This document describes Target's framework for handling errors and exceptions in data warehousing. It discusses capturing errors during data loading, setting error thresholds, purging data, and correcting inaccurate data. Errors are classified as data-related or infrastructure-related. Data exceptions involve invalid or rejected records, which may be reprocessed after correcting the source data. Infrastructure exceptions involve network, database, or system issues and generate alerts. Inaccurate data in the warehouse is detected, analyzed, and corrected by fixing requirements, designs, code, or applying patches to historical data.
Este documento proporciona instrucciones para instalar y configurar Microsoft Exchange Server 2013 en Windows Server 2012 R2. Explica los requisitos de hardware y software necesarios, y proporciona pasos detallados para instalar Exchange y configurar buzones de usuario para pruebas de correo electrónico local. También cubre la configuración de conectores de envío y recepción para permitir el intercambio de correo entre dominios.
Este documento é um registro de aula de Tecnologia da Informação em uma escola estadual. A aula abordou o tema de Computador e Computação e foi ministrada por um professor para um aluno em determinada turma e data.
1) O documento discute formas de aplicar a arquitetura de software em equipes ágeis e projetos de grande escala.
2) Apresenta os conflitos potenciais entre arquitetura de software e metodologias ágeis e formas de resolvê-los, como comunicação e documentação ágil.
3) Discutem técnicas como Scrum of Scrums para coordenar múltiplas equipes ágeis em projetos grandes e a importância de iterações na arquitetura ágil.
The document provides an overview of DB2 and discusses key concepts such as instances, databases, tablespaces, and recovery. It describes how to install and configure DB2, create instances and databases, load and move data between databases, and perform backups and recovery. Examples are given of commands used to create tablespaces and load data. The document also mentions tools for visualizing queries and monitoring performance.
10-ÅRSKONTROLL AV JORDINGSSYSTEMER - Tore Solhaug, Trainor - ELKOMP18 Trainor Elsikkerhet AS
Hva er kravet til kontroll. Det skal beregnes eller måles at det ikke kan oppstå farlige berøringsspenninger i anlegget. Eiere av høyspennings – anlegg er pålagt å dokumentere jordelektroden minst hvert 10ende år regulert gjennom forskriften FEF 2006. Videre ser vi på forskjellige målemetoder og måleinstrumenter for måling av overgangsmotstand.
Software Architecture Document | Part 1Habibur Rony
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
What are the top 100 SQL Interview Questions and Answers in 2014? Based on the most popular SQL questions asked in interview, we've compiled a list of the 100 most popular SQL interview questions in 2014.
This pdf includes oracle sql interview questions and answers, sql query interview questions and answers, sql interview questions and answers for freshers etc and is perfect for those who're appearing for a linux interview in top IT companies like HCL, Infosys, TCS, Wipro, Tech Mahindra, Cognizant etc
This list includes SQL interview questions in the below categories:
top 100 sql interview questions and answers
top 100 java interview questions and answers
top 100 c interview questions and answers
top 50 sql interview questions and answers
top 100 interview questions and answers book
sql interview questions and answers pdf
oracle sql interview questions and answers
sql query interview questions and answers
sql interview questions and answers for freshers
SQL Queries Interview Questions and Answers
SQL Interview Questions and Answers
Top 80 + SQL Query Interview Questions and Answers
Top 20 SQL Interview Questions with Answers
Sql Server Interviews Questions and Answers
100 Mysql interview questions and answers
SQL Queries Interview Questions
SQL Query Interview Questions and Answers with Examples
Mysql interview questions and answers for freshers and experienced
Machine learning topics machine learning algorithm into three main parts.DurgaDeviP2
Machine learning topics
machine learning algorithm into three main parts.
A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
Este documento proporciona instrucciones para instalar y configurar Microsoft Exchange Server 2013 en Windows Server 2012 R2. Explica los requisitos de hardware y software necesarios, y proporciona pasos detallados para instalar Exchange y configurar buzones de usuario para pruebas de correo electrónico local. También cubre la configuración de conectores de envío y recepción para permitir el intercambio de correo entre dominios.
Este documento é um registro de aula de Tecnologia da Informação em uma escola estadual. A aula abordou o tema de Computador e Computação e foi ministrada por um professor para um aluno em determinada turma e data.
1) O documento discute formas de aplicar a arquitetura de software em equipes ágeis e projetos de grande escala.
2) Apresenta os conflitos potenciais entre arquitetura de software e metodologias ágeis e formas de resolvê-los, como comunicação e documentação ágil.
