1) Incremental migration with IMIG requires exporting larger tables offline.
2) In SAP versions 4.X, the TOC file is used to restart exports.
3) For a 4.6x system migration, the R3load release must match the SAP kernel release.
This document discusses SAP system copy methods and processes. It describes heterogeneous and homogeneous system copies when the operating system, database system, or both change. Methods covered include client export/import, database refresh, R3Load&Jload, IMIG, and third party tools. The document outlines the architecture and processes for ABAP and Java data export/import, including package and table splitting techniques used to optimize runtimes. Post migration testing activities are also listed.
An AMIS Overview of Oracle database 12c (12.1)Marco Gralike
Presentation used by Lucas Jellema and Marco Gralike during the AMIS Oracle Database 12c Launch event on Monday the 15th of July 2013 (much thanks to Tom Kyte, Oracle, for being allowed to use some of his material)
M.
1. The document provides a detailed procedure for performing a homogeneous system copy of an SAP ECC 6.0 system from a source to target system using either the backup/restore or detach/attach method in Microsoft SQL Server.
2. The procedure is a 25 step process that includes creating Java export files, restoring the database, running SAP's system copy tool, installing licenses, reinitializing transports, and verifying connections.
3. Pre-copy requirements include backing up the source system, saving custom settings and configurations, identifying transports, and communicating the cut-over process.
Presentation review best ways to accomplish database load testing and analysis of database performance. Presentation targeting major RDBMS:systems - Oracle and SQL Server as well as tolls necessary for database load testing, Oracle performance tuning, SQL Server performance tuning, Windows and Linux performance optimization
This document discusses several methods for moving data in and out of Oracle databases, including:
- Using directory objects and SQL*Loader to load data from external files
- Exporting and importing data between databases using Oracle Data Pump utilities like expdp and impdp
- Creating external tables to query data files in a platform-independent manner and move data without importing
It provides overviews and examples of how to use each method, focusing on SQL*Loader, Data Pump, and external tables. It also covers related topics like performance optimization and monitoring Data Pump jobs.
Oracle supports cloning and users can easily clone existing Oracle installations. But you need to understand why cloning is useful.
This presentation covers following Topics.
When cloning useful
Different methods of Cloning
How to perform Cloning
Summary
Q&A
Presented at the Dallas Oracle Users Group
By Nabil Nawaz
sponsored by BIAS Corporation
Oracle DataPump is an excellent tool for cloning databases and schemas and it is widely used as a common toolset today among DBAs and Developers to transfer data and structure between databases. Please come and learn about new Data pump features for Oracle version 12.2. We will also be sharing a case study for a large multi-terabyte database for optimizing a data pump import process that originally ran for more than a day and then the process was tuned to run in just about 4-6 hours a nearly 90% performance enhancement. The tips that will be shared will be of great value and help to ensure you are able to have a well-tuned import process with DataPump.
Log shipping is a process that automates backing up database and transaction log files from a production SQL Server to a standby server. It keeps databases in sync by backing up transaction logs on an interval and restoring them on the standby. Log shipping consists of a primary server that is backed up and a secondary server that hosts the copies. It requires SQL Server Agent jobs to be configured for backup, copy, and restore operations between servers. Log shipping provides high availability but requires manual failover and can result in some data loss depending on backup frequency. It can be combined with other options like mirroring or replication.
This document discusses SAP system copy methods and processes. It describes heterogeneous and homogeneous system copies when the operating system, database system, or both change. Methods covered include client export/import, database refresh, R3Load&Jload, IMIG, and third party tools. The document outlines the architecture and processes for ABAP and Java data export/import, including package and table splitting techniques used to optimize runtimes. Post migration testing activities are also listed.
An AMIS Overview of Oracle database 12c (12.1)Marco Gralike
Presentation used by Lucas Jellema and Marco Gralike during the AMIS Oracle Database 12c Launch event on Monday the 15th of July 2013 (much thanks to Tom Kyte, Oracle, for being allowed to use some of his material)
M.
