Complete Practical and #Real-time Training on Snowflake. Snowflake Training course from SQL School will make you master the fundamentals of #datawarehousing capabilities as well as dealing with #data and #analytics, Our best #Intructor LED LIVE #OnlineTraining #course teaches you all important concepts like snowflake objects, cloning, undrop, fail-safe and analytics solutions. At the end of this Snowflake #Certification Training program, you will work on useful real-time #projects.
Course Details at https://sqlschool.com/Snowflake-Online-Training.html
Free Demo on Feb 19th at 8 PM India Time, Register at https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
End to End ETL with DWH Implementations, External Data Sources, Integations, Azure Cloud Data Access, Snow Pipes,
Incremental Loads, Power BI Analytics and more.. !
Snowflake Training Details at: https://sqlschool.com/Snowflake-Training.html
Snowflake Demo Video at: https://www.youtube.com/watch?v=Ir5TALslXvY&t=9s
Snowflake Training Schedules at: https://sqlschool.com/Register.html
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL SchoolSequelGate
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.
Microsoft SQL Server Training Course is exclusively designed for aspiring Data Analysts, #BusinessAnalysts, #DataScientists, #MSBI / #PowerBI Engineers and #SQL Database #Developers. This SQL Server and T-SQL Training Course is designed for both starters as well as for experienced #professionals.
#Course Info at : https://sqlschool.com/TSQL-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
# #tsql # #sqlprojects #sqlqueries #training #projects #jobs #SQLSchool #SQLSchool_TrainingInstitute #sqlschool_besttraining #sqltraining #azure #cloud #liveonlinetraining #videotraining #sqlschooltraining #hyderabadtrainings #sqlusa #sqlindia #sqljobs #storedprocedures #azuredataengineer
10 Reasons Snowflake Is Great for AnalyticsSenturus
Learn why Snowflake analytic data warehouse makes sense for BI including data loading flexibility and scalability, consumption-based storage and compute costs, Time Travel and data sharing features, support across a range of BI tools like Power BI and Tableau and ability to allocate compute costs. View this on-demand webinar: https://senturus.com/resources/10-reasons-snowflake-is-great-for-analytics/.
Senturus offers a full spectrum of services in business intelligence and training on Cognos, Tableau and Power BI. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
#Free #Demo from #SQLSchool #Training #InstituteSequelGate
#Free #Demo from #SQLSchool #Training #Institute
#PowerBI with #SQL Server #OnlineTraining (LIVE, #Practical, #Interactive) with #DA100 #Certification
Complete #Real-time and Practical Power BI with #SQLServer Training with Real-time Scenarios. Power BI is a #cloud-based, elegant end-to-end business #analytics tool that enables anyone to #visualize, #analyze, #forecast any type of #data with greater speed, efficiency, and understanding.
Complete #Course Info at : https://sqlschool.com/PowerBI-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
#SQLSchool #powerbi #sqlserver #businessanalyst #bideveloper #analyst #analysis #powerbireporting #reportingtools #dax #mdx #dataanalytics #onlinetraining #videotraining #videos #projects #realtime #powerbiusa #powerbi_india #traininginstitute #training #Hyderabad #powerbijobs #jobs #freshersjobs
#PowerBI with #SQL Server #OnlineTraining (LIVE, #Practical, #Interactive) wi...SequelGate
Complete #Real-time and Practical Power BI with #SQLServer Training with Real-time Scenarios. Power BI is a #cloud-based, elegant end-to-end business #analytics tool that enables anyone to #visualize, #analyze, #forecast any type of #data with greater speed, efficiency, and understanding.
Complete #Course Info at : https://sqlschool.com/PowerBI-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
#SQLSchool #powerbi #sqlserver #businessanalyst #bideveloper #analyst #analysis #powerbireporting #reportingtools #dax #mdx #dataanalytics #onlinetraining #videotraining #videos #projects #realtime #powerbiusa #powerbi_india #traininginstitute #training #Hyderabad #powerbijobs #jobs #freshersjobs
This whitepaper discusses best practices for using Tableau with Snowflake. Snowflake is a cloud-based data warehouse as a service that provides limitless scalability and no hardware or software to install and maintain. Tableau is a business intelligence software that allows users to visualize, explore and analyze data. The whitepaper provides an overview of Snowflake and Tableau and discusses topics such as connecting Tableau to Snowflake, working with different data types and structures in Snowflake, implementing security, and optimizing performance.
This document discusses the emergence of logical data warehouses and how they can help organizations address challenges posed by big data. A logical data warehouse takes a virtualized approach to integrating data from multiple sources like relational databases, NoSQL stores, and file systems. It provides a single, unified view of data while keeping the underlying systems decoupled. The document also describes how organizations can use techniques like data virtualization and offloading to optimize workloads between their enterprise data warehouse and Hadoop data lake. This helps reduce costs while improving query performance and resource utilization.
End to End ETL with DWH Implementations, External Data Sources, Integations, Azure Cloud Data Access, Snow Pipes,
Incremental Loads, Power BI Analytics and more.. !
Snowflake Training Details at: https://sqlschool.com/Snowflake-Training.html
Snowflake Demo Video at: https://www.youtube.com/watch?v=Ir5TALslXvY&t=9s
Snowflake Training Schedules at: https://sqlschool.com/Register.html
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL SchoolSequelGate
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.
Microsoft SQL Server Training Course is exclusively designed for aspiring Data Analysts, #BusinessAnalysts, #DataScientists, #MSBI / #PowerBI Engineers and #SQL Database #Developers. This SQL Server and T-SQL Training Course is designed for both starters as well as for experienced #professionals.
#Course Info at : https://sqlschool.com/TSQL-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
# #tsql # #sqlprojects #sqlqueries #training #projects #jobs #SQLSchool #SQLSchool_TrainingInstitute #sqlschool_besttraining #sqltraining #azure #cloud #liveonlinetraining #videotraining #sqlschooltraining #hyderabadtrainings #sqlusa #sqlindia #sqljobs #storedprocedures #azuredataengineer
10 Reasons Snowflake Is Great for AnalyticsSenturus
Learn why Snowflake analytic data warehouse makes sense for BI including data loading flexibility and scalability, consumption-based storage and compute costs, Time Travel and data sharing features, support across a range of BI tools like Power BI and Tableau and ability to allocate compute costs. View this on-demand webinar: https://senturus.com/resources/10-reasons-snowflake-is-great-for-analytics/.
