Everything You Need to Know About ETL (2023)

CEO, Portable

What is ETL?

ETL (extract, transform, and load) is the process of extracting data from one system, transforming the information (cleaning, formatting, sorting), and loading the data into a target system.

There are three steps in the ETL process:

  1. Extract - The extraction step involves identifying and retrieving data from various sources.

  2. Transform - The transform step involves cleaning, formatting, and sorting the data.

  3. Load - The loading step syncs data to the target system (typically a data warehouse or data lake) where it can be used to create business value.

Using an ETL pipeline to get data from source systems into a centralized processing environment is one of the most important steps in architecting a scalable data pipeline.

What is the purpose of ETL?

The purpose of an ETL process is to allow enterprise data teams to pull information from disparate source systems (databases, applications, file stores) and to centralize the data for analytics, automation, or product development.

As you would expect, everything revolves around creating business value.

With reliable ETL pipelines in place, data-driven organizations are able to seamlessly access, analyze, and leverage their data effectively. 

Strong enterprise data integration capabilities allows businesses to:

  • Eliminate data silos

  • Ensure data consistency

  • Improve data quality

  • Automate data processing

  • Reduce manual data manipulation

If the company can't depend on its data pipelines, why should anyone trust the insights the team generates?

Now that we understand the benefits of an ETL process, let's dig into the 3 ways to generate value from data.

ETL: Key Use Cases

ETL pipelines create business value in 3 ways.

  1. Business intelligence

  2. Process automation

  3. Product development

ETL for business intelligence

Analytics - also known as business intelligence - is the most common use case for ETL.

Business intelligence involves turning raw data into reports, visualizations, and data insights to improve strategic decision making.

To power analytics, the first step is always to extract data from disparate source systems. Enterprise data typically lives in databases, applications, or file stores and must be accessed and consolidated before analysis can take place.

Most business intelligence teams use tools from across the Modern Data Stack to ensure scalability, streamline workflows, and automate workflows in a reliable manner.

This is where ETL for business intelligence comes into play. ETL pipelines handle data access, transformation into a common format, and preparation for downstream analysis. This frees up valuable time for business analysts to focus on turning data into valuable insights.

When building a great business intelligence team, it's important to put in place the necessary infrastructure to seamlessly move data so your team can focus on generating business value instead of troubleshooting common data quality issues.

ETL for process automation

Manual tasks are the worst enemy of every company - they can lead to wasted resources, human error, incorrect data, and tedious tasks.

ETL can be used to move data and automate business processes - removing the need for manual work and increasing business efficiency.

To create value from process automation, an ETL pipeline can:

  • Centralize data to automate KPI dashboards

  • Sync data to a destination that would otherwise require manual intervention

  • Push data into an environment that is used as the source of data for downstream business processes

Regardless of the business vertical (finance, healthcare, technology, retail, etc.), manual processes consume resources, and ETL pipelines can help automate the flow of information.

With ETL pipelines and process automation, companies can reduce operational costs, improve their bottom line, and build a more scalable foundation for innovation.

ETL for product development

  • ETL for product development turns raw data into valuable products. The purpose is to create new or improved products that customers can buy. ETL pipelines can power external products with pre-packed insights, automate workflows, or raw data feeds for purchase.

  • ETL plays a crucial role in product development. It integrates data from multiple sources and loads it into a target system for product development. It enables companies to quickly develop and release new products, services, or features.

  • Using ETL for product development provides several benefits. An example is leveraging existing data assets to create new products or services. Another benefit is providing new revenue streams for the business, and driving innovation.

  • ETL for product development in e-commerce, finance, and healthcare. By using ETL pipelines, companies can create value for customers. This helps to improve their bottom line and stay ahead of the competition.

Other ETL use cases

  • ETL is a versatile tool that can be used in a variety of use cases. Here are a few more use cases that you can use ETL for.

  • ETL can help migrate data from one system to another. This can be seen when upgrading to a new software system.

  • ETL can integrate customer data from various sources. Popular examples are CRM systems, social media, and marketing campaigns.

  • ETL can be used for marketing analytics. it can integrate marketing data from various sources. This can provide a comprehensive view of marketing performance.

  • ETL for machine learning can help organizations analyze data more efficiently and accurately. This enables better decision-making.

  • ETL for IoT data analysis can help organizations identify patterns and insights in sensor data.

  • ETL can be used to integrate data from different financial systems. This will improve financial reporting and analysis.

Regardless of the use case, it's important to understand who uses an ETL solution and how value is created for those business users.

Who manages an ETL pipeline?

When it comes to managing an ETL pipeline, there are various stakeholders involved in the process. Data teams are responsible for the setup, management, and maintenance of ETL pipelines. They are responsible for ensuring that the data is flowing smoothly and efficiently.

Let's take a closer look at each stakeholder's role and how they contribute to the success of an ETL pipeline.

  1. Analysts - Analysts define the business requirements and the key performance indicators (KPIs) that the ETL pipeline should support.

  2. Analytics engineers - Analytics engineers develop and deploy the ETL pipelines. They ensure that the data is extracted, transformed, and loaded efficiently.