3) Discutem técnicas como Scrum of Scrums para coordenar múltiplas equipes ágeis em projetos grandes e a importância de iterações na arquitetura ágil.
The document provides an overview of DB2 and discusses key concepts such as instances, databases, tablespaces, and recovery. It describes how to install and configure DB2, create instances and databases, load and move data between databases, and perform backups and recovery. Examples are given of commands used to create tablespaces and load data. The document also mentions tools for visualizing queries and monitoring performance.
10-ÅRSKONTROLL AV JORDINGSSYSTEMER - Tore Solhaug, Trainor - ELKOMP18 Trainor Elsikkerhet AS
Hva er kravet til kontroll. Det skal beregnes eller måles at det ikke kan oppstå farlige berøringsspenninger i anlegget. Eiere av høyspennings – anlegg er pålagt å dokumentere jordelektroden minst hvert 10ende år regulert gjennom forskriften FEF 2006. Videre ser vi på forskjellige målemetoder og måleinstrumenter for måling av overgangsmotstand.
Software Architecture Document | Part 1Habibur Rony
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like depression and anxiety.
What are the top 100 SQL Interview Questions and Answers in 2014? Based on the most popular SQL questions asked in interview, we've compiled a list of the 100 most popular SQL interview questions in 2014.
This pdf includes oracle sql interview questions and answers, sql query interview questions and answers, sql interview questions and answers for freshers etc and is perfect for those who're appearing for a linux interview in top IT companies like HCL, Infosys, TCS, Wipro, Tech Mahindra, Cognizant etc
This list includes SQL interview questions in the below categories:
top 100 sql interview questions and answers
top 100 java interview questions and answers
top 100 c interview questions and answers
top 50 sql interview questions and answers
top 100 interview questions and answers book
sql interview questions and answers pdf
oracle sql interview questions and answers
sql query interview questions and answers
sql interview questions and answers for freshers
SQL Queries Interview Questions and Answers
SQL Interview Questions and Answers
Top 80 + SQL Query Interview Questions and Answers
Top 20 SQL Interview Questions with Answers
Sql Server Interviews Questions and Answers
100 Mysql interview questions and answers
SQL Queries Interview Questions
SQL Query Interview Questions and Answers with Examples
Mysql interview questions and answers for freshers and experienced
Machine learning topics machine learning algorithm into three main parts.DurgaDeviP2
Machine learning topics
machine learning algorithm into three main parts.
A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this iterative “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.
WA CA 7 Edition r12 Database Conversion - CA Workload Automation Technology S...Extra Technology
To book your place at the next WATS event, please get in touch with CA Technologies' Preferred Partner Extra Technology using http://www.extratechnology.com/contact.
This presentation by CA Technologies' Bill Sherwin (@CA7WA_Expert) explains how to upgrade CA 7 r11.3 database to r12. This session was presented at the 'CA Workload Automation Technology Summit (WATS) 2015' event on 7 October 2014 at the Grange City Hotel, London, UK.
CA7, AutoSys and dSeries UK User Groups meetings are incorporated into WATS. Customers agree that WATS is a must-attend event for the CA Workload Automation community, showcasing CA's Workload Automation products including iDash, AutoSys, CA7, dSeries and Workload Agents.
WATS is a free-of-charge event, sponsored and arranged by Workload Automation experts Extra Technology. It features guest speakers from CA Technologies and customers.
#WATS #CA7 #WorkloadAutomation #r11.3 #r12 #DatabaseConversion #Upgrade #Datacom @CAinc @CA_Community @extratechnology
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.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
Software architecture case study - why and why not sql server replicationShahzad
This document discusses using replication to consolidate check list data from multiple ships into a central database server. Replication is suitable because the data from different ships does not require real-time consistency and each ship's records will be updated independently. However, the document notes that an alternative like periodic file transfers may work as well depending on the volume and frequency of data changes. The key factors in choosing replication or another method are how critical up-to-date data consolidation is and how much data needs to be transferred.
Data Integration is a data processing technique that collects data from different sources (such as data cubes, multiple databases, and flat files) and offers a unified view of the data to the users. Data integration in data mining connects with issues such as duplicate data, inconsistent data, old systems, etc. Manual data integration can be achieved through middleware and applications. There are two major system for data integration which are tight coupling method and loose coupling method.
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.