Senturus offers a full spectrum of services in business intelligence and training on Cognos, Tableau and Power BI. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
#Free #Demo from #SQLSchool #Training #InstituteSequelGate
#Free #Demo from #SQLSchool #Training #Institute
#PowerBI with #SQL Server #OnlineTraining (LIVE, #Practical, #Interactive) with #DA100 #Certification
Complete #Real-time and Practical Power BI with #SQLServer Training with Real-time Scenarios. Power BI is a #cloud-based, elegant end-to-end business #analytics tool that enables anyone to #visualize, #analyze, #forecast any type of #data with greater speed, efficiency, and understanding.
Complete #Course Info at : https://sqlschool.com/PowerBI-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
#SQLSchool #powerbi #sqlserver #businessanalyst #bideveloper #analyst #analysis #powerbireporting #reportingtools #dax #mdx #dataanalytics #onlinetraining #videotraining #videos #projects #realtime #powerbiusa #powerbi_india #traininginstitute #training #Hyderabad #powerbijobs #jobs #freshersjobs
#PowerBI with #SQL Server #OnlineTraining (LIVE, #Practical, #Interactive) wi...SequelGate
Complete #Real-time and Practical Power BI with #SQLServer Training with Real-time Scenarios. Power BI is a #cloud-based, elegant end-to-end business #analytics tool that enables anyone to #visualize, #analyze, #forecast any type of #data with greater speed, efficiency, and understanding.
Complete #Course Info at : https://sqlschool.com/PowerBI-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
#SQLSchool #powerbi #sqlserver #businessanalyst #bideveloper #analyst #analysis #powerbireporting #reportingtools #dax #mdx #dataanalytics #onlinetraining #videotraining #videos #projects #realtime #powerbiusa #powerbi_india #traininginstitute #training #Hyderabad #powerbijobs #jobs #freshersjobs
This whitepaper discusses best practices for using Tableau with Snowflake. Snowflake is a cloud-based data warehouse as a service that provides limitless scalability and no hardware or software to install and maintain. Tableau is a business intelligence software that allows users to visualize, explore and analyze data. The whitepaper provides an overview of Snowflake and Tableau and discusses topics such as connecting Tableau to Snowflake, working with different data types and structures in Snowflake, implementing security, and optimizing performance.
This document discusses the emergence of logical data warehouses and how they can help organizations address challenges posed by big data. A logical data warehouse takes a virtualized approach to integrating data from multiple sources like relational databases, NoSQL stores, and file systems. It provides a single, unified view of data while keeping the underlying systems decoupled. The document also describes how organizations can use techniques like data virtualization and offloading to optimize workloads between their enterprise data warehouse and Hadoop data lake. This helps reduce costs while improving query performance and resource utilization.
Snowflake is a cloud data platform that allows users to store, process and analyze data across cloud services like AWS, GCP and Azure. Some key features include:
- It uses a shared data architecture that decouples storage, compute and management services allowing them to scale independently. Data is stored in a centralized storage layer and processed using virtual warehouses.
- Snowflake supports both structured and semi-structured data and users can query data using standard SQL. It also supports stored procedures, user defined functions and external integrations.
- The platform offers different editions with features for security, governance, compute resource management and data integration. It also has tools and connectors for data warehousing, science and sharing workflows.
Logical Data Warehouse: How to Build a Virtualized Data Services LayerDataWorks Summit
The document discusses the emergence of logical data warehouses in response to big data. It describes how a logical data warehouse uses virtualization, distributed processing, and other techniques to provide a unified view of data across different repositories like Hadoop, relational databases and NoSQL stores. It also discusses how organizations can optimize resources by offloading analytical workloads from their enterprise data warehouse to Hadoop clusters to reduce costs while still using existing code and applications.
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...Deepak Chandramouli
PayPal Data Lake Journey | 2017-Oct | San Diego | Teradata Edge of Next
Gimel [http://www.gimel.io] is a Big Data Processing Library, open sourced by PayPal.
https://www.youtube.com/watch?v=52PdNno_9cU&t=3s
Gimel empowers analysts, scientists, data engineers alike to access a variety of Big Data / Traditional Data Stores - with just SQL or a single line of code (Unified Data API).
This is possible via the Catalog of Technical properties abstracted from users, along with a rich collection of Data Store Connectors available in Gimel Library.
A Catalog provider can be Hive or User Supplied (runtime) or UDC.
In addition, PayPal recently open sourced UDC [Unified Data Catalog], which can host and serve the Technical Metatada of the Data Stores & Objects. Visit http://www.unifieddatacatalog.io to experience first hand.
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Session 8 - Creating Data Processing Services | Train the Trainers ProgramFIWARE
This technical session for Local Experts in Data Sharing (LEBDs), this session will explain how to create data processing services that are key to i4Trust.
ME_Snowflake_Introduction_for new students.pptxSamuel168738
Snowflake is a cloud-based data warehouse that runs entirely on cloud infrastructure like AWS or Azure. It uses a shared-disk and shared-nothing architecture. Data is stored in an optimized columnar format in cloud storage. Queries are executed using virtual warehouses that are independent compute clusters. Snowflake provides a SQL interface and connectors to load, query, and analyze data without having to manage any hardware or software.
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
GDG Cloud Southlake #16: Priyanka Vergadia: Scalable Data Analytics in Google...James Anderson
Do you know The Cloud Girl? She makes the cloud come alive with pictures and storytelling.
The Cloud Girl, Priyanka Vergadia, Chief Content Officer @Google, joins us to tell us about Scaleable Data Analytics in Google Cloud.
Maybe, with her explanation, we'll finally understand it!
Priyanka is a technical storyteller and content creator who has created over 300 videos, articles, podcasts, courses and tutorials which help developers learn Google Cloud fundamentals, solve their business challenges and pass certifications! Checkout her content on Google Cloud Tech Youtube channel.
Priyanka enjoys drawing and painting which she tries to bring to her advocacy.
Check out her website The Cloud Girl: https://thecloudgirl.dev/ and her new book: https://www.amazon.com/Visualizing-Google-Cloud-Illustrated-References/dp/1119816327
Snowflake’s Cloud Data Platform and Modern AnalyticsSenturus
Snowflake's Cloud Data Platform provides a fully managed data warehouse as a service. It offers elastic scaling of storage and compute independently, with multiple clusters accessing a shared set of data. Its architecture separates storage, compute, and services across independent cloud infrastructure for high availability and resilience. Snowflake handles all data management tasks and provides automatic scaling and updates with no downtime.