  3. Engineers - Engineers manage the underlying infrastructure, They ensure that the ETL pipelines are running smoothly and efficiently.

  4. Data scientists - Data scientists use the data generated by the ETL pipeline. They build predictive models, identify trends, and discover insights.


Business analysts and data analytics are key stakeholders in the ETL process. Their primary responsibility is to turn raw data into actionable insights. These insights can help the business make informed decisions.

In order to do this, analysts must have strong SQL skills to extract data from various sources across the enterprise. However, even with SQL proficiency, the data may not always be in a usable format.

This is where ETL pipelines come in. ETL pipelines can transform the data into a clean and structured format that is easier for analysts to work with.

Analysts should also have a strong understanding of the business and the specific metrics and KPIs that are important to track. They should be able to identify which data sources are relevant and prioritize them accordingly.

Communication and collaboration skills are also important for analysts. The reason is they often need to work closely with other stakeholders such as engineers and data scientists.

Some of the actions performed by analysts are

  • Define data transformation rules

  • Ensure proper data mapping and transformation

  • Collaborate with stakeholders to identify data sources

  • Create data models

For smaller teams, analysts may also be responsible for managing the data infrastructure for the ETL pipeline. This requires a deeper understanding of the ETL process and the tools involved. As a result, data analysts are common stakeholders in the build vs. buy decision for ETL pipelines.


Engineers play a crucial role in ETL pipeline management. They are responsible for developing, implementing, and maintaining data integration solutions.

The responsibilities of engineers in ETL pipeline management include designing and building data pipelines, ensuring data quality, and monitoring and troubleshooting data integration issues. They should have a strong background in programming languages like Python, Java, or Scala. They also should have knowledge of data structures, algorithms, and database technologies. (SQL, NoSQL, or Hadoop)

Engineers are critical thinkers with problem-solving skills. They should be able to work under pressure to identify and solve complex technical issues.

Engineers should be capable of writing high-quality, efficient, and maintainable code that can process large volumes of data. A team player attitude is also important, as they often work collaboratively with other stakeholders to ensure successful data integration.

Engineers usually perform the following actions when managing ETL pipelines.

  • Developing and managing custom ETL connector

  • Writing code on top of open source frameworks

  • Designing and maintaining data infrastructure

  • Collaborating with other data stakeholders such as analysts and data scientists

  • Ensuring data quality and accuracy

  • Troubleshooting ETL pipeline issues

  • Implementing ETL best practices and optimizing pipeline performance

As you can see, most build vs. buy decisions either directly or indirectly involve engineers.

Analytics engineers

Analytics engineers ensure that the data extracted, transformed, and loaded is of high quality and can be used to power downstream applications. They are responsible for designing, building, and optimizing data models to support business intelligence and analytics initiatives.

Analytics engineers should have a strong understanding of data modeling and database design. They also need to be proficient in programming languages like SQL, Python, and R. They should also have experience working with big data technologies like Hadoop and Spark.

In addition to version-controlling data transformations, analytics engineers also work to ensure data quality. They do it by implementing automated data quality checks and monitoring data pipelines for errors or issues.

Some of the actions that analytics engineers perform include

  • Designing and building data models

  • Optimizing data pipelines for performance

  • Implementing data quality checks

  • Monitoring data pipelines for issues

Finally, there is a key difference between analysts and analytics engineers. Analysts focus on building dashboards and developing insights directly. Analytics engineers approach data through the lens of self-service business intelligence - providing clean data for downstream users to build their own insights.

Data scientists

Data scientists often work with large datasets. They require deep technical expertise in data analysis, statistics, and programming. They are responsible for analyzing and interpreting complex data, and building models to extract meaningful insights.

Data scientists may work on a range of use cases. These include predictive analytics, machine learning, and natural language processing.

Data scientists don't typically involve in the development of ETL pipelines. However, they play a crucial role in the design and implementation of data processing workflows.

Data scientists can be involved in designing and testing data quality checks. They can also develop custom transformations to extract insights from data. Moreover, they can optimize data processing workflows to meet performance and accuracy requirements.

Data scientists also work closely with analysts and analytics engineers to develop models and dashboards that enable data-driven decision-making.

Overall they perform the following tasks in managing ETL pipelines.

  • Analyzing complex data sets

  • Building and testing machine learning models

  • Developing custom data transformations

  • Collaborating with data engineers, analysts, and other stakeholders

  • Ensuring data quality and accuracy

  • Building custom data visualizations and dashboards

Other stakeholders in ETL

There are more stakeholders that are commonly involved in the procurement process for ETL solutions.

The finance and procurement departments are involved in the purchase decisions for ETL solutions. Information technology departments and technical leadership, such as architects or CTOs, also have a say in design decisions.

These stakeholders may be concerned with a wide range of decisions. Some of them are the cost of the ETL solution, its compatibility with existing systems, and its scalability. It's important to consider the needs and requirements of all stakeholders when evaluating and selecting an ETL solution.

Here are some additional stakeholders that may be involved in the ETL process.

1. Project Managers: Responsible for overseeing the implementation of ETL projects. They ensure that ETL projects are completed on time and within budget.

2. Data Governance Officers: Responsible for ensuring that the ETL process complies with data governance policies and regulations.