CA 7 r11.3 to r12 DB Conversion Presentation - CA Workload Automation Technol...Extra Technology
This document provides an overview of the process for converting databases from CA 7 Release 11.3 to Release 12.0. It discusses preparing the R11.3 database by running validation and correction jobs. The conversion process exports the R11.3 data into a new format, imports it into the R12.0 database, and validates that the new database functions equivalently to the original R11.3 data. If validation fails, the issues must be addressed before continuing. The document outlines potential causes of validation failure and the options for resolving them.
This document discusses the key aspects of designing and developing a database. It covers database concepts like entity-relationship modeling, normalization, and database development methodologies like SSADM. SSADM involves phases like feasibility study, requirements analysis, logical design, and physical design. The document provides examples of one-to-one, one-to-many and many-to-many relationships. It also discusses applying normalization rules and the database development cycle to design a database for storing product and customer data for a computer hardware store.
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.
This white paper discusses how file fragmentation can lead to various reliability and stability issues for systems and applications. It identifies seven common problems caused by fragmentation: crashes and system hangs, slow boot up times, slow backup times and aborted backups, file corruption and data loss, errors in programs, increased RAM and cache usage, and potential hard drive failures. Fragmentation places additional stress on disk input/output systems and can expose faults in device drivers or software that might otherwise go unnoticed in non-fragmented environments. Addressing fragmentation is important to reduce troubleshooting burdens on IT staff and prevent unnecessary downtime.
This document describes LinkedIn's Databus, a distributed change data capture system that reliably captures and propagates changes from primary data stores. It has four main components - a fetcher that extracts changes from data sources, a log store that caches changes, a snapshot store that maintains a moving snapshot, and a subscription client that pulls changes. Databus uses a pull-based model where consumers pull changes based on a monotonically increasing system change number. It supports capturing transaction boundaries, commit order, and consistent states to preserve consistency from the data source.
Logical replication allows migration between different hardware, operating systems, and Oracle versions with minimal downtime. It works by reading the redo logs of the source database in real time and applying the changes to the target database. Some preparation is required, such as testing and validating the migration. If issues occur during cutover to the 12c target, the original production system remains intact with no data risk. Logical replication provides an effective method for migrating to Oracle 12c with zero or near-zero downtime.
The document discusses data integration and the ETL process. It provides details on:
1. Data integration, which combines data from different sources to create a unified view, supporting business analysis. It involves extracting, transforming, and loading data.
2. The general approach of integration, which can be achieved through application, business process, and user interaction integration. Techniques include ETL, data federation, and data propagation.
3. Data integration for data warehousing, focusing on the "reconciled data layer" which harmonizes data from sources before loading into the warehouse. This involves transforming operational data characteristics.
Discussion 1 The incorrect implementation of databases ouhuttenangela
Discussion 1 :
The incorrect implementation of databases ought to purpose demanding situations for the entire device. An instance of a database that has been carried out poorly is one which has redundancy. Depending on the number of statistics that the agency is anticipated to have or hold, the corporation wishes to have the surroundings setup enjoyable the needs of the requirement. In these days' global, there are many alternatives to store our statistics. As an instance, the cloud database, which runs on the cloud computing platform and is provided as-a-service. Relational databases are designed in this kind of way that each object represents one and simplest element, so there have to keep away from duplication
I would really like to explain a scenario wherein the role of the database performs a key role. Wrong designing and implementation of databases machine can reason many challenges and provide now not so properly paintings experience. For instance, a database designed poorly is one that has redundancy. As an instance, in some instances in which statistics fields are identical for diverse factors within the identical desk and all through updating of this fields can create manual mistakes or device error, that allows you to end up presenting a non-preferred end result. Extra error prevalence can result by way of guide mistakes and in this situation data admin who controls a database and does now not have centralized facts manipulate, and which results in pointless duplication. Therefore, redundancy can be prevented or reduced by way of having centralized information manage, wherein insertions and statistics up to date may be monitored to avoid duplicates. The other technique that removes redundancy is normalization because it gets rid of data duplicates from the tables. Alongside those, repetition may be stayed away from or reduced by using having brought together database manipulate, wherein inclusions and information refreshed can be checked to keep away from duplication. We have been getting mismatched information referring to the credit score report of the patron coming from the 0. 33-birthday party source. Consequently, I would really like to conclude that databases have to implement in each group, be it electronic or guide facts, make certain facts integrity and security of records.
Refs:
Andelman, S. J., & Fagan, W. F. (2000). Umbrellas and flagships: efficient conservation surrogates or expensive mistakes?. Proceedings of the National Academy of Sciences, 97(11), 5954-5959.
