The document discusses machine learning and artificial intelligence applications inside and outside of Snowflake's cloud data warehouse. It provides an overview of Snowflake and its architecture. It then discusses how machine learning can be implemented directly in the database using SQL, user-defined functions, and stored procedures. However, it notes that pure coding is not suitable for all users and that automated machine learning outside the database may be preferable to enable more business analysts and power users. It provides an example of using Amazon Forecast for time series forecasting and integrating it with Snowflake.
From the Data Work Out event:
Performant and scalable Data Science with Dataiku DSS and Snowflake
Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data.
A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.
This document outlines an agenda for a 90-minute workshop on Snowflake. The agenda includes introductions, an overview of Snowflake and data warehousing, demonstrations of how users utilize Snowflake, hands-on exercises loading sample data and running queries, and discussions of Snowflake architecture and capabilities. Real-world customer examples are also presented, such as a pharmacy building new applications on Snowflake and an education company using it to unify their data sources and achieve a 16x performance improvement.
Sydney: Certus Data 2.0 Vault Meetup with Snowflake - Data Vault In The Cloud Certus Solutions
Snowflake is a cloud data platform company that was founded in 2012. It has over 640 employees, 1500+ customers, and has raised $923 million in funding. Snowflake provides an elastic data warehouse that allows customers to instantly scale compute and storage resources. It offers a fully managed service with no infrastructure to manage and allows customers to consolidate siloed datasets and analyze data across multiple cloud regions and accounts.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Delivering rapid-fire Analytics with Snowflake and TableauHarald Erb
Until recently, advancements in data warehousing and analytics were largely incremental. Small innovations in database design would herald a new data warehouse every
2-3 years, which would quickly become overwhelmed with rapidly increasing data volumes. Knowledge workers struggled to access those databases with development intensive BI tools designed for reporting, rather than exploration and sharing. Both databases and BI tools were strained in locally hosted environments that were inflexible to growth or change.
Snowflake and Tableau represent a fundamentally different approach. Snowflake’s multi-cluster shared data architecture was designed for the cloud and to handle logarithmically larger data volumes at blazing speed. Tableau was made to foster an interactive approach to analytics, freeing knowledge workers to use the speed of Snowflake to their greatest advantage.
Vivint Smart Home's journey with Snowflake and migrating from SQL Server. We describe how we have setup snowflake from a people, process, and technology perspective.
From the Data Work Out event:
Performant and scalable Data Science with Dataiku DSS and Snowflake
Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data.
A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.
This document outlines an agenda for a 90-minute workshop on Snowflake. The agenda includes introductions, an overview of Snowflake and data warehousing, demonstrations of how users utilize Snowflake, hands-on exercises loading sample data and running queries, and discussions of Snowflake architecture and capabilities. Real-world customer examples are also presented, such as a pharmacy building new applications on Snowflake and an education company using it to unify their data sources and achieve a 16x performance improvement.
Sydney: Certus Data 2.0 Vault Meetup with Snowflake - Data Vault In The Cloud Certus Solutions
Snowflake is a cloud data platform company that was founded in 2012. It has over 640 employees, 1500+ customers, and has raised $923 million in funding. Snowflake provides an elastic data warehouse that allows customers to instantly scale compute and storage resources. It offers a fully managed service with no infrastructure to manage and allows customers to consolidate siloed datasets and analyze data across multiple cloud regions and accounts.
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
New! Real-Time Data Replication to SnowflakePrecisely
Your business is adopting the Snowflake cloud data platform to rapidly deliver data insights and lower the costs of your data warehouse. But you have a problem – what happens when data changes on your mainframe and IBM i systems? How do you make sure Snowflake is always up-to-date and in sync with these systems of record?
If you can’t integrate changes occurring on your mainframe and IBM i systems to Snowflake, your business will miss the critical data it needs to drive real-time insights and decision making.
Join us to learn how the latest enhancements to Precisely Connect help your business meet its data-driven goals by sharing changes made on legacy, mainframe, and IBM systems to Snowflake in real time.
