SlideShare a Scribd company logo
1 of 7
*
*
*Survey broadly all the various aspects of the
data extraction ,transformation and
loading(ETL)functions
*Examine the data extraction function,its
challenges,its tecniques,and learn how to
evaluate and apply the tecniques
*Discuss the wide range of tasks and types of
the transformation function
*Understand the meaning of data integration
and consolidation
*Percieve the importance of data load function
*ETL functions reshape the relevant datd from
the source systems into useful information to
be stored in the data warehouse.
*Without these function,there would be no
strategic information in the data warehouse.
*If the source data is not extracted
correctly,cleansed,and integrated in the proper
formats,query processing,the backbone of the
data warehouse,could not happen.
*
*The architecture divided into three functional areas such as
data acquisition, data storage and information delivery.
*ETL encompass the area of data acquisition and data storage.
*These are back-end processes that cover the extraction of
data from the source systems.
*Next, source data into the exact formats and structures
appropriate for data storage in data warehouse.
*After data transformation of the data, moving data into the
data warehouse repository.
*
*Data extraction-why not extract all of operational data and
dump into data warehouse?
*Avoid creating

More Related Content

What's hot

Date warehousing concepts
Date warehousing conceptsDate warehousing concepts
Date warehousing conceptspcherukumalla
 
Etl process in data warehouse
Etl process in data warehouseEtl process in data warehouse
Etl process in data warehouseKomal Choudhary
 
Data warehouse concepts
Data warehouse conceptsData warehouse concepts
Data warehouse conceptsobieefans
 
Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional ModellingVincent Rainardi
 
Classification of data mart
Classification of data martClassification of data mart
Classification of data martkhush_boo31
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecturepcherukumalla
 
ETL Testing Training Presentation
ETL Testing Training PresentationETL Testing Training Presentation
ETL Testing Training PresentationApurba Biswas
 
Introduction of Oracle
Introduction of Oracle Introduction of Oracle
Introduction of Oracle Salman Memon
 
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
 
Etl overview training
Etl overview trainingEtl overview training
Etl overview trainingMondy Holten
 

What's hot (20)

Data warehouse
Data warehouseData warehouse
Data warehouse
 
Date warehousing concepts
Date warehousing conceptsDate warehousing concepts
Date warehousing concepts
 
Etl process in data warehouse
Etl process in data warehouseEtl process in data warehouse
Etl process in data warehouse
 
Data warehouse concepts
Data warehouse conceptsData warehouse concepts
Data warehouse concepts
 
OLAP v/s OLTP
OLAP v/s OLTPOLAP v/s OLTP
OLAP v/s OLTP
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
Data Mining: Data processing
Data Mining: Data processingData Mining: Data processing
Data Mining: Data processing
 
Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional Modelling
 
Different data models
Different data modelsDifferent data models
Different data models
 
Classification of data mart
Classification of data martClassification of data mart
Classification of data mart
 
What is ETL?
What is ETL?What is ETL?
What is ETL?
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
ETL Testing Training Presentation
ETL Testing Training PresentationETL Testing Training Presentation
ETL Testing Training Presentation
 
Introduction of Oracle
Introduction of Oracle Introduction of Oracle
Introduction of Oracle
 
Ppt
PptPpt
Ppt
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...
 
Etl overview training
Etl overview trainingEtl overview training
Etl overview training
 
ETL
ETLETL
ETL
 
Database Administrator
Database AdministratorDatabase Administrator
Database Administrator
 

Viewers also liked

Dw & etl concepts
Dw & etl conceptsDw & etl concepts
Dw & etl conceptsjeshocarme
 
Data Warehouse (ETL) testing process
Data Warehouse (ETL) testing processData Warehouse (ETL) testing process
Data Warehouse (ETL) testing processRakesh Hansalia
 
data warehouse , data mart, etl
data warehouse , data mart, etldata warehouse , data mart, etl
data warehouse , data mart, etlAashish Rathod
 
Etl Overview (Extract, Transform, And Load)
Etl Overview (Extract, Transform, And Load)Etl Overview (Extract, Transform, And Load)
Etl Overview (Extract, Transform, And Load)LizLavaveshkul
 
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?Clinical Data Repository vs. A Data Warehouse - Which Do You Need?
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?Health Catalyst
 

Viewers also liked (8)

Le processus ETL (Extraction, Transformation, Chargement)
Le processus ETL (Extraction, Transformation, Chargement)Le processus ETL (Extraction, Transformation, Chargement)
Le processus ETL (Extraction, Transformation, Chargement)
 
Dw & etl concepts
Dw & etl conceptsDw & etl concepts
Dw & etl concepts
 
Data Warehouse (ETL) testing process
Data Warehouse (ETL) testing processData Warehouse (ETL) testing process
Data Warehouse (ETL) testing process
 
ETL Process
ETL ProcessETL Process
ETL Process
 
data warehouse , data mart, etl
data warehouse , data mart, etldata warehouse , data mart, etl
data warehouse , data mart, etl
 
Etl Overview (Extract, Transform, And Load)
Etl Overview (Extract, Transform, And Load)Etl Overview (Extract, Transform, And Load)
Etl Overview (Extract, Transform, And Load)
 
Introduction to ETL and Data Integration
Introduction to ETL and Data IntegrationIntroduction to ETL and Data Integration
Introduction to ETL and Data Integration
 
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?Clinical Data Repository vs. A Data Warehouse - Which Do You Need?
Clinical Data Repository vs. A Data Warehouse - Which Do You Need?
 