Bernheim, R. G. (2016). Public Engagement in Emergency Preparedness and Response. Emergency Ethics, 155-185. doi:10.1093/med/9780190270742.003.0005
Discussion 2 :
Designing and architecting a database in a proper way could reduce the problems that come during the development and deployment process. Any small type of mistake in database design would give a lot of pain to the DBA’s, developers and managers alike. In the following discussion, I wi ...
Snowflake and Oracle Autonomous Data Warehouse are two leading cloud data warehouse services. While both aim to simplify data warehousing, Oracle Autonomous Data Warehouse provides more complete automation through its self-driving, self-securing, and self-repairing capabilities. The document finds that Oracle Autonomous Data Warehouse outperforms Snowflake in several key areas including simplicity, automation, performance, security, flexibility and cost. Specifically, Oracle Autonomous Data Warehouse requires less manual intervention, achieves better performance through full automation, and offers significantly lower costs through its superior performance and elasticity controls.
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.
IRJET - The 3-Level Database Architectural Design for OLAP and OLTP OpsIRJET Journal
This document proposes a 3-level database design to improve performance for both OLTP and OLAP operations. It involves categorizing tables based on usage and applying different techniques at each level. Highly transactional tables are partitioned and stored in memory. Frequently used small tables are kept solely in memory. Larger analytical tables use partitioning. Archived data uses compression. This stratified design aims to optimize access speeds and query performance by placing frequently and recently used data in faster memory tiers while compressing less used historical data.
Mocca International GmbH _Q500 analysis and Recommendations_Finalhjperry
The document provides a technical analysis and recommendations for improving the performance of the Q500 database. It finds that the database suffers from file and internal fragmentation, an inefficient database layout that forces load onto few disks, and almost full database files. It recommends tuning the current system, improving database recovery time, implementing disaster recovery, and reviewing IT staff responsibilities before considering a platform change.
Change data capture the journey to real time biAsis Mohanty
This document discusses change data capture (CDC) methodologies for tracking changes to enterprise data. It describes four common CDC methodologies: 1) capturing changes by comparing table versions, 2) using timestamps and status flags, 3) implementing database triggers, and 4) reading transaction logs. It also discusses using CDC tools along with ETL processes. CDC aims to identify changed data to take action on or integrate into a data warehouse. Methodologies have different advantages depending on factors like data volume, change frequency, and ensuring data integrity.
The document discusses the challenges of traditional on-premise data warehouses and how cloud data warehouses address them. It outlines issues like scalability, performance, data resilience, and cost with on-premise solutions. It then explains how cloud data warehouses can dynamically scale based on workload demands, provide high performance through serverless architectures, ensure data resilience through backups, and offer a pay-as-you-go cost model with no upfront infrastructure costs. The document also provides a comparison of features and pricing for several cloud data warehouse offerings from Amazon Redshift, Snowflake, Microsoft Azure, and Google BigQuery.
The document discusses different cloud data architectures including streaming processing, Lambda architecture, Kappa architecture, and patterns for implementing Lambda architecture on AWS. It provides an overview of each architecture's components and limitations. The key differences between Lambda and Kappa architectures are outlined, with Kappa being based solely on streaming and using a single technology stack. Finally, various AWS services that can be used to implement Lambda architecture patterns are listed.
WA CA 7 Edition r12 Database Conversion - CA Workload Automation Technology S...Extra Technology
To book your place at the next WATS event, please get in touch with CA Technologies' Preferred Partner Extra Technology using http://www.extratechnology.com/contact.
This presentation by CA Technologies' Bill Sherwin (@CA7WA_Expert) explains how to upgrade CA 7 r11.3 database to r12. This session was presented at the 'CA Workload Automation Technology Summit (WATS) 2015' event on 7 October 2014 at the Grange City Hotel, London, UK.
CA7, AutoSys and dSeries UK User Groups meetings are incorporated into WATS. Customers agree that WATS is a must-attend event for the CA Workload Automation community, showcasing CA's Workload Automation products including iDash, AutoSys, CA7, dSeries and Workload Agents.
WATS is a free-of-charge event, sponsored and arranged by Workload Automation experts Extra Technology. It features guest speakers from CA Technologies and customers.
#WATS #CA7 #WorkloadAutomation #r11.3 #r12 #DatabaseConversion #Upgrade #Datacom @CAinc @CA_Community @extratechnology
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.
Data warehousing change in a challenging environmentDavid Walker
This white paper discusses the challenges of managing changes in a data warehousing environment. It describes a typical data warehouse architecture with source systems feeding data into a data warehouse and then into data marts or cubes. It also outlines the common processes involved like development, operations and data quality processes. The paper then discusses two major challenges - configuration/change management as there are frequent changes from source systems, applications and technologies that impact the data warehouse. The other challenge is managing and improving data quality as issues from source systems are often replicated in the data warehouse.