1. The document provides a detailed procedure for performing a homogeneous system copy of an SAP ECC 6.0 system from a source to target system using either the backup/restore or detach/attach method in Microsoft SQL Server.
2. The procedure is a 25 step process that includes creating Java export files, restoring the database, running SAP's system copy tool, installing licenses, reinitializing transports, and verifying connections.
3. Pre-copy requirements include backing up the source system, saving custom settings and configurations, identifying transports, and communicating the cut-over process.
Presentation review best ways to accomplish database load testing and analysis of database performance. Presentation targeting major RDBMS:systems - Oracle and SQL Server as well as tolls necessary for database load testing, Oracle performance tuning, SQL Server performance tuning, Windows and Linux performance optimization
This document discusses several methods for moving data in and out of Oracle databases, including:
- Using directory objects and SQL*Loader to load data from external files
- Exporting and importing data between databases using Oracle Data Pump utilities like expdp and impdp
- Creating external tables to query data files in a platform-independent manner and move data without importing
It provides overviews and examples of how to use each method, focusing on SQL*Loader, Data Pump, and external tables. It also covers related topics like performance optimization and monitoring Data Pump jobs.
Oracle supports cloning and users can easily clone existing Oracle installations. But you need to understand why cloning is useful.
This presentation covers following Topics.
When cloning useful
Different methods of Cloning
How to perform Cloning
Summary
Q&A
Presented at the Dallas Oracle Users Group
By Nabil Nawaz
sponsored by BIAS Corporation
Oracle DataPump is an excellent tool for cloning databases and schemas and it is widely used as a common toolset today among DBAs and Developers to transfer data and structure between databases. Please come and learn about new Data pump features for Oracle version 12.2. We will also be sharing a case study for a large multi-terabyte database for optimizing a data pump import process that originally ran for more than a day and then the process was tuned to run in just about 4-6 hours a nearly 90% performance enhancement. The tips that will be shared will be of great value and help to ensure you are able to have a well-tuned import process with DataPump.
Log shipping is a process that automates backing up database and transaction log files from a production SQL Server to a standby server. It keeps databases in sync by backing up transaction logs on an interval and restoring them on the standby. Log shipping consists of a primary server that is backed up and a secondary server that hosts the copies. It requires SQL Server Agent jobs to be configured for backup, copy, and restore operations between servers. Log shipping provides high availability but requires manual failover and can result in some data loss depending on backup frequency. It can be combined with other options like mirroring or replication.
Apache Flink enables stream processing on continuously produced data through its DataStream and DataSet APIs. It allows for streaming and batch processing as first class citizens. Flink programs are composed of sources that ingest data, transformations on those data streams, and sinks that output the results. Queryable state in Flink allows for querying the system state without writing to an external database, improving performance over traditional architectures that rely on writing intermediate results to external key-value stores. Flink's use of lightweight snapshots for fault tolerance and its log-based approach to persistence allows queryable state to have high throughput and low latency.
R12.2 introduces several new features for database administrators including:
1. Dual file system support for online patching which uses a patch edition to apply updates while the run edition remains online.
2. Simplified installation procedures using Rapidwiz which can install directly onto existing RAC clusters or ASM.
3. The new adop utility automates the online patching process across multiple nodes using edition-based redefinition and cross-edition triggers.
4. Changes to the file system layout separate application code and configuration files to improve maintenance.
The document provides an overview of Oracle 10g database architecture including its physical and logical structures as well as processes. Physically, a database consists of datafiles, redo logs, and control files. Logically, it is divided into tablespaces containing schemas, segments, and other objects. The Oracle instance comprises the system global area (SGA) shared memory and background processes that manage tasks like writing redo logs and checkpointing data blocks. User processes connect to the database through sessions allocated in the program global area.
