What Are the Important ETL Requirements: Explained in Detail

Building data pipelines requires a highly technical skill set, which your organization can accomplish by hiring a data engineering team or purchasing an ETL tool or data integration platform such as Hevo Data to minimize the engineering work involved.  Before building an ETL pipeline, you must review ETL requirements and principles, consider why you’re building a pipeline first, and identify its benefits to your business.

What Are ETL Requirements?

In a data-driven organization with a large volume of data from multiple sources—such as databases, APIs, or flat files—a data engineer must ensure they understand the business goals and what the organization aims to achieve with the extract, transform, load (ETL) process. It is crucial to define the infrastructure required for ETL, including selecting the appropriate ETL tool to perform transformations, choosing the right data warehouse (cloud-based or on-premises) based on the organization’s size, and ensuring the ETL process aligns with stakeholders’ expectations and budget constraints.

The ETL process involves extracting raw data sources, transforming them for data cleaning, aggregation, and modeling, as defined by the business requirements, and loading them into a data warehouse for analysts to perform advanced queries. 

Why Are ETL Requirements Important?

1. Data Accuracy and Consistency: Establishing clear requirements before building an ETL data pipeline helps maintain data integrity by correctly transforming and loading extracted data from multiple sources into the target system. This reduces data inconsistencies, duplication, and missing values that could impact business decisions.
2. Enhancing Efficiency: Careful pipeline design optimizes resource flow, eliminating bottlenecks and delays for smooth project progression. Automation enhances efficiency by reducing manual effort through workflow management and task scheduling.
3. Cost Efficiency and Scalability: With increasing data volume and complexity, defining project requirements for ETL, such as infrastructure, data storage, and maintenance costs, ensures the system can achieve cost efficiency and an improved return on investment (ROI). 
4. Facilitating Collaboration: Pipeline design is a blueprint for project teams to collaborate effectively. Defining the workflow, dependencies, and communication channels helps ensure that teams or individuals involving multiple stakeholders can collaborate toward a common goal crucial to large-scale projects. 

What Are the 7 Important ETL Requirements?

1. Understand Business Objectives

When defining the requirements for an ETL pipeline, it is important to ask questions from a business perspective, such as what business outcomes the data pipeline will meet, who the pipeline stakeholders are, how much control they have, and how, once the pipeline is deployed, we measure its success in business terms.

The data engineering team should actively collaborate with data analysts, scientists, and stakeholders to understand their needs and clearly understand the business goals and the stakeholders’ aim with the ETL process. They are the likely end users of the transformed and enriched data and will depend on the data pipeline daily to retrieve data that brings value to the organization.

2. Determine Your Data Sources

While evaluating business objectives, whether to improve operational efficiency, increase revenue in one sector, or data migration to the cloud for centralized data warehousing, it is important to understand what kind of data your organization is dealing with and if the data are of any value. Based on your data sources, such as an API, SaaS apps, or flat files like (CSV/XML), and the velocity at which the data is being delivered, one needs to choose a data infrastructure or data lake.

Suppose one needs real-time data coming from social media or sensor data. In that case, one must choose a tool like Kafka to load it into a data warehouse seamlessly. If it is a batch-based ETL process, one needs to evaluate how big the data is so that it is processed in a spark-based environment to perform the transformations before loading it to a data warehouse such as Snowflake.

3. Identify Your Ingestion Strategy 

To determine how you will integrate the pipeline with the source system, one must determine if they need real-time data or if batch processing is sufficient. This depends on the latency that’s acceptable for your data pipeline outcome. 

For example, real-time processing is necessary if a ride-sharing app needs to process continuous data streams (e.g., IoT devices and social media feeds) to track driver locations and match them with nearby passengers. Otherwise, batch-based data is acceptable if you need to process large volumes of daily sales data to generate a report. 

The batch vs. real-time distinction is important because the two have fundamentally different architectures. The architecture must be implemented starting at the ingestion step and can then be carried through the rest of the pipeline.

3.a) Batch-Based Data Pipeline 

Batch data pipelines follow a classic extract, transform, load (ETL) framework. The pipeline scans the data source at a preset interval and collects all the new data that appears during that time. 

Batch processing is generally more cost-effective for large-scale data because resources can be allocated during off-peak hours (e.g., nightly runs). Scaling batch systems requires careful resource management to avoid bottlenecks during peak processing times. Tools like Apache Spark or Hadoop can process large datasets efficiently on distributed systems.

3.b) Real-Time Data Pipeline 

New data should be prioritized and ingested from databases, IoT devices, log files, messaging systems, and other sources without delay. Log-based change data capture (CDC) for databases is ideal for managing and streamlining real-time data. Real-time pipelines detect new data as soon as it appears in a source. The change is encoded as a message and sent immediately to the destination. Ideally, these pipelines also transform data and perform validation in real-time.

Real-time pipelines are considered challenging to engineer due to their high complexity. This complexity is due to real-time constraints and constant resource allocation, which can lead to higher operational costs. Real-time systems must scale dynamically to handle fluctuating data streams, which can be difficult. Tools like Apache Kafka or Apache Flink require dedicated infrastructure for streaming data and are designed to scale horizontally for high-throughput streaming.

4. Design the Data Transformation Phase

Before data is utilized in the destination system, it should be validated or transformed into data compatible with that system. Each dataset should conform to a schema to make it easy to understand, query, and work with.

Transactional data in operational systems are stored in OLTP databases, which are highly normalized to ensure data integrity and minimize redundancy. In contrast, data warehouses, which support OLAP processes, require denormalized data to optimize query performance and analytical processing.

Therefore, analyzing the data mapping requirements is important to understand how data will be transformed from source to target systems. Identify the necessary transformations, mappings, conversions, aggregations, and calculations that must be applied. This step ensures data integrity and consistency throughout the ETL process.

