Slides are created to demonstrate about ETL Testing, some one who want to start and learn ETL Tesing can make use of this ppt. It includes contents related all ETL Testing schema
The document discusses ETL (extract, transform, load) which is a process used to clean and prepare data from various sources for analysis in a data warehouse. It describes how ETL extracts data from different source systems, transforms it into a uniform format, and loads it into a data warehouse. It also provides examples of ETL tools, the purpose of ETL testing including testing for data accuracy and integrity, and SQL queries commonly used for ETL testing.
The document discusses testing processes for data warehouses, including requirements testing, unit testing, integration testing, and user acceptance testing. It describes validating that requirements are complete and testable. Unit testing checks ETL procedures and mappings. Integration testing verifies initial and incremental loads as well as error handling. Integration testing scenarios include count validation, source isolation, and data quality checks. User acceptance testing tests full functionality for production use.
The document discusses tips for designing test data before executing test cases. It recommends creating fresh test data specific to each test case rather than relying on outdated standard data. It also suggests keeping personal copies of test data to avoid corruption when multiple testers access shared data. The document provides examples of how to prepare large data sets needed for performance testing.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
The document discusses various concepts related to data warehousing and ETL processes. It provides definitions for key terms like critical success factors, data cubes, data cleaning, data mining stages, data purging, BUS schema, non-additive facts, conformed dimensions, slowly changing dimensions, cube grouping, and more. It also describes different types of ETL testing including constraint testing, source to target count testing, field to field testing, duplicate check testing, and error handling testing. Finally, it discusses the differences between an ODS and a staging area, with an ODS storing recent cleaned data and a staging area serving as a temporary work area during the ETL process.
Testing data warehouse applications by Kirti BhushanKirti Bhushan
This document outlines a data warehouse testing strategy. It begins with an introduction that defines a data warehouse and discusses the need for data warehouse testing and challenges it presents. It then describes the testing model, including phases for project definition, test design, development, execution and acceptance. Next, it covers the goals of data warehouse testing like data completeness, transformation, quality and various types of non-functional testing. Finally, it discusses roles, artifacts, tools and references related to data warehouse testing.
Etl And Data Test Guidelines For Large ApplicationsWayne Yaddow
This document provides guidelines for testing the quality of data, ETL processes, and SQL queries during the development of a data warehouse. It outlines steps to verify data extracted from source systems, transformed and loaded into staging tables, cleansed and consolidated in staging, and finally transformed and loaded into the data warehouse operational tables and data marts. The guidelines describe analyzing source data quality, verifying ETL processes, matching consolidated data, and transforming data according to business rules.
The document discusses ETL (extract, transform, load) which is a process used to clean and prepare data from various sources for analysis in a data warehouse. It describes how ETL extracts data from different source systems, transforms it into a uniform format, and loads it into a data warehouse. It also provides examples of ETL tools, the purpose of ETL testing including testing for data accuracy and integrity, and SQL queries commonly used for ETL testing.
The document discusses testing processes for data warehouses, including requirements testing, unit testing, integration testing, and user acceptance testing. It describes validating that requirements are complete and testable. Unit testing checks ETL procedures and mappings. Integration testing verifies initial and incremental loads as well as error handling. Integration testing scenarios include count validation, source isolation, and data quality checks. User acceptance testing tests full functionality for production use.
The document discusses tips for designing test data before executing test cases. It recommends creating fresh test data specific to each test case rather than relying on outdated standard data. It also suggests keeping personal copies of test data to avoid corruption when multiple testers access shared data. The document provides examples of how to prepare large data sets needed for performance testing.
What is a Data Warehouse and How Do I Test It?RTTS
ETL Testing: A primer for Testers on Data Warehouses, ETL, Business Intelligence and how to test them.
Are you hearing and reading about Big Data, Enterprise Data Warehouses (EDW), the ETL Process and Business Intelligence (BI)? The software markets for EDW and BI are quickly approaching $22 billion, according to Gartner, and Big Data is growing at an exponential pace.
