ETL Validator gives quick and easy way to create test cases for checking conformance with list of values. Here, we will create a test case that will identify records from Customers table that 'Marital Status' <> 'Married' or 'Single' or 'Divorced'
ETL Validator gives quick and easy way to compare Metadata changes between source & target data sources OR changes occurring within the same Database at different points of time. Here, we will create a test case with the Metadata Compare Tool.
ETL Validator Usecase - Testing Transformations or Derived fieldsDatagaps Inc
ETL Validator gives quick and easy way to create test cases for mapping and comparing transformed data between Source and Target data sources. Here, we will create a test case that will identify differences between Source and transformed data in Target table.
ETL Validator Usecase - Checking for DuplicatesDatagaps Inc
ETL Validator gives quick and easy way to create test cases for identifying Duplicates in data sources. Here, we will create a test case that will identify duplicates of First Name + Last Name.
ETL Validator Usecase - Transformation logic in input data sourceDatagaps Inc
This document discusses using ETL Validator to test derived fields in target data by using transformation logic defined in source data. It provides step-by-step instructions to create a test case validating a 'cust_level' field derived in target based on logic in source. The test case executes the queries, identifies differences between target and transformed source data, and provides results that can be exported or viewed as a report. ETL Validator allows comprehensive testing of ETL processes through automation, repeatability, and validation of data across sources and targets.
ETL Validator Usecase - Validating Measures, Counts with VarianceDatagaps Inc
ETL Validator gives quick and easy way to create test cases for comparing counts and measures of source & target data sources. A variance can be specified too. Here, we will create a Checksum test case that will compare measures and counts. The same functionality is also implemented in Component test case using 'Measure Validation'.
ETL Validator Usecase - Check for Mandatory FieldsDatagaps Inc
ETL Validator gives quick and easy way to create test cases for checking mandatory fields. Here, we will create a test case that will identify records from Customers table that have a blank value in ‘First Name’ field and null value in ‘Marital Status’ field.
ETL Validator Usecase - Data Profiling and ComparisonDatagaps Inc
ETL Validator gives quick and easy way to create test cases for profiling and comparing source & target data sources. Here, we will create a test case that will profile the data with various aggregates.
BI-Validator Usecase - Stress Test PlanDatagaps Inc
This document describes how to use the Stress Test Plan feature in BI Validator to load test a BI environment. The Stress Test Plan allows simulating a varied number of parallel users without scripting. Key steps include naming the test plan, selecting reports and dashboards to load test, configuring settings like number of users and runtimes, running the test, and viewing results in graphs and reports. Load testing with BI Validator helps determine if a BI configuration and hardware can perform well under expected loads.
ETL Validator gives quick and easy way to compare Metadata changes between source & target data sources OR changes occurring within the same Database at different points of time. Here, we will create a test case with the Metadata Compare Tool.
ETL Validator Usecase - Testing Transformations or Derived fieldsDatagaps Inc
ETL Validator gives quick and easy way to create test cases for mapping and comparing transformed data between Source and Target data sources. Here, we will create a test case that will identify differences between Source and transformed data in Target table.
ETL Validator Usecase - Checking for DuplicatesDatagaps Inc
ETL Validator gives quick and easy way to create test cases for identifying Duplicates in data sources. Here, we will create a test case that will identify duplicates of First Name + Last Name.
ETL Validator Usecase - Transformation logic in input data sourceDatagaps Inc
This document discusses using ETL Validator to test derived fields in target data by using transformation logic defined in source data. It provides step-by-step instructions to create a test case validating a 'cust_level' field derived in target based on logic in source. The test case executes the queries, identifies differences between target and transformed source data, and provides results that can be exported or viewed as a report. ETL Validator allows comprehensive testing of ETL processes through automation, repeatability, and validation of data across sources and targets.