Oracle 21c: New Features and Enhancements of Data Pump & TTSChristian Gohmann
At the end of the year 2020, Oracle released 21c on its Cloud infrastructure. The on-premises version will follow later this year. As with every new Oracle version, the Data Pump utility gets new features or enhancements for existing features.
This presentation gives an overview of the enhancements of Data Pump and Transportable Tablespaces. The following list is an excerpt of the points I will talk about
- Simultaneous use of EXCLUDE and INCLUDE
- Parallelized import of metadata during a TTS import operation
- Checksum support for dump files
- Direct access to Oracle Cloud Object Store for exports and imports
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
Introduction to Apache Apex - The next generation native Hadoop platform. This talk will cover details about how Apache Apex can be used as a powerful and versatile platform for big data processing. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch alerts, real-time actions, threat detection, etc.
Bio:
Pramod Immaneni is Apache Apex PMC member and senior architect at DataTorrent, where he works on Apache Apex and specializes in big data platform and applications. Prior to DataTorrent, he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs.
Flink Forward Berlin 2017: Tzu-Li (Gordon) Tai - Managing State in Apache FlinkFlink Forward
Over the past year, we've seen users build entire event-driven applications such as social networks on top of Apache Flink (Drivetribe.com), elevating the importance of state management in Flink to a whole new level. Users are placing more and more data as state in Flink, using it as a replacement to conventional databases. With such mission-critical data entrusted to Flink, we need to provide similar capabilities of a database to users. One of such capabilities is being flexible in how data is persisted and represented. Specifically, how can I change how my state is serialized and stored, or even the schema of my state, as business logic changes over time? In this talk, we'll provide details on the latest state management features in Flink that allows users to do exactly that. We'll talk about how Flink manages the state for you, how it provides flexibility to the user to adapt to evolving state serialization formats and schemas, and the best practices when working with it.
Oracle 12c offers many new features and upgrading database can bring many advantages to organization. There are various upgrade and migration methods available and the best method to use for your upgrade/migration scenario depends on the source database version, the source and destination operating systems, your downtime requirements, and the personal preference of the DBA. Based upon factors there is a method available to best fit your organization needs.
Ameerpet Online Training gives you an effective and proven online learning option with an extensive learning catalog and the freedom to attend from virtually anywhere. We have trained nearly 1500+ Students on all technologies.
We are offering 10% off on Oracle Training and we will arrange a free demo at your flexible timings
New Features for Database Administrator of Oracle 12c Database. Here are some of excellent Oracle 12c new features with examples for learning purpose. SQL,Backup and recovery, Database management, Oracle RAC, Oracle ASM included.
This document provides an overview of Oracle 12c Sharded Database Management. It defines what sharding is, how it works, and the benefits it provides such as extreme scalability, fault isolation, and cost reduction. It discusses Oracle's implementation of sharding using database partitioning and Global Data Services (GDS). Key concepts covered include shards, chunks, consistent hashing, and how Oracle supports operations across shards through GDS request routing.
Ingesting Data from Kafka to JDBC with Transformation and EnrichmentApache Apex
Presenter - Dr Sandeep Deshmukh, Committer Apache Apex, DataTorrent engineer
Abstract:
Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. Apache Apex Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. Apache Apex Data Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project.
In this series of talks, we would cover how Hadoop Ingestion is made easy using Apache Apex. The third talk in this series would focus on ingesting unbounded data from Kafka to JDBC with couple of processing operators -Transform and enrichment.
This document discusses connecting Hadoop and Oracle databases. It introduces the author Tanel Poder and his expertise in databases and big data. It then covers tools like Sqoop that can be used to load data between Hadoop and Oracle databases. It also discusses using query offloading to query Hadoop data directly from Oracle as if it were in an Oracle database.
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive PresentationNabil Nawaz
This document provides an overview of Oracle Dataguard including:
- Dataguard allows configuration of up to 30 physical or logical standby databases for high availability and disaster recovery.