Are you being tasked to test these environments or would you like to learn about them and be prepared for when you are asked to test them?
RTTS, the Software Quality Experts, provided this groundbreaking webinar, based upon our many years of experience in providing software quality solutions for more than 400 companies.
You will learn the answer to the following questions:
• What is Big Data and what does it mean to me?
• What are the business reasons for a building a Data Warehouse and for using Business Intelligence software?
• How do Data Warehouses, Business Intelligence tools and ETL work from a technical perspective?
• Who are the primary players in this software space?
• How do I test these environments?
• What tools should I use?
This slide deck is geared towards:
QA Testers
Data Architects
Business Analysts
ETL Developers
Operations Teams
Project Managers
...and anyone else who is (a) new to the EDW space, (b) wants to be educated in the business and technical sides and (c) wants to understand how to test them.
The document discusses various concepts related to data warehousing and ETL processes. It provides definitions for key terms like critical success factors, data cubes, data cleaning, data mining stages, data purging, BUS schema, non-additive facts, conformed dimensions, slowly changing dimensions, cube grouping, and more. It also describes different types of ETL testing including constraint testing, source to target count testing, field to field testing, duplicate check testing, and error handling testing. Finally, it discusses the differences between an ODS and a staging area, with an ODS storing recent cleaned data and a staging area serving as a temporary work area during the ETL process.
Testing data warehouse applications by Kirti BhushanKirti Bhushan
This document outlines a data warehouse testing strategy. It begins with an introduction that defines a data warehouse and discusses the need for data warehouse testing and challenges it presents. It then describes the testing model, including phases for project definition, test design, development, execution and acceptance. Next, it covers the goals of data warehouse testing like data completeness, transformation, quality and various types of non-functional testing. Finally, it discusses roles, artifacts, tools and references related to data warehouse testing.
Etl And Data Test Guidelines For Large ApplicationsWayne Yaddow
This document provides guidelines for testing the quality of data, ETL processes, and SQL queries during the development of a data warehouse. It outlines steps to verify data extracted from source systems, transformed and loaded into staging tables, cleansed and consolidated in staging, and finally transformed and loaded into the data warehouse operational tables and data marts. The guidelines describe analyzing source data quality, verifying ETL processes, matching consolidated data, and transforming data according to business rules.
ETL Testing Interview Questions and AnswersH2Kinfosys
This document discusses interview questions related to ETL testing for business intelligence projects. It begins with questions about challenges in BI testing, what BI and data warehousing are, and key concepts like the data flow in a data warehouse. It then provides examples of different types of testing done on a data warehouse, including attribute checks, duplicate checks, original key checks, and reconciliation checks using sample SQL queries. Finally, it discusses tools that a QA team may use for ETL testing.
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
Creating a Data validation and Testing StrategyRTTS
This document discusses strategies for creating an effective data validation and testing process. It provides examples of common data issues found during testing such as missing data, wrong translations, and duplicate records. Solutions discussed include identifying important test points, reviewing data mappings, developing automated and manual testing approaches, and assessing how much data needs validation. The presentation also includes a case study of a company that improved its process by centralizing documentation, improving communication, and automating more of its testing.
ETL (Extract, Transform, Load) is a process that allows companies to consolidate data from multiple sources into a single target data store, such as a data warehouse. It involves extracting data from heterogeneous sources, transforming it to fit operational needs, and loading it into the target data store. ETL tools automate this process, allowing companies to access and analyze consolidated data for critical business decisions. Popular ETL tools include IBM Infosphere Datastage, Informatica, and Oracle Warehouse Builder.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
This document discusses different types of slowly changing dimensions in a data warehouse: Type 1, Type 2, and Type 3. Type 1 dimensions involve corrections to existing data. Type 2 dimensions track true changes over time by adding new rows. Type 3 dimensions store both old and new attribute values in the same row. The document also covers junk dimensions, large dimensions, and rapidly changing dimensions.