During this webinar, you will learn more about:
- How to easily support data replication from mainframe and IBM i to Snowflake
- Connect’s enhanced data replication capabilities for cloud data platforms
- How customers are using Connect to support their cloud data platform strategies
Delivering rapid-fire Analytics with Snowflake and TableauHarald Erb
Until recently, advancements in data warehousing and analytics were largely incremental. Small innovations in database design would herald a new data warehouse every
2-3 years, which would quickly become overwhelmed with rapidly increasing data volumes. Knowledge workers struggled to access those databases with development intensive BI tools designed for reporting, rather than exploration and sharing. Both databases and BI tools were strained in locally hosted environments that were inflexible to growth or change.
Snowflake and Tableau represent a fundamentally different approach. Snowflake’s multi-cluster shared data architecture was designed for the cloud and to handle logarithmically larger data volumes at blazing speed. Tableau was made to foster an interactive approach to analytics, freeing knowledge workers to use the speed of Snowflake to their greatest advantage.
Vivint Smart Home's journey with Snowflake and migrating from SQL Server. We describe how we have setup snowflake from a people, process, and technology perspective.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Launching a Data Platform on SnowflakeKETL Limited
This document discusses launching a data platform on Snowflake and the skills and technology required. It outlines that Snowflake provides a low barrier to entry with pay-per-use pricing and the ability to scale compute resources up and down as needed. Running a data platform requires data modeling skills and being able to work in an agile environment. The company's platform is a wrapper service built on Snowflake that extracts, loads, transforms data and provides a semantic layer for business users.
AWS Summit Singapore 2019 | Snowflake: Your Data. No LimitsAWS Summits
This document discusses Snowflake, a cloud data platform. It describes Snowflake's mission to enable organizations to be data-driven. It outlines problems with traditional data architectures like complexity, limited scalability, inability to consolidate data, and rigid costs. Snowflake's solution is a cloud-native data warehouse delivered as a service that offers instant elasticity, end-to-end security, and the ability to query structured and semi-structured data using SQL. Key benefits of Snowflake include supporting any scale of data, users and workloads; paying only for resources used; and providing simplicity, scalability, flexibility and elasticity.
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
Chug building a data lake in azure with spark and databricksBrandon Berlinrut
- The document discusses building a data lake in Azure using Spark and Databricks. It begins with an introduction of the presenter and their experience.
- The rest of the document is organized into sections that discuss decisions around why to use a data lake and Azure/Databricks, how to build the lake by ingesting and organizing data, using Delta Lake for integrated and curated layers, securing the lake, and enabling analytics against the lake.
- The key aspects covered include getting data into the lake from various sources using custom Spark jobs, organizing the lake into layers, cataloging data, using Delta Lake for transactional tables, implementing role-based security, and allowing ad-hoc queries.
This document provides an introduction and overview of implementing Data Vault 2.0 on Snowflake. It begins with an agenda and the presenter's background. It then discusses why customers are asking for Data Vault and provides an overview of the Data Vault methodology including its core components of hubs, links, and satellites. The document applies Snowflake features like separation of workloads and agile warehouse scaling to support Data Vault implementations. It also addresses modeling semi-structured data and building virtual information marts using views.
Cloud Data Warehousing presentation by Rogier Werschkull, including tips, bes...Patrick Van Renterghem
Presentation on "Cloud Data Warehousing: What, Why and How?" by Rogier Werschkull (RogerData), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
This document discusses using Azure DevOps and Snowflake to enable continuous integration and continuous deployment (CI/CD) of database changes. It covers setting up source control in a repository, implementing pull requests for code reviews, building deployment artifacts in a build pipeline, and deploying artifacts to development, test, and production environments through a release pipeline. The document also highlights key Snowflake features like zero-copy cloning that enable testing deployments before production.
Melbourne: Certus Data 2.0 Vault Meetup with Snowflake - Data Vault In The Cl...Certus Solutions
Snowflake is a cloud data warehouse that provides elasticity, scalability, and simplicity. It allows organizations to consolidate their diverse data sources in one place and instantly scale up or down their compute capacity as needed. Aptus Health, a digital marketing company, used Snowflake to break down data silos, integrate disparate data sources, enable broad data sharing, and provide a scalable and cost-effective solution to meet their analytics needs. Snowflake addressed both business needs for timely access to centralized data and IT needs for flexibility, extensibility, and reducing ETL work.
HOW TO SAVE PILEs of $$$BY CREATING THE BEST DATA MODEL THE FIRST TIME (Ksc...Kent Graziano
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk?
