How the world of data analytics, science and insights is failing and how the principles from Agile, DevOps, and Lean are the way forward. #DataOps Given at DevOps Enterprise Summit 2019
seven steps to dataops @ dataops.rocks conference Oct 2019DataKitchen
The document outlines seven steps for implementing DataOps to improve data analytics projects: 1) orchestrate the data journey from access to production, 2) add automated tests and monitoring, 3) use version control for code, 4) enable branching and merging of code, 5) use multiple environments, 6) reuse and containerize components, and 7) parameterize processing. It also discusses three additional steps: data architecture, inter- and intra-team collaboration, and process analytics for measurement. The goal of DataOps is to increase project success rates by integrating testing, monitoring, collaboration and automation practices across the entire data and analytics workflow.
Low-tech, Low-cost data management: Six insights from national reporting on f...srjbridge
A cheap easy way to deliver data products faster with no loss of accuracy, using GCDOCS, MS Office products and other low cost solutions. Props to Datakitchen.io for great foundational ideas.
Do Agile Data in Just 5 Shocking Steps!DataKitchen
For over 10 years, we have been doing agile for software development yet people struggle to do agile for data, BI, and analytics. After a quick review of the agile manifesto and principles, this talk looks at which agile practices have worked for data and which are still hard. Then, with analyst requirements in mind, this talk reveals the 5 shocking steps to actually do agile with data.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Data kitchen 7 agile steps - big data fest 9-18-2015DataKitchen
This document discusses applying agile principles and practices to data and analytics teams to address the complexity they face. It outlines seven steps to doing agile data work: 1) adding tests, 2) modularizing and containerizing work, 3) using branching and merging, 4) employing multiple environments, 5) giving analysts tools to experiment, 6) using simple storage, and 7) supporting small team, feature branch, and data governance workflows. The goal is to enable rapid experimentation and integration of new data sources through these agile practices adapted for analytics teams and their unique needs.
Here is an overview of the Bridged framework that CodeData uses to deliver data driven solutions to our customers. The Bridged framework covers all aspects of such solutions - strategy. leadership, process, technology, education and operations.
How the world of data analytics, science and insights is failing and how the principles from Agile, DevOps, and Lean are the way forward. #DataOps Given at DevOps Enterprise Summit 2019
seven steps to dataops @ dataops.rocks conference Oct 2019DataKitchen
The document outlines seven steps for implementing DataOps to improve data analytics projects: 1) orchestrate the data journey from access to production, 2) add automated tests and monitoring, 3) use version control for code, 4) enable branching and merging of code, 5) use multiple environments, 6) reuse and containerize components, and 7) parameterize processing. It also discusses three additional steps: data architecture, inter- and intra-team collaboration, and process analytics for measurement. The goal of DataOps is to increase project success rates by integrating testing, monitoring, collaboration and automation practices across the entire data and analytics workflow.
Low-tech, Low-cost data management: Six insights from national reporting on f...srjbridge
A cheap easy way to deliver data products faster with no loss of accuracy, using GCDOCS, MS Office products and other low cost solutions. Props to Datakitchen.io for great foundational ideas.
Do Agile Data in Just 5 Shocking Steps!DataKitchen
For over 10 years, we have been doing agile for software development yet people struggle to do agile for data, BI, and analytics. After a quick review of the agile manifesto and principles, this talk looks at which agile practices have worked for data and which are still hard. Then, with analyst requirements in mind, this talk reveals the 5 shocking steps to actually do agile with data.
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
This presentation introduces the audience to the DataOps and AIOps practices. It deals with organizational & tech aspects, and provide hints to start you data journey.
Data kitchen 7 agile steps - big data fest 9-18-2015DataKitchen
This document discusses applying agile principles and practices to data and analytics teams to address the complexity they face. It outlines seven steps to doing agile data work: 1) adding tests, 2) modularizing and containerizing work, 3) using branching and merging, 4) employing multiple environments, 5) giving analysts tools to experiment, 6) using simple storage, and 7) supporting small team, feature branch, and data governance workflows. The goal is to enable rapid experimentation and integration of new data sources through these agile practices adapted for analytics teams and their unique needs.
