ETL (Extract, Transformation and Load) is an important process in transforming raw data to meaningful insights. The ETL process is facilitated by data engineering services. Traditionally, the ETL process was slow and often conducted manually. However, now through data engineering services the ETL process has been transformed by introducing automation, cloud-based services, advanced processing frameworks etc.
For modern enterprises, where every business is looking to transform their data into a steady and organized computing platform, the modern ETL process is a relief.
Through the ETL process, workflows can be streamlined, reducing operational bottlenecks and generating more meaningful insights with minimal to no manual efforts.
In this blog, we will look at how the ETL process has evolved over time, and the key ways data engineering services are transforming ETL.
Evolution of ETL Workflows
Today, businesses are shifting from batch processing to real-time data processing. Batch processing is a method of processing large amounts of data in which the data is collected, processed and stored at different intervals rather than processing it all together. It includes tasks like, payroll processing, billing and so on.
This creates a lot of challenges like, such as scalability, latency and data quality issues.
Modern enterprises are looking for cloud solutions like AWS Kinesis, Google Pub/Sub, and Azure Event Hubs offers scalable, real-time data processing techniques. Technologies like Apache Kafka and Flink together help in reducing delays in data availability, making real-time analytics possible.
Key Ways Data Engineering Services are Transforming ETL
Automation and Orchestration
Traditional ETL processes were manually relied on using SQL, Python, or shell scripts. This would lead to limited logging and monitoring. Through a modern data engineering approach tools like Apache Airflow, Prefect, and Dagster automate scheduling, dependency management, and monitoring. This helps in a fast and error-free ETL process, as there is a less, manual scripted approach.
At Prescience Decision Solution, we have helped an e-commerce businesses to implement AWS Step Functions to automate manual processes, significantly reducing manual effort from 3-4 hours to 5 minutes.
Real-time data processing
Traditionally, batch processing method, was the standard method for ETL processes. In batch processing method the data pipelines are scheduled at different intervals, thus causing delays in real-time analysis of data. However, with modern Real-time ETL powered by Apache Kafka, Apache Flink, and Spark Streaming helps in faster monitoring and better decision-making.
Scalability and cloud-integration
Traditionally, on-premises data warehouses required high-cost infrastructure. They were often difficult to process because ETL operations were slow and required additional hardware. Modern data architecture includes cloud data platforms, which helps in auto-scaling. The tools include AWS Glue, Google Cloud Dataflow, and Azure Data Factory. This approach helps in improve the scaling process and enhance reliability in the data platform.
AI-ML optimization
Traditionally, data validation, anomaly detection, and data consistency, etc were manually processed based on rigid rules. These rules led to a rigid ETL process. AI and ML algorithms help detects anomalies and missing data very quickly in response to changing data patterns. This helps in maintaining data accuracy and consistency throughout the ETL process.
For data validation at Prescience, we implemented Python modules for an e-commerce business, across the pipelines to validate various ETL scenarios, including file size issues, duplicate data ingestion, schema validation, and other critical checks.
A 100% improvement was observed in file size validation and duplicate data handling, while schema validation and other scenarios saw a 60-70% improvement. The process still required some manual effort in such scenarios
Conclusion
Data engineering services are redefining how enterprises handle large-scale data processing. ETL, being a core component part facilitated by data engineering services, has evolved significantly, making its advancement of utmost importance.
This shift in data management has empowered businesses by providing real-time data management, improving scalability, and enhancing accuracy. Today every business need data engineers to scale up their data platforms; thus ETL landscape is also evolving along with it.
At Prescience Decision Solutions, we provide AI and ML solutions across various industries, helping businesses stay ahead in the tech space. Our expertise spans analytics, business intelligence, and data engineering. Additionally, our current approach to the ETL process across various project leverages AWS Glue, which is built on Apache Spark, along with other mentioned tools.
Explore our customer success stories here.
Prescience Team