Similar to Data extraction, transformation, and loading

Data flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousingData flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousingDr. Dipti Patil
 
CHAPTER-1- Introduction to data structure.pptx
CHAPTER-1- Introduction to data structure.pptxCHAPTER-1- Introduction to data structure.pptx
CHAPTER-1- Introduction to data structure.pptxOnkarModhave
 
1. Data structures introduction
1. Data structures introduction1. Data structures introduction
1. Data structures introductionMandeep Singh
 
IRJET- Comparative Study of ETL and E-LT in Data Warehousing
IRJET- Comparative Study of ETL and E-LT in Data WarehousingIRJET- Comparative Study of ETL and E-LT in Data Warehousing
IRJET- Comparative Study of ETL and E-LT in Data WarehousingIRJET Journal
 
Dwdm unit 1-2016-Data ingarehousing
Dwdm unit 1-2016-Data ingarehousingDwdm unit 1-2016-Data ingarehousing
Dwdm unit 1-2016-Data ingarehousingDhilsath Fathima
 
An Overview on Data Quality Issues at Data Staging ETL
An Overview on Data Quality Issues at Data Staging ETLAn Overview on Data Quality Issues at Data Staging ETL
An Overview on Data Quality Issues at Data Staging ETLidescitation
 
Module 1_Data Warehousing Fundamentals.pptx
Module 1_Data Warehousing Fundamentals.pptxModule 1_Data Warehousing Fundamentals.pptx
Module 1_Data Warehousing Fundamentals.pptxnikshaikh786
 
ETL Process & Data Warehouse Fundamentals
ETL Process & Data Warehouse FundamentalsETL Process & Data Warehouse Fundamentals
ETL Process & Data Warehouse FundamentalsSOMASUNDARAM T
 
data warehousing need and characteristics. types of data w data warehouse arc...
data warehousing need and characteristics. types of data w data warehouse arc...data warehousing need and characteristics. types of data w data warehouse arc...
data warehousing need and characteristics. types of data w data warehouse arc...aasifkuchey85
 
Meta Data and it's Type
Meta Data and it's TypeMeta Data and it's Type
Meta Data and it's TypeFaisal Liaqat
 
Introduction To Data WareHouse
Introduction To Data WareHouseIntroduction To Data WareHouse
Introduction To Data WareHouseSriniRao31
 
Extract, Transform and Load.pptx
Extract, Transform and Load.pptxExtract, Transform and Load.pptx
Extract, Transform and Load.pptxJesusaEspeleta
 
(Lecture 2)Data Warehouse Architecture.pdf
(Lecture 2)Data Warehouse Architecture.pdf(Lecture 2)Data Warehouse Architecture.pdf
(Lecture 2)Data Warehouse Architecture.pdfMobeenMasoudi
 
Visible Governance: How to set up data governance using Visible Analyst Comme...
Visible Governance: How to set up data governance using Visible Analyst Comme...Visible Governance: How to set up data governance using Visible Analyst Comme...
Visible Governance: How to set up data governance using Visible Analyst Comme...Michael Cesino
 

Similar to Data extraction, transformation, and loading (20)

Data flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousingData flow in Extraction of ETL data warehousing
Data flow in Extraction of ETL data warehousing
 
Data Warehouse
Data WarehouseData Warehouse
Data Warehouse
 
CHAPTER-1- Introduction to data structure.pptx
CHAPTER-1- Introduction to data structure.pptxCHAPTER-1- Introduction to data structure.pptx
CHAPTER-1- Introduction to data structure.pptx
 
1. Data structures introduction
1. Data structures introduction1. Data structures introduction
1. Data structures introduction
 
IRJET- Comparative Study of ETL and E-LT in Data Warehousing
IRJET- Comparative Study of ETL and E-LT in Data WarehousingIRJET- Comparative Study of ETL and E-LT in Data Warehousing
IRJET- Comparative Study of ETL and E-LT in Data Warehousing
 
Dwdm unit 1-2016-Data ingarehousing
Dwdm unit 1-2016-Data ingarehousingDwdm unit 1-2016-Data ingarehousing
Dwdm unit 1-2016-Data ingarehousing
 
An Overview on Data Quality Issues at Data Staging ETL
An Overview on Data Quality Issues at Data Staging ETLAn Overview on Data Quality Issues at Data Staging ETL
An Overview on Data Quality Issues at Data Staging ETL
 
Module 1_Data Warehousing Fundamentals.pptx
Module 1_Data Warehousing Fundamentals.pptxModule 1_Data Warehousing Fundamentals.pptx
Module 1_Data Warehousing Fundamentals.pptx
 
stack.pptx
stack.pptxstack.pptx
stack.pptx
 
ETL Process & Data Warehouse Fundamentals
ETL Process & Data Warehouse FundamentalsETL Process & Data Warehouse Fundamentals
ETL Process & Data Warehouse Fundamentals
 
data warehousing need and characteristics. types of data w data warehouse arc...
data warehousing need and characteristics. types of data w data warehouse arc...data warehousing need and characteristics. types of data w data warehouse arc...
data warehousing need and characteristics. types of data w data warehouse arc...
 