Here is an overview of the Bridged framework that CodeData uses to deliver data driven solutions to our customers. The Bridged framework covers all aspects of such solutions - strategy. leadership, process, technology, education and operations.
The Importance of DataOps in a Multi-Cloud WorldDATAVERSITY
There’s no denying that Cloud has evolved from being an outlying market disruptor to a mainstream method for delivering IT applications and services. In fact, it’s not uncommon to find that Enterprises use the services of more than one cloud at the same time. However, while a multi-cloud strategy offers many benefits, it also increases data management complexity and consequently reduces data availability. This webinar defines the meaning of DataOps and why it’s a crucial component for every multi-cloud approach.
Learn more - http://www.talend.com/products/talend-6
When you’re ready to move to Big Data, connect in the cloud, and across the Internet of Things, Talend 6 streamlines the process. Convert traditional data integration jobs and MapReduce jobs to Spark with the click of a button, and realize the potential of real-time data-driven decision making. Learn more about Talend and Spark.
Talend 6 also brings continuous delivery, MDM REST API, plus data masking and semantic discovery to our products.
Moving to the Cloud: Modernizing Data Architecture in HealthcarePerficient, Inc.
The document discusses moving healthcare data architecture to the cloud. It describes a large health system that implemented an enterprise data warehouse (EDW) on the cloud to provide cost savings and flexibility. This consolidated multiple clinical repositories and reduced infrastructure costs. It also describes an academic health center that integrated patient records across its organizations using a cloud-based EDW. This improved analytics and reduced operating costs by 50% while improving patient care. Both organizations benefited from the scalability, cost savings and innovation the cloud enabled for their clinical analytics and research.
Webinar: The Death of Traditional Data IntegrationSnapLogic
In this webinar, we hear from industry analyst, middleware expert and author David Linthicum on why “existing approaches to data integration won’t meet future needs as the use of technology continues to change.” David also says that, “drastic measures must be taken now to prepare enterprises for the arrival of this technology, and to position enterprises to take full advantage.”
This webinar will show you how the game is changing, and what you can do about it right now. We summarize the changes that are happening, and review new and emerging patterns of data integration, as well as data integration technology that you can buy today that lives up to these new expectations.
To learn more, visit: www.snaplogic.com/big-data
Mike Tuche, CEO of Talend: Enabling the Data Driven EnterpriseTalend
See what's new in our latest version - http://www.talend.com/products
Talend Connect 2016 Keynote. Talend CEO Mike Tuchen describes how Talend is enabling the new future of the data-driven enterprise.
Embracing Cloud Agility to Maximize Flexibility & Performance Talend
The solution to going faster is the cloud. This is true for Talend and that is why you can see us putting significant efforts into our cloud platform. For you, the cloud means lower costs with no servers to buy and via more flexible elastic computing models; it means you can deliver change faster – you can try things quickly and you can deliver the change that your business needs. In this chart by armory you can see that these successful companies shown are deploying changes at a very high rate. This continuous integration and deployment process allows them to deliver change to their business and their customers. Finally, as we look to innovate in our business with machine learning, AI and more. The cloud is where these technologies are coming to life.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Data Engineering Efficiency @ Netflix - Strata 2017Michelle Ufford
Slides from Strata 2017 talk, "Data Engineering Efficiency @ Netflix."
Michelle Ufford explains how Netflix’s data engineering and analytics team is using data to find common patterns among the chaos that enable the company to automate repetitive and time-consuming tasks and discover ways to improve data quality, reduce costs, and quickly identify and respond to issues. Michelle provides a quick overview of Netflix’s analytics environment before diving into some of the major challenges facing the company’s data engineers. Along the way, Michelle shares how Netflix is building more intelligent data platform services and tools to improve data quality, automate data maintenance, alert on job optimization opportunities, and more.