In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.
1. The document discusses a Gartner report that assesses 20 vendors of data science and machine learning platforms. It evaluates the platforms' abilities to support the full data science life cycle.
2. The report places vendors in four categories - Leaders, Challengers, Visionaries, and Niche Players. It outlines the strengths and cautions of platforms from vendors like Amazon Web Services, Alteryx, and Anaconda.
3. Key criteria for evaluating the platforms include ease of use, support for different personas, capabilities for tasks like modeling and deployment, and growth and innovation. The report aims to help users choose the right platform for their needs.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Self-serve analytics journey at Celtra: Snowflake, Spark, and DatabricksGrega Kespret
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both.
Topics include:
- Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline
- How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution
- How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency
- How Snowflake + Spark enables production and ad hoc analytics on a single repository of data
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Customer Presentation - IBM Cloud Pak for Data Overview (Level 100).PPTXtsigitnist02
This document provides instructions for using a presentation deck on Cloud Pak for Data. It instructs the user to:
1. Delete the first slide before using the deck.
2. Customize the presentation for the intended audience as the deck covers various topics and using all slides may not fit a single meeting.
3. The deck contains 6 embedded video records for a demo that takes 15-25 minutes to present. Guidance on pitching the demo is available.
The appendix contains slides on Cloud Pak for Data licensing and IBM's overall strategy.
Take the Bias out of Big Data Insights With Augmented AnalyticsTyler Wishnoff
Is bias impacting your Big Data insights? Learn how augmented analytics and the latest advancements in OLAP technology are making analytics (including on cloud) from business intelligence, data science, and machine learning more accurate and impactful. Learn more at https://kyligence.io
ADV Slides: 2021 Trends in Enterprise AnalyticsDATAVERSITY
The document discusses trends in enterprise advanced analytics for 2021 and beyond. Some key trends include remote work continuing, strong tech spending rebound led by cloud capabilities, leading organizations increasing focus on AI/ML with model deployment taking center stage, more edge AI, rise of data lakes, new technology stacks focusing on data fabrics and AI pipelines, increased automation, open source becoming more prevalent, Kubernetes becoming the standard analytics stack, and general AI beginning to emerge. Winning approaches for 2021 include cloud, AI, data lakes, data warehousing, MDM, agile development, Kubernetes, automation, data quality, and DevOps/MLOps.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
Launching a Data Platform on SnowflakeKETL Limited
This document discusses launching a data platform on Snowflake and the skills and technology required. It outlines that Snowflake provides a low barrier to entry with pay-per-use pricing and the ability to scale compute resources up and down as needed. Running a data platform requires data modeling skills and being able to work in an agile environment. The company's platform is a wrapper service built on Snowflake that extracts, loads, transforms data and provides a semantic layer for business users.
AWS Summit Singapore 2019 | Snowflake: Your Data. No LimitsAWS Summits
This document discusses Snowflake, a cloud data platform. It describes Snowflake's mission to enable organizations to be data-driven. It outlines problems with traditional data architectures like complexity, limited scalability, inability to consolidate data, and rigid costs. Snowflake's solution is a cloud-native data warehouse delivered as a service that offers instant elasticity, end-to-end security, and the ability to query structured and semi-structured data using SQL. Key benefits of Snowflake include supporting any scale of data, users and workloads; paying only for resources used; and providing simplicity, scalability, flexibility and elasticity.
Smartsheet’s Transition to Snowflake and Databricks: The Why and Immediate Im...Databricks
Join this session to hear why Smartsheet decided to transition from their entirely SQL-based system to Snowflake and Databricks, and learn how that transition has made an immediate impact on their team, company and customer experience through enabling faster, informed data decisions.
Chug building a data lake in azure with spark and databricksBrandon Berlinrut
- The document discusses building a data lake in Azure using Spark and Databricks. It begins with an introduction of the presenter and their experience.
- The rest of the document is organized into sections that discuss decisions around why to use a data lake and Azure/Databricks, how to build the lake by ingesting and organizing data, using Delta Lake for integrated and curated layers, securing the lake, and enabling analytics against the lake.
- The key aspects covered include getting data into the lake from various sources using custom Spark jobs, organizing the lake into layers, cataloging data, using Delta Lake for transactional tables, implementing role-based security, and allowing ad-hoc queries.