Software architecture case study - why and why not sql server replicationShahzad
This document discusses using replication to consolidate check list data from multiple ships into a central database server. Replication is suitable because the data from different ships does not require real-time consistency and each ship's records will be updated independently. However, the document notes that an alternative like periodic file transfers may work as well depending on the volume and frequency of data changes. The key factors in choosing replication or another method are how critical up-to-date data consolidation is and how much data needs to be transferred.
Data Integration is a data processing technique that collects data from different sources (such as data cubes, multiple databases, and flat files) and offers a unified view of the data to the users. Data integration in data mining connects with issues such as duplicate data, inconsistent data, old systems, etc. Manual data integration can be achieved through middleware and applications. There are two major system for data integration which are tight coupling method and loose coupling method.
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.
CA 7 r11.3 to r12 DB Conversion Presentation - CA Workload Automation Technol...Extra Technology
This document provides an overview of the process for converting databases from CA 7 Release 11.3 to Release 12.0. It discusses preparing the R11.3 database by running validation and correction jobs. The conversion process exports the R11.3 data into a new format, imports it into the R12.0 database, and validates that the new database functions equivalently to the original R11.3 data. If validation fails, the issues must be addressed before continuing. The document outlines potential causes of validation failure and the options for resolving them.
This document discusses the key aspects of designing and developing a database. It covers database concepts like entity-relationship modeling, normalization, and database development methodologies like SSADM. SSADM involves phases like feasibility study, requirements analysis, logical design, and physical design. The document provides examples of one-to-one, one-to-many and many-to-many relationships. It also discusses applying normalization rules and the database development cycle to design a database for storing product and customer data for a computer hardware store.
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.
This white paper discusses how file fragmentation can lead to various reliability and stability issues for systems and applications. It identifies seven common problems caused by fragmentation: crashes and system hangs, slow boot up times, slow backup times and aborted backups, file corruption and data loss, errors in programs, increased RAM and cache usage, and potential hard drive failures. Fragmentation places additional stress on disk input/output systems and can expose faults in device drivers or software that might otherwise go unnoticed in non-fragmented environments. Addressing fragmentation is important to reduce troubleshooting burdens on IT staff and prevent unnecessary downtime.
This document describes LinkedIn's Databus, a distributed change data capture system that reliably captures and propagates changes from primary data stores. It has four main components - a fetcher that extracts changes from data sources, a log store that caches changes, a snapshot store that maintains a moving snapshot, and a subscription client that pulls changes. Databus uses a pull-based model where consumers pull changes based on a monotonically increasing system change number. It supports capturing transaction boundaries, commit order, and consistent states to preserve consistency from the data source.
Logical replication allows migration between different hardware, operating systems, and Oracle versions with minimal downtime. It works by reading the redo logs of the source database in real time and applying the changes to the target database. Some preparation is required, such as testing and validating the migration. If issues occur during cutover to the 12c target, the original production system remains intact with no data risk. Logical replication provides an effective method for migrating to Oracle 12c with zero or near-zero downtime.
The document discusses data integration and the ETL process. It provides details on:
1. Data integration, which combines data from different sources to create a unified view, supporting business analysis. It involves extracting, transforming, and loading data.
2. The general approach of integration, which can be achieved through application, business process, and user interaction integration. Techniques include ETL, data federation, and data propagation.
3. Data integration for data warehousing, focusing on the "reconciled data layer" which harmonizes data from sources before loading into the warehouse. This involves transforming operational data characteristics.
Discussion 1 The incorrect implementation of databases ouhuttenangela
Discussion 1 :
The incorrect implementation of databases ought to purpose demanding situations for the entire device. An instance of a database that has been carried out poorly is one which has redundancy. Depending on the number of statistics that the agency is anticipated to have or hold, the corporation wishes to have the surroundings setup enjoyable the needs of the requirement. In these days' global, there are many alternatives to store our statistics. As an instance, the cloud database, which runs on the cloud computing platform and is provided as-a-service. Relational databases are designed in this kind of way that each object represents one and simplest element, so there have to keep away from duplication
I would really like to explain a scenario wherein the role of the database performs a key role. Wrong designing and implementation of databases machine can reason many challenges and provide now not so properly paintings experience. For instance, a database designed poorly is one that has redundancy. As an instance, in some instances in which statistics fields are identical for diverse factors within the identical desk and all through updating of this fields can create manual mistakes or device error, that allows you to end up presenting a non-preferred end result. Extra error prevalence can result by way of guide mistakes and in this situation data admin who controls a database and does now not have centralized facts manipulate, and which results in pointless duplication. Therefore, redundancy can be prevented or reduced by way of having centralized information manage, wherein insertions and statistics up to date may be monitored to avoid duplicates. The other technique that removes redundancy is normalization because it gets rid of data duplicates from the tables. Alongside those, repetition may be stayed away from or reduced by using having brought together database manipulate, wherein inclusions and information refreshed can be checked to keep away from duplication. We have been getting mismatched information referring to the credit score report of the patron coming from the 0. 33-birthday party source. Consequently, I would really like to conclude that databases have to implement in each group, be it electronic or guide facts, make certain facts integrity and security of records.