ETL Validator Usecase - Validating Measures, Counts with VarianceDatagaps Inc
ETL Validator gives quick and easy way to create test cases for comparing counts and measures of source & target data sources. A variance can be specified too. Here, we will create a Checksum test case that will compare measures and counts. The same functionality is also implemented in Component test case using 'Measure Validation'.
ETL Validator Usecase - Check for Mandatory FieldsDatagaps Inc
ETL Validator gives quick and easy way to create test cases for checking mandatory fields. Here, we will create a test case that will identify records from Customers table that have a blank value in ‘First Name’ field and null value in ‘Marital Status’ field.
ETL Validator Usecase - Data Profiling and ComparisonDatagaps Inc
ETL Validator gives quick and easy way to create test cases for profiling and comparing source & target data sources. Here, we will create a test case that will profile the data with various aggregates.
BI-Validator Usecase - Stress Test PlanDatagaps Inc
This document describes how to use the Stress Test Plan feature in BI Validator to load test a BI environment. The Stress Test Plan allows simulating a varied number of parallel users without scripting. Key steps include naming the test plan, selecting reports and dashboards to load test, configuring settings like number of users and runtimes, running the test, and viewing results in graphs and reports. Load testing with BI Validator helps determine if a BI configuration and hardware can perform well under expected loads.
ETL Validator gives quick and easy way to create test cases for mapping and comparing data between Input and Output data sources. Here, we will create a test case that will compare the data between a Source and Target table.
ETL Validator Usecase - checking for valid field and data formatDatagaps Inc
This document describes how to use ETL Validator to check field formats through the creation of test cases and SQL queries. It provides step-by-step instructions to create a test plan, select tables and fields, add SQL queries to find records that violate field format rules, run the test, and view results. The key benefits of ETL Validator are listed as 100% test coverage, repeatability, cost reduction, faster time to market, and end to end testing.
BI Validator Usecase - Scheduler and NotificationDatagaps Inc
BI Validator is a testing automation tool that provides 100% test coverage of BI applications to reduce costs and speeds up the testing process. It allows scheduling of test plan runs, automatic reruns, and sending notifications by email. Users can schedule test plans to run on a recurring basis, view history of scheduled and run jobs, and integrate testing with continuous integration tools using the command line interface.
This document provides instructions for using various formula auditing, data validation, and complex problem solving tools in Excel Chapter 8. It describes how to trace precedents and dependents, add data validation rules, use trial and error and goal seeking to solve problems, circle invalid data, use the Solver tool to find optimal solutions, and add and remove watches in the Watch Window. The objectives are to analyze worksheets, establish data validation, propose problem solving strategies, and consider steps for finalizing workbooks.
This document discusses using the data mining tool WEKA to perform classification and regression tasks. It provides step-by-step instructions for using WEKA to build a decision tree classification model to predict customer behavior using bank data, and a linear regression model to understand factors influencing CPU performance. The classification model achieved 89% accuracy, and the regression output showed CPU performance was most dependent on CHMAX and CACHE attributes.
A valueset is a group of valid values that can be used for validation or to retrieve data. There are five types of valuesets: format only, independent, dependent, subset, and table. Table valuesets retrieve values from application tables and can be used in formulas and extracts when no database item exists. SQL queries can retrieve valueset details from the FND_FLEX_VALUE_SETS and FND_FLEX_VALUES tables and retrieve data from application tables for table valuesets.
This document provides an overview of the user interface and functionality of Actuate E-Report Designer Pro. It describes the main windows and palettes for designing reports, including the structure pane, layout pane, and palettes for components, data, controls, graphics, and pages. It also discusses tools for querying databases, browsing data, and editing queries, components, expressions, variables, and methods.
Data validation in Excel allows users to restrict the type of data entered into cells. This includes creating drop-down lists, restricting dates or numbers, and custom validation rules. The document provides steps to apply data validation to a cell by selecting the cell, going to the data validation menu, choosing the type of validation such as a list, selecting the source of the list options, and setting input and error messages. Data validation helps ensure accurate data entry by limiting users to valid options.