3. Security Teams: Responsible for ensuring that the ETL process is secure and meets the organization's security requirements.

4. Compliance Officers Responsible for ensuring that the ETL process is compliant with legal and regulatory requirements.

Now that we understand the use cases and stakeholders involved, let's dive deeper into the ETL process.

What are the 3 steps of the ETL process?

The 3 steps of the ETL process are:

  1. Extract

  2. Transform

  3. Load

Step 1: Extract

  • During the Extract stage in the ETL process, data is pulled from various source systems and prepared for downstream transformation.

  • To extract data, you first need to find an upstream interface from which data can be withdrawn. This can involve connecting to databases, file systems, APIs, or other sources of data.

  • Common techniques used for data extraction include batch processing, change data capture (CDC), and real-time streaming.

  • Batch processing involves extracting large volumes of data on a scheduled basis, while CDC captures changes to data in real time.

  • Real-time streaming involves continuously extracting data as it becomes available. Effective data extraction is critical for ensuring that the downstream ETL processes have access to accurate and complete data.

  • Data engineers should also consider factors such as data security, performance, and scalability when designing and implementing their data extraction processes.

  • In addition, the data extraction process should be documented and easily repeatable. This ensures that data is consistently and reliably extracted for downstream use.

Data extraction interfaces (APIs, databases, files, etc.)

There are common interfaces from which you can extract data. Those are APIs, databases, file sources, webhooks, and event data creation.

  • APIs (Application Programming Interfaces) help ETL pipelines pull data from business applications and third-party software. Some of them are CRM and ERP systems, social media platforms, and marketing automation systems. As a data engineer, it's important to know how to authenticate with APIs and how to handle pagination and rate limiting to avoid hitting API usage limits.

  • Databases are a common source for extracting data in ETL. They can be queried directly or through change logs (for change data capture). Extracting data from databases requires knowledge of SQL, and familiarity with database schema and table structures.

  • Data engineers must also consider the amount of data they are extracting. The reason is the large volumes of data can significantly impact database performance. Additionally, they must ensure that proper security measures are in place to protect sensitive data during the extraction process.

  • File sources are a helpful way to replicate large data sets from one location to another in ETL processes. File-based data sources can include CSV files, log files, or even binary files like images or videos. When working with file sources, data engineers need to ensure that the data is formatted correctly and can be easily transformed and loaded into the target system.

  • Webhooks offer a low-latency mechanism for receiving information from upstream systems as data changes or new information is created. With webhooks, developers can specify a URL to receive notifications from an external system when an event occurs. When working with webhooks, it's important to make sure that the receiving system is secure and can handle high volumes of incoming data.

  • Event data creation can take place via a pixel on a website or a software development kit (SDK) installed in a mobile application. Event tracking allows developers to capture user behavior data, such as clicks, views, and transactions. When working with event data, it's important to ensure that the data is structured correctly, and the events are tracked consistently across all channels.

Once you have an interface for extraction, you then need to determine how best to extract the data - incremental vs. full data extraction.

Incremental data extraction vs. full extraction

Incremental data extraction is ideal for immutable data sets. Immutable data sets are information that will not be deleted or modified. With incremental data extraction, only the changes to an upstream data set (i.e. new data records) are extracted to then be applied downstream.

Incremental extraction is common when working with:

  • Database change logs

  • Event data created from websites or mobile applications

  • Large file sources and API interfaces (where upstream data is immutable or where only incremental changes are exposed)Full data extraction is preferred for historical syncs and extracting data sets that change.

With full data extraction, all of the data sets in the upstream system is extracted. The data is then replicated into the downstream system overwriting data that already exists.

Full data extraction is preferred when:

  • data is extracted for the first time (i.e. populating all historical data), or

  • when data is not immutable (i.e. information in an upstream file, API, or database can change without notice). 

Step 2: Transform

  • The transform step ensures that data is in a format suitable for loading into the target system. This involves cleaning the data, checking it for accuracy, and transforming it into the required format.

  • Tasks performed during the Transformation process include data mapping, data validation, data cleansing, data enrichment, data standardization, data aggregation, and data normalization.

  • These tasks can be performed using a variety of tools. You could either use a programming language like Python and R, or ETL tools that offer a graphical user interface.

  • The biggest consideration during the Transform stage is the amount of data analysis that will take place. Data analysts and business users often require a high level of data transformation, which can be done during the ETL process. However, there may be situations where too much data transformation can cause delays in loading the data.

  • This is the crux of the question: Should we ETL or ELT data? It's a decision that requires a thorough understanding of the data and the use case, as well as an assessment of the available resources, including hardware, software, and personnel.


  • ETL and ELT (Extract, Load, Transform) are two commonly used data integration paradigms. ETL pipelines first extract data from source systems, transform it in a staging area, and then load it into the destination system. On the other hand, ELT pipelines load data directly into the destination system and then perform transformations downstream.

  • Nowadays, most data integration platforms are shifting towards the ELT paradigm, as it allows for more flexibility and scalability.

  • That being said, even ELT pipelines conduct some level of transformation and validation before loading into the destination. It's common to reformat data, define schemas/data types, and validate data before loading. Having clean and structured data can make transformation simpler after loading is complete.