Refs:
Andelman, S. J., & Fagan, W. F. (2000). Umbrellas and flagships: efficient conservation surrogates or expensive mistakes?. Proceedings of the National Academy of Sciences, 97(11), 5954-5959.
Bernheim, R. G. (2016). Public Engagement in Emergency Preparedness and Response. Emergency Ethics, 155-185. doi:10.1093/med/9780190270742.003.0005
Discussion 2 :
Designing and architecting a database in a proper way could reduce the problems that come during the development and deployment process. Any small type of mistake in database design would give a lot of pain to the DBA’s, developers and managers alike. In the following discussion, I wi ...
Snowflake and Oracle Autonomous Data Warehouse are two leading cloud data warehouse services. While both aim to simplify data warehousing, Oracle Autonomous Data Warehouse provides more complete automation through its self-driving, self-securing, and self-repairing capabilities. The document finds that Oracle Autonomous Data Warehouse outperforms Snowflake in several key areas including simplicity, automation, performance, security, flexibility and cost. Specifically, Oracle Autonomous Data Warehouse requires less manual intervention, achieves better performance through full automation, and offers significantly lower costs through its superior performance and elasticity controls.
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.
IRJET - The 3-Level Database Architectural Design for OLAP and OLTP OpsIRJET Journal
This document proposes a 3-level database design to improve performance for both OLTP and OLAP operations. It involves categorizing tables based on usage and applying different techniques at each level. Highly transactional tables are partitioned and stored in memory. Frequently used small tables are kept solely in memory. Larger analytical tables use partitioning. Archived data uses compression. This stratified design aims to optimize access speeds and query performance by placing frequently and recently used data in faster memory tiers while compressing less used historical data.
Mocca International GmbH _Q500 analysis and Recommendations_Finalhjperry
The document provides a technical analysis and recommendations for improving the performance of the Q500 database. It finds that the database suffers from file and internal fragmentation, an inefficient database layout that forces load onto few disks, and almost full database files. It recommends tuning the current system, improving database recovery time, implementing disaster recovery, and reviewing IT staff responsibilities before considering a platform change.
Change data capture the journey to real time biAsis Mohanty
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2. Table of Contents
1. Exception Handling Overview (ref 2.5.2) ....................................................................................... 3
1.1. Data Reprocessing......................................................................................................................... 5
1.2. Infrastructure Exception Handling ................................................................................................ 7
1.3. Data Correction in DWH................................................................................................................ 9
2. Error Processing – High Level .............................................................................................................. 11
2.1. Capturing..................................................................................................................................... 11
2.2. Error threshold ............................................................................................................................ 11
2.3. Purging ........................................................................................................................................ 12
2.3.1. Landing Area ....................................................................................................................... 12
2.3.2. Staging Area ........................................................................................................................ 12
2.3.3. EDW..................................................................................................................................... 12
2.3.4. Datamart ............................................................................................................................. 12
2.4. Purge threshold........................................................................................................................... 12
2.5. Appendix ..................................................................................................................................... 12
2.5.1. About Target ....................................................................................................................... 12
2.5.2. Reference ............................................................................................................................ 13
2.5.3. Other Contributors.............................................................................................................. 13
Page 2 of 13
3. 1. Exception Handling Overview (ref 2.5.2)
Exception Handling deals with any abnormal termination, unacceptable event or incorrect data that
can impact the data flow or accuracy of data in the warehouse/mart.
Exceptions in ETL could be classified as Data Related Exceptions and Infrastructure Related
Exceptions.
Please Note: In Infrastructure Related exception, Infrastructure glitches are not classified as exception
as they are temporary and are resolved by the time the job(s) is/are rerun. But, the logs are tracked
and maintained.