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.SequelGate
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.
Microsoft SQL Server Training Course is exclusively designed for aspiring Data Analysts, #BusinessAnalysts, #DataScientists, #MSBI / #PowerBI Engineers and #SQL Database #Developers. This SQL Server and T-SQL Training Course is designed for both starters as well as for experienced #professionals.
#Course Info at : https://sqlschool.com/TSQL-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
Vitalii Bondarenko - Масштабована бізнес-аналітика у Cloud Big Data Cluster. ...Lviv Startup Club
This document discusses different architectures and approaches for scalable business intelligence (BI) on cloud big data clusters. It covers OLAP approaches using relational databases, key-value stores like Apache Kylin, big data compute engines like Hive LLAP, and column stores like Druid. It also discusses in-memory aggregations with MemSQL and the results of testing different solutions. Traditional BI approaches on SQL servers are discussed alongside modern approaches using Hadoop, Spark, and cloud services from AWS, Azure, and Google Cloud.
Why you need benchmarks
Finding the right database solution for your use case can be an arduous journey. The database deployment touches aspects of throughput performance, latency control, high availability and data resilience.
You will need to decide on the infrastructure to use: Cloud, on-premise or a hybrid solution.
Data models also have an impact on finding the right fit for the use case. Once you establish a requirements set, the next step is to test your use case against the databases of choice.
In this workshop, we will discuss the different data points you need to collect in order to get the most realistic testing environment.
We will cover:
Data model impact on performance and latency
Client behavior related to database capabilities
Failover and high availability testing
Hardware selection and cluster configuration impact
We will show 2 benchmarking tools you can use to test and benchmark your clusters to identify the optimal deployment scenario for your use case.
Attend this virtual workshop if you are:
Looking to minimize the cost of your database deployment
Making a database decision based on performance and scale data
Planning to emulate your workload on a pre-production system where you can test, fail fast and learn.
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld
VMworld 2013
Michael Corey, Ntirety, Inc
Jeff Szastak, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
This technical workshop equips you with the insights to modernize your legacy Windows and SQL Server applications. We will walk through the common Amazon Web Services (AWS) solutions and proven customer approaches to deploy and migrate SQL Server 2008 to the cloud.
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
This document discusses the evolution of data warehousing and the modern data platform. It outlines some common problems with traditional data warehousing approaches like long setup times, poor performance and scalability issues. The modern data platform combines cloud-based data warehousing, data modeling principles, and data warehouse automation tools to provide highly scalable and agile solutions. Key components demonstrated are the Snowflake data platform for scalable data storage and processing, Fivetran for automated data integration, and capabilities like cloning data for testing and time travel to access historical data.
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Many NoSQL DBaaS vendors limit what cloud platform you can run on, the size of the data you can run and require you to over-provision cloud infrastructure resources while failing to deliver performance and low latency at scale.
In this session, we will compare the performance and Total Cost of Ownership (TCO) of competing NoSQL DBaaS offerings. We will also review how to migrate to Scylla Cloud, our fully managed database service.
You will learn:
- The true cost of ownership for selected NoSQL DBaaS offerings
- The 8 essentials for selecting a NoSQL DBaaS
- Migration options from Apache Cassandra, DynamoDB and other databases
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
More Related Content
Similar to #Snowflake #Training from #SQLSchool Training #Institute.
Snowflake is a cloud data platform that allows users to store, process and analyze data across cloud services like AWS, GCP and Azure. Some key features include:
- It uses a shared data architecture that decouples storage, compute and management services allowing them to scale independently. Data is stored in a centralized storage layer and processed using virtual warehouses.
- Snowflake supports both structured and semi-structured data and users can query data using standard SQL. It also supports stored procedures, user defined functions and external integrations.
- The platform offers different editions with features for security, governance, compute resource management and data integration. It also has tools and connectors for data warehousing, science and sharing workflows.
Logical Data Warehouse: How to Build a Virtualized Data Services LayerDataWorks Summit
The document discusses the emergence of logical data warehouses in response to big data. It describes how a logical data warehouse uses virtualization, distributed processing, and other techniques to provide a unified view of data across different repositories like Hadoop, relational databases and NoSQL stores. It also discusses how organizations can optimize resources by offloading analytical workloads from their enterprise data warehouse to Hadoop clusters to reduce costs while still using existing code and applications.
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...Deepak Chandramouli
PayPal Data Lake Journey | 2017-Oct | San Diego | Teradata Edge of Next
Gimel [http://www.gimel.io] is a Big Data Processing Library, open sourced by PayPal.
https://www.youtube.com/watch?v=52PdNno_9cU&t=3s
Gimel empowers analysts, scientists, data engineers alike to access a variety of Big Data / Traditional Data Stores - with just SQL or a single line of code (Unified Data API).
This is possible via the Catalog of Technical properties abstracted from users, along with a rich collection of Data Store Connectors available in Gimel Library.
A Catalog provider can be Hive or User Supplied (runtime) or UDC.
In addition, PayPal recently open sourced UDC [Unified Data Catalog], which can host and serve the Technical Metatada of the Data Stores & Objects. Visit http://www.unifieddatacatalog.io to experience first hand.
Demystifying Data Warehouse as a Service (DWaaS)Kent Graziano
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Session 8 - Creating Data Processing Services | Train the Trainers ProgramFIWARE
This technical session for Local Experts in Data Sharing (LEBDs), this session will explain how to create data processing services that are key to i4Trust.
ME_Snowflake_Introduction_for new students.pptxSamuel168738
Snowflake is a cloud-based data warehouse that runs entirely on cloud infrastructure like AWS or Azure. It uses a shared-disk and shared-nothing architecture. Data is stored in an optimized columnar format in cloud storage. Queries are executed using virtual warehouses that are independent compute clusters. Snowflake provides a SQL interface and connectors to load, query, and analyze data without having to manage any hardware or software.
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
GDG Cloud Southlake #16: Priyanka Vergadia: Scalable Data Analytics in Google...James Anderson
Do you know The Cloud Girl? She makes the cloud come alive with pictures and storytelling.