  • This is especially the case when downstream systems are relational (target databases, data warehouses, some data lakes).

  • One important consideration when deciding between ETL and ELT is the types of transformation required for the data. In general, ETL pipelines are better suited for complex transformations, while ELT pipelines are better suited for simple transformations.

  • Another consideration is the type of downstream systems. Relational systems such as data warehouses and databases work well with both ETL and ELT pipelines. However, other systems such as data lakes may work better with ELT pipelines.

  • Let's now discuss the types of transformation that are common for both ETL and ELT pipelines.

Types of data transformation

It is common for data to undergo a variety of transformations while in motion to the destination. It ensures that it is prepared for analysis and easily accessible to downstream users. Some of the most common transformations include standardization, formatting, verification, data cleansing, deduplication, and sorting.

  • Standardization ensures data is presented consistently across disparate sources. It involves mapping, merging, and transforming data to ensure it meets a standardized structure, format, and naming convention. This helps to simplify analysis and makes it easier to work with the data.

  • Formatting is another common transformation that involves standardizing data formats across sources. This can include converting data to a standard date format or ensuring consistent use of units of measure. This helps to ensure data is easily understandable and can be easily analyzed.

  • Verification validates data as it is moved from source to destination. This can include checks for missing or invalid data. it also ensures data meets pre-defined quality standards.

  • Data cleansing is the process of detecting and correcting or removing corrupt or inaccurate data from a dataset. It involves identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data. Then it modifies, replaces, or deletes them.

  • Deduplication is the process of removing duplicate data entries within a dataset. This can involve identifying duplicates by matching data against pre-defined criteria and removing duplicates based on specific rules.

  • Data sorting is a process that arranges data in a particular order, such as alphabetical or numerical. This helps to make data more easily accessible and understandable for downstream users and can be important for certain types of analysis.

SQL data transformation

With SQL, users can perform a wide variety of data transformations, including data cleaning, aggregation, and pivoting. SQL allows users to quickly and efficiently transform large datasets, making it a popular choice for data analysts and engineers.

Some actions performed with SQL are:

  1. Data cleaning - Use SQL to remove duplicates or null values from a dataset.

  2. Aggregation - Use SQL to group data by a specific field and aggregate data using functions like SUM or AVG.

  3. Pivoting - Use SQL to transform rows of data into columns, allowing for easier analysis and visualization.

  4. Joins - Use SQL to combine data from multiple tables based on common fields.

  5. Data type conversion - Use SQL to convert data types, such as converting a string to a date or a number.

Step 3: Load

The final step in the ETL process is the Load phase. This is where the transformed and cleansed data is loaded into the target system. The target system could be a database, a data warehouse, or a data lake. The Load phase involves inserting, updating, or appending the data to the target system in a structured manner.

ETL for data warehouses

Data warehouses are designed to store and analyze large amounts of data. Therefore, data warehouses are ideal for loading. They offer a centralized repository for data from different sources. This makes it easier to access and analyze.

Data warehouses provide a structured approach to data storage. This enables quick and easy data analysis. They can handle complex queries and analytics. They can also uncover insights that would be impossible with raw data alone.

  • Snowflake ETL: Snowflake is a cloud-based data warehousing ETL that offers a range of ETL capabilities, including bulk loading, change data capture, and ELT. Snowflake's unique architecture separates storage from compute, allowing for on-demand scaling and cost-effective data warehousing.

  • BigQuery ETL: BigQuery ETL is a fully managed, cloud-native data warehouse solution. It is specifically designed for large-scale data processing and analytics in the cloud. BigQuery ETL allows for real-time data streaming and integration with other Google Cloud Platform services.

  • Redshift ETL: Amazon Redshift is a petabyte-scale cloud data warehouse. It offers high-performance processing and storage, with support for complex SQL queries and machine learning. Redshift also integrates with a wide range of other AWS services for enhanced data processing and management. Additionally, Redshift supports a variety of data sources and formats, making it a versatile tool for ETL operations.

ETL for relational databases

Relational databases are often used as a destination in the load phase of an ETL process due to their ability to store structured data in tables and rows. They provide a flexible and scalable solution for data storage and can easily integrate with other applications. Additionally, relational databases have the ability to enforce data integrity rules and ensure consistency across data sets. ETL pipelines can load data into a relational database using SQL commands or other integration tools

PostgreSQL ETL, MySQL ETL, and SQL Server are popular relational database management systems. They are well-suited for ETL because they support SQL. It is a language commonly used for querying and manipulating data in the ETL process.

They also have robust data management capabilities, including the ability to handle large volumes of data, provide data security features, and offer efficient indexing for fast querying. Additionally, they are widely used and well-supported, with active communities.

ETL for data lakes

Data lakes can be used as a destination in the load phase of ETL. The reason is their ability to handle vast amounts of data with varying structures and formats. They offer benefits such as lower storage costs, and flexibility in the data processing. Moreover, they also provide the ability to store unstructured and raw data for future analysis. However, data lakes can also introduce challenges such as data quality issues, data governance concerns, and potential security risks.

