Connecting To MS SQL Server With Mulesoft (Stored Procedure To Insert data)Jitendra Bafna
This document discusses how to connect Mulesoft to MS SQL Server to call stored procedures and insert data into tables. It provides steps for setting up the prerequisite JDBC driver and configuring a database connector in Mulesoft. It then explains how to create a HTTP listener, use the database component to call a stored procedure, map input parameters to a JSON payload, and test the integration using Postman.
Our software becomes more complex with every iteration we spent on it.
There are new systems we need to integrate with or if you're working in
microservice eco-system there are dozens of new services in your
environment every week (especially at the beginning the growth is rapid).
When it comes to testing entire system on end-2- end basis, so far it
seemed we had only 2 choices:
1. Deploy the system with a prod-like configuration (with all the
collaborators) and perform heavy manual tests followed by heavy manual
regression tests... but as Maynard James Keenanaas sings "Boredom's
not a burden anyone should bare" - anyone who's been doing manual
regression tests knows exactly what I (and Maynard ;-) am talking about
2. Deploy the system with a prod-like configuration (similarly as above)
and automate all the end-2- end tests that will eventually end-up as a
regression test suite
Consumer driven contract gives us a third option:
3. Establish contracts with all the collaborators and test your service in
isolation using contracts whenever it comes to contacting an external
system, regardless of whether it is developed within the company or
outside (I must admit that the former case requires some more
orchestration on managerial level but it is doable)
This workshop will give you a hands-on experience. You will create two
REST services integrated with each other, and implement an API change
using consumer contracts. This approach will allow us to do the change in
both services in separation, in a BDD style.
This document discusses triggering and scheduling flows in MuleSoft. It covers:
1) Triggering flows when files or database records are added or updated
2) Scheduling flows to run on a defined schedule
3) Persisting and sharing data across flow executions
4) Publishing and consuming JMS messages
5) Processing items in a collection sequentially or records asynchronously in batch
Minerals have several key identifying properties including their chemical composition, crystalline structure, and physical characteristics such as color, crystal form, luster, hardness, streak, cleavage, and fracture. A mineral's properties are used to determine its classification and identity. Hardness, streak, and reaction to acid are especially important diagnostic properties.
2009 seminar - tim m - vs 2010 developer editionTim Mahy
Team Developer 2010 provides new features for test impact analysis, historical debugging, and multithreaded debugging. It also includes improvements to code analysis and profiling. Test impact analysis identifies related tests to rerun after code changes. Historical debugging allows debugging past application states without rerunning. Multithreaded debugging adds windows to view parallel stacks and tasks. Code analysis includes 8 new rules and custom rule sets. Profiling offers just my code, memory allocation, contention, and concurrency profiling with comparison reports. Load testing also integrates functional tests and extends data collection.
Minnesota Amateur Radio Marathon Support Erik Westgard
Amateur Radio and Icom D-Star supporting the Medtronic Twin Cities Marathon. How volunteer ham radio operators provide medical communications support using modern digital technology. This talk was presented at ARRL TAPR DCC 2013 in Seattle on Sept 20 2013 Erik Westgard NY9D www.14567.org
Connecting To MS SQL Server With Mulesoft (Stored Procedure To Insert data)Jitendra Bafna
This document discusses how to connect Mulesoft to MS SQL Server to call stored procedures and insert data into tables. It provides steps for setting up the prerequisite JDBC driver and configuring a database connector in Mulesoft. It then explains how to create a HTTP listener, use the database component to call a stored procedure, map input parameters to a JSON payload, and test the integration using Postman.
Our software becomes more complex with every iteration we spent on it.
There are new systems we need to integrate with or if you're working in
microservice eco-system there are dozens of new services in your
environment every week (especially at the beginning the growth is rapid).