- It provides benefits such as offloading backups and reporting without impacting primary database performance.
- Key concepts include primary and standby databases, redo transport, and different protection modes for data replication.
Oracle dba interview questions with answerupenpriti
This document contains 10 questions about Oracle DBA interview questions and their answers. It covers topics like components of the SGA, the order in which Oracle processes SQL statements, mandatory datafiles for an Oracle 11g database, and how sessions communicate with the database. The questions test knowledge of Oracle architecture, processes, memory structures, and common administrative tasks.
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Stream data processing is becoming increasingly important to support business needs for faster time to insight and action with growing volume of information from more sources. Apache Apex (http://apex.apache.org/) is a unified big data in motion processing platform for the Apache Hadoop ecosystem. Apex supports demanding use cases with:
* Architecture for high throughput, low latency and exactly-once processing semantics.
* Comprehensive library of building blocks including connectors for Kafka, Files, Cassandra, HBase and many more
* Java based with unobtrusive API to build real-time and batch applications and implement custom business logic.
* Advanced engine features for auto-scaling, dynamic changes, compute locality.
Apex was developed since 2012 and is used in production in various industries like online advertising, Internet of Things (IoT) and financial services.
PostgreSQL has advanced in many ways but bloat remains a challenge. A solution for this in development is zheap, a new storage format in which only the latest version of the data is kept in main storage and the old version will be moved to an undo log. In this presentation delivered at Postgres Vision 2018, Robert Haas, a Major Contributor to the PostgreSQL project who is leading development of zheap at EnterpriseDB, where he is Vice President, Chief Database Architect, explains the project.
Oracle Database 12c offers new enhancements and additions in Recovery Manager (RMAN). The features listed in this article will help you transport data across platforms and reduce downtime by 8x versus tradition migration approach, recover table and table partitions to point-in-time without affecting other objects in the database, and audit RMAN-related events using unified auditing. Take advantage of these new features for efficient backup and recovery.
Oracle Database 12c introduces several new features including pluggable databases (PDB) that allow multiple isolated databases to be consolidated within a single container database (CDB). It also introduces new administrative privileges (SYSBACKUP, SYSDG, SYSKM) and features such as transparent data encryption, invisible columns, object tables, and enhancements to RMAN and SQL.
Checkpointing saves database status and changes to data files. It reduces recovery time after a fault. Regular checkpointing is performed automatically or manually. Reasons redo logs may not delete after checkpointing include active transactions or archiving in Archivelog mode. Media recovery restores missing or corrupted data files using backups, archive logs, and log anchor files in Archivelog mode.
This document provides an overview and comparison of Informix's streaming technologies: Change Data Capture (CDC), Smart Triggers, Asynchronous Triggers, and V-II Socket Streaming. CDC processes database transaction logs to capture all changes and send them to clients. Smart Triggers use selective triggers and filtering to capture specific data changes. Asynchronous Triggers use post-commit triggers to route data to user-defined routines. V-II Socket Streaming sends triggered data to MQTT brokers but is not officially supported. The document also includes code examples and diagrams demonstrating how these technologies integrate with applications.
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...Flink Forward
In Zalando's microservice architecture, each service continuously generates streams of events for the purposes of inter-service communication or data integration. Some of these events describe business processes, e.g. a customer has placed an order or a parcel has been shipped. Out of this, the need to materialize event streams from the central event bus into persistent cloud storage evolved. The temporarily persisted data is then integrated into our relational data warehouse. In this talk we present a materialization engine backed by Apache Flink. We show how we employ Flink’s RESTful API, custom accumulators and stoppable sources to provide another API abstraction layer for deploying, monitoring and controlling our materialization jobs. Our jobs compact event streams depending on event properties and transform their complex JSON structures into flat files for easier integration into the data warehouse.
