ETL Architecture Explained With Diagram [A Data Engineer's Guide]

Smartphones, tablets, computers, smartwatches, and more generate large amounts of data every day. Analyzing this data can be crucial for your business as it provides insights into your customers’ preferences. You can leverage these insights and improve your product development process or marketing campaigns.

The major challenge lies in consolidating data from multiple sources and transforming it into a usable format to extract insights and drive business growth. Having an established ETL process within your organization will save a lot of time and resources. This article will provide an overview of the ETL architecture and how it can enhance data management and decision-making.

What Is an ETL Architecture?

ETL stands for Extract, Transform, and Load, a core concept in modern data integration and analytics. It provides a structured approach for moving data from multiple sources, transforming it into a desirable format, and loading it to a destination system for analysis.

Key Components of ETL Architecture

An ETL process involves data passing through different modules. Each module is crucial in ensuring the data is accurately processed from the source to reach the destination.

Data Sources 

The initial step in an ETL process is to identify the data sources from which the data needs to be extracted, such as databases, APIs, or sensor data in IoT devices. Data resides in various formats, such as CSV, JSON, or XML.

Landing Area

The landing area is a temporary storage area that stores your extracted data from the sources before you perform any transformations. It may store multiple data batches before moving it through the ETL pipeline.

Staging Area

Data staging is an automated process for efficiently transforming your data. It facilitates the removal of NA or NULL values, saving storage and computing resources. You can also set rules for data cleaning and filtering to eliminate irrelevant or unwanted data. The cleaned data gets enriched and undergoes final validation checks to eliminate any discrepancies.

Destination

The transformed data is stored in the destination or centralized storage system, often a data lake or a data warehouse. This data is then used for analysis using visualization tools or BI tools to gain valuable insights and make informed decisions. 

ETL Process in Detail

A well-defined ETL process enables streamlined data flow without disrupting your existing workflows. Here is a detailed description of the ETL stages:

Extraction

The first process involves collecting data from multiple sources. 

Transformation

The second process includes converting data into a consistent and accurate format based on your target system compatibility and analytics needs. The data transformation techniques include:

• Data cleaning is the process of identifying and managing inaccurate records from a dataset by removing duplicates, handling missing values, and detecting outliers.
• ‍Data enrichment involves enhancing existing data by adding additional information to make it more comprehensive and valuable. It involves tasks such as adding metadata or merging external data.
• ‍Data formatting includes converting data into a specific format suitable for efficient analysis, storage, and reporting. This might involve tasks such as standardizing text and restructuring data.
• ‍Data filtering is selecting a subset of data based on specific criteria to focus on relevant information. Data can be filtered using time-based filtering and conditional subset filtering.
• ‍Data validation ensures that the transformed data meets certain quality standards and is ready for analysis.

Loading

The final stage of the ETL process is loading. It involves loading the transformed data into your destination system, such as a data warehouse, data lake, or business intelligence tool.

Here are common loading methods:

• Full Loading: Transferring all data to the destination system. It is often used for initial loads.‍
• Incremental Loading: Loading only the new or updated data. You can use this approach to keep up with the ongoing updates.

Key Factors to Consider When Designing an ETL Architecture

Consider the following key factors when designing an ETL architecture:

Understand Your Business Requirements

This is the fundamental step for any successful ETL implementation. This involves clearly identifying your project's objectives and constraints.

When your business requirements are clearly defined, you can design an ETL architecture that will help you identify the data sources, destinations, and transformation requirements. This step helps you establish data quality standards and control mechanisms throughout the ETL pipeline. 

Identify the Data Origin and Destination

Knowing from where your data originates and resides is another crucial factor in the ETL architecture. The data can be present across multiple systems, databases, or file formats across different locations. Proper identification ensures the data is collected, processed, and loaded without any inconsistencies.

For example, suppose you want to analyze customer behavior. In that case, you must extract data from the website clickstream and CRM system and then load the transformed data into a data warehouse or BI tool for further analysis.

Data Volume

With exponential growth in data volumes, the traditional ETL process might need some adjustments. Consider implementing incremental or real-time data processing, where you handle new or updated data rather than the entire dataset. This has many advantages, such as immediate insights, reduced latency, scalability, and cost optimization.

Determine Batch vs. Streaming ETL

The choice between batch and streaming ETL depends on your specific business needs and use case:

• Batch ETL: This approach allows you to process data in batches at scheduled intervals. It is an ideal solution when dealing with large volumes of data that are not time-sensitive.
• ‍Streaming ETL: This is an alternative to batch processing that processes data as soon as it arrives. This approach is ideal for real-time data processing that requires timely actions, such as stock market analysis.