Pivotal the new_pivotal_big_data_suite_-_revolutionary_foundation_to_leverage...EMC
The document discusses Pivotal's big data suite and business data lake offerings. It provides an overview of the components of a business data lake, including storage, ingestion, distillation, processing, unified data management, and action components. It also defines various data processing approaches like streaming, micro-batching, batch, and real-time response. The goal is to help organizations build analytics and transactional applications on big data to drive business insights and revenue.
5 Simple Steps to Unleash Big Data Talend ConnectTalend
The pace of business disruption is accelerating, meaning today’s organizations need to become more data driven in order to compete and innovate. It’s no secret that big data initiatives are becoming more pervasive. But in order to process this vast amount of data, companies need data science and machine learning to find valuable insights. As they move to build smart applications powered by Big Data and new emerging technologies like IoT, new challenges are arising including how to move data science and machine learning into production. Oftentimes it is a laborious, manual-coding process that can take up weeks or months.
The Model Enterprise: A Blueprint for Enterprise Data GovernanceEric Kavanagh
What gets measured, gets managed; but what gets governed, generates real value. That's one major reason why data governance has risen to a top priority for most organizations. Another reason is the rapid onboarding of big data, which often comes from beyond the traditional firewall. And then there are the authorities: issues like privacy, security and fiduciary responsibility are combining to make data governance a must-have. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why governance should be viewed as a positive change agent for the modern enterprise. He'll be briefed by Ron Huizenga of IDERA, who will discuss a practical, model-based approach to enterprise data governance, with a focus on Master Data Management.
Webinar: The 5 Most Critical Things to Understand About Modern Data IntegrationSnapLogic
In this webinar, we talk to industry analyst, author and practitioner David Linthicum who provides a state-of-the-technology explanation of big data integration.
David also provides 5 critical and lesser known data integration requirements, how to understand today's requirements, and guidance for choosing the right approaches and technology to solve these problems.
To learn more, visit: www.snaplogic.com/big-data
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Achieving Agility and Scale for Your Data Lake - TalendTalend
Most organization who going through Digital Transformation need to break down their data silos as well as leverage existing and new data sources. Here is how to build a data lake for data change in your organization.
The document discusses how GitLab.com builds its data services and products. It describes how GitLab.com uses its own DevOps platform to build an Enterprise Data Platform that analyzes data from GitLab.com. The data team faces challenges around scaling, visibility, and speed. To address these, the team takes actions like open sourcing tools, adopting DevOps practices, and establishing roles, processes, and technologies to build a trusted data model and framework. The key takeaways emphasize continuous iteration, discipline, automation, and living the company values.
Big Data Day LA 2015 - Transforming into a data driven enterprise using exist...Data Con LA
Leading entrepreneurial outfits are disrupting traditional companies by rapidly building data-driven apps. They employ top software talent and effectively use storage, analytics and app-dev tools from various open source ecosystems. We show how companies of all sizes are now transforming into data-driven enterprises using their existing software skill sets by leveraging a single platform that combines flexible data storage systems, advanced analytics and agile app-dev PaaS frameworks, all available now in open source forums.
The Future of Data Warehousing and Data IntegrationEric Kavanagh
The rise of big data, data lakes and the cloud, coupled with increasingly stringent enterprise requirements, are reinventing the role of data warehousing in modern analytics ecosystems. The emerging generation of data warehouses is more flexible, agile and cloud-based than their predecessors, with a strong need for automation and real-time data integration.
Join this live webinar to learn:
-Typical requirements for data integration
-Common use cases and architectural patterns
-Guidelines and best practices to address data requirements
-Guidelines and best practices to apply architectural patterns
In this webinar, we talk with experts from Integration Developer News about the SnapLogic Elastic Integration Platform and adoption trends for iPaaS in the enterprise.
To learn more, visit: http://video.snaplogic.com/webinars/
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
The Importance of DataOps in a Multi-Cloud WorldDATAVERSITY
There’s no denying that Cloud has evolved from being an outlying market disruptor to a mainstream method for delivering IT applications and services. In fact, it’s not uncommon to find that Enterprises use the services of more than one cloud at the same time. However, while a multi-cloud strategy offers many benefits, it also increases data management complexity and consequently reduces data availability. This webinar defines the meaning of DataOps and why it’s a crucial component for every multi-cloud approach.