ETL Validator: Testing for Referential IntegrityDatagaps Inc
The document describes a referential integrity test conducted by a data testing company. The test aims to ensure that a company's data warehouse meets referential integrity requirements. It allows the user to select a foreign key, database connection, and entities or joins to test. Running the test will display results and show the underlying query used.
The document introduces Oracle Application Testing Suite's e-Load and ServerStats tools. ServerStats can monitor performance across application tiers in real-time with charts and alarms. It saves data to e-Reporter to correlate with load test results from e-Load for bottleneck analysis. Key server metrics discussed include CPU usage, memory usage, hits/requests per second, queue times, and disk and database activity. The document provides instructions for configuring new ServerStats monitors and metrics profiles to collect specific performance data from web servers, application servers, and database servers during load tests.
ETL Validator: Table to Table ComparisonDatagaps Inc
This document provides instructions for using an ETL validator tool to compare data between two tables. The process involves logging in, connecting to databases, selecting the target and source tables, building queries, executing tests, and viewing any differences in data found between the tables. The tool allows users to package tests into plans and schedule routine validation of data flows.
The document introduces several features of Oracle Application Testing Suite's auto data banking capabilities:
- Auto Map automatically creates variables and mappings for each parameter in a visual script
- Auto Bind creates a data file with headers for each variable and binds the variables to data bank fields
- Synthesize enables adding/editing data in a databank file according to a defined pattern
It also discusses potential issues with data banking like navigations breaking if HTML changes and provides solutions like re-recording or modifying the navigation.
The Experimenter allows users to set up machine learning experiments, run them across multiple machines, and analyze the results. It automates the experimental process, stores performance statistics in ARFF format, and distributes computing loads. As a demonstration, it compares the J48 decision tree method against the OneR baseline on selected datasets. Users first set up the experiment, run it by clicking Start, and then analyze the results on the Analyze tab, which indicates statistically better or worse performers compared to the baseline.
The document introduces performance testing basics and methodology using Oracle Application Testing Suite. It covers types of performance testing like load testing, stress testing, and volume testing. It emphasizes the importance of setting up realistic user scenarios and test scripts. The testing environment should replicate production and use dedicated agent machines to generate load. Performance testing helps identify bottlenecks and determine scalability.
The document describes the Knowledge Flow Interface in Weka, which allows users to visually connect components like data loaders, classifiers, and evaluators to build machine learning workflows. As a demonstration, it walks through building a workflow that loads ARFF data, performs attribute assignment, 10-fold cross-validation with a J48 decision tree classifier, and evaluates the classifier performance. Additional components are included to view the decision trees generated for each fold of cross-validation.
Tableau Dashboard - Change data sourceImprovado.io
This document provides instructions for changing the data source for linked worksheets in a Looker dashboard. It explains that all worksheets are connected to the same data source, identified by a blue checkmark. It then provides steps to connect to a new PostgreSQL data source, select the relevant table, and replace the data source for the entire dashboard with the new connection.
- Oracle Business Rules is a lightweight business rules product that is part of Oracle Fusion Middleware and can be used in SOA and BPM suites. It allows business processes to be more agile and align with changing business demands by acting as a central rules repository.
- The document demonstrates how to create a rule in Oracle Business Rules using JDeveloper to calculate student grades based on average marks and test it using various methods like a debugging function, the Enterprise Manager console, and SOAP UI web services calls.
- A decision table rule is created to return a grade based on comparing average marks to ranges in a bucketset. The rule can then be tested by passing sample data and evaluating the output.
Data Validation Option is an ETL testing tool that comes with Informatica PowerCenter. It reads table definitions from PowerCenter repositories and validates data by checking for inconsistencies. It can verify that data moved or transformed by PowerCenter workflows is complete, accurate, and unchanged. Data Validation Option defines validation rules, runs tests against those rules, and examines results to identify errors in the ETL process.