Best Practices for Designing ETL Architecture

This section outlines the best practices that you should follow while implementing a robust ETL architecture. These practices will ensure efficient, reliable, and scalable data integration for your data analytics platform.

Independent Microservices

You can break down your ETL architecture into independent modular stages—extract, transform, and load. This helps you distribute computing tasks like parallel processing across multiple stages, improving overall efficiency.

Additionally, you can also isolate failures to specific modules, preventing them from affecting the entire data pipeline.

Incremental Data Synchronizations

The initial data extraction may pull large volumes of data from source systems. However, ensure the subsequent extractions capture only new or updated data with an incremental approach. This will significantly help reduce the volume of data to be processed during each cycle and save computing resources. 

Metadata Management

Implement metadata management, which will add an extra layer of simplification to the data-loading process. The metadata will consist of all the details like the project name and purpose, rejected data, pre/post- processing steps, frequency of execution, etc. This metadata will help you decide whether the data is suitable for processing.

Selecting the Right ETL Tool

There are many factors that can influence your decision to choose the right ETL tool for your organization. You can consider the points below to help you evaluate the tools based on specific criteria:

• Connectivity and Integration: Check whether the ETL tool offers pre-built connectors for your data sources and target systems. Ensure the tool can easily integrate without disrupting your existing workflow and data architecture.
• ‍Ease of Use: Select the tools that are user-friendly for both technical and non-technical teams, allowing for easy setup and operation.‍
• Security and Compliance: Select the tool that meets your industry's security requirements and data regulations. Evaluate how it handles data encryption, access control, and auditing.
• ‍Cost and Scalability: Check whether the tool can accommodate your growing data volumes and business needs. Decide whether the pricing structure fits your budget and if it is worth paying for the amount of flexibility and scalability it offers.
• ‍Customer Support: Enquire whether the vendor can offer quick and knowledgeable customer support. Look for the resources they provide for troubleshooting and employee training.

Data Security

Data security enables you to set regulations that safeguard data throughout its lifecycle, ensuring data integrity, privacy, and security. Effective data security procedures involve understanding the data, who can access it, and how to process it during the ETL stages.

When designing ETL architecture, it's important to adhere to relevant privacy regulations, such as GDPR (General Data Protection Regulation) or HIPAA (Health Insurance Portability and Accountability Act). 

Logging and Monitoring

Practice data observability by logging each ETL subprocess. These logs include the current status, timestamps, and amount of data processed, enabling you to audit data processing activities and track anomalies in your ETL pipeline.

Effortlessly Unify Your Data with Airbyte 

Airbyte takes the complexity out of the data integration process. This is a no-code platform that allows you to seamlessly move data from diverse sources. With its expansive repository of over 350 pre-built connectors, Airbyte enables you to extract data from popular data sources like DynamoDB, Firebolt, Asana, and many more.

To transform your data, you can integrate Airbyte with dbt (Data Build Tool). This empowers you to define and execute complex SQL-based transformations to get your data exactly how you need it.

Finally, you can load your transformed data into popular data warehouses and data lakes like S3, Blob Storage, BigQuery, Redshift, etc.

But that’s not all! If you don’t find your required connector in the pre-built list, Airbyte gives you the flexibility to create a custom one using the Connector Development Kit (CDK) in just 30 minutes.

Unique Features of Airbyte

• Airbyte facilitates multiple user-friendly interfaces such as UI, API, Terraform Provider, and PyAirbyte, serving various preferences and necessities.
• Its support for Change Data Capture (CDC) enables you to capture the changes made to source systems and reflect them in the destination. This optimization minimizes data redundancy and resource utilization, making it efficient for handling large datasets.
• Airbyte adheres to stringent security standards such as HIPAA, ISO, SOC 2, and GDPR. This allows you to secure your data’s reliability and confidentiality.

To explore more about Airbyte, you can refer to the official documentation. 

Conclusion

The ETL architecture plays a crucial role in transforming raw data from a wide range of sources into a clean and understandable format for analysis. In this article, you learned about what an ETL architecture framework looks like, the key factors you should consider when designing it, and the best practices to implement. By understanding these concepts and leveraging robust tools, you can establish a robust ETL pipeline that will empower you to unlock valuable insights from your data to make data-driven decisions.

FAQs

What is an ETL architecture?

ETL architecture provides a structured approach for moving data from multiple sources, transforming it into a desirable format, and loading it to a destination system for analysis.

How do you ensure data quality in ETL processes?

Ensuring data quality in an ETL process involves data cleansing, validation, auditing, and logging. These measures help you analyze source data for inconsistencies and check them against predefined rules for transparency and troubleshooting.