This document provides an introduction and overview of implementing Data Vault 2.0 on Snowflake. It begins with an agenda and the presenter's background. It then discusses why customers are asking for Data Vault and provides an overview of the Data Vault methodology including its core components of hubs, links, and satellites. The document applies Snowflake features like separation of workloads and agile warehouse scaling to support Data Vault implementations. It also addresses modeling semi-structured data and building virtual information marts using views.
Cloud Data Warehousing presentation by Rogier Werschkull, including tips, bes...Patrick Van Renterghem
Presentation on "Cloud Data Warehousing: What, Why and How?" by Rogier Werschkull (RogerData), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
This document discusses using Azure DevOps and Snowflake to enable continuous integration and continuous deployment (CI/CD) of database changes. It covers setting up source control in a repository, implementing pull requests for code reviews, building deployment artifacts in a build pipeline, and deploying artifacts to development, test, and production environments through a release pipeline. The document also highlights key Snowflake features like zero-copy cloning that enable testing deployments before production.
Melbourne: Certus Data 2.0 Vault Meetup with Snowflake - Data Vault In The Cl...Certus Solutions
Snowflake is a cloud data warehouse that provides elasticity, scalability, and simplicity. It allows organizations to consolidate their diverse data sources in one place and instantly scale up or down their compute capacity as needed. Aptus Health, a digital marketing company, used Snowflake to break down data silos, integrate disparate data sources, enable broad data sharing, and provide a scalable and cost-effective solution to meet their analytics needs. Snowflake addressed both business needs for timely access to centralized data and IT needs for flexibility, extensibility, and reducing ETL work.
HOW TO SAVE PILEs of $$$BY CREATING THE BEST DATA MODEL THE FIRST TIME (Ksc...Kent Graziano
A good data model, done right the first time, can save you time and money. We have all seen the charts on the increasing cost of finding a mistake/bug/error late in a software development cycle. Would you like to reduce, or even eliminate, your risk of finding one of those errors late in the game? Of course you would! Who wouldn't? Nobody plans to miss a requirement or make a bad design decision (well nobody sane anyway). No data modeler or database designer worth their salt wants to leave a model incomplete or incorrect. So what can you do to minimize the risk?
In this talk I will show you a best practice approach to developing your data models and database designs that I have been using for over 15 years. It is a simple, repeatable process for reviewing your data models. It is one that even a non-modeler could follow. I will share my checklist of what to look for and what to ask the data modeler (or yourself) to make sure you get the best possible data model. As a bonus I will share how I use SQL Developer Data Modeler (a no-cost data modeling tool) to collect the information and report it.
1. The document discusses a Gartner report that assesses 20 vendors of data science and machine learning platforms. It evaluates the platforms' abilities to support the full data science life cycle.
2. The report places vendors in four categories - Leaders, Challengers, Visionaries, and Niche Players. It outlines the strengths and cautions of platforms from vendors like Amazon Web Services, Alteryx, and Anaconda.
3. Key criteria for evaluating the platforms include ease of use, support for different personas, capabilities for tasks like modeling and deployment, and growth and innovation. The report aims to help users choose the right platform for their needs.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Self-serve analytics journey at Celtra: Snowflake, Spark, and DatabricksGrega Kespret
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both.
Topics include:
- Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline
- How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution
- How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency
- How Snowflake + Spark enables production and ad hoc analytics on a single repository of data
Snowflake: Your Data. No Limits (Session sponsored by Snowflake) - AWS Summit...Amazon Web Services
Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
Data mesh is a decentralized approach to managing and accessing analytical data at scale. It distributes responsibility for data pipelines and quality to domain experts. The key principles are domain-centric ownership, treating data as a product, and using a common self-service infrastructure platform. Snowflake is well-suited for implementing a data mesh with its capabilities for sharing data and functions securely across accounts and clouds, with built-in governance and a data marketplace for discovery. A data mesh implemented on Snowflake's data cloud can support truly global and multi-cloud data sharing and management according to data mesh principles.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Customer Presentation - IBM Cloud Pak for Data Overview (Level 100).PPTXtsigitnist02
This document provides instructions for using a presentation deck on Cloud Pak for Data. It instructs the user to:
1. Delete the first slide before using the deck.
2. Customize the presentation for the intended audience as the deck covers various topics and using all slides may not fit a single meeting.
