Refresher on data processing concepts
Data
March 03, 2023
Just a refresher on some data processing concepts and related patterns.
Data Processing
Data processing is the process of transforming raw data into meaningful information. It involves the collection, manipulation, and analysis of data to produce useful insights. Data processing can be done manually or using automated tools and techniques.
Data processing techniques and patterns
There are several data processing techniques and related architectural patterns that are commonly used to process data. Some of the most common techniques and patterns include:
- Batch Processing
- Stream Processing
- ETL (Extract, Transform, Load)
- ELT (Extract, Load, Transform)
- Online Analytical Processing (OLAP)
- Online Transaction Processing (OLTP)
Batch Processing
- Batch Processing is a data processing technique that is used to process data in large volumes. It involves collecting data over a period of time and processing it in batches. Batch processing is typically used for tasks that do not require real-time processing, such as generating reports, updating databases, and running analytics.
Stream Processing
- Stream Processing is a data processing technique that is used to process data in real-time. It involves processing data as it is generated, allowing for real-time insights and decision-making. Stream processing is typically used for tasks that require real-time processing, such as monitoring, alerting, and event-driven applications.
- ETL (Extract, Transform, Load) is a data processing pattern that is used to extract data from one or more sources, transform it into a usable format, and load it into a target system. ETL is typically used for data integration, data migration, and data warehousing applications.
- ELT (Extract, Load, Transform) is a data processing pattern that is used to extract data from one or more sources, load it into a target system, and then transform it into a usable format. ELT is typically used for data integration, data migration, and data warehousing applications.
Online Analytical Processing (OLAP)
- Online Analytical Processing (OLAP) is a data processing technique that is used to analyse data from multiple perspectives. It is a way to analyse data in a multidimensional way, allowing users to drill down into the data to gain insights. OLAP is typically used for business intelligence and data warehousing applications. OLAP essentially focuses on analyzing large datasets for trends, patterns, and insights. OLAP models are typically multidimensional, allowing users to slice and dice data along different dimensions (e.g., time, product, region).
Online Transaction Processing (OLTP)
- Online Transaction Processing (OLTP) is a data processing technique that is used to manage transaction-oriented applications. It is a way to process data in real-time, allowing users to perform transactions such as insert, update, and delete. OLTP is typically used for transactional systems such as e-commerce, banking, and order processing. OLTP essentially focuses on managing individual transactions and ensuring data integrity. OLTP typically deals with smaller datasets and is optimized for read and write operations. OLTP models prioritize fast data retrieval, updates, and insertions, often using normalized tables for efficiency.
Conclusion
Data processing is a critical aspect of any data-driven organization. It involves the collection, manipulation, and analysis of data to produce useful insights. By understanding the various data processing techniques and related architectural patterns, organizations can make informed decisions about how to process and manage their data effectively.