Snowflake is a cloud-native data warehouse solution that uniquely allows you to scale storage and compute independently. Applying both Snowflake and AWS best practices enable customer enjoyment of the platforms.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
The session will be a deep dive introduction to Snowflake that includes Snowflake architecture, Virtual Warehouses, Designing a real use case, Loading data into Snowflake from a Data Lake.
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
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.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
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.
Introducing Direct Database Access with Snowflake + IntrinioIntrinio
Intrinio is proud to announce our new integration with Snowflake. In keeping with our mission to make data simpler and more useful, we’re now offering direct database access to our business customers. Watch the full webinar here: https://www.youtube.com/watch?v=zobU34StN2I&t=6s
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
The session will be a deep dive introduction to Snowflake that includes Snowflake architecture, Virtual Warehouses, Designing a real use case, Loading data into Snowflake from a Data Lake.
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
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.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
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.
Introducing Direct Database Access with Snowflake + IntrinioIntrinio
Intrinio is proud to announce our new integration with Snowflake. In keeping with our mission to make data simpler and more useful, we’re now offering direct database access to our business customers. Watch the full webinar here: https://www.youtube.com/watch?v=zobU34StN2I&t=6s
In the past few years, the term "data lake" has leaked into our lexicon. But what exactly IS a data lake? Some IT managers confuse data lakes with data warehouses. Some people think data lakes replace data warehouses. Both of these conclusions are false. Their is room in your data architecture for both data lakes and data warehouses. They both have different use cases and those use cases can be complementary.
Todd Reichmuth, Solutions Engineer with Snowflake Computing, has spent the past 18 years in the world of Data Warehousing and Big Data. He spent that time at Netezza and then later at IBM Data. Earlier in 2018 making the jump to the cloud at Snowflake Computing.
Mike Myer, Sales Director with Snowflake Computing, has spent the past 6 years in the world of Security and looking to drive awareness to better Data Warehousing and Big Data solutions available! Was previously at local tech companies FireMon and Lockpath and decided to join Snowflake due to the disruptive technology that's truly helping folks in the Big Data world on a day to day basis.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
This document outlines a 30-day plan to address common data struggles around loading, integrating, analyzing, and collaborating on data using Snowflake's data platform. It describes setting up a team, defining goals and scope, loading sample data, testing and deploying business logic transformations, creating warehouses for business intelligence tools, and connecting BI tools to the data. The goal is that after 30 days, teams will be collaborating more effectively, able to easily load and combine different data sources, have accurate business logic implemented, and gain more insights from their data.
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
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 is an analytic data warehouse provided as software-as-a-service (SaaS). It uses a unique architecture designed for the cloud, with a shared-disk database and shared-nothing architecture. Snowflake's architecture consists of three layers - the database layer, query processing layer, and cloud services layer - which are deployed and managed entirely on cloud platforms like AWS and Azure. Snowflake offers different editions like Standard, Premier, Enterprise, and Enterprise for Sensitive Data that provide additional features, support, and security capabilities.
This document provides instructions for a hands-on lab guide to explore the Snowflake data warehouse platform using a free trial. The lab guide walks through loading and analyzing structured and semi-structured data in Snowflake. It introduces the key Snowflake concepts of databases, tables, warehouses, queries and roles. The lab is presented as a story where an analytics team loads and analyzes bike share rider transaction data and weather data to understand riders and improve services.
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.
The document discusses Azure Data Factory V2 data flows. It will provide an introduction to Azure Data Factory, discuss data flows, and have attendees build a simple data flow to demonstrate how they work. The speaker will introduce Azure Data Factory and data flows, explain concepts like pipelines, linked services, and data flows, and guide a hands-on demo where attendees build a data flow to join customer data to postal district data to add matching postal towns.
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.
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.
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.
- Google App Engine is a platform for easily developing and hosting scalable web applications, with no need for complex server management. It automatically scales the applications and handles all the operational details.
- App Engine applications run on Google's infrastructure and benefit from automatic scaling across multiple servers. It also provides security isolation and quotas to prevent applications from disrupting others.
- The platform uses a stateless, request-based architecture and scales applications automatically as traffic increases by distributing requests across multiple servers. It also uses quotas to ensure fairness among applications.
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
Snowflake is a cloud data warehouse that offers scalable storage, flexible compute capabilities, and a shared data architecture. It uses a shared data model where data is stored independently from compute resources in micro-partitions in cloud object storage. This allows for elastic scaling of storage and compute. Snowflake also uses a virtual warehouse architecture where queries are processed in parallel across nodes, enabling high performance on large datasets. Data can be loaded into Snowflake from external sources like Amazon S3 and queries can be run across petabytes of data with ACID transactions and security at scale.
