ETL

ETL (extract, transform, and load) is about integrating data from various sources, transforming them into a standard format, and loading into the destination. 

Extract - Obtain and combine data from source points like APIs, cloud services, SaaS applications, and other data systems. The data can be in a semi-structured, unstructured, or structured format.

Transform - Extracted data goes through transformation like aggregation, cleansing, formatting, deduplication, etc., to fit into a standard format.

Load - Loading the transformed data into the target location. It can be a data warehouse, data mart, or other repositories.

For effective utilization of ETL, data professionals perform data discovery process to understand what data is required, where it resides, its structure, and relationships with other sources.

Characteristics of ETL

What are some properties of ETL that get you meaningful insights?

Scalablity: ETL can handle your company’s increasing data volumes without affecting the performance. 

Data quality: Despite the volumes of data being processed, ETL workloads ensure that accurate data is loaded into the destination. This reliability and consistency makes it suitable for analytics and data-based decision making.

Automation: Doesn’t require manual intervention. Schedules and runs manually. 

Error handling: Ability to go back and start from the point of failure. 

Flexibility: To change and adapt itself to different types of sources, formats, environments, business needs, etc.

Performance: Can exhibit high performance and efficiency, which is required to handle high volumes of data workloads.

Security: Ensure safety, security, and encryption to the data while it’s being ingested, processed, and loaded.

Multi-purpose: Can reuse the same ETL pipelines or components for different projects and use cases.

Why is ETL important?

Data integration: Integrating sources is important to get required business insights for decision making.

Maintain governance: Can ensure that your data meets standards and complies with regulatory requirements.

Accurate and fast reporting: Accuracy and timeliness are important for business intelligence and reporting purposes, which ETL can offer.

Less redundancy: Reduces duplication, minimizes redundancy of data, and thereby saving and processing costs.

Low IT costs: No need for manual handling of data. ETL processes data at low costs, reducing the need for a large data team.

Real-time analytics: Get insights in real time or near real time processing capabilities for critical decision-making using ETL.

Types of ETL

Batch processing: This is when extracting, transformation, and load process happens in bulk or during scheduled interviews, like monthly, hourly, or custom timelines.  

Real-time ETL: Data processing and loading happens in real-time, often with time sensitive or streaming forms of data.

Incremental ETL: It doesn’t transfer and replace the entire data sets rather, updates only the changes or updates in the data. Hence, it’s faster and more efficient.

Cloud ETL: When the data collection and processing happens in the cloud, utilizing the flexibility and scalability of cloud systems. 

Some common use cases of ETL

Data warehousing and data marts: If establishing a centralized or department wide data storage is your goal, you will require ETL for setting up the entire backend process.

Business intelligence tools: Own business intelligence and visualization tools like Power BI for generating reports? ETL is again the backbone, integrating every data source needed for reports.

Customer 360: It’s an application that integrates all customer touch points and data sources to create a 360 view of data.

AI use cases: AI and ML use cases like fraud detection, logistics management, inventory sourcing, vehicle routing, and other applications require ETL pipelines for their streaming data requirements.

Common ETL tools

Enterprise ETL tools: Informatica PowerCenter, IBM DataStage, Microsoft SQL Server Integration Services, etc.

ETL tools cloud version: Microsoft Azure Data Factory, Stitch, Matillion, FiveTran, etc. 

Free and open-source ETL tools: Talend Open Studio, Apache Spark, Apache Airflow, etc.

ETL vs. ELT

There are many differences between ETL and ELT. While ETL stands for Extract, Transform, and Load, ELT stands for Extract, Load, and Transform. With ETL, the extracted and transformed data is moved to a data warehouse or other repositories. Whereas, with ELT, the extracted data is first loaded into the destination and gets transformed to the required format.

ETL is suitable for companies with traditional storage systems that accept only cleaned and formatted data. Whereas, ELT is suitable for big data use cases. ELT can come in handy if you have modern data warehouses and Lakes, where data format isn’t a concern. Choosing between ETL and ELT totally depends on your company requirements and data stack.