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Different Task Types Available in CDI (Cloud Data Integration)
Introduction
Cloud
Data Integration (CDI) is a crucial component of modern data-driven
businesses, enabling seamless data movement and transformation across diverse
cloud and on-premises environments. Informatica Cloud Data Integration (CDI)
offers various task types to help organizations manage, process, and transform
their data efficiently. Understanding these task types is essential for
optimizing data workflows and ensuring smooth operations. Informatica
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Different Task Types Available in CDI (Cloud Data Integration) |
1. Mapping Task
A Mapping Task in CDI is designed to execute data integration
mappings created using the Mapping Designer. It allows users to define
source-to-target data flows with transformations applied in between. Mapping
tasks support multiple data sources, transformations, and load strategies,
ensuring seamless data movement and transformation.
Key
Features:
·
Executes predefined mappings
·
Supports advanced transformations like aggregation, filtering, and
lookup
·
Handles complex data integration workflows
2. Synchronization Task
A Synchronization Task is used to synchronize data between
different sources and targets. It is typically employed to keep cloud
applications, databases, or data warehouses updated with the latest data. Informatica
IICS Training
Key
Features:
·
Supports incremental and full data synchronization
·
Provides multiple scheduling options for automated updates
·
Allows data replication between on-premises and cloud systems
3. Replication Task
A Replication Task enables users to replicate data from a source
system to a target system without complex transformations. It is useful for
backup, disaster recovery, and data archival purposes.
Key
Features:
·
Allows full or incremental data replication
·
Supports relational and cloud databases
·
Offers automatic schema drift handling
4. Data Transfer Task
A Data
Transfer Task is designed for quickly transferring data from one
location to another without applying transformations. It is commonly used for
moving bulk data across cloud storage services and databases.
Key
Features:
·
Supports high-volume data movement
·
Works with multiple cloud storage solutions
·
Provides easy scheduling and automation
5. Masking Task
A Masking Task is used for data security by applying data masking
techniques to sensitive information. This is particularly useful for compliance
with regulations like GDPR and HIPAA. Informatica
Cloud Training
Key
Features:
·
Protects sensitive data using dynamic or static masking
·
Ensures compliance with data privacy standards
·
Supports various data masking techniques (e.g., substitution,
encryption, shuffling)
6. Taskflow
A Taskflow helps automate and orchestrate multiple CDI tasks in a
predefined sequence. It is used for building complex workflows involving
dependencies and conditions.
Key
Features:
·
Allows sequential and parallel execution of tasks
·
Supports decision-making using conditions
·
Integrates with external applications and APIs
7. Linear Taskflow
A Linear
Taskflow is a simplified version of taskflows where tasks execute in a
sequential manner without complex branching logic.
Key
Features:
·
Executes tasks one after another
·
Best suited for straightforward workflows
·
Provides basic monitoring and error-handling capabilities
8. PowerCenter Task
A PowerCenter Task is designed for users migrating from
Informatica PowerCenter to CDI. It allows running PowerCenter workflows within
the cloud environment. IICS
Online Training
Key
Features:
·
Enables migration of on-premises PowerCenter processes to the cloud
·
Supports PowerCenter transformations and mappings
·
Ensures backward compatibility with PowerCenter assets
Conclusion
CDI
provides a range of task types to facilitate seamless data integration,
transformation, and management. Whether you need to synchronize data, replicate
databases, mask sensitive information, or automate workflows, CDI has a task
type suited to your needs. Understanding these different task types enables
organizations to build robust and efficient data pipelines, ensuring
high-performance data management in the cloud era.
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