Learn more - http://www.talend.com/products/talend-6
When you’re ready to move to Big Data, connect in the cloud, and across the Internet of Things, Talend 6 streamlines the process. Convert traditional data integration jobs and MapReduce jobs to Spark with the click of a button, and realize the potential of real-time data-driven decision making. Learn more about Talend and Spark.
Talend 6 also brings continuous delivery, MDM REST API, plus data masking and semantic discovery to our products.
Moving to the Cloud: Modernizing Data Architecture in HealthcarePerficient, Inc.
The document discusses moving healthcare data architecture to the cloud. It describes a large health system that implemented an enterprise data warehouse (EDW) on the cloud to provide cost savings and flexibility. This consolidated multiple clinical repositories and reduced infrastructure costs. It also describes an academic health center that integrated patient records across its organizations using a cloud-based EDW. This improved analytics and reduced operating costs by 50% while improving patient care. Both organizations benefited from the scalability, cost savings and innovation the cloud enabled for their clinical analytics and research.
Webinar: The Death of Traditional Data IntegrationSnapLogic
In this webinar, we hear from industry analyst, middleware expert and author David Linthicum on why “existing approaches to data integration won’t meet future needs as the use of technology continues to change.” David also says that, “drastic measures must be taken now to prepare enterprises for the arrival of this technology, and to position enterprises to take full advantage.”
This webinar will show you how the game is changing, and what you can do about it right now. We summarize the changes that are happening, and review new and emerging patterns of data integration, as well as data integration technology that you can buy today that lives up to these new expectations.
To learn more, visit: www.snaplogic.com/big-data
Mike Tuche, CEO of Talend: Enabling the Data Driven EnterpriseTalend
See what's new in our latest version - http://www.talend.com/products
Talend Connect 2016 Keynote. Talend CEO Mike Tuchen describes how Talend is enabling the new future of the data-driven enterprise.
Embracing Cloud Agility to Maximize Flexibility & Performance Talend
The solution to going faster is the cloud. This is true for Talend and that is why you can see us putting significant efforts into our cloud platform. For you, the cloud means lower costs with no servers to buy and via more flexible elastic computing models; it means you can deliver change faster – you can try things quickly and you can deliver the change that your business needs. In this chart by armory you can see that these successful companies shown are deploying changes at a very high rate. This continuous integration and deployment process allows them to deliver change to their business and their customers. Finally, as we look to innovate in our business with machine learning, AI and more. The cloud is where these technologies are coming to life.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Data Engineering Efficiency @ Netflix - Strata 2017Michelle Ufford
Slides from Strata 2017 talk, "Data Engineering Efficiency @ Netflix."
Michelle Ufford explains how Netflix’s data engineering and analytics team is using data to find common patterns among the chaos that enable the company to automate repetitive and time-consuming tasks and discover ways to improve data quality, reduce costs, and quickly identify and respond to issues. Michelle provides a quick overview of Netflix’s analytics environment before diving into some of the major challenges facing the company’s data engineers. Along the way, Michelle shares how Netflix is building more intelligent data platform services and tools to improve data quality, automate data maintenance, alert on job optimization opportunities, and more.
Pivotal the new_pivotal_big_data_suite_-_revolutionary_foundation_to_leverage...EMC
The document discusses Pivotal's big data suite and business data lake offerings. It provides an overview of the components of a business data lake, including storage, ingestion, distillation, processing, unified data management, and action components. It also defines various data processing approaches like streaming, micro-batching, batch, and real-time response. The goal is to help organizations build analytics and transactional applications on big data to drive business insights and revenue.