When it comes to testing entire system on end-2- end basis, so far it
seemed we had only 2 choices:
1. Deploy the system with a prod-like configuration (with all the
collaborators) and perform heavy manual tests followed by heavy manual
regression tests... but as Maynard James Keenanaas sings "Boredom's
not a burden anyone should bare" - anyone who's been doing manual
regression tests knows exactly what I (and Maynard ;-) am talking about
2. Deploy the system with a prod-like configuration (similarly as above)
and automate all the end-2- end tests that will eventually end-up as a
regression test suite
Consumer driven contract gives us a third option:
3. Establish contracts with all the collaborators and test your service in
isolation using contracts whenever it comes to contacting an external
system, regardless of whether it is developed within the company or
outside (I must admit that the former case requires some more
orchestration on managerial level but it is doable)
This workshop will give you a hands-on experience. You will create two
REST services integrated with each other, and implement an API change
using consumer contracts. This approach will allow us to do the change in
both services in separation, in a BDD style.
This document discusses triggering and scheduling flows in MuleSoft. It covers:
1) Triggering flows when files or database records are added or updated
2) Scheduling flows to run on a defined schedule
3) Persisting and sharing data across flow executions
4) Publishing and consuming JMS messages
5) Processing items in a collection sequentially or records asynchronously in batch
Minerals have several key identifying properties including their chemical composition, crystalline structure, and physical characteristics such as color, crystal form, luster, hardness, streak, cleavage, and fracture. A mineral's properties are used to determine its classification and identity. Hardness, streak, and reaction to acid are especially important diagnostic properties.
2009 seminar - tim m - vs 2010 developer editionTim Mahy
Team Developer 2010 provides new features for test impact analysis, historical debugging, and multithreaded debugging. It also includes improvements to code analysis and profiling. Test impact analysis identifies related tests to rerun after code changes. Historical debugging allows debugging past application states without rerunning. Multithreaded debugging adds windows to view parallel stacks and tasks. Code analysis includes 8 new rules and custom rule sets. Profiling offers just my code, memory allocation, contention, and concurrency profiling with comparison reports. Load testing also integrates functional tests and extends data collection.
Minnesota Amateur Radio Marathon Support Erik Westgard
Amateur Radio and Icom D-Star supporting the Medtronic Twin Cities Marathon. How volunteer ham radio operators provide medical communications support using modern digital technology. This talk was presented at ARRL TAPR DCC 2013 in Seattle on Sept 20 2013 Erik Westgard NY9D www.14567.org
This document summarizes 6 SQL packages that will load data from Excel files into tables in an SQL Server database for a construction company. The packages will load data into tables for employees, employee rates, clients, client groupings, divisions, and a cross-reference table for client groupings. The packages use lookups and aggregations to validate data and insert new or updated rows while handling errors.
This document describes an SSIS package that loads data from various external sources into SQL Server tables. It consists of 8 packages that load data into specific tables from Excel files and CSV files. Each package is discussed in detail including input sources, lookups, conditional processing, and error handling. A final package performs maintenance on the database after all loads complete successfully.
The document describes a SQL Server Integration Services project to load data from various Excel and CSV source files into tables in an AllWorks SQL database. Multiple packages were created to extract, transform, and load the data into tables for divisions, client groupings, employee timesheets, and other tables. The packages validate and clean the source data, load it into the tables, and send logs and error reports via email. A master package controls the order of package execution and a maintenance package performs daily backups, indexing, and truncation.
The document describes a SQL Server Integration Services project to load data from various Excel and CSV source files into tables in an AllWorks SQL database. Multiple packages were created to extract, transform, and load the data into different tables. Packages were scheduled to run nightly to update the database with new or changed data and perform backups and maintenance. Logging was implemented to monitor package success, failures, and record counts.
Software Effort Measurement Using Abstraction Techniquesaliraza786
This document proposes a framework for measuring software effort using abstraction techniques. It involves two phases: a project planning phase where software requirements are abstracted and estimates are recorded, and a post-project mortem phase where actual efforts are analyzed against estimates to refine abstractions. The framework aims to improve estimates over time by categorizing requirements into abstraction categories with associated estimate data.
This document provides documentation for an SSIS project for a fictitious company called AllWorks. It describes 8 packages that will load data from source files into tables in the AllWorks database to track employee, customer, timesheet, and other information. The packages will insert new records, update existing records, and write invalid records to error files. The master package contains and runs the other packages in a specified order and includes post-load database maintenance tasks.