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.
The document is a 20 page comparison of ETL tools. It includes an introduction, descriptions of 4 ETL tools (Pentaho Kettle, Talend, Informatica PowerCenter, Inaplex Inaport), and a section comparing the tools on various criteria such as cost, ease of use, speed and data quality. The comparison chart suggests Informatica PowerCenter is the fastest and most full-featured tool while open source options like Pentaho Kettle and Talend offer lower costs but require more manual configuration.
The document provides an overview of business intelligence, data warehousing, and ETL concepts. It defines business intelligence as using technologies to analyze data and support decision making. A data warehouse stores historical data from transaction systems and supports querying and analysis for insights. ETL is the process of extracting data from sources, transforming it, and loading it into the data warehouse for analysis. The document discusses components of BI systems like the data warehouse, data marts, and dimensional modeling and provides examples of how these concepts work together.
Data Warehouse Testing: It’s All about the PlanningTechWell
Today’s data warehouses are complex and contain heterogeneous data from many different sources. Testing these warehouses is complex, requiring exceptional human and technical resources. So how do you achieve the desired testing success? Geoff Horne believes that it is through test planning that includes technical artifacts such as data models, business rules, data mapping documents, and data warehouse loading design logic. Wayne shares planning checklists, a test plan outline, concepts for data profiling, and methods for data verification. He demonstrates how to effectively create a test strategy to discover empty fields, missing records, truncated data, duplicate records, and incorrectly applied business rules—all of which can dramatically impact the usefulness of the data warehouse. Learn common pitfalls, which can cost your business hundreds of thousands of dollars or more, when test planning shortcuts are taken. If you work in an environment that often performs data warehouse testing without proper planning and technical skills, this session is for you.
Data warehousing testing strategies cognosSandeep Mehta
The document describes a testing methodology for a data warehouse project. It will involve three phases: unit testing of ETL processes and validating data matches between source systems and the data warehouse; a conference room pilot where users can validate reports and test performance; and system integration testing where users test analytical reporting tools to answer business questions across multiple data sources.
ETL is a process that involves extracting data from multiple sources, transforming it to fit operational needs, and loading it into a data warehouse. It provides a method of moving data from various source systems into a data warehouse to enable complex business analysis. The ETL process consists of extraction, which gathers and cleanses raw data from source systems, transform, which prepares the data for the data warehouse through steps like validation and standardization, and load, which stores the transformed data in the data warehouse. ETL tools automate and simplify the ETL process and provide advantages like faster development, metadata management, and performance optimization.
1. A successful data migration requires meeting quality criteria such as agreed stakeholder impact, reliable execution, a controlled process, and being auditable.
2. Data migration is represented as a workstream in a transition program including activities such as data analysis, data quality improvement, and data mapping.
3. Data migration is typically done through a series of incremental iterations consisting of standard activities such as data analysis, data mapping, and migration testing.
An Oracle database instance consists of background processes that control one or more databases. A schema is a set of database objects owned by a user that apply to a specific application. Tables store data in rows and columns, and indexes and constraints help maintain data integrity and improve query performance. Database administrators perform tasks like installing and upgrading databases, managing storage, security, backups and high availability.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
This document provides an overview of current ETL techniques from a big data perspective. It discusses the evolution of ETL from traditional batch-based techniques to near real-time and real-time approaches. However, existing real-time ETL approaches are inadequate to address the volume, velocity, and variety characteristics of data streams. The document also surveys available ETL tools and techniques for handling data streams, and concludes that the ETL process needs to be redefined to better address issues in processing dynamic data streams.
This document provides an overview and introduction to Oracle SQL basics. It covers topics such as installing Oracle software like the database, Java SDK, and SQL Developer tool. It then discusses database concepts like what a database and table are. It also covers database fundamentals including SQL queries, functions, joins, constraints, views and other database objects. The document provides examples and explanations of SQL statements and database components.