3. The deck contains 6 embedded video records for a demo that takes 15-25 minutes to present. Guidance on pitching the demo is available.
The appendix contains slides on Cloud Pak for Data licensing and IBM's overall strategy.
Take the Bias out of Big Data Insights With Augmented AnalyticsTyler Wishnoff
Is bias impacting your Big Data insights? Learn how augmented analytics and the latest advancements in OLAP technology are making analytics (including on cloud) from business intelligence, data science, and machine learning more accurate and impactful. Learn more at https://kyligence.io
ADV Slides: 2021 Trends in Enterprise AnalyticsDATAVERSITY
The document discusses trends in enterprise advanced analytics for 2021 and beyond. Some key trends include remote work continuing, strong tech spending rebound led by cloud capabilities, leading organizations increasing focus on AI/ML with model deployment taking center stage, more edge AI, rise of data lakes, new technology stacks focusing on data fabrics and AI pipelines, increased automation, open source becoming more prevalent, Kubernetes becoming the standard analytics stack, and general AI beginning to emerge. Winning approaches for 2021 include cloud, AI, data lakes, data warehousing, MDM, agile development, Kubernetes, automation, data quality, and DevOps/MLOps.
In this presentation, we:
1. Look at the challenges and opportunities of the data era
2. Look at key challenges of the legacy data warehouses such as data diversity, complexity, cost, scalabilily, performance, management, ...
3. Look at how modern data warehouses in the cloud not only overcome most of these challenges but also how some of them bring additional technical innovations and capabilities such as pay as you go cloud-based services, decoupling of storage and compute, scaling up or down, effortless management, native support of semi-structured data ...
4. Show how capabilities brought by modern data warehouses in the cloud, help businesses, either new or existing ones, during the phases of their lifecycle such as launch, growth, maturity and renewal/decline.
5. Share a Near-Real-Time Data Warehousing use case built on Snowflake and give a live demo to showcase ease of use, fast provisioning, continuous data ingestion, support of JSON data ...
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
This document provides an agenda and notes for a DB2 Update Day event held in March 2015. The agenda includes sessions on DB2 use cases, archives, native stored procedures, and updates for developers. Additional notes provide status updates from various Nordic locations attending the event with a total of 403 attendees. The document also includes sections on IBM zSystems and enabling mobile analytics and reporting on zSystems using tools like QMF and Cognos.
Data-driven analytics is making a measurable impact on businesses performance, helping companies pinpoint new sources of revenue and streamline operations. But traditional computing systems are challenged to keep up with a rapidly evolving data management landscape.
How do you foster superior efficiency, flexibility, and economy while meeting diverse and pressing analytics needs?
SAP® Sybase IQ and Dobler Consulting can help:
Traditional database systems were meant for processing transactions, but SAP® Sybase® IQ server is a highly efficient RDBMS optimized for extreme-scale EDWs and Big Data analytics – offering you faster data loading and query performance while slashing maintenance, hardware, and storage costs. Realize exponential improvement, even as thousands of employees and massive amounts of data (structured and unstructured) enter your ecosystem.
With SAP Sybase IQ 16 you can:
• Exploit the value of Big Data and incorporate into everyday business decision-making
• Transform your business through deeper insight by enabling analytics on real-time information
• Extend the power of analytics across your enterprise with speed, availability and security.
Please join us to learn the value offered by SAP Sybase IQ 16. And, see how by tying together your organization’s data assets – from operational data to external feeds and Big Data – SAP dramatically simplifies data management landscapes for both current and next-generation business applications, delivering information at unprecedented speeds and empowering a Big Data-enabled Enterprise Data Warehouse.
Master the Multi-Clustered Data Warehouse - SnowflakeMatillion
Snowflake is one of the most powerful, efficient data warehouses on the market today—and we joined forces with the Snowflake team to show you how it works!