IBM InfoSphere Information Analyzer is a tool used for data profiling, data quality assessment, analysis and monitoring. It has capabilities for column analysis, primary key analysis, foreign key analysis, and cross-domain analysis. It provides data quality assessment, monitoring and rule design. Features include advanced analysis and monitoring, integrated rules analysis, and support for heterogeneous data. It helps users understand data structure, relationships and quality.
ETL Validator gives quick and easy way to create test cases for mapping and comparing data between Input and Output data sources. Here, we will create a test case that will compare the data between a Source and Target table.
ETL Validator Usecase - checking for valid field and data formatDatagaps Inc
This document describes how to use ETL Validator to check field formats through the creation of test cases and SQL queries. It provides step-by-step instructions to create a test plan, select tables and fields, add SQL queries to find records that violate field format rules, run the test, and view results. The key benefits of ETL Validator are listed as 100% test coverage, repeatability, cost reduction, faster time to market, and end to end testing.
BI Validator Usecase - Scheduler and NotificationDatagaps Inc
BI Validator is a testing automation tool that provides 100% test coverage of BI applications to reduce costs and speeds up the testing process. It allows scheduling of test plan runs, automatic reruns, and sending notifications by email. Users can schedule test plans to run on a recurring basis, view history of scheduled and run jobs, and integrate testing with continuous integration tools using the command line interface.
This document provides instructions for using various formula auditing, data validation, and complex problem solving tools in Excel Chapter 8. It describes how to trace precedents and dependents, add data validation rules, use trial and error and goal seeking to solve problems, circle invalid data, use the Solver tool to find optimal solutions, and add and remove watches in the Watch Window. The objectives are to analyze worksheets, establish data validation, propose problem solving strategies, and consider steps for finalizing workbooks.
This document discusses using the data mining tool WEKA to perform classification and regression tasks. It provides step-by-step instructions for using WEKA to build a decision tree classification model to predict customer behavior using bank data, and a linear regression model to understand factors influencing CPU performance. The classification model achieved 89% accuracy, and the regression output showed CPU performance was most dependent on CHMAX and CACHE attributes.
A valueset is a group of valid values that can be used for validation or to retrieve data. There are five types of valuesets: format only, independent, dependent, subset, and table. Table valuesets retrieve values from application tables and can be used in formulas and extracts when no database item exists. SQL queries can retrieve valueset details from the FND_FLEX_VALUE_SETS and FND_FLEX_VALUES tables and retrieve data from application tables for table valuesets.
This document provides an overview of the user interface and functionality of Actuate E-Report Designer Pro. It describes the main windows and palettes for designing reports, including the structure pane, layout pane, and palettes for components, data, controls, graphics, and pages. It also discusses tools for querying databases, browsing data, and editing queries, components, expressions, variables, and methods.
Data validation in Excel allows users to restrict the type of data entered into cells. This includes creating drop-down lists, restricting dates or numbers, and custom validation rules. The document provides steps to apply data validation to a cell by selecting the cell, going to the data validation menu, choosing the type of validation such as a list, selecting the source of the list options, and setting input and error messages. Data validation helps ensure accurate data entry by limiting users to valid options.
ETL Validator: Testing for Referential IntegrityDatagaps Inc
The document describes a referential integrity test conducted by a data testing company. The test aims to ensure that a company's data warehouse meets referential integrity requirements. It allows the user to select a foreign key, database connection, and entities or joins to test. Running the test will display results and show the underlying query used.
The document introduces Oracle Application Testing Suite's e-Load and ServerStats tools. ServerStats can monitor performance across application tiers in real-time with charts and alarms. It saves data to e-Reporter to correlate with load test results from e-Load for bottleneck analysis. Key server metrics discussed include CPU usage, memory usage, hits/requests per second, queue times, and disk and database activity. The document provides instructions for configuring new ServerStats monitors and metrics profiles to collect specific performance data from web servers, application servers, and database servers during load tests.
