemented using Apache Ranger and sensitive data stored at rest in encryption zones. Business data sets were developed in Hive scripts and scheduled using Oozie. Multiple reporting tools connectivity including SQL tools, Excel and Tableau were enabled for Self-service Analytics. Upon successful implementation of the initial phase, a full roadmap is established to extend the Finance data lake to over 25 data sources and enhance data ingestion to scale as well as enable OLAP tools on Hadoop.
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 meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
Hadoop based data Lakes have become increasingly popular within today’s modern data architectures for their ability to scale, handle data variety and low cost. Many organizations start slow with the data lake initiatives but as they grow bigger, they suffer with challenges on data consistency, quality and security, resulting in losing confidence in their data lake initiatives.
This talk will discuss the need for good data governance mechanisms for Hadoop data lakes and it relationship with productivity and how it helps organizations meet regulatory and compliance requirements. The talk advocates carrying a different mindset for designing and implementing flexible governance mechanisms on Hadoop data lakes.
Are You Killing the Benefits of Your Data Lake?Denodo
Watch the full webinar on-demand here: https://goo.gl/RL1ZSa
Data lakes are centralized data repositories. Data needed by data scientists is physically copied to a data lake which serves as a one storage environment. This way, data scientists can access all the data from only one entry point – a one-stop shop to get the right data. However, such an approach is not always feasible for all the data and limits it’s use to solely data scientists, making it a single-purpose system.
So, what’s the solution?
A multi-purpose data lake allows a broader and deeper use of the data lake without minimizing the potential value for data science and without making it an inflexible environment
Attend this session to learn:
• Disadvantages and limitations that are weakening or even killing the potential benefits of a data lake.
• Why a multi-purpose data lake is essential in building a universal data delivery system.
• How to build a logical multi-purpose data lake using data virtualization.
Do not miss this opportunity to make your data lake project successful and beneficial.
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
Traditional data warehouse ETL has become too slow, too complicated, and too expensive to address the torrent of new data sources and new analytic approaches needed for decision making. The new ETL environment is already looking drastically different.
In this webinar, Ralph Kimball, founder of the Kimball Group, and Manish Vipani, Vice President and Chief Architect of Enterprise Architecture at Kaiser Permanente will describe how this new ETL environment is actually implemented at Kaiser Permanente. They will describe the successes, the unsolved challenges, and their visions of the future for data warehouse ETL.
Performance Acceleration: Summaries, Recommendation, MPP and moreDenodo
Watch full webinar here: https://bit.ly/3nLHayP
Performance is critical for an organization across the board. Developers can optimize execution with Summaries, MPP, Data Movement, and more. Business users rely on the Recommendation engine to guide them to the right data. Let’s discover and learn about various performance acceleration techniques in this session.
Big Data International Keynote Speaker Mark van Rijmenam shared his vision on Hadoop Data Lakes during a Zaloni Webinar. What are the Hadoop Data Lake trends for 2016, what are the data lake challenges and how can organizations benefit from data lakes.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
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.
How to Become a Thought Leader in Your NicheLeslie Samuel
Are bloggers thought leaders? Here are some tips on how you can become one. Provide great value, put awesome content out there on a regular basis, and help others.
Transforming Insurance Operations through Data and AnalyticsDatalytyx
Analytics and big data is established in Insurance could be better. It's not joined up. Big data is an accelerator of what is possible.
Roger Oldham of Amethyst Business Consultancy explains the impact of big data and analytic technology in the Corporate / Wholesale Insurance market.
Course Notes for the design of spatial applications course. The course presents an overview of the technologies, tradition, psychology and methodology for the design of maps and other spatial applications
Data-Driven Government: Explore the Four Pillars of ValueThomas Robbins
McKinsey Global Institute estimates that government organizations together can generate $3 trillion dollars in value for themselves and their taxpayers through data and information transparency initiatives with some of these dollars being generated at the local level.
Yes, that's a staggering number, but governments like yours are realizing pieces of it already. Are you taking advantage of the enormous economic and social impacts of information transparency?
Join this vital webinar to learn more about the four pillars of value that are reshaping how government thinks not only about open data, but how it's applied and leveraged to cut costs and significantly increase government efficiency.
India, Internet of things and the role of governmentSyam Madanapalli
IoT provides an opportunity for India to technicalize the citizen services for bettering their living standards. Here are my thoughts on the Role of Indian Government towards the Internet of Things.