5 Simple Steps to Unleash Big Data Talend ConnectTalend
The pace of business disruption is accelerating, meaning today’s organizations need to become more data driven in order to compete and innovate. It’s no secret that big data initiatives are becoming more pervasive. But in order to process this vast amount of data, companies need data science and machine learning to find valuable insights. As they move to build smart applications powered by Big Data and new emerging technologies like IoT, new challenges are arising including how to move data science and machine learning into production. Oftentimes it is a laborious, manual-coding process that can take up weeks or months.
The Model Enterprise: A Blueprint for Enterprise Data GovernanceEric Kavanagh
What gets measured, gets managed; but what gets governed, generates real value. That's one major reason why data governance has risen to a top priority for most organizations. Another reason is the rapid onboarding of big data, which often comes from beyond the traditional firewall. And then there are the authorities: issues like privacy, security and fiduciary responsibility are combining to make data governance a must-have. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why governance should be viewed as a positive change agent for the modern enterprise. He'll be briefed by Ron Huizenga of IDERA, who will discuss a practical, model-based approach to enterprise data governance, with a focus on Master Data Management.
Webinar: The 5 Most Critical Things to Understand About Modern Data IntegrationSnapLogic
In this webinar, we talk to industry analyst, author and practitioner David Linthicum who provides a state-of-the-technology explanation of big data integration.
David also provides 5 critical and lesser known data integration requirements, how to understand today's requirements, and guidance for choosing the right approaches and technology to solve these problems.
To learn more, visit: www.snaplogic.com/big-data
Meg Mude, Intel - Data Engineering Lifecycle Optimized on Intel - H2O World S...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/cnU6sqd31JU
Developing meaningful AI applications requires complete data lifecycle management. Sourcing, harvesting, labelling and ensuring the conduit to consume data structures and repositories is critical for model accuracy....but, one of the least talked about subjects. Intel’s optimized technologies enable efficient delivery of complete data samples to develop (and deploy) meaningful outcomes. During this session, we’ll review the considerations and criticality of data lifecycle management for the AI production pipeline.
Bio: Meg brings more than 17 years of global product, engineering and solutions experience. She is presently a Solutions Architect with Intel Corporation specializing in Visual Compute and AAI (Analytics and AI) Architecture. She is passionate about the potential for technology to improve the quality of peoples’ lives and humanity on the whole.
Achieving Agility and Scale for Your Data Lake - TalendTalend
Most organization who going through Digital Transformation need to break down their data silos as well as leverage existing and new data sources. Here is how to build a data lake for data change in your organization.
The document discusses how GitLab.com builds its data services and products. It describes how GitLab.com uses its own DevOps platform to build an Enterprise Data Platform that analyzes data from GitLab.com. The data team faces challenges around scaling, visibility, and speed. To address these, the team takes actions like open sourcing tools, adopting DevOps practices, and establishing roles, processes, and technologies to build a trusted data model and framework. The key takeaways emphasize continuous iteration, discipline, automation, and living the company values.
Big Data Day LA 2015 - Transforming into a data driven enterprise using exist...Data Con LA
Leading entrepreneurial outfits are disrupting traditional companies by rapidly building data-driven apps. They employ top software talent and effectively use storage, analytics and app-dev tools from various open source ecosystems. We show how companies of all sizes are now transforming into data-driven enterprises using their existing software skill sets by leveraging a single platform that combines flexible data storage systems, advanced analytics and agile app-dev PaaS frameworks, all available now in open source forums.
The Future of Data Warehousing and Data IntegrationEric Kavanagh
The rise of big data, data lakes and the cloud, coupled with increasingly stringent enterprise requirements, are reinventing the role of data warehousing in modern analytics ecosystems. The emerging generation of data warehouses is more flexible, agile and cloud-based than their predecessors, with a strong need for automation and real-time data integration.
Join this live webinar to learn:
-Typical requirements for data integration
-Common use cases and architectural patterns
-Guidelines and best practices to address data requirements
-Guidelines and best practices to apply architectural patterns
In this webinar, we talk with experts from Integration Developer News about the SnapLogic Elastic Integration Platform and adoption trends for iPaaS in the enterprise.