The document provides an overview of the Extract, Transform, Load (ETL) process. It defines ETL as extracting data from databases, transforming the format or cleaning the data, and loading it into a data warehouse or data mart. It contrasts ETL tools, which move data between databases, from business intelligence (BI) tools, which allow querying and visualization of data. Key aspects of ETL covered include source and target mapping, data validation and quality checks, and testing approaches. Challenges and best practices for ETL are also discussed.
- Oracle Database 11g introduced new features to simplify database administration and automate tasks. It provided up to 44% less administration time and 47% fewer steps compared to prior versions.
- British Telecommunications consolidated thousands of databases onto an Oracle Database 11g private cloud, reducing management costs by 20% and improving business agility. The consolidation standardizes deployment and reduced application deployment time from weeks to minutes.
- Dena Bank deployed Oracle databases and storage to improve ATM and core banking application performance. This enhanced customer service by increasing ATM transaction speeds by 80% and reducing declined transactions from 10% to less than 1%.
ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
ETL Testing Interview Questions and AnswersH2Kinfosys
This document discusses interview questions related to ETL testing for business intelligence projects. It begins with questions about challenges in BI testing, what BI and data warehousing are, and key concepts like the data flow in a data warehouse. It then provides examples of different types of testing done on a data warehouse, including attribute checks, duplicate checks, original key checks, and reconciliation checks using sample SQL queries. Finally, it discusses tools that a QA team may use for ETL testing.
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
Creating a Data validation and Testing StrategyRTTS
This document discusses strategies for creating an effective data validation and testing process. It provides examples of common data issues found during testing such as missing data, wrong translations, and duplicate records. Solutions discussed include identifying important test points, reviewing data mappings, developing automated and manual testing approaches, and assessing how much data needs validation. The presentation also includes a case study of a company that improved its process by centralizing documentation, improving communication, and automating more of its testing.
ETL (Extract, Transform, Load) is a process that allows companies to consolidate data from multiple sources into a single target data store, such as a data warehouse. It involves extracting data from heterogeneous sources, transforming it to fit operational needs, and loading it into the target data store. ETL tools automate this process, allowing companies to access and analyze consolidated data for critical business decisions. Popular ETL tools include IBM Infosphere Datastage, Informatica, and Oracle Warehouse Builder.
The document provides an overview of key concepts in data warehousing and business intelligence, including:
1) It defines data warehousing concepts such as the characteristics of a data warehouse (subject-oriented, integrated, time-variant, non-volatile), grain/granularity, and the differences between OLTP and data warehouse systems.
2) It discusses the evolution of business intelligence and key components of a data warehouse such as the source systems, staging area, presentation area, and access tools.
3) It covers dimensional modeling concepts like star schemas, snowflake schemas, and slowly and rapidly changing dimensions.
This document discusses different types of slowly changing dimensions in a data warehouse: Type 1, Type 2, and Type 3. Type 1 dimensions involve corrections to existing data. Type 2 dimensions track true changes over time by adding new rows. Type 3 dimensions store both old and new attribute values in the same row. The document also covers junk dimensions, large dimensions, and rapidly changing dimensions.
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.
The document is a 20 page comparison of ETL tools. It includes an introduction, descriptions of 4 ETL tools (Pentaho Kettle, Talend, Informatica PowerCenter, Inaplex Inaport), and a section comparing the tools on various criteria such as cost, ease of use, speed and data quality. The comparison chart suggests Informatica PowerCenter is the fastest and most full-featured tool while open source options like Pentaho Kettle and Talend offer lower costs but require more manual configuration.
The document provides an overview of business intelligence, data warehousing, and ETL concepts. It defines business intelligence as using technologies to analyze data and support decision making. A data warehouse stores historical data from transaction systems and supports querying and analysis for insights. ETL is the process of extracting data from sources, transforming it, and loading it into the data warehouse for analysis. The document discusses components of BI systems like the data warehouse, data marts, and dimensional modeling and provides examples of how these concepts work together.
