Data Integration Chapter 3

Data Integration Chapter 3

• Data integration combines data from multiple sources into a unified view.
• This aims to enhance data quality, enrich data with additional information, and enable reliable data analytics.
• Integrating in-house data within a data warehouse is simplified by shared attributes and schema consistency.

Data Preprocessing

• Data preprocessing encompasses an overview, data quality, major tasks in data preprocessing, data cleaning, data integration, data reduction, and data transformation/discretization.
• Data cleaning is a crucial part of data preprocessing.
• Problems like heterogeneity among data sources (source type, schema, data type, value, and entity identification) are addressed in data integration.
• Data profiling is essential, as it involves understanding the data, identifying missing or invalid values, and providing insights for rules and data standards for data quality checks.

Data Preprocessing: An Overview

• Data quality is a core concept in data preprocessing.
• Data integration encompasses manipulating data to resolve heterogeneity issues—varied storage systems (relational, object-oriented, XML, and others).
• Data reduction involves methods for data summarization, and other techniques.
• Data Transformation and Discretization deal with specific tasks within the data preprocessing pipeline.

Data Integration: Manipulating Data

• Joining tables is a significant data manipulation technique.
• This process extracts and concurrently processes data from multiple tables.
• Different join types, such as INNER JOIN, FULL JOIN, LEFT JOIN, and RIGHT JOIN, are used depending on the required data intersection.

Manipulating Data - Joins

• INNER JOIN returns matching rows from both tables.
• FULL JOIN returns all rows from both tables, including unmatched rows.
• LEFT JOIN returns all rows from the left table, with matching rows from the right table.
• RIGHT JOIN returns all rows from the right table, with matching rows from the left table.

Data Warehousing

• A data warehouse is a system for reporting and analyzing data.
• It integrates data from various sources into a unified repository.
• The Extract-Transform-Load (ETL) process is employed to transform the data into a suitable format for analysis.

ETL Process

• ETL involves extracting data from sources, transforming it to match the target format, and loading it into a target data store.
• Extract phase: retrieving data from various source systems.
• Transform phase: modifying and structuring data to conform to the data warehouse schema (reformatting, data cleaning, and more).
• Load phase: transferring transformed data into the data warehouse.
• Pre-ETL tasks include data profiling.

Dirty Data

• Dirty data encompasses issues like missing data, cryptic data, contradicting data, and non-unique identifiers.

Data Cleaning in Integration

• Data cleaning is a vital phase in the data integration process, addressing data issues.
• This process includes tasks like parsing, combining, correcting, and standardizing data to ensure it is usable.

Data Cleaning: Parsing/Combining

• Parsing identifies individual data elements within files and isolates them.
• Combining combines data elements extracted from diverse fields, such as combining date, month, and year fields into a comprehensive date format.

Data Cleaning: Correcting

• Correcting data involves using sophisticated algorithms and secondary data sources.
• Data correction is subject to specific data rules/standards.

Data Cleaning: Standardizing

• Standardizing data involves transforming into preferred formats based on standard data conventions or custom rules.

Data Cleaning: Matching

• Searching and matching data records involves using predefined rules to handle duplication and inconsistencies in data.

Data Staging

• Data staging prepares data before loading into the final data warehouse.
• It's an interim step between data extraction and loading.
• Data staging is used for cleaning, transformation, and improving data quality.
• End users generally have no access to the staging area.

Data Transformation

• Data transformation conforms data to prescribed rules and standards.
• Examples include data reformatting, field splitting, code replacement, and aggregate transformations.

ETL Tools

• Few popular commercial and open-source ETL tools are mentioned.

This quiz covers the fundamentals of data integration and preprocessing from Chapter 3. It discusses the significance of unifying data from multiple sources and the essential tasks involved in data quality and cleaning. Gain insights into data profiling and the importance of consistency in data integration for effective analytics.