This document describes an SSIS project for an ETL process that loads data from various source files into a SQL Server database. It involves multiple packages that populate different tables with data from Excel spreadsheets, XML files and CSV files. There is also a package that backs up the database nightly and another that reindexes and shrinks the database. A master package calls all the other packages to run the full ETL process nightly.
This document provides an overview and samples of a business intelligence project using SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS). It includes descriptions of ETL packages in SSIS to load and transform data, a cube with dimensions and calculations in SSAS, and sample MDX queries and reports. The goals are to track, analyze, and report on facets of a simulated construction company.
This document summarizes a business intelligence portfolio project for a simulated construction company. It includes details on an ETL solution built in SQL Server Integration Services to load data nightly from various sources into a SQL database. It also covers an OLAP cube with a partial snowflake structure created in SQL Server Analysis Services, including sample MDX queries and KPIs. Finally, it discusses reports deployed to SharePoint using SQL Server Reporting Services and PerformancePoint Services, including gauges, charts and dashboards. The overall goal was to build a BI solution to track, analyze and report on all aspects of the company's business using Microsoft SQL Server and SharePoint technologies.
This document provides documentation for an SSAS cube project using the ALLWORKS database. It includes:
1) Details of the data source view created using 4 fact tables and 9 dimension tables from the ALLWORKS database.
2) Descriptions of the cube structure and partitions created for the cube. Two partitions were used for each fact table to optimize query performance.
3) Screenshots and explanations of 5 KPIs created using calculations and measures in the cube to analyze business metrics for clients, jobs, and overhead categories.
The document describes an SSIS project for a fictitious construction company called AllWorks. The project involves creating 11 SSIS packages to extract data from various Excel and CSV sources and load it into SQL Server tables. The packages are organized into a master package. The packages are built, deployed, and configured to run daily via a SQL Server Agent job.
This document summarizes a business intelligence project for a construction company called AllWorks. The project involves integrating various external data sources like Excel spreadsheets, XML files, and CSV files into a SQL Server database using SQL Server Integration Services (SSIS). Dimensional models are created in SQL Server Analysis Services (SSAS) from the integrated data. SQL Server Reporting Services (SSRS) and Excel are used to build reports on the data. PerformancePoint Server (PPS) is used to create dashboards with KPIs, charts, and filters that provide insights into employee, customer, timesheet, and invoice data.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides samples of work using Microsoft Business Intelligence tools, including T-SQL, SSIS, SSAS, MDX, and SSRS. It includes 3 code samples that demonstrate extracting data from a relational database using T-SQL queries, documenting an SSIS package, and building a calculation in SSAS. The document is intended to showcase the author's skills and experience with these BI tools for business executives, IT managers, and solution providers.
1. Log on to the SAP Fiori launchpad as a user with the business role Configuration Expert - Business Process Configuration.
2. Open the Configure Assistant app.
3. Select the Configure Expenses tile.
4. Define new expenses by entering an expense type and mapping the respective cost accounts.
5. Save your entries.
Transaction:
1. Log on to the SAP Fiori launchpad as a user with the business role Configuration Expert - Business Process Configuration.
2. Open transaction KK01.
3. Define new expenses by entering an expense type and mapping the respective cost accounts.
4. Save your entries.
Test
Step
Cognos Framework Manager is a metadata modeling tool.Cognos Framework Manager provides the metadata model development environment for Cognos 8.A model is a business presentation of the information from one or more data sources. The model provides a business presentation of the metadata.The model is packaged and published for report authors and query users
Live online IT Training with MaxOnlineTraining.com is an easy, effective way to maximize your skills without the travel.
Call us at For any queries, please contact:
+1 940 440 8084 / +91 953 383 7156 TODAY to join our Online IT Training course & find out how Max Online Training.com can help you embark on an exciting and lucrative IT career.
Visit www.maxonlinetraining.com
The document provides samples of work using Microsoft Business Intelligence tools including T-SQL, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS). It includes T-SQL queries, documentation of an SSIS package to load data into tables, and screenshots showing the design of SSIS control flows and data flows. The SSAS section discusses the importance of cube structure and design.