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.
Moving to the cloud; PaaS, IaaS or Managed InstanceThomas Sykes
In this session we'll look at the cloud choices available in Azure for SQL Server. Whether it's PaaS, IaaS or Managed Instance we'll look into the features provided, the major differences and the Pros and Cons of each solution and how to choose the best option available.
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.
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.
Analyzing Semi-Structured Data At Volume In The CloudRobert Dempsey
Presentation from Snowflake Computing at the November 2015 Data Wranglers DC meetup.
The Cloud, Mobile and Web Applications are producing semi-structured data at an unprecedented rate. IT professionals continue to struggle capturing, transforming, and analyzing these complex data structures mixed with traditional relational style datasets using conventional MPP and/or Hadoop infrastructures. Public cloud infrastructures such as Amazon and Azure provide almost unlimited resources and scalability to handle both structured and semi-structured data (XML, JSON, AVRO) at Petabyte scale. These new capabilities coupled with traditional data management access methods such as SQL allow organizations and businesses new opportunities to leverage analytics at an unprecedented scale while greatly simplifying data pipeline architectures and providing an alternative to the "data lake".
IBM Cloud Day January 2021 - A well architected data lakeTorsten Steinbach
- The document discusses an IBM Cloud Day 2021 event focused on well-architected data lakes. It provides an overview of two sessions on data lake architecture and building a cloud native data lake on IBM Cloud.
- It also summarizes the key capabilities organizations need from a data lake, including visualizing data, flexibility/accessibility, governance, and gaining insights. Cloud data lakes can address these needs for various roles.
SQL Server 2016 introduces several new features for In-Memory OLTP including support for up to 2 TB of user data in memory, system-versioned tables, row-level security, and Transparent Data Encryption. The in-memory processing has also been updated to support more T-SQL functionality such as foreign keys, LOB data types, outer joins, and subqueries. The garbage collection process for removing unused memory has also been improved.
This document summarizes a proposal from Singlepoint Solutions Ltd. for an AWS Well-Architected Review. The review is a free consultation where a Singlepoint AWS Certified Architect evaluates a customer's AWS workload against best practices. The architect identifies areas of risk and opportunity for improvement. Singlepoint then provides a report with recommendations and an action plan to optimize the customer's AWS architecture and usage over time. The goal is to help customers design secure, high-performing, reliable, and cost-efficient cloud infrastructure on AWS.
Many large enterprises have begun using AWS to host development and test environments while also building greenfield applications in AWS. After realizing the benefits that AWS has to offer, many Enterprise look for ways to accelerate their migration to the cloud. In beginning this journey they are often faced with a number of challenges such as determining which applications should move, how they should move, and how can they be effectively managed in the cloud. Accenture, working with AWS Solution Architects, and AWS Professional Services have developed a framework, based on our experiences, to quickly, efficiently, and successfully move enterprise applications to AWS at scale. This session will review our approach, tools, and methods that can help Enterprises evolve their cloud transformation programs.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
This document outlines a 30-day plan to address common data struggles around loading, integrating, analyzing, and collaborating on data using Snowflake's data platform. It describes setting up a team, defining goals and scope, loading sample data, testing and deploying business logic transformations, creating warehouses for business intelligence tools, and connecting BI tools to the data. The goal is that after 30 days, teams will be collaborating more effectively, able to easily load and combine different data sources, have accurate business logic implemented, and gain more insights from their data.
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
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 is an analytic data warehouse provided as software-as-a-service (SaaS). It uses a unique architecture designed for the cloud, with a shared-disk database and shared-nothing architecture. Snowflake's architecture consists of three layers - the database layer, query processing layer, and cloud services layer - which are deployed and managed entirely on cloud platforms like AWS and Azure. Snowflake offers different editions like Standard, Premier, Enterprise, and Enterprise for Sensitive Data that provide additional features, support, and security capabilities.
This document provides instructions for a hands-on lab guide to explore the Snowflake data warehouse platform using a free trial. The lab guide walks through loading and analyzing structured and semi-structured data in Snowflake. It introduces the key Snowflake concepts of databases, tables, warehouses, queries and roles. The lab is presented as a story where an analytics team loads and analyzes bike share rider transaction data and weather data to understand riders and improve services.
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.