Data Warehouse Testing: It’s All about the PlanningTechWell
Today’s data warehouses are complex and contain heterogeneous data from many different sources. Testing these warehouses is complex, requiring exceptional human and technical resources. So how do you achieve the desired testing success? Geoff Horne believes that it is through test planning that includes technical artifacts such as data models, business rules, data mapping documents, and data warehouse loading design logic. Wayne shares planning checklists, a test plan outline, concepts for data profiling, and methods for data verification. He demonstrates how to effectively create a test strategy to discover empty fields, missing records, truncated data, duplicate records, and incorrectly applied business rules—all of which can dramatically impact the usefulness of the data warehouse. Learn common pitfalls, which can cost your business hundreds of thousands of dollars or more, when test planning shortcuts are taken. If you work in an environment that often performs data warehouse testing without proper planning and technical skills, this session is for you.
Data warehousing testing strategies cognosSandeep Mehta
The document describes a testing methodology for a data warehouse project. It will involve three phases: unit testing of ETL processes and validating data matches between source systems and the data warehouse; a conference room pilot where users can validate reports and test performance; and system integration testing where users test analytical reporting tools to answer business questions across multiple data sources.
ETL is a process that involves extracting data from multiple sources, transforming it to fit operational needs, and loading it into a data warehouse. It provides a method of moving data from various source systems into a data warehouse to enable complex business analysis. The ETL process consists of extraction, which gathers and cleanses raw data from source systems, transform, which prepares the data for the data warehouse through steps like validation and standardization, and load, which stores the transformed data in the data warehouse. ETL tools automate and simplify the ETL process and provide advantages like faster development, metadata management, and performance optimization.
1. A successful data migration requires meeting quality criteria such as agreed stakeholder impact, reliable execution, a controlled process, and being auditable.
2. Data migration is represented as a workstream in a transition program including activities such as data analysis, data quality improvement, and data mapping.
3. Data migration is typically done through a series of incremental iterations consisting of standard activities such as data analysis, data mapping, and migration testing.
An Oracle database instance consists of background processes that control one or more databases. A schema is a set of database objects owned by a user that apply to a specific application. Tables store data in rows and columns, and indexes and constraints help maintain data integrity and improve query performance. Database administrators perform tasks like installing and upgrading databases, managing storage, security, backups and high availability.
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
Modern data analysis is moving beyond the Data Warehouse to the Data Lake where analysts are able to take advantage of emerging technologies to manage complex analytics on large data volumes and diverse data types. Yet, for some business problems, a Data Warehouse may still be the right solution.
If you’re on the fence, join this webinar as we compare and contrast Data Lakes and Data Warehouses, identifying situations where one approach may be better than the other and highlighting how the two can work together.
Get tips, takeaways and best practices about:
- The benefits and problems of a Data Warehouse
- How a Data Lake can solve the problems of a Data Warehouse
- Data Lake Architecture
- How Data Warehouses and Data Lakes can work together
This document provides an overview of current ETL techniques from a big data perspective. It discusses the evolution of ETL from traditional batch-based techniques to near real-time and real-time approaches. However, existing real-time ETL approaches are inadequate to address the volume, velocity, and variety characteristics of data streams. The document also surveys available ETL tools and techniques for handling data streams, and concludes that the ETL process needs to be redefined to better address issues in processing dynamic data streams.
This document provides an overview and introduction to Oracle SQL basics. It covers topics such as installing Oracle software like the database, Java SDK, and SQL Developer tool. It then discusses database concepts like what a database and table are. It also covers database fundamentals including SQL queries, functions, joins, constraints, views and other database objects. The document provides examples and explanations of SQL statements and database components.
The document provides an overview of the Extract, Transform, Load (ETL) process. It defines ETL as extracting data from databases, transforming the format or cleaning the data, and loading it into a data warehouse or data mart. It contrasts ETL tools, which move data between databases, from business intelligence (BI) tools, which allow querying and visualization of data. Key aspects of ETL covered include source and target mapping, data validation and quality checks, and testing approaches. Challenges and best practices for ETL are also discussed.