ETL Validator: Table to Table ComparisonDatagaps Inc
This document provides instructions for using an ETL validator tool to compare data between two tables. The process involves logging in, connecting to databases, selecting the target and source tables, building queries, executing tests, and viewing any differences in data found between the tables. The tool allows users to package tests into plans and schedule routine validation of data flows.
The document introduces several features of Oracle Application Testing Suite's auto data banking capabilities:
- Auto Map automatically creates variables and mappings for each parameter in a visual script
- Auto Bind creates a data file with headers for each variable and binds the variables to data bank fields
- Synthesize enables adding/editing data in a databank file according to a defined pattern
It also discusses potential issues with data banking like navigations breaking if HTML changes and provides solutions like re-recording or modifying the navigation.
The Experimenter allows users to set up machine learning experiments, run them across multiple machines, and analyze the results. It automates the experimental process, stores performance statistics in ARFF format, and distributes computing loads. As a demonstration, it compares the J48 decision tree method against the OneR baseline on selected datasets. Users first set up the experiment, run it by clicking Start, and then analyze the results on the Analyze tab, which indicates statistically better or worse performers compared to the baseline.
The document introduces performance testing basics and methodology using Oracle Application Testing Suite. It covers types of performance testing like load testing, stress testing, and volume testing. It emphasizes the importance of setting up realistic user scenarios and test scripts. The testing environment should replicate production and use dedicated agent machines to generate load. Performance testing helps identify bottlenecks and determine scalability.
The document describes the Knowledge Flow Interface in Weka, which allows users to visually connect components like data loaders, classifiers, and evaluators to build machine learning workflows. As a demonstration, it walks through building a workflow that loads ARFF data, performs attribute assignment, 10-fold cross-validation with a J48 decision tree classifier, and evaluates the classifier performance. Additional components are included to view the decision trees generated for each fold of cross-validation.
Tableau Dashboard - Change data sourceImprovado.io
This document provides instructions for changing the data source for linked worksheets in a Looker dashboard. It explains that all worksheets are connected to the same data source, identified by a blue checkmark. It then provides steps to connect to a new PostgreSQL data source, select the relevant table, and replace the data source for the entire dashboard with the new connection.
- Oracle Business Rules is a lightweight business rules product that is part of Oracle Fusion Middleware and can be used in SOA and BPM suites. It allows business processes to be more agile and align with changing business demands by acting as a central rules repository.
- The document demonstrates how to create a rule in Oracle Business Rules using JDeveloper to calculate student grades based on average marks and test it using various methods like a debugging function, the Enterprise Manager console, and SOAP UI web services calls.
- A decision table rule is created to return a grade based on comparing average marks to ranges in a bucketset. The rule can then be tested by passing sample data and evaluating the output.
Data Validation Option is an ETL testing tool that comes with Informatica PowerCenter. It reads table definitions from PowerCenter repositories and validates data by checking for inconsistencies. It can verify that data moved or transformed by PowerCenter workflows is complete, accurate, and unchanged. Data Validation Option defines validation rules, runs tests against those rules, and examines results to identify errors in the ETL process.
IBM InfoSphere Information Analyzer is a tool used for data profiling, data quality assessment, analysis and monitoring. It has capabilities for column analysis, primary key analysis, foreign key analysis, and cross-domain analysis. It provides data quality assessment, monitoring and rule design. Features include advanced analysis and monitoring, integrated rules analysis, and support for heterogeneous data. It helps users understand data structure, relationships and quality.
About Basics of IBM Rational Performance Tester Tool.
It describes what is RPT? how to do a simple script in RPT.
And how to execute it?.