The Cloud Girl, Priyanka Vergadia, Chief Content Officer @Google, joins us to tell us about Scaleable Data Analytics in Google Cloud.
Maybe, with her explanation, we'll finally understand it!
Priyanka is a technical storyteller and content creator who has created over 300 videos, articles, podcasts, courses and tutorials which help developers learn Google Cloud fundamentals, solve their business challenges and pass certifications! Checkout her content on Google Cloud Tech Youtube channel.
Priyanka enjoys drawing and painting which she tries to bring to her advocacy.
Check out her website The Cloud Girl: https://thecloudgirl.dev/ and her new book: https://www.amazon.com/Visualizing-Google-Cloud-Illustrated-References/dp/1119816327
Snowflake’s Cloud Data Platform and Modern AnalyticsSenturus
Snowflake's Cloud Data Platform provides a fully managed data warehouse as a service. It offers elastic scaling of storage and compute independently, with multiple clusters accessing a shared set of data. Its architecture separates storage, compute, and services across independent cloud infrastructure for high availability and resilience. Snowflake handles all data management tasks and provides automatic scaling and updates with no downtime.
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.SequelGate
Free Demo on #Microsoft #SQLServer & #T-SQL with #Azure from SQL School.
Microsoft SQL Server Training Course is exclusively designed for aspiring Data Analysts, #BusinessAnalysts, #DataScientists, #MSBI / #PowerBI Engineers and #SQL Database #Developers. This SQL Server and T-SQL Training Course is designed for both starters as well as for experienced #professionals.
#Course Info at : https://sqlschool.com/TSQL-Online-Training.html
#Register at : https://sqlschool.com/Register.html
Reach us (24x7)
Mail : contact@sqlschool.com
#India (+91) : 9666 44 0801
#USA (+1) : 956-825-0401
http://www.sqlschool.com
Vitalii Bondarenko - Масштабована бізнес-аналітика у Cloud Big Data Cluster. ...Lviv Startup Club
This document discusses different architectures and approaches for scalable business intelligence (BI) on cloud big data clusters. It covers OLAP approaches using relational databases, key-value stores like Apache Kylin, big data compute engines like Hive LLAP, and column stores like Druid. It also discusses in-memory aggregations with MemSQL and the results of testing different solutions. Traditional BI approaches on SQL servers are discussed alongside modern approaches using Hadoop, Spark, and cloud services from AWS, Azure, and Google Cloud.
Why you need benchmarks
Finding the right database solution for your use case can be an arduous journey. The database deployment touches aspects of throughput performance, latency control, high availability and data resilience.
You will need to decide on the infrastructure to use: Cloud, on-premise or a hybrid solution.
Data models also have an impact on finding the right fit for the use case. Once you establish a requirements set, the next step is to test your use case against the databases of choice.
In this workshop, we will discuss the different data points you need to collect in order to get the most realistic testing environment.
We will cover:
Data model impact on performance and latency
Client behavior related to database capabilities
Failover and high availability testing
Hardware selection and cluster configuration impact
We will show 2 benchmarking tools you can use to test and benchmark your clusters to identify the optimal deployment scenario for your use case.
Attend this virtual workshop if you are:
Looking to minimize the cost of your database deployment
Making a database decision based on performance and scale data
Planning to emulate your workload on a pre-production system where you can test, fail fast and learn.
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld
VMworld 2013
Michael Corey, Ntirety, Inc
Jeff Szastak, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
This technical workshop equips you with the insights to modernize your legacy Windows and SQL Server applications. We will walk through the common Amazon Web Services (AWS) solutions and proven customer approaches to deploy and migrate SQL Server 2008 to the cloud.
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
This document discusses the evolution of data warehousing and the modern data platform. It outlines some common problems with traditional data warehousing approaches like long setup times, poor performance and scalability issues. The modern data platform combines cloud-based data warehousing, data modeling principles, and data warehouse automation tools to provide highly scalable and agile solutions. Key components demonstrated are the Snowflake data platform for scalable data storage and processing, Fivetran for automated data integration, and capabilities like cloning data for testing and time travel to access historical data.
The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.
Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology.
Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus.
Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.
Many NoSQL DBaaS vendors limit what cloud platform you can run on, the size of the data you can run and require you to over-provision cloud infrastructure resources while failing to deliver performance and low latency at scale.
In this session, we will compare the performance and Total Cost of Ownership (TCO) of competing NoSQL DBaaS offerings. We will also review how to migrate to Scylla Cloud, our fully managed database service.
You will learn:
- The true cost of ownership for selected NoSQL DBaaS offerings
- The 8 essentials for selecting a NoSQL DBaaS
- Migration options from Apache Cassandra, DynamoDB and other databases
Similar to #Snowflake #Training from #SQLSchool Training #Institute. (20)
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
#Snowflake #Training from #SQLSchool Training #Institute.
1. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Complete Practical; Real-time Job Oriented Training
Snowflake Training
PLAN A PLAN B PLAN C
Applicable For (Resume Plan) Snowflake SQL & T-SQL Queries
Snowflake
SQL & T-SQL Queries
Snowflake
Power BI
Snowflake Data Warehouse ✓ ✓ ✓
Snowflake Architecture ✓ ✓ ✓
Snowflake Objects ✓ ✓ ✓
Using SnowSQL CLI ✓ ✓ ✓
Creating Virtual Warehouse ✓ ✓ ✓
Data Analytics Options Snowflake ✓ ✓ ✓
Snowflake Data LifeCycle ✓ ✓ ✓
Snowflake Databases ✓ ✓ ✓
Snowflake Schemas ✓ ✓ ✓
Snowflake Streams & Tasks ✓ ✓ ✓
Staging Operations Snowflake ✓ ✓ ✓
Snowflake Worksheets ✓ ✓ ✓
Data Pipelines in Snowflake ✓ ✓ ✓
Replication in Snowflake ✓ ✓ ✓
Business Continuity in Snowflake ✓ ✓ ✓
TSQL: Database Basics, T-SQL X ✓ ✓
TSQL : Constraints, Joins, Queries X ✓ ✓
TSQL: Views, Group By, Self Joins X ✓ ✓
Power BI: Report Design, Visuals X X ✓
Power BI: M Lang, DAX for ETL X X ✓
Power BI: Cloud, Apps, Tenant X X ✓
Power BI: Report Server, Project X X ✓
DA 100 Exams Guidance X X ✓
Total Duration 4 Weeks 6 Weeks 10 Weeks
Trainer : Mr. Sai Phanindra T [17+ Yrs of Real-time Exp]. Profile @ linkedin.com/in/saiphanindra
2. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Training Module Duration Plan A Plan B Plan C
Module 1 Snowflake Training
[Snowflake Architecture, Snowflake
Objects, Virtual Warehouse in
Snowflake, Security Management with
Snowflake, Snowflake with Azure Data
Factory, Snowflake with Python]
4 W
Module 2 SQL Basics, T-SQL Queries 2 W X
Module 3
Power BI & Big Data Analytics (DA 100)
[Power BI Cloud Service, Report Server,
REST API, Dashboards, Power Query,
DAX, Real-time Project, Resume Guide]
4 W X X
Total Duration 4 W 6 W 10 W
Module 1: Snowflake Training
Applicable for All Plans
Chapter 1: Introduction to DWH & Snowflake Introduction
Database Types & Introduction to DWH; Cloud based DWH; Snowflake: Need, Advantages; History
of Snowflake; DWaaS [DataWarehouse As A Service]; Why Snowflake? Snowflake Data
Warehouse Concepts; Virtual Warehouses and Advantages; Snowflake Career Options:
Certifications [Snowpro Core, Architect]
Chapter 2: Snowflake Architecture
Snowflake Architecture; Storage, Compute and Cloud Services Layers; Virtual Warehouses and
Databases with File System; Cloud Platforms & Regions; Releases; Metadata Manager and
Security Operations; Uses of Database Query Layer; Massively Parallel Processing [MPP]
Architecture; Real-time Uses of Snowflake; Snowflake Editions: Standard, Standard +, Business
Critical, Virtual Private Snowflake (VPS); Clustering, Performance and Security Compliance
Comparisons; Virtual Warehouse: Multi Cluster Warehouse Concepts; Maximized and Auto Scale
Options; SCALING_POLICY and Cluster Size; Credit Usage Policies;
Chapter 3: Snowflake Service Form
Setting Up Snowflake Account; Self Service Form; Snowflake Pricing; Compute & Storage;
Evaluating and Using Free Credits; Working with Suspended Accounts; End User Access Options:
Browser-based web interface, SnowSQL [CLI Client], JDBC / ODBC Client Applications and 3rd Party
Tools [Partners]; Account Identifier; Logging in Using the Web Interface; SnowSQL: Snowflake
Client Repository & Installation [Windows]. MSI File; Validing Installation & Register;
Chapter 4: Snowflake Objects
Snowflake Objects: Virtual Warehouses, Databases; Sizing and Metadata Properties; Table
Creation Options; Fields, Types and Column Usage Options; Page Navigations: Database Page,
3. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Warehouse Page, Worksheet Page; Performing Basic DDL and DML Operations; Data Load
Operations: Limitations;
Chapter 5: Using SnowSQL CLI
Using SnowSQL CLI; Creating Virtual Warehouse; Creating Databases and Tables; Data Storage and
Querying Operations; Basic Performance Optimization Options, CSV File Loads; Data Load
Limitations; Querying Tables and Reading Resultsets; Data Analytics Options with Snowflake;
Cleanup Operations;
Chapter 6: Snowflake Data
Snowflake Data LifeCycle: Organizing Data, Storing Data, Quering Data, Working with Data [DML
& MERGE] and Removing Data [TRUCATE and DROP]; Snowflake Databases: CREATE, CLONE
ALTER and DROP database operations; UNDROP, USE and SHOW database operations; Snowflake
Schemas: CREATE SCHEMA, SHOW SCHEMA operations. Transient Property, Clone, Collation and
Retention Time Operations with Schemas;
Chapter 7: Sequence in Snowflake
Snowflake Objects: Working with Tables [Standard and External Tables]; Views [Standard Views &
Materialized Views]; Working with Sequences in Snowflake; DML Operations with Snowflake:
Insert [Single and Multi Table]; Merge, Update, Delete and Truncate Operations with Snowflake
Tables;
Chapter 8: Snowflake Stream & Tasks
Snowflake Streams & Tasks: CREATE, ALTER, SHOW AND DROP; CLONE Operations with Streams
and Jobs; Using Streams for Data Loads in Snowflake; Snowflake Shares: Creating and Using
Shares; Describe Shares, Show Shares and Drop Operations; Real-time Use of Snowflake Shares;
Chapter 9: SnowSQL Operators
SnowSQL Operators: Arthimetic, Comparison, Logical / Boolean, Set Operators; Sub Query
Operators; WHERE, JOIN, WITH, TOP, PIVOT, VALUE Constructs; GROUP BY with ROLLUP, CUBE,
HAVING, QUALIFY AND ORDER BY Constructs with Snowflake; LIMIT & FETCH,
MATCHING_RECOGNIZE & TABLESAMPLE
Chapter 10: SnowSQL Concepts
SnowSQL Concepts: Scalar, Aggregate, Window, Table & System Functions; Real-time Use of
Functions in Snowflake; Loading Data into Snowflake: Data Load Considerations; Data Preperation
and Loading Options: Bulk Loads, Web Interface Data Loads & SnowPipe; Staging;
Chapter 11: Snowflake Data Loads & Validations
Bulk Data Loads from Local Systesm; Using COPY command with SnowSQL CLI; File Formats and
Staging; Staged Files, Target Data Loads; Data Cleanup Options; Data Load Errors and Solutions;