In this webinar:
- Learn how to optimize Snowflake
- Hear insider tips and tricks on how to improve performance
- Get expert insights from Craig Collier, Technical Architect from Snowflake, and Kalyan Arangam, Solution Architect from Matillion
- Find out how leading brands like Converse, Duo Security, and Pets at Home use Snowflake and Matillion ETL to make data-driven decisions
- Discover how Matillion ETL and Snowflake work together to modernize your data world
- Learn how to utilize the impressive scalability of Snowflake and Matillion
NRB - BE MAINFRAME DAY 2017 - Data spark and the data federation NRB
Frank Van der Wal - Technical Lead IBM Z BENELUX Digital Transformation Specialist
Leif Pedersen - IBM Analytics for IBM Z Specialist at IBM
Mainframe Innovation Tour (API enconomy, Hybrid Cloud, Enterprise Linux, Machine learning, Spark)
Become More Data-driven by Leveraging Your SAP DataDenodo
Watch full webinar here: https://bit.ly/3K2SaCQ
In today’s world, management of data can be a major challenge. For many systems, including SAP, data in real-time and integrating it with other disparate sources has historically been difficult to accomplish. The traditional Data Warehouse approach can also be quite expensive to keep data fresh and control access to meet new and future data protection requirements. Denodo and Gateway Architect’s Meister Core™ offers a high-performance data virtualization solution, designed to fulfill those needs.
Join Denodo, Gateway Architects and W5 Consulting to learn about the value of a logical Data Fabric and delivery platform and its role in this new solution. The webinar will overview the solution including how it provides support for SAP Migrations and sharing of SAP data across geographic boundaries. In addition, you will see how this solution provides the added value of improved agility for supply chain management, and much more. We will also share a demonstration to showcase the benefits of this solution.
Do not miss this opportunity to learn all this as well as how the Joint Denodo/Meister Core solution can:
- Create an agile, real-time, robust data virtualization solution.
- Work with combinations of SAP and Non-SAP data in “Actual” real time scenarios.
- And deliver a true 360 degree view of analytics from multiple systems and seemingly tie that to all your SAP FICO documents 10X faster then previously possible.
The document discusses the challenges of maintaining separate data lake and data warehouse systems. It notes that businesses need to integrate these areas to overcome issues like managing diverse workloads, providing consistent security and user management across uses cases, and enabling data sharing between data science and business analytics teams. An integrated system is needed that can support both structured analytics and big data/semi-structured workloads from a single platform.
OLAP on the Cloud with Azure Databricks and Azure SynapseAtScale
This presentation was part of the 2020 Global Summer Azure Data Fest. It explains how Cloud OLAP helps you to analyze large amounts of data on Azure Databricks, Azure Synapse and other data platforms without moving it. And, shows how to leverage AtScale’s Cloud OLAP perform multidimensional analysis – and derive business insights – on data sets from multiple providers – with no data prep or data engineering required.
Digital Transformation with data science and AI. implementing AI @ scale with IBM cloud pak for data. An end to end cloud-native platform easily implemented in a private cloud, public cloud or hybrid cloud. Combining the power of open source tools with enterprise support of IBM is important for organizations to realize the value fast. Accelerate their efforts to become digital companies.
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
Seeing Redshift: How Amazon Changed Data Warehousing ForeverInside Analysis
The Briefing Room with Claudia Imhoff and Birst
Live Webcast April 9, 2013
What a difference a day can make! When Amazon announced their new RedShift offering – a data warehouse in the cloud – the entire industry of information management changed. The most notable disruption? Price. At a whopping $1,000 per year for a terabyte, RedShift achieved a price-point improvement that amounts to at least two orders of magnitude, if not three when compared to its top-tier competitors. But pricing is just one change; there's also the entire process by which data warehousing is done.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Claudia Imhoff explain why a new cloud-based reality for data warehousing significantly changes the game for business intelligence and analytics. She'll be briefed by Brad Peters of Birst who will tout his company's BI solution, which has been specifically architected for cloud-based hosting. Peters will discuss several key intricacies of doing BI in the cloud, including the unique provisioning, loading and modeling requirements. Founded in 2004, Birst has nearly a decade of doing cloud-based BI and Analytics.