The document discusses Azure Data Factory V2 data flows. It will provide an introduction to Azure Data Factory, discuss data flows, and have attendees build a simple data flow to demonstrate how they work. The speaker will introduce Azure Data Factory and data flows, explain concepts like pipelines, linked services, and data flows, and guide a hands-on demo where attendees build a data flow to join customer data to postal district data to add matching postal towns.
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.
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.
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.
- Google App Engine is a platform for easily developing and hosting scalable web applications, with no need for complex server management. It automatically scales the applications and handles all the operational details.
- App Engine applications run on Google's infrastructure and benefit from automatic scaling across multiple servers. It also provides security isolation and quotas to prevent applications from disrupting others.
- The platform uses a stateless, request-based architecture and scales applications automatically as traffic increases by distributing requests across multiple servers. It also uses quotas to ensure fairness among applications.
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
Snowflake is a cloud data warehouse that offers scalable storage, flexible compute capabilities, and a shared data architecture. It uses a shared data model where data is stored independently from compute resources in micro-partitions in cloud object storage. This allows for elastic scaling of storage and compute. Snowflake also uses a virtual warehouse architecture where queries are processed in parallel across nodes, enabling high performance on large datasets. Data can be loaded into Snowflake from external sources like Amazon S3 and queries can be run across petabytes of data with ACID transactions and security at scale.
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.
Moving to the cloud; PaaS, IaaS or Managed InstanceThomas Sykes
In this session we'll look at the cloud choices available in Azure for SQL Server. Whether it's PaaS, IaaS or Managed Instance we'll look into the features provided, the major differences and the Pros and Cons of each solution and how to choose the best option available.
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.
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.
Analyzing Semi-Structured Data At Volume In The CloudRobert Dempsey
Presentation from Snowflake Computing at the November 2015 Data Wranglers DC meetup.
The Cloud, Mobile and Web Applications are producing semi-structured data at an unprecedented rate. IT professionals continue to struggle capturing, transforming, and analyzing these complex data structures mixed with traditional relational style datasets using conventional MPP and/or Hadoop infrastructures. Public cloud infrastructures such as Amazon and Azure provide almost unlimited resources and scalability to handle both structured and semi-structured data (XML, JSON, AVRO) at Petabyte scale. These new capabilities coupled with traditional data management access methods such as SQL allow organizations and businesses new opportunities to leverage analytics at an unprecedented scale while greatly simplifying data pipeline architectures and providing an alternative to the "data lake".
IBM Cloud Day January 2021 - A well architected data lakeTorsten Steinbach
- The document discusses an IBM Cloud Day 2021 event focused on well-architected data lakes. It provides an overview of two sessions on data lake architecture and building a cloud native data lake on IBM Cloud.
- It also summarizes the key capabilities organizations need from a data lake, including visualizing data, flexibility/accessibility, governance, and gaining insights. Cloud data lakes can address these needs for various roles.
SQL Server 2016 introduces several new features for In-Memory OLTP including support for up to 2 TB of user data in memory, system-versioned tables, row-level security, and Transparent Data Encryption. The in-memory processing has also been updated to support more T-SQL functionality such as foreign keys, LOB data types, outer joins, and subqueries. The garbage collection process for removing unused memory has also been improved.
This document summarizes a proposal from Singlepoint Solutions Ltd. for an AWS Well-Architected Review. The review is a free consultation where a Singlepoint AWS Certified Architect evaluates a customer's AWS workload against best practices. The architect identifies areas of risk and opportunity for improvement. Singlepoint then provides a report with recommendations and an action plan to optimize the customer's AWS architecture and usage over time. The goal is to help customers design secure, high-performing, reliable, and cost-efficient cloud infrastructure on AWS.
Many large enterprises have begun using AWS to host development and test environments while also building greenfield applications in AWS. After realizing the benefits that AWS has to offer, many Enterprise look for ways to accelerate their migration to the cloud. In beginning this journey they are often faced with a number of challenges such as determining which applications should move, how they should move, and how can they be effectively managed in the cloud. Accenture, working with AWS Solution Architects, and AWS Professional Services have developed a framework, based on our experiences, to quickly, efficiently, and successfully move enterprise applications to AWS at scale. This session will review our approach, tools, and methods that can help Enterprises evolve their cloud transformation programs.