The document discusses characteristics of good software design including component independence, high cohesion, and low coupling. It defines different types of coupling (content, common, control, stamp, data) and cohesion (coincidental, logical, temporal, procedural, communicational, functional). Examples are provided to illustrate each type of coupling and cohesion. Maintaining low coupling and high cohesion results in components that are easier to understand, modify, and reuse.
The document discusses the evolution of annotation support in Spring frameworks over different versions. It summarizes key annotations introduced in Spring 2.0, 2.5, 3.0 and beyond. It also explains how annotations like @Autowired, @Qualifier, @Component, @Repository, @Service and @Controller work and provides examples of their usage. The document additionally covers concepts like stereotype annotations, configuration classes, @Bean and JSR-250 annotations like @Inject, @Named and @Resource.
This document summarizes 6 SQL packages that will load data from Excel files into tables in an SQL Server database for a construction company. The packages will load data into tables for employees, employee rates, clients, client groupings, divisions, and a cross-reference table for client groupings. The packages use lookups and aggregations to validate data and insert new or updated rows while handling errors.
This document describes an SSIS package that loads data from various external sources into SQL Server tables. It consists of 8 packages that load data into specific tables from Excel files and CSV files. Each package is discussed in detail including input sources, lookups, conditional processing, and error handling. A final package performs maintenance on the database after all loads complete successfully.
The document describes a SQL Server Integration Services project to load data from various Excel and CSV source files into tables in an AllWorks SQL database. Multiple packages were created to extract, transform, and load the data into tables for divisions, client groupings, employee timesheets, and other tables. The packages validate and clean the source data, load it into the tables, and send logs and error reports via email. A master package controls the order of package execution and a maintenance package performs daily backups, indexing, and truncation.
The document describes a SQL Server Integration Services project to load data from various Excel and CSV source files into tables in an AllWorks SQL database. Multiple packages were created to extract, transform, and load the data into different tables. Packages were scheduled to run nightly to update the database with new or changed data and perform backups and maintenance. Logging was implemented to monitor package success, failures, and record counts.
Software Effort Measurement Using Abstraction Techniquesaliraza786
This document proposes a framework for measuring software effort using abstraction techniques. It involves two phases: a project planning phase where software requirements are abstracted and estimates are recorded, and a post-project mortem phase where actual efforts are analyzed against estimates to refine abstractions. The framework aims to improve estimates over time by categorizing requirements into abstraction categories with associated estimate data.
This document provides documentation for an SSIS project for a fictitious company called AllWorks. It describes 8 packages that will load data from source files into tables in the AllWorks database to track employee, customer, timesheet, and other information. The packages will insert new records, update existing records, and write invalid records to error files. The master package contains and runs the other packages in a specified order and includes post-load database maintenance tasks.
This document describes an SSIS project for an ETL process that loads data from various source files into a SQL Server database. It involves multiple packages that populate different tables with data from Excel spreadsheets, XML files and CSV files. There is also a package that backs up the database nightly and another that reindexes and shrinks the database. A master package calls all the other packages to run the full ETL process nightly.
This document provides an overview and samples of a business intelligence project using SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS). It includes descriptions of ETL packages in SSIS to load and transform data, a cube with dimensions and calculations in SSAS, and sample MDX queries and reports. The goals are to track, analyze, and report on facets of a simulated construction company.
This document summarizes a business intelligence portfolio project for a simulated construction company. It includes details on an ETL solution built in SQL Server Integration Services to load data nightly from various sources into a SQL database. It also covers an OLAP cube with a partial snowflake structure created in SQL Server Analysis Services, including sample MDX queries and KPIs. Finally, it discusses reports deployed to SharePoint using SQL Server Reporting Services and PerformancePoint Services, including gauges, charts and dashboards. The overall goal was to build a BI solution to track, analyze and report on all aspects of the company's business using Microsoft SQL Server and SharePoint technologies.
This document provides documentation for an SSAS cube project using the ALLWORKS database. It includes:
1) Details of the data source view created using 4 fact tables and 9 dimension tables from the ALLWORKS database.
2) Descriptions of the cube structure and partitions created for the cube. Two partitions were used for each fact table to optimize query performance.