Apache Flink enables stream processing on continuously produced data through its DataStream and DataSet APIs. It allows for streaming and batch processing as first class citizens. Flink programs are composed of sources that ingest data, transformations on those data streams, and sinks that output the results. Queryable state in Flink allows for querying the system state without writing to an external database, improving performance over traditional architectures that rely on writing intermediate results to external key-value stores. Flink's use of lightweight snapshots for fault tolerance and its log-based approach to persistence allows queryable state to have high throughput and low latency.
R12.2 introduces several new features for database administrators including:
1. Dual file system support for online patching which uses a patch edition to apply updates while the run edition remains online.
2. Simplified installation procedures using Rapidwiz which can install directly onto existing RAC clusters or ASM.
3. The new adop utility automates the online patching process across multiple nodes using edition-based redefinition and cross-edition triggers.
4. Changes to the file system layout separate application code and configuration files to improve maintenance.
The document provides an overview of Oracle 10g database architecture including its physical and logical structures as well as processes. Physically, a database consists of datafiles, redo logs, and control files. Logically, it is divided into tablespaces containing schemas, segments, and other objects. The Oracle instance comprises the system global area (SGA) shared memory and background processes that manage tasks like writing redo logs and checkpointing data blocks. User processes connect to the database through sessions allocated in the program global area.
Oracle 21c: New Features and Enhancements of Data Pump & TTSChristian Gohmann
At the end of the year 2020, Oracle released 21c on its Cloud infrastructure. The on-premises version will follow later this year. As with every new Oracle version, the Data Pump utility gets new features or enhancements for existing features.
This presentation gives an overview of the enhancements of Data Pump and Transportable Tablespaces. The following list is an excerpt of the points I will talk about
- Simultaneous use of EXCLUDE and INCLUDE
- Parallelized import of metadata during a TTS import operation
- Checksum support for dump files
- Direct access to Oracle Cloud Object Store for exports and imports
Intro to Apache Apex - Next Gen Platform for Ingest and TransformApache Apex
Introduction to Apache Apex - The next generation native Hadoop platform. This talk will cover details about how Apache Apex can be used as a powerful and versatile platform for big data processing. Common usage of Apache Apex includes big data ingestion, streaming analytics, ETL, fast batch alerts, real-time actions, threat detection, etc.
Bio:
Pramod Immaneni is Apache Apex PMC member and senior architect at DataTorrent, where he works on Apache Apex and specializes in big data platform and applications. Prior to DataTorrent, he was a co-founder and CTO of Leaf Networks LLC, eventually acquired by Netgear Inc, where he built products in core networking space and was granted patents in peer-to-peer VPNs.
Flink Forward Berlin 2017: Tzu-Li (Gordon) Tai - Managing State in Apache FlinkFlink Forward
Over the past year, we've seen users build entire event-driven applications such as social networks on top of Apache Flink (Drivetribe.com), elevating the importance of state management in Flink to a whole new level. Users are placing more and more data as state in Flink, using it as a replacement to conventional databases. With such mission-critical data entrusted to Flink, we need to provide similar capabilities of a database to users. One of such capabilities is being flexible in how data is persisted and represented. Specifically, how can I change how my state is serialized and stored, or even the schema of my state, as business logic changes over time? In this talk, we'll provide details on the latest state management features in Flink that allows users to do exactly that. We'll talk about how Flink manages the state for you, how it provides flexibility to the user to adapt to evolving state serialization formats and schemas, and the best practices when working with it.
Oracle 12c offers many new features and upgrading database can bring many advantages to organization. There are various upgrade and migration methods available and the best method to use for your upgrade/migration scenario depends on the source database version, the source and destination operating systems, your downtime requirements, and the personal preference of the DBA. Based upon factors there is a method available to best fit your organization needs.
Ameerpet Online Training gives you an effective and proven online learning option with an extensive learning catalog and the freedom to attend from virtually anywhere. We have trained nearly 1500+ Students on all technologies.