(ENT206) Migrating Thousands of Workloads to AWS at Enterprise Scale | AWS re...Amazon Web Services
Migrating workloads to AWS in an enterprise environment is not easy, but with the right approach, an enterprise-sized organization can migrate thousands of instances to AWS quickly and cost effectively. You can leave this session with a good understanding of the migration framework used to assess an enterprise application portfolio and how to move thousands of instances to AWS in a quick and repeatable fashion.
In this session, we describe the components of Accenture's cloud migration framework, including tools and capabilities provided by Accenture, AWS, and third-party software solutions, and how enterprises can leverage these techniques to migrate efficiently and effectively. The migration framework covers:
- Defining an overall cloud strategy
- Assessing the business requirements, including application and data requirements
- Creating the right AWS architecture and environment
- Moving applications and data using automated migration tools- Services to manage the migrated environment
Using AWS Well Architectured Framework for Software Architecture Evaluations ...Alexandr Savchenko
Event Lint: https://pages.awscloud.com/EMEA-field-OE-AWS-Cloud-Week-2020-reg-event.html
When you start thinking about innovations and prepare evaluations plan for AWS architecture, first of all you want to define answers to a lot of questions such as: “What methods should I use (interviews or automation tools)?”, “What questions should I ask and what categories should they cover?”, “Can I use some automation tools to define correct receipts?”, “What best practices should I recommend after evaluation and what will be the best way to implement these improvements?”.
AWS Well-Architecture Framework has answers to all of these questions and can help you to evaluate, build or improve your infrastructure and software architecture. It's a very important tool that will be useful in different phases of SDLC and you can use this on a regular basis.
This speech will expose principles of architecture evaluation using AWS WAF, show structure of framework, general design principles and common categories, materials which will help you learn this framework and AWS architecture more deeply.
An Introduction to the AWS Well Architected Framework - WebinarAmazon Web Services
This document provides an introduction to the AWS Well-Architected Framework, which consists of five pillars - security, reliability, performance efficiency, cost optimization, and operational excellence. It discusses the recent addition of the operational excellence pillar and updates to the reliability pillar. It also covers new architecture type overlays and available resources like whitepapers, online training, and reference architectures. The session is intended for architects, developers, managers, and IT professionals interested in cloud architecture best practices.
apidays LIVE New York 2021 - API for multi-cloud management platform by Pawel...apidays
apidays LIVE New York 2021 - API-driven Regulations for Finance, Insurance, and Healthcare
July 28 & 29, 2021
API for multi-cloud management platform
Pawel Skrzypek, Chief Multi Cloud Architect at 7bulls
AWS Techical Due Diligence to post transaction execution for M&A Tom Laszewski
Overview of the TDD and post transaction process, roadmap, tools, offerings, playbooks,use cases, and case studies. Covers all the resources, assets, tools, and offerings AWS utilizes for a successful acquisitions, mergers, divestitures, or carve out (M&A activity) technical due diligence and post transaction execution.
This document provides an overview of best practices for achieving performance efficiency based on the AWS Well-Architected Framework. It discusses selecting optimal compute, storage, database, and network resources to meet requirements. The document emphasizes using a data-driven approach to refine architectural choices through benchmarking and load testing. It also covers monitoring systems to ensure performance and making trade-offs like caching, partitioning, and compression to improve efficiency.
This document provides an agenda for a Post-Ignite Update event happening in November 2019. The agenda includes sessions on new features of Azure Arc, Azure Synapse Analytics, Cognitive Services, Power Platform, Dynamics 365, Microsoft 365, and security updates from Ignite. There will be presentations from Microsoft partners and teams on these topics, as well as a Q&A session at the end.
apidays LIVE Helsinki & North 2022_API for Multi-Cloud Management Platformapidays
apidays LIVE Helsinki & North: API Ecosystems - Connecting Physical and Digital
March 16 & 17, 2022
API for Multi-Cloud Management Platform
Paweł Skrzypek, Chief Multi Cloud Architect at 7bulls.com Sp. z o.o.
Adding to the bottom line - the Key Cloud plays for the Mid-Market - Adam BeavisAmazon Web Services
Learn from AWS and SI/ISV partners how we've been successful in driving a joint Data Center Migration campaign to our Mid-Market customers. More and more Mid-Market customers realise how they can improve their bottomline by moving their infrastructure and business applications to AWS. We will provide you with tips and tricks on how to successfully develop and promote differentiating solutions on AWS.
OSSCube provides consulting, development, integration and support services for open source technologies. They have expertise in areas such as PHP, CRM, marketing automation, content management, e-commerce, BI and big data. This presentation introduces AWS and discusses why organizations use AWS, common use cases, and how to get started. It describes key AWS services for application and web hosting including EC2, ELB, RDS, ElastiCache, EBS and CloudWatch and how they provide scalability, reliability, flexibility and security for applications deployed in the AWS cloud.