- Oracle Database 11g introduced new features to simplify database administration and automate tasks. It provided up to 44% less administration time and 47% fewer steps compared to prior versions.
- British Telecommunications consolidated thousands of databases onto an Oracle Database 11g private cloud, reducing management costs by 20% and improving business agility. The consolidation standardizes deployment and reduced application deployment time from weeks to minutes.
- Dena Bank deployed Oracle databases and storage to improve ATM and core banking application performance. This enhanced customer service by increasing ATM transaction speeds by 80% and reducing declined transactions from 10% to less than 1%.
ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake.
The document discusses various techniques for tuning data warehouse performance. It recommends tuning the data loading process to speed up queries and optimize hardware usage. Specific strategies mentioned include loading data in batches during off-peak hours, using parallel loading and direct path inserts to bulk load data faster, preallocating tablespace, and temporarily disabling indexes and constraints. The document also provides examples of using SQL*Loader and parallel direct path loads to efficiently bulk load data from files into tables.
Part 3 - Data Warehousing Lecture at BW Cooperative State University (DHBW)Andreas Buckenhofer
Part 3(4)
The slides contain a DWH lecture given for students in 5th semester. Content:
- Introduction DWH and Business Intelligence
- DWH architecture
- DWH project phases
- Logical DWH Data Model
- Multidimensional data modeling
- Data import strategies / data integration / ETL
- Frontend: Reporting and anaylsis, information design
- OLAP
TPC-DI - The First Industry Benchmark for Data IntegrationTilmann Rabl
This presentation was held by Meikel Poess on September 3, 2014 at VLDB 2014 in Hangzhou, China.
Full paper and additional information available at:
http://msrg.org/papers/VLDB2014TPCDI
Abstract:
Historically, the process of synchronizing a decision support system with data from operational systems has been referred to as Extract, Transform, Load (ETL) and the tools supporting such process have been referred to as ETL tools. Recently, ETL was replaced by the more comprehensive acronym, data integration (DI). DI describes the process of extracting and combining data from a variety of data source formats, transforming that data into a unified data model representation and loading it into a data store. This is done in the context of a variety of scenarios, such as data acquisition for business intelligence, analytics and data warehousing, but also synchronization of data between operational applications, data migrations and conversions, master data management, enterprise data sharing and delivery of data services in a service-oriented architecture context, amongst others. With these scenarios relying on up-to-date information it is critical to implement a highly performing, scalable and easy to maintain data integration system. This is especially important as the complexity, variety and volume of data is constantly increasing and performance of data integration systems is becoming very critical. Despite the significance of having a highly performing DI system, there has been no industry standard for measuring and comparing their performance. The TPC, acknowledging this void, has released TPC-DI, an innovative benchmark for data integration. This paper motivates the reasons behind its development, describes its main characteristics including workload, run rules, metric, and explains key decisions.
Testing Strategies for Data Lake Hosted on HadoopCitiusTech
This document discusses testing strategies for structured data in a data lake hosted on Hadoop. It covers validating the schema, data masking, data reconciliation during loads, testing the extract-load-transform framework, handling on-premise versus cloud environments, data quality checks, partitioning and compacting the data for storage. Challenges include special characters in the data, varying data formats, masking logic failures, and limitations of cloud data types and sizes.
Airbyte @ Airflow Summit - The new modern data stackMichel Tricot
The document introduces the modern data stack of Airbyte, Airflow, and dbt. It discusses how ELT addresses issues with traditional ETL processes by separating extraction, loading, and transformation. Extraction and loading involve general-purpose routines to pull and push raw data, while transformation uses business logic specific to the organization. The stack is presented as an open solution that allows composing with best of breed tools for each part of the data pipeline. Airbyte provides data integration, dbt enables data transformation with SQL, and Airflow handles scheduling. The demo shows how these tools can be combined to build a flexible, autonomous, and future proof modern data stack.