Apache Hadoop started as batch: simple, powerful, efficient, scalable, and a shared platform. However, Hadoop is more than that. It's true strengths are:
Scalability – it's affordable due to it being open-source and its use of commodity hardware for reliable distribution.
Schema on read – you can afford to save everything in raw form.
Data is better than algorithms – More data and a simple algorithm can be much more meaningful than less data and a complex algorithm.
What is data-driven government for public safety?IBM Analytics
How can governments become data-driven and capitalize on the ton of valuable insight hidden in the flood of data we generate every day? Where has this already been implemented, and what are the effects? Get the big picture on public safety and incident and emergency management at http://ibm.co/saferplanet
Architecting next generation big data platformhadooparchbook
A tutorial on architecting next generation big data platform by the authors of O'Reilly's Hadoop Application Architectures book. This tutorial discusses how to build a customer 360 (or entity 360) big data application.
Audience: Technical.
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
Watch here: https://bit.ly/2NGQD7R
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization - a paradigm shift in the approach that organizations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes centre stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission-critical.
Attend this session to learn:
- Learn how you can meet cloud and data science challenges with data virtualization.
- Why data virtualization is increasingly finding enterprise-wide adoption
- Discover how customers are reducing costs and improving ROI with data virtualization
Myth Busters 9: Data virtualization doesn’t help me with data governanceDenodo
Watch full webinar here: https://buff.ly/3OGJYur
In the past, data governance was looked upon as a thankless task, more of a hindrance to business than a facilitator. However, because of today's data privacy regulations, data governance is increasingly seen as a critical function across most organizations. Companies are investing in governance tools, data quality tools, data catalogs, and so on in an effort to improve their data governance function.
Many people think that data virtualization has no role to play in data governance, that it's just a data access layer. And this is why we're back with another episode of Myth Busters!
In this webinar, we'll look at the need for data governance within organizations and whether modern data management platforms, powered by data virtualization, can play an important role within this function. We'll not just look at the data management aspect of data governance, but also at how good data governance can enable self-service analytics while still protecting data from unauthorized access.
Join us for this Myth Busters webinar as we explore the challenges of data governance and whether data virtualization is needed to deliver the value that your company expects from these initiatives.
Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...Denodo
Sylvain Dutilh, INFORMATION INTELLIGENCE SPECIALIST, Landsbankinn
Traditional data processing leaves large pools of replicated and unsynchronized data sets behind. In an era when data grows exponentially and is disconnected and spread across silos, it has never been more unnecessary to replicate data. In this session, Sylvain from Landsbankinn will walk us through his organization's journey of implementing a Logical Data Warehouse and a data-sharing program by leveraging Data Virtualization capability that allowed it to build a central, secure business rules repository and an agile, modern data mesh architecture.
The Great Lakes: How to Approach a Big Data ImplementationInside Analysis
The Briefing Room with Dr. Robin Bloor and Think Big, a Teradata Company
Live Webcast April 7, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=4114b87441ab7b2b4c52f6b24776e5a1
The more things change in Big Data, the more they stay the same. Indeed, there are many similarities between a Hadoop-based Data Lake and today’s modern Data Warehouse. Regardless of platform, information workers must still be able to turn their assets into action quickly, without taking a hit on governance or downstream performance.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the challenges facing organizations who endeavor on Big Data projects. He’ll be briefed by Rick Stellwagen of Think Big, a Teradata Company, who will outline his company’s approach to handling Big Data implementations. Rick will discuss the role of the data lake, and how timely response of queries is critical for reporting and analysis.
Visit InsideAnalysis.com for more information.
Databases are fundamentally changing due to new technologies and new requirements. This has never been more evident than with Oracle Database 12c, which has been the most rapidly adopted release in over a decade. This session provides a technical introduction to what's new in Oracle Database 12c and Oracle’s Engineered systems. We will describe which industry transformation inspired each enhancement and explain when and how you can embrace each enhancement while preserving your existing performance.
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Contexti / Oracle - Big Data : From Pilot to ProductionContexti
Big Data is moving from hype to reality for many organisations. The value proposition is clear and sponsorship is high, but how do organisations execute?
Join Oracle and Contexti to discuss the typical journey of a big data project from concept to pilot to production.
