Data Integration is the process of combining data from various sources into a unified view, ensuring consistency and coherence in Dimensional Modeling. This step is crucial for building a comprehensive data model.

Data Wrangling is the process of cleaning, structuring, and enriching raw data to make it suitable for analysis. It involves tasks such as cleaning inconsistent data, handling missing values, and transforming data into a format usable for analysis.

Anomaly detection plays a vital role in monitoring data pipelines by identifying abnormal patterns or deviations from expected behavior. By analyzing metrics such as data latency, throughput, and error rates, anomaly detection algorithms can detect unusual spikes, drops, or inconsistencies in data flow, signaling potential issues or anomalies requiring investigation and remediation to maintain pipeline reliability and performance.

Normalization reduces data redundancy by organizing data into separate tables and linking them through relationships, which minimizes duplication and ensures each piece of information is stored only once.

Kafka is a distributed messaging system that enables real-time data processing in the Hadoop ecosystem. It allows for the publishing, subscribing, and processing of streams of records in real-time.

In real-time data processing, the process of data streaming involves analyzing continuous streams of data to detect patterns, trends, or anomalies as data flows in real-time. This process is crucial for applications requiring immediate insights or actions based on incoming data, such as fraud detection, sensor monitoring, or real-time analytics.

Incremental loading refers to the process of loading data in small increments periodically, typically to update existing datasets with new or modified data without having to reload the entire dataset.

The CAP theorem, also known as Brewer's theorem, defines three properties: Consistency, Availability, and Partition tolerance. It states that in a distributed system, it's impossible to simultaneously guarantee all three properties; you can only choose two. This theorem has profound implications for database design and scalability. For example, choosing consistency and availability sacrifices partition tolerance, impacting scalability, while prioritizing availability and partition tolerance may lead to eventual consistency models. Understanding these trade-offs is crucial for designing scalable and resilient distributed databases.

Apache Airflow is a widely used open-source workflow orchestration tool in the field of data engineering. It provides a platform for defining, scheduling, and monitoring workflows as directed acyclic graphs (DAGs). With features like task dependencies, parallel execution, and extensibility through plugins, Apache Airflow is well-suited for orchestrating data pipelines and managing data workflows in various environments.

In Dimensional Modeling, a Fact Table stores quantitative, measurable facts about a business process or event. It typically contains foreign keys that reference dimension tables for context.

Imputation is a data cleansing technique that involves filling in missing values in a dataset based on statistical methods such as mean, median, or mode imputation. It helps maintain data integrity and completeness by replacing missing values with estimated values derived from the remaining data. Imputation is commonly used in various domains, including data analysis, machine learning, and business intelligence, to handle missing data effectively and minimize its impact on downstream processes.

Determining the effectiveness of the data cleansing process involves measuring various data quality metrics such as completeness, accuracy, consistency, and timeliness. Comparing data quality metrics before and after cleansing helps assess the impact of cleansing activities on data quality improvement. Data profiling and outlier detection identify anomalies and discrepancies in the data. Soliciting feedback from stakeholders provides insights into their satisfaction with the data quality improvements.