We are offering 10% off on Oracle Training and we will arrange a free demo at your flexible timings
New Features for Database Administrator of Oracle 12c Database. Here are some of excellent Oracle 12c new features with examples for learning purpose. SQL,Backup and recovery, Database management, Oracle RAC, Oracle ASM included.
This document provides an overview of Oracle 12c Sharded Database Management. It defines what sharding is, how it works, and the benefits it provides such as extreme scalability, fault isolation, and cost reduction. It discusses Oracle's implementation of sharding using database partitioning and Global Data Services (GDS). Key concepts covered include shards, chunks, consistent hashing, and how Oracle supports operations across shards through GDS request routing.
Ingesting Data from Kafka to JDBC with Transformation and EnrichmentApache Apex
Presenter - Dr Sandeep Deshmukh, Committer Apache Apex, DataTorrent engineer
Abstract:
Ingesting and extracting data from Hadoop can be a frustrating, time consuming activity for many enterprises. Apache Apex Data Ingestion is a standalone big data application that simplifies the collection, aggregation and movement of large amounts of data to and from Hadoop for a more efficient data processing pipeline. Apache Apex Data Ingestion makes configuring and running Hadoop data ingestion and data extraction a point and click process enabling a smooth, easy path to your Hadoop-based big data project.
In this series of talks, we would cover how Hadoop Ingestion is made easy using Apache Apex. The third talk in this series would focus on ingesting unbounded data from Kafka to JDBC with couple of processing operators -Transform and enrichment.
This document discusses connecting Hadoop and Oracle databases. It introduces the author Tanel Poder and his expertise in databases and big data. It then covers tools like Sqoop that can be used to load data between Hadoop and Oracle databases. It also discusses using query offloading to query Hadoop data directly from Oracle as if it were in an Oracle database.
Nabil Nawaz Oracle Oracle 12c Data Guard Deep Dive PresentationNabil Nawaz
This document provides an overview of Oracle Dataguard including:
- Dataguard allows configuration of up to 30 physical or logical standby databases for high availability and disaster recovery.
- It provides benefits such as offloading backups and reporting without impacting primary database performance.
- Key concepts include primary and standby databases, redo transport, and different protection modes for data replication.
Oracle dba interview questions with answerupenpriti
This document contains 10 questions about Oracle DBA interview questions and their answers. It covers topics like components of the SGA, the order in which Oracle processes SQL statements, mandatory datafiles for an Oracle 11g database, and how sessions communicate with the database. The questions test knowledge of Oracle architecture, processes, memory structures, and common administrative tasks.
Apache Big Data EU 2016: Building Streaming Applications with Apache ApexApache Apex
Stream processing applications built on Apache Apex run on Hadoop clusters and typically power analytics use cases where availability, flexible scaling, high throughput, low latency and correctness are essential. These applications consume data from a variety of sources, including streaming sources like Apache Kafka, Kinesis or JMS, file based sources or databases. Processing results often need to be stored in external systems (sinks) for downstream consumers (pub-sub messaging, real-time visualization, Hive and other SQL databases etc.). Apex has the Malhar library with a wide range of connectors and other operators that are readily available to build applications. We will cover key characteristics like partitioning and processing guarantees, generic building blocks for new operators (write-ahead-log, incremental state saving, windowing etc.) and APIs for application specification.
Apache Big Data EU 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
Stream data processing is becoming increasingly important to support business needs for faster time to insight and action with growing volume of information from more sources. Apache Apex (http://apex.apache.org/) is a unified big data in motion processing platform for the Apache Hadoop ecosystem. Apex supports demanding use cases with:
* Architecture for high throughput, low latency and exactly-once processing semantics.
* Comprehensive library of building blocks including connectors for Kafka, Files, Cassandra, HBase and many more
* Java based with unobtrusive API to build real-time and batch applications and implement custom business logic.
* Advanced engine features for auto-scaling, dynamic changes, compute locality.
Apex was developed since 2012 and is used in production in various industries like online advertising, Internet of Things (IoT) and financial services.