Meta Data and it's Type
Meta Data and it's TypeMeta Data and it's Type
Meta Data and it's Type
 
Introduction To Data WareHouse
Introduction To Data WareHouseIntroduction To Data WareHouse
Introduction To Data WareHouse
 
Extract, Transform and Load.pptx
Extract, Transform and Load.pptxExtract, Transform and Load.pptx
Extract, Transform and Load.pptx
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
(Lecture 2)Data Warehouse Architecture.pdf
(Lecture 2)Data Warehouse Architecture.pdf(Lecture 2)Data Warehouse Architecture.pdf
(Lecture 2)Data Warehouse Architecture.pdf
 
Visible Governance: How to set up data governance using Visible Analyst Comme...
Visible Governance: How to set up data governance using Visible Analyst Comme...Visible Governance: How to set up data governance using Visible Analyst Comme...
Visible Governance: How to set up data governance using Visible Analyst Comme...
 
GROPSIKS.pptx
GROPSIKS.pptxGROPSIKS.pptx
GROPSIKS.pptx
 
IDUG 2015 NA Data Movement Utilities final
IDUG 2015 NA Data Movement Utilities finalIDUG 2015 NA Data Movement Utilities final
IDUG 2015 NA Data Movement Utilities final
 
Chapter 6.pptx
Chapter 6.pptxChapter 6.pptx
Chapter 6.pptx
 

More from Siddique Ibrahim (20)

List in Python
List in PythonList in Python
List in Python
 
Python Control structures
Python Control structuresPython Control structures
Python Control structures
 
Python programming introduction
Python programming introductionPython programming introduction
Python programming introduction
 
Data mining basic fundamentals
Data mining basic fundamentalsData mining basic fundamentals
Data mining basic fundamentals
 
Basic networking
Basic networkingBasic networking
Basic networking
 
Virtualization Concepts
Virtualization ConceptsVirtualization Concepts
Virtualization Concepts
 
Networking devices(siddique)
Networking devices(siddique)Networking devices(siddique)
Networking devices(siddique)
 
Osi model 7 Layers
Osi model 7 LayersOsi model 7 Layers
Osi model 7 Layers
 
Mysql grand
Mysql grandMysql grand
Mysql grand
 
Getting started into mySQL
Getting started into mySQLGetting started into mySQL
Getting started into mySQL
 
pipelining
pipeliningpipelining
pipelining
 
Micro programmed control
Micro programmed controlMicro programmed control
Micro programmed control
 
Hardwired control
Hardwired controlHardwired control
Hardwired control
 
interface
interfaceinterface
interface
 
Interrupt
InterruptInterrupt
Interrupt
 
Interrupt
InterruptInterrupt
Interrupt
 
DMA
DMADMA
DMA
 
Io devies
Io deviesIo devies
Io devies
 
Stack & queue
Stack & queueStack & queue
Stack & queue
 
Metadata in data warehouse
Metadata in data warehouseMetadata in data warehouse
Metadata in data warehouse
 

Recently uploaded

SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesZilliz
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfSeasiaInfotech2
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticscarlostorres15106
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationSlibray Presentation
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 

Recently uploaded (20)

SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector DatabasesVector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdfThe Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmaticsKotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
 
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck PresentationConnect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 

Data extraction, transformation, and loading

  • 1. *
  • 2. * *Survey broadly all the various aspects of the data extraction ,transformation and loading(ETL)functions *Examine the data extraction function,its challenges,its tecniques,and learn how to evaluate and apply the tecniques *Discuss the wide range of tasks and types of the transformation function
  • 3. *Understand the meaning of data integration and consolidation *Percieve the importance of data load function
  • 4. *ETL functions reshape the relevant datd from the source systems into useful information to be stored in the data warehouse. *Without these function,there would be no strategic information in the data warehouse. *If the source data is not extracted correctly,cleansed,and integrated in the proper formats,query processing,the backbone of the data warehouse,could not happen.
  • 5. * *The architecture divided into three functional areas such as data acquisition, data storage and information delivery. *ETL encompass the area of data acquisition and data storage. *These are back-end processes that cover the extraction of data from the source systems. *Next, source data into the exact formats and structures appropriate for data storage in data warehouse. *After data transformation of the data, moving data into the data warehouse repository.
  • 6.
  • 7. * *Data extraction-why not extract all of operational data and dump into data warehouse? *Avoid creating