• Discuss our experience with a regional Telco
• Common Use Cases across key verticals
• Defining and prioritising use cases
• The challenge of moving from Pilot to Production
• Common Operating Models for Big Data
• Funding a Big Data Capability going forward
• Pilots - common mistakes; challenges; success criteria
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
What are Data Analytics Platforms? What decision points are necessary in creating a modern, unified analytics data platform? What benefits are there to building your analytics data platform on Google Cloud Platform? Susan Pierce walks us through it all.
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
Modern Data Management for Federal ModernizationDenodo
Watch full webinar here: https://bit.ly/2QaVfE7
Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture.
This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to:
- Enable governed, inter-agency data sharing
- Simplify data acquisition, search and tagging
- Streamline data delivery for transition to cloud, data science initiatives, and more
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Similar to Webinar -Data Warehouse Augmentation: Cut Costs, Increase Power (20)
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. • Award-winning provider of enterprise data lake
management solutions:
Integrated data lake management platform
Self-service catalog and data preparation
• Data Lake Design and Implementation Services:
POC, Pilot, Production, Operations, Training
• Data Science Professional Services
3. 3 Zaloni Proprietary
About our speakers
Pradeep Varadan, Verizon Wireline, OSS Data Science Leader
Varadan is a data scientist and enterprise architect who specializes in data challenges within
telecommunications. He is tasked with providing a competitive edge focused on utilizing data
analytics to drive effective decision-making. He is skilled in creating systems that can be used to
understand and make better decisions involving rapid technology shifts, customer lifestyle and
behavior trends and relevant changes that impact the Verizon Network.
Scott Gidley, Zaloni, VP Product Management
Gidley is responsible for the strategy and roadmap of existing and future products within the Zaloni
portfolio. He is a nearly 20 year veteran of the data management software and services market.
Prior to joining Zaloni, he served as senior director of product management at SAS and was
previously CTO and cofounder of DataFlux Corporation.
4. Zaloni Confidential and Proprietary - Provided under NDA
4 Zaloni Proprietary
Current state of a corporate data flow architecture
BI/ReportingData Generators
Machines
Data Channels
Warehouses Marts
Repositories
Data stores
4 Zaloni Proprietary
5. 5 Zaloni Proprietary
Business Challenges:
• Increased processing time/reduced
response
• Lack of data lineage/lack of
visibility
• Constant CapEx for hardware
upgrade
• Lack of access to history
Key Challenges
IT Challenges:
• Multiple data transfers
• Multiple technology platforms with
data copies
• Constant performance tuning
for CPU
• Manual data offload for space
management
6. Zaloni Confidential and Proprietary - Provided under NDA
6 Zaloni Proprietary
Sources ETL Report Mart
Data Discovery
Analytics BI
ELT/Reporting/MiningETL
Resource consumption
Staging Warehouse
6 Zaloni Proprietary
7. Zaloni Confidential and Proprietary - Provided under NDA
7 Zaloni Proprietary
Typical utilization of RDBMS resources
We expend almost all CPU for low business value ETL
Business Value
CPU
ETL to Stage
Auditing
(Landing tables query)
Data Mining
(Staging query)
Ad-hoc Analysis
(Warehouse query)
ETL to Warehouse
ETL to Reporting
Reporting
(Presentation table query)
*Size indicates frequency of use
7 Zaloni Proprietary
8. Zaloni Confidential and Proprietary - Provided under NDA
8 Zaloni Proprietary
~80% of system capacity used for batch processing (ELT)
8 Zaloni Proprietary
9. Zaloni Confidential and Proprietary - Provided under NDA
9 Zaloni Proprietary
Reduce cost of ELT/ETL by offloading to Hadoop
9 Zaloni Proprietary
10. Zaloni Confidential and Proprietary - Provided under NDA
10 Zaloni Proprietary
The future of enterprise data flowFuture
10 Zaloni Proprietary
Legacy
Structured Data ETL EDW+Sandbox BI/ReportingData Marts
Transactional
Systems
Machine logs/IOT
Structured/ Unstructured
Data Lake
Modern
T-Systems
Machines ETL Sandbox
EDW BI/Reporting/
Analytics
Data Marts
Operational Dashboards/EDA/Mining/Reporting/Analytics
Transactional
Systems
EDW Data Marts ETL Sandbox
ETL
12. 12 Zaloni Proprietary
Data lake challenges
• Ingestion
• Visibility and Quality
• Privacy and Compliance
• Timeliness
• Reliance on IT
• Reusability
• Rate of Change
• Skills Gap
• Complexity
Managing: Delivering:Building:
13. Zaloni Confidential and Proprietary - Provided under NDA
13 Zaloni Proprietary
Data Lake 360°: A holistic approach to actionable big data
1. Enable the lake
2. Govern the data
3. Engage the business
• Foster a data-driven business
through self-service data
discovery and preparation
• Safeguard sensitive data and
enable regulatory compliance
• Improve data visibility, reliability
and quality to reduce time-to-
insight
• Leverage the full power of a scale-out
architecture with an actionable,
scalable data lake
14. 14 Zaloni Proprietary
• Managed Ingestion
Ability to ingest vast amounts of data
Ability to handle a wide variety of formats
(streaming, files, custom) and sources
Build in repeatability through automation to pick up incoming data and
apply pre-defined processing
• Metadata Management
Capture and manage operational, technical and business metadata
Provides visibility and reliability – key to finding data in the lake
Reduced time to insight for analytics
File and record level watermarking provides data lineage, enables
audit and traceability
Enable the lake
15. 15 Zaloni Proprietary
Govern the data
• Data Lineage
See how data moves and how it is consumed in the data lake.