You may have heard of ELT and ETL frameworks for data pipelines. The “T” in the acronym refers to the crucial transformation step. (“E” is for extraction, otherwise known as ingestion. “L” is for loading the data to its destination.) The transformation can occur at different points in the pipeline. Most pipelines will have multiple transformation stages. Regardless, the transformation must occur before the data is analyzed. In general, ETL is a safer framework than ELT because it keeps your warehouse free of raw data altogether.

5. Identify Non-Functional Requirements

Non-functional requirements define the ETL system’s qualities and constraints. They document factors such as performance expectations, scalability requirements, security and privacy considerations, data governance and compliance requirements, and regulatory or industry-specific constraints.

6. Implement Monitor Framework

Before deploying your pipeline, it is important to implement a monitoring framework for the overall process of the data pipeline. Data pipelines involve many components and dependencies, so errors are likely, so you’ll want to catch them early.

Your monitoring framework should keep you informed on system health and can protect data quality. You would need to create your own and keep track of metrics such as latency, traffic and error frequency, and saturation using output logs for diagnosing and debugging performance issues and errors. Tracing is another application of logging that captures all events in real-time and can be used to determine performance issues. Together, metrics, logs, and traces provide different levels of granularity so you can monitor your data pipeline, detect issues quickly, and debug them.

7. Get Stakeholder’s Feedback

Collaborate with stakeholders from different departments, such as business users, IT teams, and data analysts, to gather their insights on your data pipeline plan that takes into account the cost, feasibility, and projected outcome. Make sure they are involved in every step of the process so that they are on the same page. Gathering such feedback will ensure that the ETL project requirements align with the organization’s and its users’ needs.

Common Challenges With ETL Requirements

1. Neglecting the Data Transformation Phase: When building your ETL pipeline, making sure the raw data undergoes proper data profiling, such as data cleaning and enrichment, is integral to producing high-quality data for later reporting. Ignoring the transformation stage could lead to inconsistent and poor-quality data. 
2. Choosing the Correct Ingestion Strategy: Businesses frequently find it difficult to choose between batch and real-time processing, each of which has unique complications. Although expensive and resource-intensive, real-time processing is necessary for time-sensitive operations with low latencies, such as predictive maintenance and real-time system maintenance. Batch processing is more effective for large-scale data aggregation, such as generating quarterly reports and feeding tactical dashboards.
3. Maintaining Data Quality: When working with large volumes of data, it is important for the stakeholders to set up data quality frameworks and define the goals early in the stage of data collection, so that data engineers can adhere to data quality standards and pay attention to its accuracy and relevance. 
4. Overlooking Maintenance and Operational Costs: Neglecting long-term maintenance and operational costs in ETL processes can lead to significant expenses due to evolving data sources and system upgrades. 
5. Stakeholder’s Misalignment in Setting Goals: ETL projects often struggle with stakeholder misalignment due to differing priorities from the IT Team. While the IT team might opt to migrate from legacy stems to the cloud, stakeholders might be resistant to such changes due to infrastructure costs and the required IT expertise. IT leaders must communicate their rationale to align stakeholders.

By proactively addressing these challenges, organizations can build more robust, scalable, and cost-effective ETL processes that align with their long-term data strategy.

Implementing Project Requirements for ETL in Real-World Use Cases 

Building a Real-Time Pipeline for Supply Chain Optimization

In a large retail chain, stakeholders must collaborate with the IT team to strategically plan to optimize inventory levels, keep track of customer demand fluctuations, and reduce stockouts. The data sources might involve real-time sales data from e-commerce platforms to warehouse data from IoT sensors. When dealing with such streaming data sources, one should implement a real-time data ingestion strategy for extracting warehouse inventory data, and implement a Spark-based streaming platform engine to handle large volumes of real-time sales and inventory data.

The end goal of the pipeline would be to generate real-time alerts when stock levels are low and automate triggers for reordering of products for target end users, such as inventory managers, which gives real-time visibility into supply chain operations.

Building a Batch-Based Pipeline for Financial Reporting

An organization needs to recognize the value of analyzing its existing internal data to determine their financial stability through ROI  compared to competitors. To build a batch-based ETL pipeline for creating a financial report, one needs to automate the process of extracting the businesses’ historical data from various departments on a weekly or monthly basis and load it into data warehouses to gain valuable insights into the financial standings. 

Customer Churn Prediction

To reduce customer churn, an organization needs to build a data architecture that integrates an ETL and ML pipeline for data ingestion, processing, and predictive analytics. In the transformation stage for ML, feature engineering can be applied to detect customer tenure and service usage trends. Such valuable insights can be utilized by marketing and customer service teams for targeted retention campaigns, ensuring data-driven decision-making.

Conclusion

When building an ETL pipeline, it is important to define clear project requirements, such as business objectives that can help the organization find value from data and enhance their decision-making process, improving efficiencies and reducing cost. Identifying data sources and deciding proper ingestion strategies, such as batch-based or real-time streaming frameworks based on various business use cases, is important to define early on so that the data engineers can build scalable and reliable systems. Managing these systems through proper monitoring frameworks and documentation can ensure high data quality and standardized pipelines in the future. 

Beyond the technical implementation, a data engineer’s role extends into managing expectations, educating stakeholders, and advocating for best practices in data migration and pipeline initiatives. By navigating these challenges, data engineers drive technological advancements and cultural transformation within their organizations. Following best practices in ETL development empowers businesses to unlock their data’s full potential, enhance decision-making, and achieve long-term growth.

If you want to automate your ETL pipeline without the hassle of manual integration, Hevo can help. Sign up for a 14-day free trial and streamline your data workflows effortlessly. Check out our pricing plans to find the best fit for your business needs.

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