7 best practices to build a high-performing ETL infrastructure

etl development best practices

In 2022, the data integration market size was around USD 12.14 billion, and it’s anticipated to skyrocket to about USD 39.25 billion by 2032. The ETL (Extract, Transform, Load) process is a key player in this process, as it seamlessly consolidates and aggregates data from various systems, enabling informed business decisions. In this article, we explore the transformative journey of ETL, the ways it optimizes data workflows, and discuss the best ETL development and data integration practices.

Practice 1: Ensure scalability

For businesses with rapidly expanding datasets or fluctuating workloads, scalability is crucial to ensure seamless data integration, adaptability to evolving needs and sustained optimal performance. Data integration experts highlight the following factors: 

  • Adaptability to growing data volumes. As businesses expand, the data they generate and process increases exponentially. A scalable data integration process ensures the ability to handle growing datasets efficiently. 
  • Flexibility for changing business needs. Scalability allows organizations to adapt to evolving business requirements. Whether it’s incorporating new data sources, accommodating additional users, or expanding into new markets, a scalable data integration process provides the flexibility needed.
  • Enhanced performance and responsiveness. Scalability directly impacts the performance and responsiveness of data integration systems. A scalable solution ensures that processing times remain optimized even during spikes in data volume or increased complexity.

The widely known market giants have successfully implemented a scalability-oriented architecture.

Case 1: Netflix

Netflix, a leading streaming service, harnesses the power of Amazon Web Services (AWS) for scalability. The ETL process is crucial in managing Netflix’s extensive content library. By extracting, transforming, and loading data efficiently, Netflix ensures the seamless streaming experience users expect. This ETL pipeline facilitates personalized recommendations and the ability to handle dynamic user demands during peak times, contributing significantly to the platform’s scalability.

Case 2: Airbnb

Airbnb, a global online lodging marketplace, utilizes Google Cloud Platform (GCP) for scalable operations. ETL processes play a vital role in handling the substantial volume of data associated with property listings, bookings, and user interactions. Data extraction, transformation, and loading enable Airbnb to maintain a scalable and responsive platform. This ETL-driven approach supports the company’s global expansion by accommodating diverse data needs and enhancing the overall experience for hosts and guests.

Practice 2: Check proper data quality and cleansing

Data validation and quality checks are critical components of the ETL ( process, ensuring that data is accurate, consistent, and reliable as it moves through various data pipeline stages. The ETL process typically involves the following steps, each of which can incorporate different validation and quality checks:

1. Extraction (E):

  • Source system checks: Verify the source system’s availability and accessibility.
  • Data integrity checks: Ensure data integrity during extraction, identifying missing or incomplete records.

2. Transformation (T):

  • Data type checks: Validate that data types match the expected format.
  • Range checks: Examine whether data falls within acceptable ranges.
  • Format checks: Ensure data adheres to predefined formats (e.g., dates, currency).
  • Duplicate checks: Identify and eliminate duplicate records.
  • Referential integrity checks: Confirm relationships between different data sets or tables.
  • Business rules validation: Enforce specific business rules relevant to the data.

3. Load (L):

  • Completeness checks: Verify that all expected records are loaded into the destination.
  • Consistency checks: Ensure consistency between source and destination data.
  • Concurrency control: Prevent conflicts arising when multiple processes update the same data simultaneously.
  • Data quality profiling: Analyze data distributions, patterns, and outliers to detect anomalies.
  • Checksums or hashes: Generate and compare checksums or hashes to validate data integrity during loading.

4. Post-load validation:

  • Record counts: Verify that the number of records loaded matches expectations.
  • Summation checks: Confirm that summations and aggregations align with predefined values.
  • Statistical checks: Use statistical measures to identify outliers and anomalies.

5. Reconciliation:

  • Data reconciliation: Periodically reconcile data between source and destination systems to ensure ongoing accuracy.

Implementing a comprehensive set of data validation and quality checks helps organizations maintain confidence in the integrity of their data, support data-driven decision-making, and comply with regulatory requirements. Automated tools and scripts often streamline these checks and minimize manual intervention. Regularly updating and refining these validation mechanisms is essential as data sources, business rules, and requirements evolve over time.

Practice 3: Incremental loading

Incremental loading significantly reduces processing time and resource usage by focusing only on the data that has changed. This optimization is particularly valuable when dealing with large datasets, enhancing overall ETL performance. Applying incremental loading in the ETL process helps businesses clear the negative repercussions of resource strain and processing delays arising from reprocessing entire datasets. Check out this practice’s application process below:

Step 1: Identify changes since the last ETL run

Instead of processing the entire dataset in every ETL run, incremental loading begins by identifying and loading only the changes that have occurred since the last execution.

