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MindMap Gallery Data integration and interoperability

Data integration and interoperability

The Dama body of knowledge, Data Integration and Interoperability (DII), describes the processes involved in moving and integrating data within and between disparate data stores, applications and organizations.

Edited at 2024-04-03 11:29:28

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Data integration and interoperability

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Outline

Data integration and interoperability

introduction

Data integration and interoperability (DII) describes the process of moving and integrating data within and between disparate data stores, applications, and organizations

Data integration is the consolidation of data into a physical or virtual consistent format

Data interoperability is the ability to communicate between multiple systems

business drivers

1. The main purpose of data integration and interoperability is to effectively manage data movement

2. Organizations purchase applications from software vendors rather than develop custom applications, which expands the need for enterprise data integration and interoperability

3. Maintenance and management costs

4. Support the organization’s ability to comply with data processing standards and rules

goal principle

Target

1. Provide safe and compliant data in a timely manner according to the required format

2. Build and develop shared models and interfaces to reduce the cost and complexity of solutions

3. Identify meaningful events and automatically trigger warnings and actions

4. Support business intelligence, data analysis, master data management, and strive to improve operational efficiency

basic concept

1. Extraction conversion loading

At the heart of data integration and interoperability is the fundamental process of extract, transform, and load (ETL).

extract

Convert

1. Format changes

Technical format conversion, such as format conversion from ENCDIC to SACII

2. Structural changes

Such as records from denormalization to normalization

3. Semantic changes

Maintain semantic consistency when converting data values. For example, the source gender code can include 0, 1, 2, and 3, while the target gender code can be identified as UNKNOWN, FAMALE, MALE, or NOT PROVIDED.

4. Eliminate duplication

5. Reorder

load

Extract, Load, Transform (ETL)

If the target system has stronger conversion capabilities than the source system or intermediate application system, the order of data processing can be switched to ETL - extraction, loading, transformation

mapping

Mapping is a synonym for transformation. It is both the process of building a lookup matrix from a source structure to a target structure and the result of this process.

Delay

Latency refers to the time difference between the source system generating data and the target system making the data available.

1.Batch processing

2. Change Data Capture (CDC)

Is a method of reducing transmission bandwidth requirements by adding filtering to only include data that has changed within a specific time range.

Three data-based change data capture techniques

1. The source system fills in specific data elements

2. The source system process is added to a simple list of objects and identifiers when changing data, which is then used to control the selection of extracted data.

3. The source system copies the changed data. This data has been turned into independent objects as part of the transaction and is then used to extract the data

3. Quasi-real-time and event-driven

4.Asynchronous

5. Real-time, synchronized

6. Low latency or stream processing

2. Interaction model

1. Point to point

1) Impact processing

2) Management interface

3) Potential inconsistencies

2. Center and spoke type

Is a peer-to-peer alternative that consolidates shared data (physical or virtual) into a central data center that applications can use

Data center provides consistent view of data with limited impact on source system performance

3. Publish and subscribe

The publish and subscribe model involves systems that push (publish) data and other systems that accept (subscribe) data

3. Data integration and interoperability architecture concepts

1. Application coupling

2. Orchestration and process control

3. Enterprise application integration

4. Enterprise Service Bus (EBS)

It is a data integration solution for sharing data in near real-time among multiple systems. Its data center is a virtual concept that represents the standard and standardized format for data sharing in an organization.

is a system that acts as an intermediary between systems, passing messages between them

5. Service-oriented architecture (SOA)

With good service calls between applications, the ability to push data or update data (or other data services) can be provided

6. Complex event processing

7. Data federation and virtualization

When data exists in disparate data repositories, it can also be aggregated through means other than physical integration. Data Federation provides access to a collection of independent data repositories. Data Virtualization enables distributed libraries and multiple heterogeneous data stores to be accessed and viewed as a single database

8. Data as a Service (DaaS)

Software as a Service (SaaS) is a delivery and licensing model

9. Cloud integration

Also known as Integration Platform as a Service or IPaaS

Activity

Analyze data

Basic analysis includes

1. The data format defined in the data structure and the format inferred from reality

2. The amount of data, including null value, empty or default data level

3. Data values and their close relationship to a defined set of valid values

4. Patterns and relationships within the data set, such as related fields and cardinality rules

5. Relationship to other data sets

Collect business rules

Business rules are a key subset of requirements, statements that define or constrain aspects of business processing.

Business rules are divided into four categories

Business term definitions

Facts about interrelated terms

constraint or behavior assertion

derived

Achieving data integration and interoperability requires the support of business rules, including several aspects

1. Evaluate data from potential source and target datasets

2. Manage data flows in your organization

3. Monitor operational data in your organization

4. Indicate when events and alerts are automatically triggered