PostgreSQL has advanced in many ways but bloat remains a challenge. A solution for this in development is zheap, a new storage format in which only the latest version of the data is kept in main storage and the old version will be moved to an undo log. In this presentation delivered at Postgres Vision 2018, Robert Haas, a Major Contributor to the PostgreSQL project who is leading development of zheap at EnterpriseDB, where he is Vice President, Chief Database Architect, explains the project.
Oracle Database 12c offers new enhancements and additions in Recovery Manager (RMAN). The features listed in this article will help you transport data across platforms and reduce downtime by 8x versus tradition migration approach, recover table and table partitions to point-in-time without affecting other objects in the database, and audit RMAN-related events using unified auditing. Take advantage of these new features for efficient backup and recovery.
Oracle Database 12c introduces several new features including pluggable databases (PDB) that allow multiple isolated databases to be consolidated within a single container database (CDB). It also introduces new administrative privileges (SYSBACKUP, SYSDG, SYSKM) and features such as transparent data encryption, invisible columns, object tables, and enhancements to RMAN and SQL.
Checkpointing saves database status and changes to data files. It reduces recovery time after a fault. Regular checkpointing is performed automatically or manually. Reasons redo logs may not delete after checkpointing include active transactions or archiving in Archivelog mode. Media recovery restores missing or corrupted data files using backups, archive logs, and log anchor files in Archivelog mode.
This document provides an overview and comparison of Informix's streaming technologies: Change Data Capture (CDC), Smart Triggers, Asynchronous Triggers, and V-II Socket Streaming. CDC processes database transaction logs to capture all changes and send them to clients. Smart Triggers use selective triggers and filtering to capture specific data changes. Asynchronous Triggers use post-commit triggers to route data to user-defined routines. V-II Socket Streaming sends triggered data to MQTT brokers but is not officially supported. The document also includes code examples and diagrams demonstrating how these technologies integrate with applications.
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...Flink Forward
In Zalando's microservice architecture, each service continuously generates streams of events for the purposes of inter-service communication or data integration. Some of these events describe business processes, e.g. a customer has placed an order or a parcel has been shipped. Out of this, the need to materialize event streams from the central event bus into persistent cloud storage evolved. The temporarily persisted data is then integrated into our relational data warehouse. In this talk we present a materialization engine backed by Apache Flink. We show how we employ Flink’s RESTful API, custom accumulators and stoppable sources to provide another API abstraction layer for deploying, monitoring and controlling our materialization jobs. Our jobs compact event streams depending on event properties and transform their complex JSON structures into flat files for easier integration into the data warehouse.
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DB2 考试 provides a self-test with 27 multiple choice questions covering various topics related to DB2. The questions test knowledge of topics like DB2 products, utilities, SQL statements, isolation levels, locking, and more. Sample questions include identifying the correct DB2 product required to access a DB2 for OS/390 database from AIX, how to catalog a remote database, and which SQL statement would display data from multiple tables with an outer join.
Managing GCP Projects with Terraform (devfest Pisa 2018)Giovanni Toraldo
Terraform is a tool for building, changing, and versioning infrastructure safely and efficiently. It uses a declarative configuration file to describe infrastructure and allows incremental changes through a plan and apply process. The document provides an overview of Terraform and demonstrates how to set up a Google Cloud Platform project and deploy a virtual machine instance on GCP using Terraform. It also shows how to output the instance's IP address, upgrade the instance's machine type, attach additional disks, and manage multiple instances with disks using variables and counts.
ব্যাংকের কম্পিউটার বিষয়ের প্রস্তুতির জন্য ৩০টি মডেল টেস্ট (মোট এমসিকিউঃ ৩০*৩০=৯০০টি)
প্রত্যেকটি নৈব্যত্তিক প্রশ্ন বিগত সালের ব্যাংকের প্রশ্ন থেকে সংগ্রহ করা হয়েছে
Computer and information model test for bank exams
Oracle 18c includes several new database features including enhancements to multitenant architecture, patching model changes, and new RMAN and data guard capabilities. Key features include improved PDB cloning options, PDB snapshot carousels, CDB fleets for scalability, read-only Oracle homes, and no-logging options for data guard standbys. Developers can test 18c features using free Oracle cloud trial accounts or the 18c Express edition.