To learn more, visit: http://video.snaplogic.com/webinars/
The 3 Key Barriers Keeping Companies from Deploying Data Products Dataiku
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Mint.com started as a prototype created by the author using open source tools with no prior startup experience. The initial prototype focused on differentiating features like aggregating financial accounts and transactions. As users grew, performance issues arose due to increased load on servers and databases. To address these growing pains, the architecture was optimized by separating tiers, adding caching, database sharding, and more. Key lessons were to focus first on critical user problems in prototypes, continuously measure performance, and optimize based on demand to balance latency, throughput, and quality as the user base expanded.
The document contains a resume for Saketh Vadlamudi seeking an entry level machine learning or data analysis role, highlighting his skills and experience in machine learning algorithms, programming languages, and tools as well as academic and professional projects applying machine learning to problems in various domains like banking, oil prices, sentiment analysis, and credit risk classification. Vadlamudi has a Master's degree in Computer Science from Texas A&M University and is currently working as a Data and Machine Learning Engineer at Reynolds American Inc.
This presentation gives an overview of StreamCentral technology targeted for IT professionals. StreamCentral is software to model and build Big Data Solutions. StreamCentral consists of a Big Data Solutions Modeler that not only makes it easy to model traditional BI/DW and Big Data solutions but also auto deploys the model on the latest innovations in Big Data Management solutions (like HP Vertica and SQL Server Parallel Data Warehouse). StreamCentral Big Data Server executes the model definition in real-time. StreamCentral drastically reduces the time to market, risk and cost associated with building traditional BI/DW and Big Data solutions!
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Build it…will they come by Shawn TrainerData Con LA
Abstract:- The truth about enabling self-service (and why you need it) Data is growing astronomically, historically and in real-time. So is the need for exploration and discovery. One size doesn’t fit all. We’ll be covering how to efficiently deliver information on-demand and promote self-service adoption with the right data platform.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses unlocking operational intelligence from data lakes using MongoDB. It begins by describing how digital transformation is driving changes in data volume, velocity, and variety. It then discusses how MongoDB can help operationalize data lakes by providing real-time access and analytics on data stored in data lakes, while also integrating batch processing capabilities. The document provides an example reference architecture of how MongoDB can be used with a data lake (Hadoop) and stream processing framework (Kafka) to power operational applications and machine learning models with both real-time and batch data and analytics.
Using a Semantic and Graph-based Data Catalog in a Modern Data FabricCambridge Semantics
Watch this webinar to learn about the benefits of using semantic and graph database technology to create a Data Catalog of all of an enterprise's data, regardless of source or format, as part of a modern IT or data management stack and an important step toward building an Enterprise Data Fabric.
Smarter Analytics: Supporting the Enterprise with AutomationInside Analysis
The Briefing Room with Barry Devlin and WhereScape
Live Webcast on June 10, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=5230c31ab287778c73b56002bc2c51a
The data warehouse is intended to support analysis by making the right data available to the right people in a timely fashion. But conditions change all the time, and when data doesn’t keep up with the business, analysts quickly turn to workarounds. This leads to ungoverned and largely un-managed side projects, which trade short-term wins for long-term trouble. One way to keep everyone happy is by creating an integrated environment that pulls data from all sources, and is capable of automating both the model development and delivery of analyst-ready data.
Register for this episode of The Briefing Room to hear data warehousing pioneer and Analyst Barry Devlin as he explains the critical components of a successful data warehouse environment, and how traditional approaches must be augmented to keep up with the times. He’ll be briefed by WhereScape CEO Michael Whitehead, who will showcase his company’s data warehousing automation solutions. He’ll discuss how a fast, well-managed and automated infrastructure is the key to empowering faster, smarter, repeatable decision making.
Visit InsideAnlaysis.com for more information.
The document discusses leveraging the cloud to architect digital solutions. It covers state-of-the-art IoT technology, machine learning clustering and classification prototypes, Cortana analytics, and patterns and anti-patterns for building solutions. The document demonstrates table storage and machine learning clustering of data. It presents an Azure IoT reference architecture and discusses visualizing machine learning results and deriving business value from big data.