Popular options for data lakes include Amazon S3, Microsoft Azure Data Lake Storage, and Google Cloud Storage. When using a data lake ETL, it's important to carefully consider several factors. Those are data organization, security measures, and integration with other systems. This ensures the successful implementation and use of data lakes for ETL.

ETL Functionality

  1. Connectivity
  2. Data operations
  3. Infrastructure
  4. Security and compliance


In ETL, connectivity refers to the ability to connect to various data sources and destinations. Data engineers should be familiar with the process of establishing connections between their ETL tool and various data sources and destinations

  • Data sources - Data sources can be either structured or unstructured. They can come from a variety of sources, including databases, web services, files, or even the internet of things. Some ETL tools may also provide custom connectors or support for custom ETL scripts to allow for connectivity to unique data sources.

  • Unstructured vs. structured - Structured data is organized in a specific format that can easily be stored and analyzed in databases. Unstructured data refers to data that doesn't follow a specific format. Unstructured data sources can be challenging to connect to without proper data parsing and handling techniques. Techniques like data mapping, parsing, and keyword extraction can be used to convert unstructured data into structured data for downstream analysis.

  • Custom ETL - Custom ETL refers to creating a custom ETL pipeline from scratch to fit specific data sources or destinations. It requires knowledge of programming languages and database management systems. Custom ETL solutions are a good option when off-the-shelf ETL tools don't meet specific data integration requirements.

  • Custom connectors - Custom connectors are used to establish connectivity between ETL tools and data sources that are not natively supported. These connectors require customization to meet specific connectivity requirements. Developing custom connectors can be a time-intensive process. However, Portable does it in a few hours.

  • Destinations - Destinations can be relational databases, data warehouses, and data lakes. You should use the most suitable connectors to connect to destinations. This ensures compatibility and efficient data transfer.

  • Relational databases - Replication is a key consideration when connecting to relational databases. Replication is the process of copying data from a source database to a destination database. It enables organizations to maintain multiple copies of data across different locations. This improves data availability and reduces the risk of data loss.

Data operations

  • Data transformation is the process of converting and modifying data from one format to another. It improves the quality and usefulness of the data. This process can include tasks such as data cleansing, data standardization, and data enrichment.

  • Orchestration refers to the process of coordinating and managing the different components of an ETL workflow. Those are data extraction, transformation, and loading. This involves setting up and scheduling workflows, and monitoring their progress. Moreover, it also includes handling any errors or issues that arise.

  • Real-time data processing is processing and analyzing data as soon as it is generated or received. It allows businesses to make quick and informed decisions based on up-to-date information. This process can be used for a variety of purposes. Some examples are fraud detection, customer behavior analysis, and real-time recommendations.


  • Scalability - Scalability in ETL can be implemented by using distributed processing, data partitioning, and load-balancing techniques.

  • Big data ETL - For Big data ETL, techniques like Hadoop MapReduce, Spark, and Flink can be used to handle large-scale data processing. This can help in reducing the processing time and increasing efficiency.

  • Huge volumes of data - When dealing with huge volumes of data, it's important to use scalable storage systems like Hadoop Distributed File System (HDFS) or Amazon S3. This allows for the storage and processing of large amounts of data in a distributed and fault-tolerant manner. Data partitioning and indexing can also be used to improve query performance.

  • Deployment models - Deployment models in ETL refer to the way in which the ETL infrastructure is deployed, and whether it is hosted on-premises or in the cloud.

  • On premises vs. cloud solutions - On-premises deployments offer greater control over infrastructure and data. However, they require significant upfront investments in hardware and maintenance. Cloud-based solutions offer greater flexibility and scalability. They can be more expensive over the long term.

  • Regulations that impact where data can reside - Regulations around data privacy and security can impact where data can be stored and processed. To comply with regulations, organizations may need to store data within specific geographic regions or use data centers that meet specific security requirements. Cloud providers like AWS, Azure, and Google Cloud offer compliance certifications for various regulatory frameworks. Some examples are HIPAA, GDPR, and SOC2, which can help organizations ensure compliance.

  • Reliability - Reliability is a critical ETL functionality that ensures the data integration process is dependable and trustworthy. This includes ensuring that data is not lost or corrupted during transfer and transformation.

  • Fault tolerance - To ensure reliability, fault tolerance is an important consideration. This involves designing ETL workflows in such a way that they can handle errors, outages, and other issues without compromising the overall data integration process.

  • Implementing fault tolerance can involve measures such as setting up backup and recovery procedures, designing automated retry mechanisms, and building in redundancy to ensure that if one component fails, the entire ETL workflow does not fail.

  • Monitoring and alerting - Monitoring and alerting are essential features in any ETL platform. They allow data engineers to keep track of the health and performance of their data pipelines. These features enable alerts to be set up for critical errors, such as failed or slow-running jobs, to ensure they can be addressed in a timely manner.

  • Auditing and logging - Auditing and logging functionality is important in ETL pipelines, providing an audit trail of all data transformations and transactions that have taken place. This enables data engineers to identify any issues and roll back changes if necessary, as well as meet compliance requirements.