The document discusses GitOps and continuous infrastructure using Terraform. It describes how GitOps ensures that every change is driven by a change in source control, with the entire system described declaratively and the desired state versioned in Git. Approved changes can be automatically applied. Software agents ensure correctness and alert on divergence. The presenter then discusses their journey using Terraform over 5 years for various use cases and integrations. Common workflows for GitOps using Terraform Cloud, GitHub Actions, and GitLab Runner are presented.
How to build an ETL pipeline with Apache Beam on Google Cloud DataflowLucas Arruda
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Some osdb migration_question_for_the_certification_tadm70
1. 1) Which of the following statements is true? Regarding incremental migration
a) The export of the bigger tables select by IMIG must be do offline.
B) The IMIG migration make sense only when a few tables have the more date.
2) In the SAP versions 4.X which file is used to restart the export?
-) file TOC;
3) Which must be the release of R3load for the request of the migration key, in a system 4.6x ?
The release of the R3load must be the same of the SAP kernel released for the release 4.6x
4) Which programs create the CMD files ?
SAPINST, R3SETUP and MIGMON.
5) Which of the following statements is true? TABART
A) Every Table can be assigned to one TABART
B) TABART has the dimension of tables
6) For verify if the existing objects in the *.STR files are on DB which control must to be do?
7) Which things are questioned on Remote Audit Project?
8) files TSK:
-) are introduced on version 6.10
-) they has R3load execution parameters
-) Allow the restart management for export import
10) In which situation must be “CALLED” an OS/DB migration consultant?
When we are doing an Heterogeneous System Copy, in every kind of system (development, test and
production).
11) If we change an entry in task file from ‘ok’ to ‘err’ what must happen at restart the import with R3load?
11) When we are doing a Heterogeneous System Copy?
When we are changing DB or OS.
.
13) Which program produce the DBSIZE.TPL file on 4.X migrations ?
The program R3SETUP
14) R3load options
socket , continue_on_error ..
15) Which program produces the EXT files on 6.X migrations ?
2. The program R3szchk
16) Which program produce the DDL<DBS>.TPL?
The program R3ldclt
17) merge_bck option of R3load
When the merge of task file finish, the import process start automatically
The option merge_bck change the status of the entries xeq to err in task files
18) If own system crash for “POWER FAILURE” in a 4.X migration which action can be do?
Delete TOC, DUMP and LOG files from the package that was interrupted at crash time
19) When is not possible do a migration with R3LOAD ?
When an INCV conversion has not completed
When the PREPARE phase has been started
20) From which program has been created the R3LOAD TSK files ?
From R3LOAD
21) In which directory is created the file DDL<DBS>.TPL ?
a) install directory
b) data
c) DB
d) DB/DBS
e) Dump directory
22) When a certified consultant for OS/DB migration is supported by SAP ?
When use the tools released for SAP
23 ) Which kind of thinks is able to do MIGMON ??
a) Copy dump files with rcp command
24) if a customer have an CUSTOM tablespace with the correct TABART, but after reorganization activities the
tablespace can be placed on standard tablespace without modify the TABART. Indicate if the follow sentences
are true or false:
A) The export R3LOAD is interrupted because doesn’t found the correct tablespace.
B) R3SIZECHK can be able to calculate the dimension without any problem
3. 25) Which files/directory must be copied from source to target system?
a) I command files?
b) Installation directory ?
c) Dump files ?
d) Task files ?
e) DB directory ?
26) DUMP file
a) In the dump file can be found the information for the follow file dump for be imported
b) Is created by R3LOAD