Safeguard data and reduce risk, always knowing where data
has come from, where it is, and how it is being used.
• Data Quality
Rules based Data validation
Integration with the Managed Data Pipeline
Stats and metrics for reporting and actions
16. 16 Zaloni Proprietary
Govern the data
• Data Security and Privacy
Differing permissions require enhanced data security
Mask or tokenize data before published in the lake for consumption
Policy-based security
• Data lifecycle management across tiered storage environments
Hot -> Warm -> Cold on an entity level based on policies/SLAs
Across on-premise and cloud environments
Provide data management features to automate scheduling and
orchestration of data movement between heterogeneous storage
environments
17. Zaloni Confidential and Proprietary - Provided under NDA
17 Zaloni Proprietary
Engage the business
• Data Catalog
See what data is available across your enterprise
Contribute valuable business information to improve
search and usage
Use a shopping cart experience to create sandbox for ad-
hoc and exploratory analytics
• Self-service Data Preparation
Blend data in the lake without a costly IT project
Perform interactive data-driven transformations
Collaborate and share data assets and transformations
with peers
18. Zaloni Confidential and Proprietary - Provided under NDA
18 Zaloni Proprietary
Data lake reference architecture
• Data required for LOB specific views - transformed
from existing certified data
• Consumers are anyone with appropriate role-based access
• Standardized on corporate governance/ quality policies
• Consumers are anyone with appropriate role-based access
• Single version of truth
Transient
Landing Zone
Raw Zone
Refined Zone
Trusted Zone
Sandbox
Data Lake
• Temporary store of
source data
• Consumers are IT,
Data Stewards
• Implemented in highly
regulated industries
• Original source data
ready for consumption
• Consumers are ETL
developers, data
stewards, some data
scientists
• Single source of truth
with history
• Data required for LOB specific views - transformed
from existing certified data
• Consumers are anyone with appropriate role-based access
Sensors
(or other time series data)
Relational Data
Stores
(OLTP/ODS/DW)
Logs
(or other unstructured
data)
Social and
shared data
16 Zaloni Proprietary
19. 19 Zaloni Proprietary
Data lake reference architecture with Zaloni
Consumption ZoneSource
System
File Data
DB Data
ETL Extracts
Streaming
Transient
Landing Zone Raw Zone
Refined
Zone
Trusted
Zone
Sandbox
APIs
Metadata
Management
Data Quality Data Catalog Security
Data Lake
Business Analysts
Researchers
Data Scientists
DATA LAKE MANAGEMENT
& GOVERNANCE PLATFORM
Sensors
(or other time series data)
Relational Data
Stores
(OLTP/ODS/DW)
Logs
(or other unstructured
data)
Social and
shared data
EDW
Data Marts
20. 20 Zaloni Proprietary
• Save millions in storage costs
• Significantly speed up processing
• Maximize the data warehouse for BI
• Extract more value from all of your data
Four great reasons to augment with a data lake
21. 21 Zaloni Proprietary
Centralized data, decentralized access
Business Analyst Business Manager Data Scientist Business SME
What happened? What is happening? What will happen? What can we control? Can I see the data?
IT Team
Business
Users
IT Analyst Programmer DBA/Modeler Data Scientist Data Engineer
Data Lake
Code Analysis App ImplementationApp PrototypeData ModelCode Development
Operations Manager