Data Validation Issues and Using VALIDATE function with Namespace, Table & Job ID; Error Loads;
Error Row Redirect Options;
Chapter 12: Staging Operations with Snowflake
4. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Staging Operations with Snowflake; Querying Staged Data; Data Transfer Monitoring and
Querying Metadata; Query Syntax and Parameters; Supported Functions; Users Stages, Table
Stages and Named Stages; Creating and Using Named Stage; Listing and Using Staged Data Files;
File Formats and Query Examples; Working with Aliases; Loading and Unloading Data with
Snowflake; Working with JSON Data an Parquet Data with Snowflake;
Chapter 13: Unloading Data from Snowflake
Unloading Data from Snowflake; Key Concepts and Best Practices; Data Preparation for Unloads;
Unloading to Snowflake Stage; Unloading to Microsoft Azure; Using Snowflake Worksheets; user
Preferences; Using Worksheets for Queries, DDL & DML; Using History Page for Query Monitoring;
Snowsight Usage & Monitoring
Chapter 14: Virtual Warehouse in Snowflake
Virtual Warehouse in Snowflake; Warehouse Creation & Usage; Multi-Cluster Warehouses;
Warehouse Considerations, Warehouse Working and Monitoring; Continuous Data Pipelines;
Data Pipelines Creation and Usage; Change Tracking using Table Streams; Tasks and Schedules;
Monitoring and Using Data Pipelines;
Chapter 15: Replication and Business Continuity
Replication and Business Continuity; Disaster Recovery Scenarios; Database Replication and
Failover / Failback Options; Client Redirect; Region Outage, Fail-safe; Data Restore and Time
Travel Options; SQL Extentions; Enabling / Disabling Time Travel; Data Retention; History and
Clone Options; Drop and Restore Operations;
Chapter 16: Security Management with Snowflake
Security Management with Snowflake; ACCOUNTADMIN, SYSADMIN & SECURITYADMIN;
Authentication, Securing Objects and Authorization; Object Access Control; Sharing Data Securely
in Snowflake; Granting Privileges to Other Roles; Data Sharing Usage and Working with Shared
Data @ Web Interface; Security Options;
Chapter 17: Snowflake with Azure Data Factory
Snowflake with Azure Data Factory: Creating ADF Linked Services for Snowflake; Using Copy Data
Tool [CDT] in ADF Studio; Data Imports & Pipelines; DIUs; Snowflake with Python: Application
Development Interface for Python; JDBC and ODBC Connection Options; Supported Python
Versions; Pandas DataFrames;
Chapter 18: Managing Governance in Snowflake
Managing Governance in Snowflake: Governance Features, Column Level Security, Row Access
Policies, Access History and Object Tagging; Continuous Data Protection; Managing Account in
Snowflake: Account Identifiers; System Usage and Billing; Parameter Management, User
Management, Change Release Management;
5. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Module 2: Database Basics, SQL, T-SQL Queries
Applicable for Snowflake Plans B, C
Chapter 1: SQL SERVER INTRODUCTION
Data, Databases and RDBMS Software; Database Types : OLTP, DWH, OLAP; Microsoft SQL Server
Advantages, Use; Versions and Editions of SQL Server; SQL : Purpose, Real-time Usage Options;
SQL Versus Microsoft T-SQL [MSSQL]; Microsoft SQL Server - Career Options; Database Engine
Component and OLTP; BI Components, Data Science Components; ETL, MSBI and Power BI
Components; Course Plan, Resume, Project; 24 x 7 Lab; Software Installation Pre-Requisites;
Chapter 2: SQL SERVER INSTALLATIONS
System Configuration Checker Tool; Versions and Editions of SQL Server; SQL Server Pre-
requisites: S/W, H/W; SQL Server 2016 / 2017 Installation; SQL Server 2019 Installation; Instance
Name; Instances : Types; Default Instance, Named Instances; Port Numbers; Service and Service
Account; Authentication Modes and Logins; FileStream, Collation Properties;
Chapter 3: SSMS Tool, SQL BASICS - 1
SQL Server Management Studio; Local and Remote Connections; System Databases: Master and
Model; MSDB, TempDB, Resource Databases; Creating Databases : Files [MDF, LDF]; Creating
Tables in GUI; Data Insertion & Storage; SQL : Real-time Usage; DDL, DML, SELECT, DCL and TCL
Statements; Data Storage, Inserts - Basic Level; SELECT; Table Data Retrieval;
Chapter 4: SQL BASICS - 2
Creating Databases & Tables in SSMS; Single Row Inserts, Multi Row Inserts; Rules for Data
Insertion Statements; SELECT Statement @ Data Retrieval; SELECT with WHERE Conditions; AND
and OR; IN and NOT IN; Between, Not Between; LIKE and NOT LIKE; UPDATE Statement; DELETE
& TRUNCATE; Logged and Non-Logged Operations; ADD, ALTER and DROP Statements;
Chapter 5: SQL BASICS - 3, T-SQL Introduction
Schemas : Group Tables in Database; Using Schemas for Table Creation; Using Schemas in Table
Relations; Table Migrations across Schemas; Default Schema : "dbo"; Import and Export Wizard;
Bulk Operations; Excel File Imports / Exports; SQL Server Native Client; Executing SSIS Packages,
Data Loads; Local and Global Temporary Tables; # & ## Prefix; Temporary Vs Permanent Tables;
Chapter 6: CONSTRAINTS & INDEXES BASICS
Constraints and Keys - Data Integrity; NULL, NOT NULL Property on Tables; UNIQUE KEY
Constraint; PRIMARY KEY Constraint; FOREIGN KEY Constraint, References; CHECK Constraint;
DEFAULT Constraint; Identity Property : Seed & Increment; Database Diagrams and ER Models;
Relationships Verification and Links; Indexes : Basic Types and Creation; Index Sort Options,
Search Advantages; Clustered and Non Clustered Indexes; Primary Key and Unique Key Indexes;
REAL-TIME CASE STUDY - 1 (SALES & RETAIL)
Chapter 7: JOINS and TSQL Queries : Level 1
6. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
JOINS - Table Comparisons; INNER JOINS For Matching Data; OUTER JOINS For (non) Match Data;
Left Outer Joins; Right Outer Joins - Example Queries; FULL Outer Joins; One-way and Two Way
