Module 2: Data Integration

Unified data access

organizations often have multiple databases for different applications, such as CRM, ERP, and HR Systems. These databases may store related data that needs to be accessed collectively.

Eliminate Data Silos

Standalone databases can lead to "data silos", where information is isolated. Preventing efficient collaborative and decision-making.

Real-time Data Synchronization

many business operations rely on up-to-date information to function properly, such as inventory management or financial reporting.

Enhanced-Decision Making

data-driven decisions require accurate and comprehensive information from multiple sources.

Improved Efficiency and Automation

Manual data entry and reconciliation between disconnected databases are time-consuming and error-prone.

Support for Advanced Analytics

advanced analytics, such as predictive modeling or machine learning, require large and diverse.

Scalability for Business Growth

as businesses grow, they often adopt new tools and systems, each with its own database.

Compliance and Data Governance

regulatory requirements often mandate consistent and accurate data management across systems.

Data Mapping

the process of establishing relationships between data elements from different sources. For example, mapping a "Cust_ID" field in one database to a "CustomerNumber" field in another.

Data Transformation

the process of converting data from one format or structure into another to meet the requirements of the target system.

Data Cleansing

removing errors, duplicates, and inconsistencies from the data.

Data Aggregation

combining data from multiple sources, such as calculating total sales for a specific period.

Data Filtering

selecting specific data based on certain criteria.

Data Formatting

changing the format of data, such as converting date formats or currency symbols.

Data Enrichment

adding missing information to the data from external sources.

ETL (Extract, Transform, Load)

is a three-stage process used to integrate data from various sources into a central repository, such as a data warehouse.

Extract

data is gathered from various sources, such as relational databases, flat files, and APIs.

Transform

the extracted data is cleaned, formatted, and validated to ensure that it meets the requirements of the target system.

Load

the transformed data is imported into the target system, such as a data warehouse or data mart.

Data Profiling

 the process of analyzing data to understand its structure, quality, and content.

Data Validation

the process of checking data against predefined rules to ensure its accuracy and completeness.

Data Standardization

the process of ensuring that data follows a consistent format across all systems.

Data Deduplication

 the process of identifying and removing duplicate records from the data.

Master Data Management (MDM)

the process of creating a single, consistent view of critical data, such as customer or product information.

Change Data Capture (CDC)

the process of identifying and capturing only the changes made to a database, reducing the amount of data that needs to be transferred.

Data Virtualization

the process of providing a unified view of data from multiple sources without physically moving it.

Data Federation

a type of data virtualization that combines data from multiple sources in real-time.

Data Streaming

the process of integrating data in real-time as it is generated, such as using Apache Kafka.

Data Security and Privacy

protecting sensitive data during the integration process.

Scalability

ensuring that the integration process can handle increasing volumes of data.

Complexity

 managing multiple data sources, formats, and transformation rules.

Cost

the cost of software, hardware, and personnel.

Data Governance

establishing policies and procedures for data management.