Skip to content

JA8. Extract, Transform, Load (ETL) Process

Statement

For organizations to gain any value from their big data repositories, the data must first be integrated from its source, transformed based on business and data rules then finally loaded into the data warehouse that holds the data. This process is called ETL (Extract, Transform and Load) and is used by data analysts and architecture to load the data warehouse for analysis.

Explain the process of ETL in data integration for big data repository, and describe each step and its importance in the process.

Answer

Introduction

Big data analysis involves processing an organization’s data to extract insights that answer questions the business has. However, due to the heterogeneity and variety of the data sources, data is rarely useful in its raw form, nor it is compatible with each other or with the next tools in the pipeline. Hence, the ETL process prepares the raw data for analysis.

ETL (Extract, Transform, Load) is a three-step process where each step uses the output of the previous step as its input and must be scalable, reliable, fault-tolerant, and efficient to handle large volumes of data (RudderStack, 2020). ETL was designed initially for batch processing, but it now supports stream processing of real-time and near-real-time data (Haider, 2023).

ETL is also important, since objects’ shapes change over time, to include historical data without doing migrations on the actual data (e.g. when compliance regulations prevent that). ETL also raises the data quality before it’s put into processing. ETL process is an iterative process that is repeated as new data is added to the warehouse (GeeksForGeeks, 2019).

Extract

Extract is the first step in the ETL process, where data is read from its sources, which can be databases, files, APIs, or other sources. The data is then copied to a staging area waiting for the next steps. The copied data in the staging area is not modified, but it is saved into a certain file format as the formats coming from the sources may vary from database records, CSV, JSON, XML, or other natural text formats (IBM, 2021).

The extract process can be incremental or full, where incremental extraction only extracts new or updated data since the last extraction, while full extraction extracts all data from the source. The loading process may be time-consuming and should be respectful to other systems (e.g. not to overload external APIs) and thus need to be scheduled at low-traffic times; hence, good notifications, monitoring, and logging systems are needed to ensure the process is running smoothly (Haider, 2023).

Transform

Transform is the main step in the ETL process, where various operations are performed on the extracted data according to what’s needed in the next steps or the state of the data in the staging area. The goal of this step is to make the data consistent, clean, compatible, simple, high-quality, and ready for analysis in the target warehouse. The transformation process may include one or more of the operations listed below.

Transform operations may include filtering out irrelevant or unwanted data, cleaning by filling up missing or null values with sensible defaults, splitting complex fields into simple ones, flattening nested structures, and sorting data in a way that makes it easier to analyze (GeeksForGeeks, 2019).

Transform operations may also include correcting data errors such as typos and arithmetic errors, removing duplicate records and assigning unique identifiers to each record, and doing the necessary de-normalization steps to replace foreign keys with actual records which reduces computation complexity and data dependencies; however, due to storage constraints, sometimes normalization is needed to reduce storage usage (Haider, 2023).

Transform operations may also include revising data format where character sets, measurement units, date and time values are all unified to a common format, deriving new data fields by computing values from existing data according to business rules, summarizing data by aggregating values such as merging multiple low-value transactions into a single one that has the sum of their payments, and encrypting sensitive data to protect it from unauthorized access (AWS, 2022).

Load

Load is the final step in the ETL process, where the transformed data is loaded into the target data warehouse, which is a database or a data lake that holds the data for further analysis. The loading process may be full where all the data in the staging area is loaded and replaced data in the warehouse, or incremental where only the new or updated data is loaded into the warehouse (IBM, 2021).

The loaded data is well-structured in the format that the warehouse needs such as relational tables, NoSQL documents, or raw data files. The data is indexed and partitioned to make it easier to query and analyze, and it is stored in a way that makes it easy to access and retrieve.

Conclusion

ETL decouples raw data from the analytics processes, allowing the same raw data to be used in multiple ways and analytics processes where each has its own ETL process that reflects its needs. The ETL process is important for data integration in big data repositories as it prepares the raw data for analysis by extracting it from its sources, transforming it into a consistent, clean, compatible, simple, high-quality format, and loading it into the target data warehouse.

References