  • Notification support - Notification support in ETL platforms enables data engineers to receive alerts and notifications of important events or errors in their data pipelines via various communication channels such as email or messaging apps. This feature enables teams to collaborate effectively and quickly address any issues that arise during the ETL process.

Security and compliance

In an ETL process, you need to ensure sensitive data is protected and that legal requirements are met. ETL tools should provide a secure way to transfer data and encrypt sensitive data at rest and in transit. They also should authenticate and authorize access to data sources and destinations.

Furthermore, data governance policies and compliance regulations must be followed. Some examples are GDPR, HIPAA, and PCI DSS.

Two more important concepts in ETL security are hashing and compliance.

  • Hashing is the process of converting data of any length or format into a fixed-length string. It ensures data integrity by producing a unique hash value for each set of data.

  • Tokenization is the process of replacing sensitive data with a token. It is a random string of characters that cannot be reversed. This method helps to protect personally identifiable information by keeping the original data inaccessible.

Build vs. Buy for ETL

The decision to build or buy an ETL solution depends on factors such as budget, in-house expertise, and specific requirements. Building a custom ETL solution offers maximum flexibility and control, but may require significant investment in skilled developers and ongoing maintenance. On the other hand, buying a pre-built ETL solution can save time and resources, while still providing robust capabilities.

No code vs. low code vs. code

Selecting the right ETL platform depends on your organization's technical expertise and requirements.

No code ETL tools are easy to use and require no programming knowledge. This makes them accessible to a wider range of users. Low-code platforms need minimal coding skills, offering a balance between ease of use and customization. Code-based platforms demand strong programming skills and provide the most flexibility for building custom ETL solutions.

  • Code-based ETL solutions cater to organizations with specific data integration needs. They allow developers to write custom scripts for data extraction, transformation, and loading processes, enabling maximum customization. Popular programming languages for code-based ETL include Python, Java, and SQL.

  • Batch processing is a common ETL strategy that processes data in groups or batches. This approach is efficient for large datasets and helps consolidate resources, improving performance. However, batch processing may lead to latency. This makes real-time data processing a more suitable alternative for certain use cases.

  • Open-source ETL tools offer cost-effective solutions with strong developer communities, providing ongoing updates and improvements. Examples of popular open-source ETL tools include Apache NiFi, Talend, and Apache Kafka. These tools typically require more technical expertise compared to no-code or low-code platforms.

  • Cloud-based ETL solutions are increasingly popular due to their numerous advantages. They offer scalability and flexibility, allowing organizations to easily adjust their data processing capabilities as their needs change. Cloud-based solutions also reduce infrastructure costs, as they eliminate the need for expensive on-premises hardware.

ETL cost considerations

  • ETL development costs - ETL development costs can vary. They depend on several factors. The chosen ETL solution plays a significant role. No code and low code platforms have lower development costs. They require less programming expertise. Code-based and open-source solutions need skilled developers. This can increase costs.

  • The complexity of the project also affects development costs. Simple ETL tasks require less time and effort. Complex tasks need more resources. Data sources, target systems, and transformations influence costs. Customization and integration with existing systems can also raise expenses. Factor in these elements when planning your ETL project.

  • Ongoing data pipeline maintenance - Data pipeline maintenance is crucial. It ensures smooth data flow. It guarantees accurate and reliable data. Maintenance costs can be ongoing. They include monitoring, troubleshooting, and updating ETL processes.

  • Monitoring is essential for identifying issues. It involves tracking performance and data quality. Tools like Apache NiFi and Logstash help with monitoring. Troubleshooting fixes errors and prevents data loss. Regular updates keep ETL processes optimized. They adapt to changing data requirements and technologies.

  • Training staff on ETL tools and processes is part of maintenance costs. Skilled professionals are necessary for effective maintenance. Outsourcing maintenance is an option. It can reduce in-house costs. Weigh the advantages and disadvantages before making a decision.

ETL options (in-house ETL development, buying ETL software, hiring ETL consultants)

When considering ETL, organizations have several options to choose from. Here are some of them.

  • Develop in-house ETL pipelines

  • Buy ETL software

  • Find an ETL consultant

  • Alternatives and substitutes

Each option has its own benefits and drawbacks. It's important to carefully evaluate each one to determine the best fit for your organization's needs and resources.

Option 1: In-house ETL Development

In-house ETL development involves building and maintaining ETL pipelines internally within an organization. When considering in-house ETL development, there are several factors to consider.

ETL design principles

ETL design principles are essential to ensure that the data flow is accurate, efficient, and optimized.

The ETL design process involves mapping out the flow of data from the data source to the data store or repository. It should take into consideration the data storage and data repository systems that are in place.

Organizations must ensure that their ETL design is scalable and can handle unstructured data.

Effective ETL design principles include

  • Data profiling to understand the structure of the source data

  • Data mapping to identify the correct fields and relationships

  • Data quality checks to ensure data consistency

ETL design principles also include identifying the ETL performance metrics. These metrics will be tracked to ensure that the ETL process is efficient and meets the organization's data integration needs.

Open source ETL frameworks

Open-source ETL frameworks like Apache NiFi and Talend offer a cost-effective solution for in-house ETL development. These frameworks can provide a wide range of features and capabilities for data integration, including support for data storage and data repository systems.