ETL is a process that extracts data from multiple sources, transforms it to fit operational needs, and loads it into a data warehouse or other destination system. It migrates, converts, and transforms data to make it accessible for business analysis. The ETL process extracts raw data, transforms it by cleaning, consolidating, and formatting the data, and loads the transformed data into the target data warehouse or data marts.
The document provides a resume summary for Jithender GundaJithender Reddy, a DWH/ETL Test Engineer. It outlines his 3+ years of experience in ETL testing using tools like Informatica, Data Stage, Teradata, Oracle, and Netezza. It also lists 4 projects he has worked on testing ETL processes and data loads for clients in various industries. The summary highlights his skills in testing data warehouses and ETL processes, writing SQL queries, and using test management tools like ALM.
GoldenGate and ODI - A Perfect Match for Real-Time Data WarehousingMichael Rainey
Oracle Data Integrator and Oracle GoldenGate excel as standalone products, but paired together they are the perfect match for real-time data warehousing. Following Oracle’s Next Generation Reference Data Warehouse Architecture, this discussion will provide best practices on how to configure, implement, and process data in real-time using ODI and GoldenGate. Attendees will see common real-time challenges solved, including parent-child relationships within micro-batch ETL.
Presented at RMOUG Training Days 2013 & KScope13.
This document discusses strategies for efficiently loading and transforming large datasets in PostgreSQL for analytics use cases. It presents several case studies:
1) Loading a large CSV file - different methods like pgloader, COPY, and temporary foreign tables are compared. Temporary foreign tables perform best when filtering columns.
2) Pre-aggregating ("rolling up") data into multiple tables at different granularities for optimized querying. Chained INSERTs and CTEs are more efficient than individual inserts.
3) Creating a "dumb rollup table" using GROUPING SETS to pre-aggregate into a single temp table and insert into final tables in one pass. This outperforms multiple round trips or inserts.
This document provides an overview of data warehousing concepts including:
- Data warehouses store historical data from operational systems for analysis and reporting. The data passes through a staging area and operational data store for cleaning before loading into the data warehouse.
- Common data warehouse architectures include star schemas with fact and dimension tables and snowflake schemas with normalized dimensions. Data marts contain summarized data for specific business questions.
- ETL processes extract, transform, and load the data in three phases. Transformation cleans and prepares the data before loading into dimensional schemas.
- Data warehouses typically contain historical data, derived data generated from existing data, and metadata describing the data and schemas.
Data platform architecture principles - ieee infrastructure 2020Julien Le Dem
This document discusses principles for building a healthy data platform, including:
1. Establishing explicit contracts between teams to define dependencies and service level agreements.
2. Abstracting the data platform into services for ingesting, storing, and processing data in motion and at rest.
3. Enabling observability of data pipelines through metadata collection and integration with tools like Marquez to provide lineage, availability, and change management visibility.
The document describes a migration from an Oracle database topology to a PostgreSQL database topology at ACI. It discusses the starting Oracle topology with issues around operational complexity and non-ACID compliance. It then describes the target PostgreSQL topology with improved performance, availability and lower costs. The document outlines decisions around tools, extensions, code changes and testing approaches needed for the migration. It also discusses options for migrating the data and cutting over to the new PostgreSQL environment.
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#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Agenda
◎Datawarehouse Architecture
◎What is ETL?
◎Why ETL is a separate Testing Type?