Visit: http://www.insideanalysis.com
Snowflake + Syncsort: Get Value from Your Mainframe DataPrecisely
Your business wants to solve problems for your customers, not spend time managing silos of disconnected data that comes from on-premises solutions and new cloud applications. More and more organizations are looking to solve this problem by investing in cloud-based storage and analytics platforms such as Snowflake. However, data from systems such as mainframes can be a challenge to bring into cloud data warehouses. Together, Snowflake and Syncsort offer you the ability to get the full picture of your data – whether its mainframe or from a cloud application. View this webinar on how Snowflake and Syncsort are working together to get you back to what is essential for your business.
View this webcast on-demand to learn:
• Best practices for extracting your mainframe data
• Advantages of using Snowflake for your cloud data warehouse needs
• Common challenges faced by businesses trying to access mainframe data for use in cloud data warehouses
• How Syncsort is helping organizations gain strategic value from their mainframe data
In the digital world, semi-structured data is as important as transactional, structured data. Both need to be analyzed to create a competitive advantage. Unfortunately, neither the data lake nor the data warehouse are adequate to handle the analysis of both data types.
These slides—based on the webinar from EMA Research and Vertica—delve into the push toward the innovative unified analytics warehouse (UAW), a merging of the data lake and data warehouse.
Is Your Data Paying You Dividends? Data innovation is a means to an end where data as an asset can be managed, developed, monetized, and eventually expected to pay dividends to the business.While 70% of CEOs surveyed expect investments in data, analytics, ML and AI initiatives to improve their bottom-line, 56% stated concerns over the integrity of their data1. Data science teams are now tasked to deliver true business value but fundamental issues remain in data preparation, data cleansing which impedes speed to market.Join Karan Sachdeva as he demonstrates capabilities of the all-new IBM Cloud Private for Data –a single containerized platform - that bridges the gap between data consumability, governance, integration, and visualization, accelerating speed to market and dividends to your business.by Karan Sachdeva, Sales Leader Big Data Analytics, IBM Asia Pacific
Machine Learning - Eine Challenge für ArchitektenHarald Erb
Aufgrund vielfältiger potenzieller Geschäftschancen, die Machine Learning bietet, starten viele Unternehmen Initiativen für datengetriebene Innovationen. Dabei gründen sie Analytics-Teams, schreiben neue Stellen für Data Scientists aus, bauen intern Know-how auf und fordern von der IT-Organisation eine Infrastruktur für "heavy" Data Engineering & Processing samt Bereitstellung einer Analytics-Toolbox ein. Für IT-Architekten warten hier spannende Herausforderungen, u.a. bei der Zusammenarbeit mit interdisziplinären Teams, deren Mitglieder unterschiedlich ausgeprägte Kenntnisse im Bereich Machine Learning (ML) und Bedarfe bei der Tool-Unterstützung haben.
The document discusses Oracle's cloud-based data lake and analytics platform. It provides an overview of the key technologies and services available, including Spark, Kafka, Hive, object storage, notebooks and data visualization tools. It then outlines a scenario for setting up storage and big data services in Oracle Cloud to create a new data lake for batch, real-time and external data sources. The goal is to provide an agile and scalable environment for data scientists, developers and business users.
Do you know what k-Means? Cluster-Analysen Harald Erb
Cluster-Analysen sind heute "Brot und Butter"-Analysetechniken mit Verfahren, die zur Entdeckung von Ähnlichkeitsstrukturen in (großen) Datenbeständen genutzt werden, mit dem Ziel neue Gruppen in den Daten zu identifizieren. Der K-Means-Algorithmus ist dabei einer der einfachsten und bekanntesten unüberwachten Lernverfahren, das in verschiedenen Machine Learning Aufgabenstellung einsetzbar ist. Zum Beispiel können abnormale Datenpunkte innerhalb eines großen Data Sets gefunden, Textdokumente oder Kunden¬segmente geclustert werden. Bei Datenanalysen kann die Anwendung von Cluster-Verfahren ein guter Einstieg sein bevor andere Klassifikations- oder Regressionsmethoden zum Einsatz kommen.
In diesem Talk wird der K-Means Algorithmus samt Erweiterungen und Varianten nicht im Detail betrachtet und ist stattdessen eher als ein Platzhalter für andere Advanced Analytics-Verfahren zu verstehen, die heute „intelligente“ Bestandteile in modernen Softwarelösungen sind bzw. damit kombiniert werden können. Anhand von zwei Kurzbeispielen wird live gezeigt: (1) Identifizierung von Kunden-Cluster mit einem Big Data Discovery Tool und Python (Jupyter Notebook) und (2) die Realisierung einer Anomalieerkennung direkt im Echtzeitdatenstrom mit einer Stream Analytics Lösung von Oracle.