Its a brief idea about RPT
Data-driven testing (DDT) is a common term in the computer software testing area. Its define a testing has been done using a table of conditions directly as test inputs and verifiable outputs. Also, in the data-driven testing process, the test environment settings and control are not hard-coded.
This testing process is quite popular and normally applied right after the record and playback mode. In other words, once a set of testing object under a test case is captured, testers will need to add more input values from the external data files to ensure the application can handle the most number of scenarios in the future.
Source: https://www.katalon.com/resources-center/tutorials/data-driven-testing/
This document discusses how to build and leverage a data model in ETL Validator for query construction, testing referential integrity, and identifying noise in a data warehouse. It explains how to select tables and define join conditions between tables to create an entity data model that can then be reused over time for these purposes. The data model can be used in the query builder to build constrained queries and in a referential integrity test plan to automatically identify records without valid parents.
The document discusses test case generation for verifying and testing database functionalities. It describes test case generation as the process of writing SQL test cases and designing them based on the functionalities of an application. The purpose is to check the output against expected results. Multiple techniques for generating test cases are discussed, including goal-oriented, random, specification-based, and source-code-based approaches. Best practices for writing quality test cases are also provided.
Data validation in Microsoft Excel allows users to restrict the type of data entered into cells. This includes creating drop down lists, restricting dates, numbers, text, and custom validation rules. When applied to a cell, data validation ensures only the defined options can be selected rather than invalid or confusing entries. To set up data validation, select the cell and choose data validation from the Data tab. Options can be set as a list by selecting the predefined list of valid entries. This helps guide users to make accurate selections.
Automate data warehouse etl testing and migration testing the agile wayTorana, Inc.
Data Warehouse, ETL & Migration projects are exposed to huge financial risks due to lack of QA automation. At iCEDQ, we suggest the agile rules based testing approach for all data integration projects.
Performance testing for web applications – techniques, metrics and profilingTestCampRO
The document discusses performance testing techniques for web applications. It covers three main areas: techniques, metrics analysis, and profiling. For techniques, it describes how to stage the testing environment, build test assets by analyzing client data and building scenarios, and running manual and automated tests. For metrics analysis, it discusses automating data collection and processing and analyzing system, database, and network issues. Finally, for profiling it explains analyzing load distribution, database profiling through workload summaries and SQL queries, and application profiling through method-level timing. The goal is to publish results from multiple test iterations to identify performance bottlenecks.
Three principles of Apex testing are provided:
1. Use assertions to validate expected behavior and outputs. Every test method should include at least one assertion.
2. Use Test.startTest() and Test.stopTest() to isolate code from setup and ensure governor limits are hit.
3. Write both positive and negative tests. Positive tests validate expected behavior, while negative tests validate exceptions are properly handled for invalid inputs.
As an Apex developer, you can harness testing as a way of improving software quality. Every piece of code you write needs coverage in order to push it into production. Join us as we cover the ways you can set up your org for successful and painless test development. You'll learn common testing patterns, how to use test data factories and mocks, and learn best practices for various types of tests, including callout integration.
This document discusses test effectiveness and techniques to improve it. It defines test effectiveness as how well a system satisfies customer requirements and specifications. Test effectiveness can be enhanced through testing principles like early testing to prevent defects, and test design techniques like structure-based/white-box testing of internal logic and flows. Specification-based/black-box testing focuses on external behavior and validates techniques like equivalence partitioning and boundary value analysis.
This is a presentation I did back in 2006 about using Selenium and Grinder. Looking at it today (2011) it doesn't focus enough on the Selenium Grinder intersection....
This document provides an overview of software testing and its history. It discusses how software testing has matured and become a professional discipline integral to the software development lifecycle. It describes different testing types like unit, integration, system, and user acceptance testing. It also discusses test planning, test case creation, and use of test management and defect tracking tools like TestRail and JIRA.