3) Screenshots and explanations of 5 KPIs created using calculations and measures in the cube to analyze business metrics for clients, jobs, and overhead categories.
The document describes an SSIS project for a fictitious construction company called AllWorks. The project involves creating 11 SSIS packages to extract data from various Excel and CSV sources and load it into SQL Server tables. The packages are organized into a master package. The packages are built, deployed, and configured to run daily via a SQL Server Agent job.
This document summarizes a business intelligence project for a construction company called AllWorks. The project involves integrating various external data sources like Excel spreadsheets, XML files, and CSV files into a SQL Server database using SQL Server Integration Services (SSIS). Dimensional models are created in SQL Server Analysis Services (SSAS) from the integrated data. SQL Server Reporting Services (SSRS) and Excel are used to build reports on the data. PerformancePoint Server (PPS) is used to create dashboards with KPIs, charts, and filters that provide insights into employee, customer, timesheet, and invoice data.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This document provides samples of work using Microsoft Business Intelligence tools, including T-SQL, SSIS, SSAS, MDX, and SSRS. It includes 3 code samples that demonstrate extracting data from a relational database using T-SQL queries, documenting an SSIS package, and building a calculation in SSAS. The document is intended to showcase the author's skills and experience with these BI tools for business executives, IT managers, and solution providers.
1. Log on to the SAP Fiori launchpad as a user with the business role Configuration Expert - Business Process Configuration.
2. Open the Configure Assistant app.
3. Select the Configure Expenses tile.
4. Define new expenses by entering an expense type and mapping the respective cost accounts.
5. Save your entries.
Transaction:
1. Log on to the SAP Fiori launchpad as a user with the business role Configuration Expert - Business Process Configuration.
2. Open transaction KK01.
3. Define new expenses by entering an expense type and mapping the respective cost accounts.
4. Save your entries.
Test
Step
Cognos Framework Manager is a metadata modeling tool.Cognos Framework Manager provides the metadata model development environment for Cognos 8.A model is a business presentation of the information from one or more data sources. The model provides a business presentation of the metadata.The model is packaged and published for report authors and query users
Live online IT Training with MaxOnlineTraining.com is an easy, effective way to maximize your skills without the travel.
Call us at For any queries, please contact:
+1 940 440 8084 / +91 953 383 7156 TODAY to join our Online IT Training course & find out how Max Online Training.com can help you embark on an exciting and lucrative IT career.
Visit www.maxonlinetraining.com
The document provides samples of work using Microsoft Business Intelligence tools including T-SQL, SQL Server Integration Services (SSIS), SQL Server Analysis Services (SSAS), and SQL Server Reporting Services (SSRS). It includes T-SQL queries, documentation of an SSIS package to load data into tables, and screenshots showing the design of SSIS control flows and data flows. The SSAS section discusses the importance of cube structure and design.
The document discusses characteristics of good software design including component independence, high cohesion, and low coupling. It defines different types of coupling (content, common, control, stamp, data) and cohesion (coincidental, logical, temporal, procedural, communicational, functional). Examples are provided to illustrate each type of coupling and cohesion. Maintaining low coupling and high cohesion results in components that are easier to understand, modify, and reuse.
The document discusses the evolution of annotation support in Spring frameworks over different versions. It summarizes key annotations introduced in Spring 2.0, 2.5, 3.0 and beyond. It also explains how annotations like @Autowired, @Qualifier, @Component, @Repository, @Service and @Controller work and provides examples of their usage. The document additionally covers concepts like stereotype annotations, configuration classes, @Bean and JSR-250 annotations like @Inject, @Named and @Resource.
11. Job Master Table Package: Job Master Table Description: For every valid client, this package checks to see if the Job is in the Job Master Table. If the job exist it will update the row. If not it will add the Job to the Job Master Table.
29. Master Package Package: Master Package Description: The package will execute other packages as one package. The packages will run in the following order. EmployeeMaster Package EmployeeRate Client Client Grouping DivisionMaster ClientGroupingXclient JobMaster TimeSheetsLoad After these packages run, the maintenance packages will run.