The process of recovering or gracefully exiting when an exception occurs is called exception handling.
Page 3 of 13
4. Data related exceptions are caused because of incorrect data format, incorrect value, incomplete
data from the source system. This leads to Data validation exceptions and Data Rejects. The process of
handling the Data Rejects is called Data Reprocessing.
Page 4 of 13
5. Infrastructure related exceptions are caused because of issues in the Network , the Database and the
Operating System. Common Infrastructure exceptions are FTP failure, Database connectivity failure,
File system full etc.
The data related exceptions are usually documented in the requirements, if not they must be because
if the data related exceptions are not handled they lead to inaccurate data in the warehouse/mart. We
also keep a threshold of maximum number of validation or reject failures allowed per load. Any value
above the threshold would mean the data would be too inaccurate because to too many rejections.
There is one more exception which is the presence of inaccurate or incorrect data in the warehouse.
This could happen due to
1. Incorrect requirement or missed, leading to incorrect ETL.
2. Incorrect interpretation of requirements leading to incorrect ETL.
3. Uncaught coding defects.
4. Incorrect data from source.
The process of Correction of the data already loaded in the warehouse involves fixing the data already
loaded and also preventing the inaccuracy to persist in the future.
1.1. Data Reprocessing
Reprocessing is is an exception handling process which involves the correction of the data that is could
not be loaded into the warehouse/mart.
There could be many reasons why source data
gets rejected from DWH. Most common of
them are
Data Rejection - Source data not
matching critical business codes/attributes.
This is called Lookup Failure in ETL.
Data Cleansing - Source data
containing junk values for business critical
fields hence getting rejected during data
validation.
There are 3 options to deal with the rejected
records. One, We could leave the rejected
data out of DWH or, two we could correct it
based on whether the rejected field is critical
to business and is worth reprocessing, and
then load it into DWH, and last option is to
The process of correcting the rejected data
and then loading into DWH is called Data
Reprocessing.
Page 5 of 13
6. As depicted in the figure above, we reject the data during the data validation process, data cleansing
process and data transformation process. The rejected data is collected in temporary files on the ETL
server while the ETL is running. Once the ETL is complete, the rejected data is moved into the Landing
Area.
The end user and the business analyst are provided interfaces to read the reject data in landing area.
They take this as the input, analyze the cause of rejection and correct the data at the source itself.
Once the data is corrected at the source, it is again extracted (depicted in Brown line in the figure).
The corrected data is not expected to get rejected again unless the correction provided was
insufficient.
Page 6 of 13
7. In some business critical data warehouses which have very very low tolerance towards inaccurate data,
we would need a sophisticated and a fast mechanism of handling rejected data in the landing area.
Here we consider a database to land the data. The database schema is the same as that of source
files/tables. We add two more columns to the schema, one to flag whether the record got rejected in
ETL, and the other to identify the date when the data was sent by the source system. Having a
database gives us an option of easily create applications to access and update the data in the landing
area.
Please note that adding a database in the landing area adds the infrastructure and maintenance costs.
Adding the database would also increase the number of processes in the extraction process, thereby
affecting the performance of ETL.
1.2. Infrastructure Exception Handling
Infrastructure related exceptions are caused because of issues in the Network connectivity, the
Database operations and the Operating System.
Common Infrastructure exceptions are
Page 7 of 13
8. Database Errors like db connection error, Referential integrity constraint failure, primary key
constraint failure, incorrect credentials, data type mismatch, Null in Not Null fields.
Network connection failure causing FTP failure.
Operating system issues on ETL server full causing aborts due to memory insufficiency, un-
mounted file systems, 100% CPU utilization, incorrect file/directory permissions.
The diagram below depicts the
exceptions and the process to handle
them.
The process of detecting the
abovementioned exception is generally
caught by the ETL scheduler which
checks whether there is a non zero value
returned by the ETL process.
If an exception occurs, we make a log
entry, send email or alerts to the users
to notify that the ETL process has
aborted and exit to the Operating
System with a Non Zero value.
The notification process alerts the IS
team to take appropriate action so that
the ETL process can be restarted once
the infrastructure issue is resolved.
Page 8 of 13
9. 1.3. Data Correction in DWH
The data in the DWH could be
incorrect or inaccurate due to a
variety of reasons, mainly
1. Incorrect requirement or
missed, leading to incorrect ETL.
2. Incorrect interpretation
of requirements leading to
incorrect ETL.
3. Uncaught coding defects.
4. Incorrect data from
source.
The reason 1, 2, and 3 would
require us to revisit the ETL code
with respect to the incorrect
requirements, missed
requirements and uncaught
defects.