◎Discuss some ETL Jargons
◎ETL Loading Strategies
◎ETL Testing Types
◎Preparing Test Data for ETL Testing
◎ETL Testing Challenges
◎Best Practices on ETL Testing
◎Demo Example
2
4. ETL – Extract, Transformation and Load
◎ Data is taken (extracted) from a source system,
converted (transformed) into a format that can be
analyzed, and stored (loaded) into a data warehouse or
other system
4
5. ETL - Separate Testing Type?
◎Validation of Data Migration (End – to – End)
○ Source to Target record count match
○ Source to Target data match
○ Transformation of Data
○ Loading Techniques – Full, Incremental
◎Comparison – Current (Legacy) vs Future system
○ Reports / Data comparison
○ Loading time
5
6. Contd..
◎Validation of Business use cases
○ Transformation of data in different format for downstream
systems
○ File Transfer
6
7. ETL Jargons
◎File Systems
○ Structured - clearly defined data types
(CSV, Database, Tab-separated, etc..)
○ Unstructured - not as easily searchable
(Email, Web-pages, videos, etc..)
◎Dimensions
○ Descriptive attributes that are textual fields
○ Dimensions like people, products, place and time
7
8. Contd..
◎Facts
○ Consists of business facts and foreign keys that refer to
primary keys in the dimension tables provide the
measurement of an enterprise
8
9. Contd..
◎Staging Layer
○ Staging area is a place where you hold temporary tables
on data warehouse server
◎Look-up
○ Reference tables – used to fetch the matching values
○ Target tables – used to find the delta records or perform
incremental load
9
10. ETL Loading Strategies
◎Full Load – Truncate and Load
○ Truncating the target table before loading new data (Staging
Area)
◎Incremental Load
○ Incremental load is a process of loading data incrementally
○ Only new and changed data is loaded to the destination
○ Used to keep historical data
○ Uses Timestamps, Flags, Business key to fetch delta records
10
11. SCD types
◎A Slowly Changing Dimension (SCD) is a dimension
that stores and manages both current and historical
data over time in a data warehouse.
◎It is considered and implemented as one of the most
critical ETL tasks in tracking the history of dimension
records
11
12. Contd..
◎Type 0 SCDs– Fixed Dimension
○ No changes allowed, dimension never changes
◎Type 1 SCDs – Overwriting
○ Existing data is lost as it is not stored anywhere else
○ Default type of dimension you create
◎Type 2 SCDs - Creating another dimension record
○ When the value of a chosen attribute changes, the current record is
closed. A new record is created -becomes the current record
○ Each record contains the effective time and expiration time
12
13. ETL Testing Types
◎Production Validation Testing
○ Table balancing or product reconciliation. It is performed on
data before or while being moved into the production system in
the correct order.
◎Source To Target Testing
○ Performed to validate the data values after data transformation.
◎Application Upgrade
○ Check data extracted from an older application or repository are
exactly same as the data in a repository or new application.
13
14. Contd..
◎Data Transformation Testing:
○ Multiple SQL queries are required to be run for each and
every row to verify data transformation standards.
◎Data Completeness Testing:
○ Verify if the expected data is loaded at the appropriate
destination as per the predefined standards.
14
15. Preparing Test Data
◎Can be Generated
○ Manually
○ Mass copy of data from production to testing environment
○ Mass copy of test data from legacy client systems
○ Automated Test Data Generation Tools
◎How to select data for testing
○ Data profiling
○ Full field length data
○ Null records
○ Lookup values
15
16. ETL Testing Challenges
◎ Testers have no privileges to execute ETL jobs by their own
◎ Volume and complexity of data are very huge
◎ Incompatible and duplicate data
◎ Loss of data during ETL process
◎ Fault in business process and procedures
◎ Trouble acquiring and building test data
◎ Unstable testing environment
◎ Missing business flow information
16
17. Best Practices
◎Make sure data is transformed correctly
◎Without any data loss and truncation projected data
should be loaded into the data warehouse
◎Ensure that ETL application appropriately rejects and
replaces with default values and reports invalid data
◎Ensure appropriate load occurs at each data layer
17
18. Contd..
◎Need to ensure that the data loaded in data
warehouse within prescribed and expected time
frames to confirm scalability and performance
◎Ensure records are updated as per appropriate
Business Key in the target database tables
◎Ensure coding standards are in place while designing
ETL mappings
18