Kevin Poorman presented 10 principles of Apex testing. The principles included using asserts to validate expected behavior, using StartTest and StopTest to reset limits, writing both positive and negative tests, testing with different user profiles and permission sets, generating own test data rather than using real data, using helper libraries to facilitate testing, mocking external services to enable unit testing, writing code in a testable way, and leveraging continuous integration to catch failing tests early. Continuous integration was said to help with multiple developers working in parallel and keeping aware of test coverage.
This document reviews how to debug a load testing script in Oracle OpenScript that is failing during playback. It provides steps to identify the root cause, including comparing request headers between recording and playback, and applying manual correlation to resolve issues with dynamic session IDs. The sample script provided is failing at step 3 and step 4 due to hardcoded session IDs. The steps show how to correlate the "javax.faces.ViewState" parameter to fix the failure at step 3, and that the same correlation technique needs to be applied to step 4 to fully resolve the script errors.
This document discusses different types of database testing including:
1) Checking the database connection is valid by executing test queries and handling exceptions if the connection fails.
2) Validating data types by checking that received values match the expected number and type of values.
3) Performing input verification by checking input field lengths and formatting.
4) Ensuring data integrity by verifying related entities after data is inserted, updated or deleted.
5) Testing backups by restoring data and verifying accuracy by comparing table structure and rows to the source database.
The document discusses various black box testing strategies including equivalence partitioning, boundary value analysis, decision table testing, state transition testing, and error guessing. Black box testing involves testing software modules independently without knowledge of internal implementation. It aims to test functional validity and interface errors by examining expected outputs for given inputs based on requirements.
Similar to ETL Validator Usecase - checking for LoV conformance (20)
The document discusses using ETL Validator's Metadata Compare Tool to identify differences in database metadata between two snapshots of a table. It demonstrates taking snapshots of a sample table's metadata before and after changes, and using the Metadata Compare Tool to display the differences in column names and data lengths between the two snapshots. The tool can also identify new or unmatched tables between two environments or points in time.
Web Service Connection - using Login OperationDatagaps Inc
The document discusses connecting to a SOAP web service data source using ETL Validator. It involves the following steps:
1. Selecting a SOAP web service data source and providing the WSDL file.
2. Creating an authentication using a login operation, specifying session ID and password parameters and testing the login request.
3. Saving the authentication details to connect to the web service and extract data using SOAP requests.
This document provides steps to create a connection to a Tableau server in BI Validator for the purpose of automating tests. It outlines getting the necessary Tableau server details, downloading and installing TabCmd, configuring the TabCmd location in BI Validator settings, adding a new BI connection in BI Validator by selecting Tableau and entering the signin URL, Restful URL, and testing and saving the connection.
Subject Area Testing Automation in OBI EnvironmentDatagaps Inc
BI Validator is a business intelligence testing automation platform that allows business analysts and QA teams to ensure dimensions and facts are properly designed in subject areas to prevent runtime errors. Users can select subject areas of interest, types of tests, and run automated tests that check for exceptions, marking failed tests. This significantly cuts down the manual testing time needed to check subject areas in a BI environment compared to doing so manually.
Importing Queries using Mass Import ToolDatagaps Inc
ETL Validator is a data testing automation platform that allows users to import source and target queries from an existing CSV file to quickly get started testing in ETLV. The CSV file must be in the proper format, with fields in a specific order, including parameters like select, connections, and file. Once imported, ETL Validator will automatically generate a "Query Test Case" for each row in the CSV to test the queries.
Query parameterization in ETL ValidatorDatagaps Inc
ETL Validator is a data testing automation platform that leverages reusable query parameters. It allows users to create parameters that can be used in building queries and modifying parameters values without having to edit the actual queries. Parameters can be created, reused across multiple queries, and modified either in the parameter tab or at the test plan level for flexibility. This streamlines data testing by avoiding repetitive query edits and enabling dynamic testing through parameterization.