Step 2: Utilize unique identifiers

To pinpoint changes, the ETL process compares timestamps or unique identifiers in the source data with the records already existing in the target system. This step ensures the system can distinguish between new, updated, or unchanged records.

Step 3: Leverage incremental identifiers in source data

Each record in the source data carries a timestamp or an incremental identifier indicating its creation or last modification. These timestamps or identifiers help determine which records are new or updated during the incremental loading process.

Depending on the ETL tool and the specific requirements, the loading strategy may involve insert, update, or upsert operations.

  • Insert operation involves adding entirely new records from the source data to the target system. It is used when the source data contains new records in the target system.
  • Update operation modifies existing records in the target system based on changes detected in the source data. It is applied when the source data indicates modifications or updates to records already present in the target system.
  • Upsert (a blend of «update» and «insert») operation combines elements of both insert and update. It inserts new records and updates existing records as needed. It is ideal when the source data may contain a mix of new records and modifications to existing records, and you want to ensure the target system reflects these changes accurately.

Comprehensive logging and auditing are crucial in incremental loading to track the changes processed, identify any issues, and provide a clear history for troubleshooting and compliance purposes. By implementing incremental loading, ETL processes can significantly reduce processing time and resource requirements, making them more scalable and responsive to changes in the source data.

Practice 4: Manage metadata

Metadata refers to data that provides information about other data. It offers context, meaning, and details about various aspects of the data, facilitating the primary data’s understanding, management, and use. Metadata is crucial in describing and organizing data assets in data management. Here are some of its key components:

  1. Structural metadata describes data types, field lengths, and relationships between tables. These could be database schema, table structures, and field definitions.
  2. Descriptive metadata provides information about the content of the data, such as titles, keywords, and abstracts, such as data descriptions, business glossaries, and data dictionaries.
  3. Administrative metadata includes information related to data management processes, such as data ownership, access controls, and versioning. The examples may include access permissions, data ownership, and version history.
  4. Technical metadata focuses on technical details relevant to data processing and integration, including data formats (CSV, JSON), ETL specifications, and storage locations.
  5. Operational metadata capture log files, execution times, and resource utilization during systems’ and processes’ performance.
  6. Business metadata relates to the business context of the data, including its purpose, usage, and relevance to organizational goals. A well-known example is key performance indicators (KPIs).

Metadata management involves organizing, documenting, and maintaining metadata throughout the data lifecycle. Its validation is verifying the consistency and accuracy of metadata to ensure that it aligns with expected standards and accurately reflects the state of the underlying data.

For example, if the metadata indicates that a certain field should contain numerical values only, metadata validation would check whether the actual data in that field conforms to this expectation. Therefore, this practice helps maintain the reliability and integrity of the metadata, which, in turn, enhances the overall reliability of the data management processes.

Practice 5: Implement error-handling mechanisms

Certainly, ETL processes can encounter various errors that impact data integrity, quality, and overall reliability. Let’s look at them in detail, as well as at mitigation tips suggested by the Lighpoint experts.

etl error handling

Error 1: Data type mismatch

Data mismatch can occur when the values extracted from source systems do not match the expected data types in the destination system or conversions. For example, suppose the source system uses floating point numbers to represent monetary values, while the destination system expects currency amounts in integer format — an error will logically occur.

Mitigation tips:

  • Implement comprehensive data profiling and schema validation during the ETL design phase to identify and handle potential data type mismatches.
  • Use explicit data type conversions or transformations in the ETL logic to ensure consistency between source and destination data types.
  • Regularly update data dictionaries and metadata to reflect any changes in data types.

Error 2: Incomplete or missing data

Data losses can occur during the extraction, transformation, or loading stages due to network failures, data source changes, or errors in the ETL logic.

Mitigation tips:

  • Implement robust error handling and logging mechanisms to capture and report instances of missing or incomplete data.
  • Conduct thorough data profiling and quality assessments to identify and address any gaps in the data before, during, and after the ETL process.
  • Implement data validation checks to ensure the completeness of the data at different stages of the ETL pipeline.

Error 3: Data dupes

Duplicates can emerge when extracting data from source systems, during transformations, or due to collateral errors. Duplicates in a system can manifest in various ways: as exact duplicates where all field values match, creating a complete replica of the data; case sensitivity duplicates arising from variations in letter casing (e.g., «John Doe» vs. «john doe»); synonymous duplicates due to different representations of the same concept (e.g., «USA» vs. «United States»); numeric duplicates in numerical fields, and more.

