Data quality is an essential factor in determining the success of the ETL (Extract, Transform, Load) process. High-quality data ensures accurate analytics and decision-making, leading to better outcomes.

Database normalization primarily aims to minimize redundancy and dependency in a database schema, leading to improved data integrity and reducing anomalies such as update, insertion, and deletion anomalies.

Normalization involves decomposing a large table into smaller, related tables to eliminate redundancy and improve data integrity by reducing the chances of anomalies.

Accuracy is a data quality metric that assesses the correctness and precision of data against a trusted reference source. It involves comparing the data values in a dataset with known or authoritative sources to determine their level of agreement. Accurate data ensures that information is reliable and dependable for decision-making and analysis purposes.

Data profiling involves analyzing the structure, content, and quality of data to understand its characteristics at a specific point in time. It helps identify data anomalies, patterns, and inconsistencies, which are essential for understanding data quality issues. On the other hand, data monitoring involves continuously observing data quality over time to detect deviations from expected patterns or thresholds. It ensures that data remains accurate, consistent, and reliable over time, allowing organizations to proactively address data quality issues as they arise.

Descriptive statistics are commonly used for data quality assessment as they provide a summary of the key characteristics of a dataset, such as measures of central tendency (mean, median, mode), dispersion (range, variance, standard deviation), and distribution (histograms, box plots). These statistics help analysts understand the underlying patterns, trends, and outliers in the data, enabling them to assess data quality and make informed decisions based on the findings.

A well-defined data governance framework helps ensure that data is consistent, accurate, and reliable across the organization by establishing policies, standards, and processes for managing data throughout its lifecycle. This includes defining data quality standards, data classification policies, data access controls, and data stewardship responsibilities. By implementing a robust data governance framework, organizations can improve data quality, enhance decision-making, and ensure regulatory compliance.

Checkpointing is a critical feature in streaming processing frameworks that enables fault tolerance and state recovery by periodically saving intermediate processing results to durable storage. This mechanism allows the system to resume processing from a consistent state in case of failures or system restarts, ensuring data integrity and reliability in continuous data processing pipelines.

Sharding in a distributed database system involves horizontally partitioning data across multiple servers or nodes. Each shard contains a subset of the overall data, enabling better scalability by distributing the data workload and reducing the burden on individual nodes. This approach facilitates handling large volumes of data and accommodating increased read and write operations in a distributed environment.

The primary goal of data loading in a database is to import data into the database for storage and analysis, enabling users to query and manipulate the data effectively.

Matillion is well-suited for seamless integration with cloud services like AWS and Azure. Its native integration with cloud platforms, drag-and-drop interface, and scalability make it an ideal choice for building data integration solutions in cloud environments.

Change Data Capture (CDC) captures only the changes made to the source data since the last extraction. This approach ensures near real-time analytics by transferring only the modified data, reducing the processing time and allowing for quicker insights.