01 - Singleplex
Singleplex: One to One Mapping
The singleplex ingestion pattern refers to the process of ingesting data from a single source into a data platform, such as Databricks, typically for further processing, transformation, and analysis. This pattern is often used when dealing with a specific data source that provides critical data for business operations or analytics.
Key Characteristics of Singleplex Ingestion Pattern
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Single Data Source: The pattern involves ingesting data from one specific source, such as a database, a file system, a streaming service, or an API.
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Simplicity: This pattern is simpler compared to multiplex ingestion patterns, which involve multiple data sources. It focuses on a straightforward data flow from the source to the target system.
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Use Case Specific: Often employed for specific use cases where the data from a single source is critical for the application or analysis, such as sales data from a transactional database or log data from a web server.
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Performance Optimization: By focusing on a single source, it allows for optimization techniques specific to that source, ensuring efficient data ingestion and minimizing potential bottlenecks.
Steps in Singleplex Ingestion
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Source Identification: Identify and understand the single data source, including its structure, format, and update frequency.
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Data Extraction: Use appropriate tools and techniques to extract data from the source. This could involve SQL queries for databases, API calls for web services, or file readers for file systems.
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Data Transformation: Transform the data as needed to fit the schema and format required by the target system. This can include data cleaning, normalization, and enrichment.
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Data Loading: Load the transformed data into the target system, such as Databricks, ensuring it is stored efficiently and is accessible for further processing and analysis.
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Monitoring and Maintenance: Implement monitoring to ensure the data ingestion process is running smoothly and maintain the process to handle changes in the source data or requirements.
Advantages
- Focused Performance Tuning: Allows for targeted optimization and tuning specific to the data source.
- Simplicity and Manageability: Easier to manage and troubleshoot compared to complex multi-source ingestion pipelines.
- Consistency: Ensures a consistent flow of data from the single source, reducing the risk of data discrepancies.
Use Cases
- ETL for a Specific Database: Ingesting transactional data from a single database for reporting and analysis.
- Log Data Ingestion: Collecting and processing log data from a single application or server for monitoring and analysis.
- APIs and Web Services: Ingesting data from a single API for integration into a larger data platform.