Open-source ETL frameworks can handle structured and unstructured data. They can also support data in various formats such as XML, CSV, and JSON. The benefit of using open-source ETL frameworks is that they are flexible and can be customized to fit specific data integration needs.

They also have an active community of developers who contribute to their development and maintenance, ensuring that they are up-to-date and relevant.

On-premises vs. cloud ETL

In-house ETL development can be done on-premises or in the cloud.

On-premises ETL development provides organizations with complete control over their data management process. In contrast, cloud ETL development offers scalability and flexibility. They allow organizations to scale their data management capabilities up or down depending on their needs.

Organizations must carefully evaluate their data management requirements to determine whether on-premises or cloud ETL development is the best option.

On-premises ETL development may be more suitable for organizations that have a large amount of data that needs to be processed and stored locally. Cloud ETL development may be more suitable for organizations that need to process and store data in a distributed environment or have a need for rapid data integration.

Python for ETL

Python is a popular programming language for ETL development due to its ease of use and extensive libraries. Python can handle a wide range of data formats and sources, making it a versatile tool for in-house ETL development.

Python can be used to perform data profiling and data cleaning tasks, as well as data transformation and data enrichment tasks. Python can also be used to build custom ETL pipelines, making it a flexible and powerful tool for managing data integration tasks.

The benefits of using Python for ETL include its ability to handle unstructured data, its scalability, and its compatibility with other programming languages.

ETL testing

ETL testing is a crucial component of in-house ETL development. It ensures the accuracy and integrity of the data being processed. Organizations must establish clear protocols for ETL testing, including data profiling, data validation, and data reconciliation.

ETL testing involves checking the data at every stage of the ETL process, from data extraction to data loading. Effective ETL testing protocols can help organizations identify and address data quality issues early on in the data integration process. This minimizes the risk of data errors and inconsistencies. Automated ETL testing can also be used to increase the efficiency and speed of the ETL testing process, reducing the time and effort required for ETL testing.

Effective ETL testing protocols can also help organizations meet regulatory compliance requirements and ensure data privacy and security.

Additionally, ETL testing can provide valuable insights into the performance of the ETL process. This allows organizations to identify bottlenecks and inefficiencies and optimize their data integration processes over time.

Option 2: Buying ETL Software

Evaluating ETL solutions is an important aspect of the buying process.

Recommended Read: ETL Tool Comparison Matrix: Costs, Features & FAQs

Evaluating ETL solutions

When evaluating ETL software, two options that are commonly compared are cloud SaaS and open-source solutions.

  • Cloud SaaS vs. Open Source -Cloud SaaS solutions provide the benefits of a cloud-based model, including scalability, cost-effectiveness, and ease of use. Open-source solutions provide flexibility and customizability. It is important to evaluate the needs of your organization and choose a solution that meets your specific requirements.

  • Data integration tools - Data integration tools are another important consideration when evaluating ETL solutions. These tools allow for seamless integration with a variety of data sources and destinations. They allow organizations to leverage the power of data for business intelligence, analytics, and other purposes. When evaluating data integration tools, it is important to consider factors such as ease of use, scalability, and security.

Cloud ETL pricing

Cloud ETL pricing is typically based on one of two models: pay-as-you-go or subscription-based.

Pay-as-you-go plans allow customers to pay only for what they use, making it an attractive option for those with sporadic or unpredictable ETL needs. This model typically charges per execution, data transfer, or processing time.

Subscription-based plans offer a fixed cost over a specified period, such as monthly or annually. This model is beneficial for businesses with predictable ETL needs, as it allows them to plan their budgets accordingly.

Top ETL software

The top ETL software providers include Portable.io, Informatica PowerCenter, Talend, and Microsoft SQL Server Integration Services (SSIS). These tools offer a range of features for data extraction, transformation, and loading.

  • The ETL landscape - The ETL landscape is constantly evolving with new tools and technologies emerging. Many ETL software providers now offer cloud-based solutions that allow for more flexible and scalable data integration.

  • Top tools - Top tools refer to the most widely used and popular ETL software in the market. These tools are typically feature-rich, and robust, and offer various functionalities like data transformation, data cleansing, and data integration

  • Free tools - Free tools are ETL solutions that offer their services at no cost. These tools often have some limitations, such as a limited number of connectors or data volume restrictions. However, they are a great option for small businesses or organizations with limited budgets.

  • Real-time ETL tools / Streaming ETL - Real-time ETL tools or Streaming ETL refers to ETL solutions that enable users to process and transform data in real-time or near real-time. These tools offer faster processing of data, which is especially useful in use cases where immediate insights are required.

  • ETL tools from cloud providers - ETL tools from cloud providers are ETL solutions offered by cloud service providers like AWS and GCP. These tools provide users with the ability to scale their ETL operations and take advantage of cloud services like storage, processing, and analysis.

  • AWS - AWS offers ETL solutions like AWS Glue, which is a fully managed ETL service that allows users to move data between various AWS data stores.

  • GCP - GCP offers ETL solutions like Dataflow, which is a fully-managed ETL service that can be used to perform batch and streaming data processing.