Don't Let Your Shoppers Drop; 5 Rules for Today’s eCommerceDataStax
The definition of eCommerce has totally changed, expanding from a purely retail perspective to mean "the place where your customers meet you online." Whether you offer mortgage services or catering recommendations, you must think of your online transaction application as an eCommerce site.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Data and Application Modernization in the Age of the Cloudredmondpulver
Data modernization is key to unlocking the full potential of your IT investments, both on premises and in the cloud. Enterprises and organizations of all sizes rely on their data to power advanced analytics, machine learning, and artificial intelligence.
Yet the path to modernizing legacy data systems for the cloud is full of pitfalls that cost time, money, and resources. These issues include high hardware and staffing costs, difficulty moving data and analytical processes to cloud environments, and inadequate support for real-time use cases. These issues delay delivery timelines and increase costs, impacting the return on investment for new, cutting-edge applications.
Watch this webinar in which James Kobielus, TDWI senior research director for data management, explores how enterprises are modernizing their mainframe data and application infrastructures in the cloud to sustain innovation and drive efficiencies. Kobielus will engage John de Saint Phalle, senior product manager at Precisely, in a discussion that addresses the following key questions:
When should enterprises consider migrating and replicating all their data assets to modern public clouds vs. retaining some on-premises in hybrid deployments?How should enterprises modernize their legacy data and application infrastructures to unlock innovation and value in the age of cloud computing?What are the key investments that enterprises should make to modernize their data pipelines to deliver better AI/ML applications in the cloud?What is the optimal data engineering workflow for building, testing, and operationalizing high-quality modern AI/ML applications in the cloud?What value does real-time replication play in migrating data and applications to modern cloud data architectures?What challenges do enterprises face in ensuring and maintaining the integrity, fitness, and quality of the data that they migrate to modern clouds?What tools and methodologies should enterprise application developers use to refactor and transform legacy data applications that have migrated to modern clouds
Simplifying Building Automation: Leveraging Semantic Tagging with a New Breed...Memoori
Memoori's 10th Webinar in the 2019 Smart Buildings Series. We spoke with Chris Irwin, VP Sales EMEA & Asia at J2 Innovations about the FIN 5 software framework and “Simplifying Building Automation by Leveraging Semantic Tagging with a New Breed of Software”.
The Shifting Landscape of Data IntegrationDATAVERSITY
This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
Similar to Overcoming DataOps hurdles for ML in Production (20)
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
1. 1
Overcoming
DataOps hurdles for
ML in Production
August 2020
SANDEEP UTTAMCHANDANI
CHIEF DATA OFFICER and VP OF ENGINEERING
sandeep@unraveldata.com
5. Levels of
Automation
Gather technical metadata
Gather operational metadata
Aggregate tribal
knowledge
1. “I thought the attribute means something else”
Battlescar:
Incorrect assumptions about the meaning of attributes, whether it is the
source of truth, owner/common users, versioning, whether dataset is
trustworthy?
Metric:
Time to
Interpret
Building a Self-Service Metadata Catalog
6. 1. “I thought the attribute means something else?”
Battlescar:
Incorrect assumptions about the meaning of attributes, whether it is the
source of truth, owner/common users, versioning, whether dataset is
trustworthy?
Metric:
Time to
Interpret
Building a Self-Service Metadata Catalog
Intuit
7. 7
2. “Where is the dataset I need for my model?”
Battlescar:
Building a customer support forecasting model. Data was silo’ed across
business units. 4+ months of connecting to data stewards to locate the data
attributes required for building the model
Building a Self-Service Search Service
Levels of
Automation
Indexing of datasets &
artifacts
Search Relevance ranking
Access control of
search results
Metric:
Time to
Find
8. 8
Battlescar:
Building a customer support forecasting model. Data was silo’ed across
business units. 4+ months of connecting to data stewards to locate the data
attributes required for building the model
Building a Self-Service Search Service
Metric:
Time to
Find
2. “Where is the dataset I need for my model?”