This document discusses migrations and application modernization. It provides an overview of migration strategies and approaches, including assessing applications, planning a migration, executing the migration, and optimizing in the cloud. It also discusses modernizing applications to be cloud-native through re-architecting or re-platforming approaches. Key benefits of modernization include making applications more cost-efficient, scalable, and automated. The document also highlights archive storage as a solution for low-cost, secure storage of infrequently accessed data.
Srinivas Palle is a solutions architect with over 9 years of experience in AWS migration, DevOps, Linux systems administration, and cloud computing. He has expertise in designing and deploying scalable architectures on AWS using services like EC2, ELB, VPC, and monitoring with CloudWatch. Palle is currently seeking a challenging position to make use of his technical skills and leadership experience managing teams and projects.
Presenting the newest version of Cloudify - 4.6 including a orchestrated SD-WAN demo from MEF18 where Cloudify is used as the orchestration platform for uCPE based on containers.
AWS Public Sector Symposium 2014 Canberra | Test and Development on AWSAmazon Web Services
Organisations today are increasingly looking for faster and cost effective ways to develop and test products before deployment. Those managing this process must determine when a product is ready to be deployed to production. But before this decision is made, the entire testing and development process should be carefully planned, managed, and reviewed. Amazon Web Services' utility computing model provides a great backbone to achieve this goal. With AWS you can spin up infrastructure on an as-needed basis for development and testing. Run workloads for a certain amount of time, and then stop running them – and stop paying for them – when you don’t.
CloudServiceManagement discusses strategic implications of cloud computing for IT service management. It provides an overview of cloud basics, industry dynamics, OpenStack as an open source cloud platform, and HP's approach and portfolio for helping customers adopt cloud. HP's Helion integration program maps requirements across three phases to deliver a virtual private cloud offering integrated with IT service management tools.
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
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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."
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
1. CONTACT US: For more information on this workshop, please call +1.781.577.6770
Snowflake on AWS
Architecture Workshop
Performance, Security, Reliability, Operational Maturity,
and Cost Optimization
AWS Well-Architected Design
Principles
Based on a decade of real-world
experience, AWS has developed
a framework to review cloud
projects for:
• Operational Excellence
• Security
• Reliability
• Performance
• Cost Optimization
Workshop Goals:
• Business and Technical
Alignment
• Architectural Visibility
• Recommendations
Why Cloud for Data
Management?
To take advantage of new data
sources and accelerate analytics,
organizations are turning to cloud
solutions for data management.
Operational leaders are leveraging
self-service and on-demand
scalability to rapidly address their
changing analytic needs. Cloud-
native data warehouse platform are
appearing to further simplify the use
of data as-is and to scale up and
down to meet demand.
Every database solution has its own nuances that build on its architecture and
that of the underlying cloud platform. Snowflake is well-known for its ease of
scalability, data flexibility, and management. These benefits depend on the
proper utilization of Snowflake’s underlying clustering and partitioning
features. Further, Snowflake builds on native cloud services such as S3, EC2,
IAM and VPC to deliver its service experience. The combination of Snowflake
and AWS services, ensure a scalable and secure data warehouse platform.
This workshop reviews both the proposed Snowflake architecture patterns and
the foundational cloud services. The workshop is conducting by subject-matter
experts in both Snowflake and AWS with multiple years of experience scaling
the platform across multiple real-world use cases and industries. AWS’s Well-
Architected framework evaluates your solution for 1) Operational excellence,
2) Scalability, 3) Security, 4) Availability, and 5) Cost.
Part One
1. General Snowflake Experience Review: Integra
2. Review of Functional Requirements - Client
3. Review of Non-Functional Requirements – Client
4. Feedback: Areas of Interest for Architecture and Testing - Integra
5. Review and suggestions for POC Plan – Integra
Part Two
1. Overview of Key Cloud Services enabling Snowflake
2. Introduction to AWS Well-Architected – Integra
a. Well-Architected Principles
b. Hands-On with the Well-Architected Tool
c. Available Follow-on options with AWS Architecture teams
3. Ask Anything – Integra
4. Review of Cost Management Tools
5. Review of Open Question - Client and Integra
Deliverables
1. Snowflake architectural recommendations
2. AWS Well-Architected tool report
3. Recommended triage of issues uncovered