Comparisons; "ON" Conditions; Join Unrelated Tables; NULL, IS NULL in Joins; CROSS JOIN and
CROSS APPLY; Join Options: Merge, Loop and Hash Joins; Performance Advantages;
Chapter 8: GROUP BY, T-SQL Queries : Level 2
GROUP BY Queries and Aggregations; Group By Queries with Having Clause; Group By Queries
with Where Clause; Using WHERE and HAVING in T-SQL; Rollup : Usage and T-SQL Queries; Cube
: Usage and T-SQL Queries; UNION and UNION ALL Operator; EXISTS Operator, Query Conditions;
Sub Queries; Joins with Group By Queries; Nested Sub Queries; UNION and UNION ALL; Nested
Sub Queries with Group By, Joins; Comparing WHERE, HAVING Conditions;
Chapter 9: JOINS & T-SQL Queries : Level 3
GetDate, Year, Month, Day Functions; Date & Time Styles, Data Formatting; DateAdd and
DateDiff Functions; Cast and, Convert Functions in Queries; String Functions: SubString, Relicate;
Len, Upper, Lower, Left and Right; LTrim, RTrim, CharIndex Functions; MERGE Statement -
Comparing Tables; WHEN MATCHED and NOT MATCHED; Incremental Load with MERGE
Statement; IIF() Function for Value Compares; CASE Statement : WHEN, ELSE, END;
ROW_NUMBER() and RANK() Queries; Dense Rank and Partition By Queries;
Chapter 10: View, Procedure, Function Basics
Views : Types, Usage in Real-time; System Predefined Views and Audits; Listing Databases, Tables,
Schemas; Functions : Types, Usage in Real-time; Scalar, Inline and Multi-Line Functions; System
Predefined Functions, Audits; DBId, DBName, ObjectID, ObjectName; Variables & Parameters;
User & System Predefined Procedures; Parameters; Sp_help, Sp_helpdb and sp_helptext;
sp_pkeys, sp_rename and sp_help; When to use Which Database Objects;
Chapter 11: Triggers & Transactions
Triggers - Purpose, Real-world Usage; FOR/AFTER Triggers; INSTEAD OF Triggers; INSERTED,
DELETED Memory Tables; DML Automations using Memory Tables; Read Only Tables using DML
Triggers; Enable Triggers and Disable Triggers; Database Level, Server Level Triggers; Transactions
& ACID Properties; Auto Commit; EXPLICIT & IMPLICIT; COMMIT and ROLLBACK; Open Transaction; Query
Blocking Scenarios @ Real-time; NOLOCK and READPAST Lock Hints;
Chapter 12: ER MODELS, NORMAL FORMS
Normal Forms for Entity Relationships; First, Second, Third Normal Forms Usage; Boycee-Codd Normal
Form: BNCF : Usage; 4 NF, EKNF, ETNF. Functional Dependency; Multi-Valued, Transitive Dependencies;
Composite Keys and Composite Indexes; 1:1, 1:M, M:1, M:M Relationship Types; SQL Queries Access in
Reporting Tools; Storing SQL Queries into Views; Creating Office Data Connection Files; Excel Pivot Reports
and Reports; SQL Queries (Auto Generated) in BI Tools; FETCH OFFSET, NEXT ROWS; Data Refresh
(Manual, Automated);
REAL-TIME CASE STUDY - 2 (Sales & Retail), EXCEL INTEGRATION
7. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Module 3: Power BI (Reports, Cloud, Server, Analytics)
Applicable for Snowflake Plan C
Chapter 1 : POWER BI BASICS
Power BI Job Roles in Real-time; Power BI Data Analyst Job Roles; Business Analyst - Job Roles; Power
BI Developer - Job Roles; Power BI for Data Scientists Comparing MSBI and Power BI; Comparing
Tableau and Power BI; DA 100 Exam Guidance; Types of Reports in Real-World; Interactive & Paginated
Reports; Analytical & Mobile Reports; Data Sources Types in Power BI; Licensing Plans; Power BI
Training : Lab Plan; Power BI Dev, Prod Environments;
Chapter 2 : BASIC REPORT DESIGN
Power BI Desktop Installation; Data Sources & Visual Types; Canvas, Visualizations and Fields; Get Data
and Memory Tables; In-Memory xvelocity Database; Table and Tree Map Visuals; Format Button and
Data Labels; Legend, Category and Grid; PBIX and PBIT File Formats; Visual Interaction, Data Points;
Disabling Visual Interactions; Edit Interactions - Format Options; SPOTLIGHT & FOCUSMODE; CSV and
PDF Exports. Tooltips; Power BI EcoSystem, Architecture;
Chapter 3 : VISUAL SYNC, GROUPING
Slicer Visual : Real-time Usage; Orientation, Selection Properties; Single & Multi Select, CTRL Options;
Slicer : Number, Text and Date Data; Slicer List and Slicer Dropdowns; Visual Sync Limitations; Disabling
Slicers; Grouping : Real-time Use, Examples; List Grouping and Binning Options; Grouping Static / Fixed
Data Values; Grouping Dynamic / Changing Data; Bin Size and Bin Limits (Max, Min); Bin Count and
Grouping Options; Grouping Binned Data, Classification;
Chapter 4 : HIERARCHIES, FILTERS
Creating Hierarchies in Power BI; Independent Drill-Down Options; Dependant Drill-Down Options;
Conditional Drilldowns, Data Points; Drill Up Buttons and Operations; Expand & Show Next Level
Options; Dynamic Data Drills Limitations; Show Data and See Records; Filters : Types and Usage in Real-
time; Visual Filter, Page Filter, Report Filter; Basic, Advanced and TOP N Filters; Category and Summary
Level Filters; DrillThru Filters, Drill Thru Reports; Keep All Filters" Options in DrillThru; CrossReport
Filters, Include, Exclude;
Chapter 5 : BOOKMARKS, AZURE, MODELING
Drill-thru Filters, Page Navigations; Bookmarks : Real-time Usage; Bookmarks for Visual Filters;
Bookmarks for Page Navigations; Selection Pane with Bookmarks; Buttons, Images with Actions;
Buttons, Actions and Text URLs; Bookmarks View & Selection Pane; OLTP Databases, Big Data Sources;
Azure Database Access, Reports; Import &Direct Query with Power BI; Enter Data; Data Modeling :
Currency, Relations; Summary, Format, Synonyms; Web & Mobile View in PBI;
Chapter 6 : VISUALIZATION PROPERTIES
Stacked Charts and Clustered Charts; Line Charts, Area Charts, Bar Charts; 100% Stacked Bar and
Column Charts; Map Visuals: Tree, Filled, Bubble; Cards, Funnel, Table, Matrix; Scatter Chart : Play Axis,
Labels; Series Clusters; Waterfall Chart; ArcGIS Maps; Infographics; Color Saturation, Sentiment Colors;