Component Test Case Wizard in ETL ValidatorDatagaps Inc
The document describes how to use the Component Test Case Wizard in ETL Validator to identify differences between tables by leveraging the integration between Informatica and ETL Validator. The process involves selecting the source and target databases and tables, choosing whether to use queries from the Informatica log file or enter them manually, mapping source to target columns, running the test, and viewing any differences between the source and target tables.
This data testing company provides a data profile test plan tool that allows QA engineers to define rules for data entities to ensure data adheres to those rules. The presentation explains how to design an entity model in their ETL Validator tool so the test plans can be reused over time. Users can select an entity, choose attributes to define rules for, run the test, and view results that focus on data that failed to meet the defined rules.
This document describes a referential integrity test tool that allows QA engineers to test that referential integrity requirements are met in a data warehouse. The tool allows the user to select a foreign key, database connection, and entities or joins to test. It runs the test and displays results, and allows the user to view the underlying query. The goal is to ensure referential integrity is maintained between different database tables.
ETL Validator: Metadata Comparison Test PlanDatagaps Inc
The document outlines a test plan for comparing metadata across multiple environments to ensure the structure is exactly the same. The plan involves selecting the data entities to use as a baseline, capturing snapshots of the metadata from each connection to compare, picking the connections and specific metadata to analyze, and clicking compare to display any differences found between the environments.
Oracle Business Intelligence, ETL Testing, Flat File Testing, Data Validation, Table to Table Comparison, Data Migration, Source to Target Comparison, CSV Testing,
BI Validaor: Regression Testing of Oracle Business Intelligence DashboardsDatagaps Inc
datagaps: Regression Testing Business Intelligence Dashboards is a major challenge. These slides demonstrate how to do that using BI Validator from datagaps.
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
4. Usecase :
LoV Conformance Check
Create a test case:
Identify records from
Customers table which
have ‘Marital Status’
values other than
‘Married’ or ‘Single’ or
‘Divorced’
Start with creating a
new Data Rules Test
Plan
5. Usecase:
Name the test plan.
Select the Database
Connection.
Navigate to the next
screen.
LoV Conformance Check
6. Usecase:
Expand the schema; in
this example, ‘public’.
Click on ‘Add Custom
Rule’.
Rule Builder window
opens. Click on ‘Add
New Attribute Rule’
LoV Conformance Check
8. Usecase:
Enter value ‘Married’.
So a rule is created where
cust_marital_status <>
‘Married
Create 2 more rules -
cust_marital_status <>
‘Single’
cust_marital_status <>
‘Divorced’
LoV Conformance Check
9. Usecase:
Click on ‘Build Query’
Data grid below shows
records which didn’t satisfy
the rules we set up in ‘Query
Conditions’.
The ‘SQL’ pane shows the sql
that was generated as per the
rules we specified.
Name the query.
Click on ‘Save Query’.
LoV Conformance Check
10. Usecase:
The new rule ‘Lov_Check’ is
displayed in Data Rules
window.
Navigate to Next Screen
where these test cases are
run.
Before clicking on ‘Run’, click
on settings and unselect all
the tables except
‘Customers’.
‘Save’ the selection.
LoV Conformance Check
11. Usecase:
Click on ‘Run’.
All the Rules set up for
‘Customers’ table will be
executed.
‘FAILED’ indicates that there are
records that didn’t satisfy the
rule.
The grid shows results from first
test case in the list above.
Click on subsequent rows to see
those results.
Click on ‘View Report in Browser’
to see same results in web
layout.
LoV Conformance Check
12. Usecase:
Same info is
displayed in web
layout.
The link can be
shared with others.
Click on the upward
arrow to see the
records
LoV Conformance Check
13. More with ETL Validator….
• Validating Field and Data Format
• Data counts validation with allowed variance
• Check Data Quality using Data Rules Test Plan
• Advanced ETL Testing using a Component Test Case
• Avoiding inline views on your queries in ETL Validator
• Checking for Mandatory Fields
www.datagaps.com