All about Oracle Golden Gate by Udaya Kumar Pyla

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The document discusses Oracle Golden Gate, a data replication tool. It provides an overview of Golden Gate's components including the manager, extract, trails, data pump, collector, and replicat. It also covers the logical architecture and supported topologies. Golden Gate enables real-time data replication across heterogeneous databases and platforms.

Datos y análisis

Copyright © 2017 Tech Mahindra. All rights reserved.
Oracle Golden Gate
By
Uday Kumar P

Copyright © 2017 Tech Mahindra. All rights reserved.
 Introduction & Overview of Golden Gate
 Golden Gate components
 Logical Architecture
 Supported Topologies
Agenda

Copyright © 2017 Tech Mahindra. All rights reserved.
Introduction & Overview of Golden Gate
What is Oracle Golden Gate ?
Oracle’s Strategic Data Replication solution
Delivers Real-time access of Real-time information
Improves the availability, reliability and performance of
critical data across the enterprise

Copyright © 2017 Tech Mahindra. All rights reserved.
 Enables the exchange and manipulation of data at
transaction level
 Available across multiple, heterogeneous platforms
across the enterprise
 Modular architecture gives flexibility to extract and
replicate selected data records, transactional changes
and changes to DDL across a variety of topologies.
Introduction & Overview of Golden Gate Cont..
What does Golden Gate do ?

Copyright © 2017 Tech Mahindra. All rights reserved.
Golden Gate Components
 Manager
 Extract
 Trails
 Data Pump
 Collector
 Replicat

Copyright © 2017 Tech Mahindra. All rights reserved.
Overview of Manager
 Control process of Oracle Golden Gate
 Must be running on each system in the Oracle Golden
Gate configuration
 One manger process can control many extract or replicat
processes

Copyright © 2017 Tech Mahindra. All rights reserved.
Overview of Extract
 Extraction Mechanism in Oracle Golden Gate
 Can be configured for the following
 Initial loads
 Change Synchronization
 Can be captured from a data source of :
 Table
 Database Recovery logs or Transactional Logs
 Third party capture module

Copyright © 2017 Tech Mahindra. All rights reserved.
Overview of Trails
 Supports the continuous extraction and replication of
database changes
 Golden Gate stores the records of captured changes
temporarily on disk in a series of files called a trail
 A trail can exist on
 Source system (Extract Trail)
 Intermediary system
 Target System (Remote Trail)

Copyright © 2017 Tech Mahindra. All rights reserved.
Overview of Data Pump
 Secondary extract group within the source
 Primary extract group “Extract” writes to a trail on the
source
 Data Pump reads the trail and sends the data over the
network to a remote trail on the target
 Can perform data filtering, mapping and conversion or it
can be configured pass through mode.

Copyright © 2017 Tech Mahindra. All rights reserved.
Overview of Collector
 Runs on the target system
 Receives extracted database changes that are sent by
extract and write them to a Trail file
 Can receive information from only one Extract process,
So there is one collector for each extract that you use.
 Collector terminates when the associated extract process
terminates

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Overview of Replicat
 Runs on the target system
 Reads the trail on the target system
 Re-constructs the DML or DDL operation and applies
them to the target database
 Like the extract can be configured for initial loads and
change synchronization

Copyright © 2017 Tech Mahindra. All rights reserved.
Golden Gate Supports
 Business continuance & High Availability
 Zero Downtime migration
 Data Integration
 Live Reporting, Real time Data Warehousing
 Live Standby database
 Supports Heterogeneous environments.
Multiple business requirements:

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Supported Databases & Methods

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Logical Architecture of Golden Gate

Copyright © 2017 Tech Mahindra. All rights reserved.
Supported Topologies

Copyright © 2017 Tech Mahindra. All rights reserved.
Source / Prod Server
Golden gate replication
Require Live Production Data
Target Server
Requesting
data
Performance
impact
Load
Balancing
Live data
from Target
Real time access scenario

Copyright © 2017 Tech Mahindra. All rights reserved.

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Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

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As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.

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3. Copyright © 2017 Tech Mahindra. All rights reserved. Introduction & Overview of Golden Gate What is Oracle Golden Gate ? Oracle’s Strategic Data Replication solution Delivers Real-time access of Real-time information Improves the availability, reliability and performance of critical data across the enterprise

4. Copyright © 2017 Tech Mahindra. All rights reserved.  Enables the exchange and manipulation of data at transaction level  Available across multiple, heterogeneous platforms across the enterprise  Modular architecture gives flexibility to extract and replicate selected data records, transactional changes and changes to DDL across a variety of topologies. Introduction & Overview of Golden Gate Cont.. What does Golden Gate do ?

6. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Manager  Control process of Oracle Golden Gate  Must be running on each system in the Oracle Golden Gate configuration  One manger process can control many extract or replicat processes

7. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Extract  Extraction Mechanism in Oracle Golden Gate  Can be configured for the following  Initial loads  Change Synchronization  Can be captured from a data source of :  Table  Database Recovery logs or Transactional Logs  Third party capture module

8. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Trails  Supports the continuous extraction and replication of database changes  Golden Gate stores the records of captured changes temporarily on disk in a series of files called a trail  A trail can exist on  Source system (Extract Trail)  Intermediary system  Target System (Remote Trail)

9. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Data Pump  Secondary extract group within the source  Primary extract group “Extract” writes to a trail on the source  Data Pump reads the trail and sends the data over the network to a remote trail on the target  Can perform data filtering, mapping and conversion or it can be configured pass through mode.

10. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Collector  Runs on the target system  Receives extracted database changes that are sent by extract and write them to a Trail file  Can receive information from only one Extract process, So there is one collector for each extract that you use.  Collector terminates when the associated extract process terminates

11. Copyright © 2017 Tech Mahindra. All rights reserved. Overview of Replicat  Runs on the target system  Reads the trail on the target system  Re-constructs the DML or DDL operation and applies them to the target database  Like the extract can be configured for initial loads and change synchronization

12. Copyright © 2017 Tech Mahindra. All rights reserved. Golden Gate Supports  Business continuance & High Availability  Zero Downtime migration  Data Integration  Live Reporting, Real time Data Warehousing  Live Standby database  Supports Heterogeneous environments. Multiple business requirements:

16. Copyright © 2017 Tech Mahindra. All rights reserved. Source / Prod Server Golden gate replication Require Live Production Data Target Server Requesting data Performance impact Load Balancing Live data from Target Real time access scenario

17. Copyright © 2017 Tech Mahindra. All rights reserved. INDIA USA China Business User BI Data Replicating Data Replicating Data Replicating User requesting data Data received Item Brand Qty Gend er Jeans Lee Cooper 300 Male Jacket Roadste r 150 Female Item Brand Qty Gend er Jeans Lee Cooper 200 Male Jacket Roadste r 200 Female Item Brand Qty Gend er Jeans Lee Cooper 120 Male Jacket Roadste r 400 Female Item Country Total Qty Jeans India 200 Jeans USA 300 Jeans China 120 Business Scenario

All about Oracle Golden Gate by Udaya Kumar Pyla

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