The figure below depicts the
process to be followed to correct
the data already loaded in DWH.
Detection
Most important is the detection
of the inaccurate or incorrect
data in DWH. Incorrect data
loaded in DWH is usually
detected long after the it has
been loaded when some end-user
identifies it in his/her report.
Analysis
Once reported, we analyze the
report and its metadata. This
would require understanding the
report metadata, calculation and
the SQL generated by the report.
Page 9 of 13
10. If there is a no issue in the report definition, we analyze the data in DWH. Once we have pin pointed
the table, attributes and the data in DWH where the inaccuracy is, we perform the root cause of the
inaccuracy.
The root cause would require us to check the data with respect to the requirements, design and code.
The root cause helps us identify the next course of action.
Missing Requirements - If the root cause is massing requirements, then we go to the users and get the
complete requirements.
Misinterpretation of Requirements - Here too we go to the end user and clarify on the misinterpreted
requirement.
Defect in the code - There is a possibility of missing detecting bugs during the testing phase. If
undetected, the bug could cause inaccuracy in data.
Correction Process
In case of missing requirements,
1. Get the new requirements from the users.
2. Document the new requirements.
3. Design the new ETL.
4. Code the new ETL.
5. Test the new ETL.
6. Make the DWH offline.
7. Perform the History Load for the new Requirements. This could be possible only when we have
added new tables or new attributes in the data model.
8. Check the report for new requirements.
9. If the reports are correct, then implement the new ETL into the regular ETL.
10. Perform the catch-up load for the duration the DWH was offline.
11. Bring the DWH online.
In case of misinterpreted requirements or undetected bugs,
1. Analyze the ETL and identify the changes in it.
2. Update the design.
3. Correct the code.
4. Test the code.
5. Create a patch to update the historical data (data already in DWH) to correct it.
6. Test the patch.
7. Bring the DWH offline.
8. Run the patch.
9. Check the report for correction.
10. If the reports are correct, then implement the corrected ETL.
11. Perform the catch-up load for the duration the DWH was offline.
12. Bring the DWH online.
Page 10 of 13
11. 2. Error Processing – High Level
The error processing in Target is unique and flawless.
2.1. Capturing
All the various source system data is dumped into the landing area as is. All the records
in the landing area are marked as valid in the first instance during the load.
On a given schedule, the records are processed from landing area to the staging area
and all the business validation are executed on these records. Once the staging load is
finished, all the records which have not been loaded into the staging area are marked as
invalid record in landing area.
Information of all the rejected records which have failed will be stored into the error
tables with error code. There is another table having all reference to the error code.
Depending on the table(s), we would have multiple business validations for a each
record. Hence could end up having multiple entries in the error table(s) for a given
source record.
The records which have been marked as invalid would be processed for every staging
load until they are purged or if a corrected record is sent from the source.
2.2. Error threshold
If the no. of rejections reach a given threshold limit, mail is sent to EAM / Business data
quality team informing the abnormal behavior and job is aborted.
Page 11 of 13
12. Based on the feedback the jobs are rerun/re-triggered manually.
2.3. Purging
Purging is to delete the previous records which are no more required by a given
business process.
Following are the logic applied on various data.
Purging logic is based on the following:-
2.3.1. Landing Area
1. Valid records – Valid records which have been loaded into the Staging area
will retain only previous 7 days of data. Rest will be purged.
2. Invalid records - Invalid records which have been errored out from Staging
area will be retained for 30 days. Rest will be purged.
2.3.2. Staging Area
Truncate and load. An Area where we load and make sure data is good before
we do any changes to warehouse table.
2.3.3. EDW
Depending on Business need, data is maintained in EDW.
2.3.4. Datamart
Depending on Business need, data is maintained in EDW.
2.4. Purge threshold
During purging, the business can set a threshold limit to the number of records being
purged. If while deleting the threshold limit is crossed. The Purge jobs are automatically
aborted and a mail sent to the EAM / Business data quality team for confirmation.
Once the business confirms, the aborted jobs are later triggered manually.
2.5. Appendix
2.5.1. About Target
TBU
Page 12 of 13
13. 2.5.2. Reference
The Exception Handling Overview is an extract from www.dwhinfo.com written
by Krishan.Vinayak@target.com
2.5.3. Other Contributors
Krishan.Vinayak – Delivery Manager
Devanathan.Rajagopalan – Senior Technical Architect
Asis.Mohanty – BI Manager
Joseph.Raj – Technical Architect
Page 13 of 13