Exploratory Analysis in the Data Lab - Team-Sport or for Nerds only?Harald Erb
Talk held at DOAG 2016 conference (2016.doag.org/de/home) discussing a data lab concept incl.architecture blueprint, collaboration and tool examples based on Oracle solutions like Oracle Big Data Discovery (in combination with Jupyter Notebook)
Big Data Discovery + Analytics = Datengetriebene Innovation!Harald Erb
Vortrag von der DOAG 2015-Konferenz: Die Umsetzung von Datenprojekten muss man nicht zwangsläufig den sog. Data Scientists allein überlassen werden. Daten- und Tool-Komplexität im Umgang mit Big Data sind keine unüberwindbaren Hürden mehr für die Teams, die heute im Unternehmen bereits für Aufbau und Bewirtschaftung des Data Warehouses sowie dem Management bzw. der Weiterentwicklung der Business Intelligence-Plattform zuständig sind. In einem interdisziplinären Team bringen neben den technischen Rollen auch Fachanwender und Business Analysten von Anfang an ihr Domänenwissen in das Datenprojekt mit ein,
Oracle Big Data Discovery working together with Cloudera Hadoop is the fastest way to ingest and understand data. Powerful data transformation capabilities mean that data can quickly be prepared for consumption by the extended organisation.
DOAG News 2012 - Analytische Mehrwerte mit Big DataHarald Erb
Seit einigen Monaten wird „Big Data“ intensiv aber auch kontrovers diskutiert. Stellt dieser Ansatz die bestehende relationale Datenbankdominanz in Frage, zumindest für ausgewählte analytische Problemstellungen? Dieser Artikel zeigt nach einem einführenden Überblick anhand von Anwendungsfällen auf, wo die geschäftlichen Mehrwerte von Big Data Projekten liegen und wie diese neuen Erkenntnisse in die bestehenden Data Warehouse und Business Intelligence Projekte integriert werden können.
Oracle Unified Information Architeture + Analytics by ExampleHarald Erb
Der Vortrag gibt zunächst einen Architektur-Überblick zu den UIA-Komponenten und deren Zusammenspiel. Anhand eines Use Cases wird vorgestellt, wie im "UIA Data Reservoir" einerseits kostengünstig aktuelle Daten "as is" in einem Hadoop File System (HDFS) und andererseits veredelte Daten in einem Oracle 12c Data Warehouse miteinander kombiniert oder auch per Direktzugriff in Oracle Business Intelligence ausgewertet bzw. mit Endeca Information Discovery auf neue Zusammenhänge untersucht werden.
Endeca Web Acquisition Toolkit - Integration verteilter Web-Anwendungen und a...Harald Erb
Das einzig Beständige ist der Wandel: Kritische Informationen, die Unternehmen täglich als Entscheidungsgrundlage benötigen, unterliegen der permanenten Veränderung und sind noch dazu über viele interne und externe Quellen verteilt. Sei es in Dokumenten, E-Mails, auf Portalen und Websites, etc. – überall finden sich relevante Daten, die wertvolle Erkenntnisse für fundierte Geschäftsentscheidungen liefern können.
Technisch betrachtet müssen die zum Teil sehr schwer zugänglichen Informationen zunächst einmal von den verteilten Anwendungen und Datenquellen beschafft werden bevor die eigentliche Weiterverarbeitung im Data Warehouse stattfindet. Als graphisches Entwicklungswerkzeug setzt das Endeca Web Acquisition Toolkit (Endeca WAT) genau an diesem Punkt an, indem es das Erstellen synthetischer Schnittstellen ermöglicht. Z.B. sollen von einer kommerziellen Website Preisdaten und/oder Kundenbewertungen akquiriert werden, für die der Website-Betreiber keine API bereitstellt. Der nachfolgende Artikel bzw. Vortrag skizziert, wie das Endeca Web Acquisition Toolkit Integrationsaufgaben zur Anbindung externer Datenquellen im Rahmen der aktuellen Oracle Information Management Reference Architecture übernehmen kann
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.