9. 9
3. “1000 rows in source database -- why only 50 rows in
data lake?”
Battlescar:
Issues in correctness, completeness, timeliness in moving data
daily/hourly from transactional datastores to centralized data lake
Metric:
Time to
Move
Building a Self-Service Data Movement service
Data Ingestion Configuration
Data Transformation
Change Mgt
Levels of
Automation
10. 10
4. “Job completed but dashboard graphs have data missing?”
Battlescar:
Jobs are orchestrated using schedulers (such as Airflow, Oozie). Several
times, the job dependencies are incorrect, leading to reporting or model
training jobs to be triggered prematurely.
Metric:
Time to
Orchestrate
Building a Self-Service orchestration Service
Levels of
Automation
Defining Job Dependencies
Robust Job Execution
Production
Monitoring
11. 11
5. “Data processing was supposed to complete at 8 am. Its 4pm
and my model retraining job is still waiting?”
Battlescar:
Writing efficient Big Data processing applications is non-trivial. With
plethora of technologies, gaining broad expertise is difficult even for
expert data engineers.
Metric:
Time to
Optimize
Building a Self-Service query optimization Service
Levels of
Automation
Aggregating query, cluster,
resource Stats
Analyzing & correlating
stats
Tuning Jobs
12. 12
6. “Customer changed preference to no marketing emails. Why are
we still including in email campaigns?”
Battlescar:
Without a consistent primary key to identify the customer across data
silos, where recurring issues arise. Emerging Data Rights such as
GDPR, CCPA, require complying with customer preferences on what
data is collected, how it is used, deleted on request.
Metric:
Time to
Comply
Building a Self-Service data rights governance Service
Levels of
Automation
Tracking customer data lifecycle
and preferences
Executing customer’s
data rights requests
Use-case
based access
control
13. 13
7. “Job pipeline ran for 15 hours and now we detect data
quality issue upon completion -- could we be proactive?”
Battlescar:
Data issues in a long running business critical job leads to missing
insights. Only when results don't look correct that we realize there is an
issue.
Metric:
Time to
Insights
Quality
Building a Self-Service data observability Service
Levels of
Automation
Verify accuracy of data
Detect anomalies
Avoid data
quality issues
14. 14
8. “Using the best polyglot datastores -- how do I now write
queries effectively across this data?”
Battlescar:
Significant time spent in planning, design, and writing queries that
process data across datastores
Metric:
Time to
Virtualize
Datastores
Building a Self-Service data virtualization Service
Levels of
Automation
Automatic query routing
Managing datastore
specific queries
Joining across
transactional
sources
15. 15
9. “I ran a A/B experiment -- need to build time-consuming
data pipelines to now analyze the data”
Battlescar:
Analyzing experimental results in a consistent fashion is a nightmare. No
consistent definitions between metrics used for experimental analysis
and business reporting
Metric:
Time to A/B
Test
Building a Self-Service A/B Testing Service
Levels of
Automation
16. 16
10. “Data processing jobs last week cost us 30% more. Why?”
Battlescar:
Especially in the cloud, $ cost is linear to usage. Tracking budgets and
spend to effectively optimize requires non-trivial effort.
Metric:
Time to
Cost
Governance
Building a Self-Service cost governance Service
Levels of
Automation
Expenditure Observability
Matching
Supply-Demand
Continuous Cost
Optimization
23. 23
Call for Action: Making DataOps Self-Service
1. Measure
Create your
Time-to-Insight Scorecard
Self-Service
DataOps
2. Learn
Shortlist 1-2 scorecard
metrics to improve level
of automation
3. Build
Implement well-known
design patterns in your
data platform to make the
metrics self-service
24. 24
Upcoming Book: The Self-Service Data Roadmap
Available Sept’20
Early Release Available on O’Reilly:
https://www.oreilly.com/library/view/the-self-service-data/9781492075240/
25. 25
CONTACT US TO SCHEDULE A DATA OPERATIONS HEALTH CHECK TODAY
hello@unraveldata.com