Mitigation tips:

  • Use unique identifiers or keys to detect and eliminate duplicates during ETL.
  • Implement validation checks to identify duplicate records and handle them appropriately, either by removing duplicates or updating existing records.
  • Utilize data profiling tools to identify patterns and anomalies on time.

It’s important to note that error prevention is just as crucial as error mitigation. Regularly testing and validating the ETL processes, maintaining up-to-date documentation, and incorporating data quality checks into the workflow can help identify and resolve potential issues early in the development and production phases. Additionally, establishing a robust monitoring and alerting system allows for prompt detection of errors and facilitates quick response and resolution.

Practice 6: Parallel processing

Parallel processing, or, in other words, workload distribution, helps businesses to process larger volumes of data in less time. Businesses can achieve improved performance by strategically distributing workloads during the ETL process and optimize resource utilization.

Ensure the following process steps for distributing workloads in ETL:

1. Data partitioning:

  • Divide the dataset into partitions based on certain criteria (e.g., ranges of values, date ranges).
  • Partitioning allows for parallel processing of data subsets, optimizing performance.

2. Parallel processing:

  • Execute ETL tasks concurrently across multiple processing units or nodes.
  • Use parallel processing frameworks or technologies to distribute the workload effectively.

3. Task dependency management:

  • Identify dependencies among ETL tasks and sequences.
  • Prioritize and schedule tasks to ensure that dependent tasks are executed in the correct order.

4. Data distribution:

  • Distribute data across multiple nodes or servers for parallel processing.
  • Choose an appropriate distribution strategy based on the characteristics of the data and the processing tasks (e.g., hash-based distribution, range-based distribution).

5. Load balancing:

  • Ensure a balanced distribution of workloads across processing units to avoid bottlenecks.
  • Monitor and adjust the distribution of tasks dynamically to optimize resource utilization.

By utilizing parallelization, businesses can leverage existing hardware more efficiently, avoiding the need for massive infrastructure upgrades and reducing operational costs.

etl performance monitoring

Practice 7: Fine-tune monitoring and performance

Integration strategies are on the rise, but implementing them can be difficult. That’s why businesses pay more and more attention to monitoring tools, as it is much easier to prevent disaster than to deal with consequences. We can examine three use cases highlighting the importance of constant data monitoring and logging.

  1. Data quality issues: Inaccurate financial reporting. A financial institution ran ETL processes to consolidate financial data from various branches. Due to a lack of real-time monitoring, unnoticed data quality issues led to inaccuracies in financial reports, resulting in executives’ flawed decision-making and financial losses due to incorrect assessments of the organization’s financial health.
  2. Increased downtime: Customer service disruptions. A telecommunications company relied on ETL processes to update customer records in real-time. Without real-time monitoring, data transformation or loading errors caused downtime in customer service systems. This led to disrupted services and frustrated customers.
  3. Compliance and security risks: Data breach and regulatory penalties. In a healthcare organization managing sensitive patient data through ETL processes, the absence of real-time monitoring allowed security vulnerabilities to go unnoticed. In the event of a data breach, patient confidentiality was compromised, leading to severe regulatory penalties and damage to the organization’s reputation.

How to avoid these challenges? We suggest maintaining detailed logs of ETL activities for auditing purposes and conducting systematic recording of activities and changes made during the data integration process. The key steps may include:

  • Capture metadata: Record metadata information about each step in the ETL process, including data extraction, transformation, and loading. Capture details such as timestamp, user, source, destination, and the nature of the transformation or operation.
  • Monitor the performance: Implement logging mechanisms to capture errors, warnings, and informational messages during the ETL process.
  • Set performance metrics: Record performance metrics, such as execution time, resource utilization, and data throughput. Use this information for performance tuning and optimization of the ETL process.
  • Control the current versions: Maintain version control for your ETL scripts and workflows. This helps track changes over time and facilitates rollback in case of issues.

This proactive approach helps address potential problems before they impact the data pipeline. Therefore, you can create a comprehensive audit trail for your ETL processes, providing transparency, accountability, and the ability to quickly identify and resolve issues.


The essential requisites for modern ETL solutions encompass seamless ingestion, enrichment, and management of transactions, coupled with real-time support for structured and unstructured data from diverse sources — be it on-premises or in the cloud. Thus, the practices of scalability, parallel processing, timely error handling, incremental loading, and others are the keys to shaping a fully utilized ETL infrastructure that is adaptable to changes in data sources, business requirements, and technological advancements, allowing for easy integration of new data formats or sources.

An experienced ETL service provider can carry out existing ETL processes and help continuously improve them. Contact Lightpoint team for seamless data integration services that evolve with your needs.