Column Series, Column Axis in Lines; Join Types : Round, Bevel, Miter; Shapes, Markers, Axis, Plot Area;
Data Colors; Series, Custom Series and Legends;
Chapter 7 : POWER QUERY LEVEL 1
8. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Power Query Architecture and M Language; Data Types, Literals and Values; Power Query
Transformation Types; Table & Column; Text & Number Transformations; Date, Time and Structured
Data; List, Record & Table; let, source, in statements @ M Lang; Power Query Functions, Parameters;
Invoke Functions; Get Data, Table Creations, Edit; Merge and Append Transformations; Join Kinds,
Advanced Editor, Apply; ETL Operations with Power Query;
Chapter 8 : POWER QUERY LEVEL 2
Query Duplicate, Query Reference; Group By and Advanced Options; Aggregations with Power Query;
Transpose, Header Row Promotion; Reverse Rows and Row Count; Data Type Changes & Detection;
Replace Columns: Text, NonText; Replace Nulls: Fill Up, Fill Down; PIVOT, UNPIVOT; Move Column and
Split Column; Extract, Format; Date & Time Transformations; Deriving Year, Quarter, Month, Chapter;
Add Column : Query Expressions; Query Step Inserts and Step Edits;
Chapter 9 : POWER QUERY LEVEL 3
Creating Parameters in Power Query; Parameter Data Types, Default Lists; Static/Dynamic Lists For
Parameters; Removing Columns and Duplicates; Convert Tables to List Queries; Linking Parameters to
Queries; Parameters and PBI Canvas; Multi-Valued Parameter Lists; Creating Lists in Power Query;
Converting Lists to Table Data; Advanced Edits and Parameters; Data Type Conversions, Expressions;
Columns From Examples, Indexes; Conditional Columns, Expressions;
Chapter 10 : DAX Functions - Level 1
DAX : Importance in Real-time; Real-world usage of Excel, DAX; DAX Architecture, Entity Sets; DAX Data
Types, Syntax Rules; DAX Measures and Calculations; ROW Context and Filter Context; DAX Operators,
Special Characters; DAX Functions, Types in Real-time; Vertipaq Engine, DAX Cheat Sheet; Creating,
Using Measures with DAX; Creating, Columns with DAX; Quick Measures; SUM, AVERAGEX,
KEEPFILTERS; Dynamic Expressions, IF in DAX;
Chapter 11 : DAX Functions - Level 2
Data Modeling Options in DAX; Detecting Relations for DAX; Using Calculated Columns in DAX; Using
Aggregated Measures in DAX; Working with Facts & Measures; Modeling : Missing Relations; Modeling
: Relation Management; CALCULATE Function Conditions; CALCULATE & ALL Member Scope; RELATED
& COUNTROWS in DAX; Slicing; Dynamic Expressions, RETURN; Date, Time, Text Functions; Logical,
Mathematical Functions; Running Total, EARLIER Function;
Chapter 12 : DAX FUNCTIONS - Level 3
1:1, 1:M and M:1 Relations; Connection with CSV, MS Access; AVERAGEX and AVERAGE in DAX;
KEEPFILTERS and CALCUALTE; COUNTROWS, RELATED, DIVIDE; PARALLELPERIOD, DATEDADD;
CALCULATE & PREVIOUSMONTH; USERELATIONSHIP, DAX Variables; TOTALYTD , TOTALQTD; DIVIDE,
CALCULATE, Conditions; IF..ELSE..THEN Statement; SELECTEDVALUE, FORMAT; SUM, DATEDIFF
Examples; TOCHAPTER, DATE, CHAPTER with DAX; Time Intelligence Functions;
Chapter 13 : POWER BI CLOUD - 1
Power BI Service Architecture; Power BI Cloud Components, Use; App Workspaces, Report Publish
Related Datasets Cloud; Creating New Reports in Cloud; Report Publish and Report Uploads;
Dashboards Creation and Usage; Adding Tiles to Dashboards; Pining Visuals and Report Pages; Visual
Pin Actions in Dashboards; LIVE Interaction in Dashboard; Adding Images, Custom Links; Videos &
Embed Links; API Data Sources; Streaming Dataset Tiles (REST API);
9. www.sqlschool.com For Free Demo: Reach us on +91 9666 44 0801 or +1 956.825.0401 (24x7)
Chapter 14 : POWER BI CLOUD - 2
Dashboards Actions, Report Actions; DataSet Actions: Create Report; Share, Metrics and Exports;
Mobile View & Dashboard Themes; Q & A [Cortana], Pin Visuals; Export, Subscribe, Subscribe;
Favourite, Insights, Embed Code; Featured Dashboards and Refresh; Gateways Configuration, PBI
Service; Gateway Types; Gateway Clusters, Data Refresh : Manual, Automatic; PBIEngw Service;
DataFlows, Power Query Expressions; Adding Entities, JSON Files;
Chapter 15 : EXCEL, ROW LEVEL SECURITY
Import and Upload Options in Excel; Excel Workbooks and Dashboards; Datasets in Excel and
Dashboards; Using Excel Analyzer in Power BI; Using Excel Publisher in PBI Cloud; Excel Workbooks,
PINS in Power BI; Excel ODC Connections, Power Pivot; Row Level Security (RLS) with DAX; Need for
RLS in Power BI Cloud; Data Modelling; DAX Roles Creation and Testing; Power BI Users to Roles;
Custom Visualizations; Histogram, Gantt Chart, Info graphics;
Chapter 16: REPORT SERVER, REPORT BUILDER
Need for Report Server in PROD; Install, Configure Report Server; Report Server DB, Temp Database;
Webservice URL, Webportal URL; Creating Hybrid Cloud with Power BI; Using Power BI DesktopRS;
Uploading Interactive Reports; Report Builder; Report Builder For Power BI Cloud; Designing Paginated
Reports (RDL); Deploy to Power BI Report Server; Data Source Connections, Report; Power BI Report
Server to Cloud; Tenant IDs; Mobile Report Publisher;
Chapter 17: AZURE BI INTEGRATIONS WITH POWER BI
Power BI with SQL Server Source; Power BI with Azure SQL Database; Power BI with Azure Data
Warehouse (Synapse); Power BI with Azure Data Lake; Power BI with Azure Databricks; Power BI with
Azure Cosmos DB; Power BI with Azure BLOB Storage; Azure AD Authentication;
Chapter 18: Real-time Project [Sales & Customers]
Resume, Project Oriented FAQs and Solutions
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Contact Us Today:
Email : contact@sqlschool.com
Skype: SQL School Training Institute
Website: www.sqlschool.com
Call Us (India): 24 x 7
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saiphanindrait@gmail.com
+91 9030040801
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