ETL Tools List

  • Portable - Portable is an ideal data integration tool for teams that work with long-tail data sources. This ETL platform boasts over 300 connectors for unusual data sources, giving it an edge over other ETL tools. The Portable team will create custom connectors for clients on request, with rapid turnaround times of just a few hours

  • Hevo - Hevo Data is a cloud-based data integration platform that supports over 100 data sources, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt. With Hevo, you can replicate data in real time, automate data pipelines, and transform data without code. Hevo also offers data quality and governance features, multi-cloud support, and scalability. Businesses can easily integrate data from various sources without worrying about infrastructure maintenance.

  • Stitch - Stitch is a user-friendly data pipeline tool that is now a part of Talend. It allows for easy data extraction and manipulation with a built-in GUI or various programming languages. The tool also includes Talend Data Quality and Talend Profiling services. Its top features include replication frequency, warehouse views, scalability, and high availability.

  • AWS Glue - AWS Glue is a fully managed ETL solution designed to move data between data sources. It makes it easy to organize ETL processes and automatically discover and categorize data, making it simple to search and query. The Glue Data Catalog is a single metadata repository that stores and tracks data location, schema, and runtime metrics. With features like an integrated data catalog, serverless architecture, job authoring, and integration with other AWS services, AWS Glue is a popular choice for ETL pipelines in the cloud.

  • Fivetran - Fivetran is a cloud-based data integration platform that helps enterprises automate data transfer from various sources to a central data warehouse. It offers a fully managed, zero-maintenance architecture that automates data translation, data quality checks, and data deduplication. Fivetran's top features include complete integration, fast deployment, important notifications, and fully managed services.

Option 3: Hiring ETL Consultants

When organizations require specialized expertise for ETL projects, hiring ETL consultants can be a viable option. There are several options to consider when hiring ETL consultants.


Freelancers can be a valuable option when hiring ETL consultants for organizations that require specific expertise in certain aspects of the data integration process. Freelancers often have diverse skill sets and can offer specialized knowledge in data mapping, data cleansing, and other areas of ETL.

One advantage of hiring freelancers is that they can be a cost-effective solution for short-term projects. Additionally, organizations can leverage the expertise of multiple freelancers to build a team of ETL specialists for larger projects. However, it is important to carefully evaluate the skills and experience of freelancers before hiring them to ensure they have the necessary knowledge and capabilities to complete the job effectively.

Another consideration when hiring freelancers is data storage and management. Organizations must ensure that their data is secure and protected while it is being handled by freelancers. It is crucial to establish clear communication and protocols for data storage and management with the freelancers to avoid any potential security breaches or data loss.

Data integration consultants

Data integration consultants are professionals who provide a range of services related to ETL, from strategy development to implementation and maintenance. They can offer specialized expertise in specific industries, such as healthcare or finance.

One of the advantages of hiring data integration consultants is their ability to develop a comprehensive ETL strategy that aligns with the organization's specific data management needs. They can also provide guidance on data storage and management best practices and ensure data quality and integrity throughout the integration process.

Data integration consultants can also help organizations select the right ETL tools and technologies for their specific data integration needs. They can provide insights into the latest industry trends and advancements in ETL technologies, which can help organizations stay ahead of the curve in terms of data integration capabilities.

However, hiring data integration consultants can be a costly option for organizations, especially for longer-term projects. It is important to carefully evaluate the experience and expertise of data integration consultants and ensure that they have a track record of successful ETL projects in the relevant industry.

Top ETL services

Top ETL services are end-to-end solutions for data integration and management that can provide a wide range of features and capabilities for organizations. These services offer data visualization, data quality assurance, and data governance features that can help organizations manage their data effectively.

One advantage of using top ETL services is their scalability and flexibility. These services can scale up or down based on an organization's specific data management needs, and they can offer a variety of pricing options to suit different budgets. Additionally, top ETL services often provide 24/7 support and maintenance, which can ensure data integration processes are running smoothly at all times.

Another advantage of using top ETL services is their ability to integrate with a wide range of data sources and formats. These services often have pre-built connectors for popular data sources, which can save organizations time and resources in the data integration process.

However, it is important for organizations to evaluate the cost and scalability of top ETL services before making a decision. While these services can offer a variety of benefits, they may not be suitable for all organizations, especially smaller organizations with limited data management needs.

Option 4: ETL Alternatives And Substitutes

ETL vs. iPaaS

ETL solutions are traditional data integration tools. They involve the development of in-house ETL pipelines for transforming and transferring data between different systems. iPaaS, on the other hand, is a cloud-based platform that provides pre-built connectors and APIs for data integration and management.

One of the key differences between ETL and iPaaS is the level of control and customization they offer. ETL provides greater control over data flow and transformation, allowing organizations to create custom pipelines to meet their specific needs. iPaaS, on the other hand, offers less control but provides more scalability and flexibility, making it ideal for handling complex data integration requirements.

ETL Pros:

  • Greater control and customization over data flow

  • More efficient for handling large volumes of data

  • Provides specific data transformation capabilities

ETL Cons:

  • May require significant resources and expertise to develop and maintain

  • Not as scalable as iPaaS for handling complex data integration needs

iPaaS Pros:

  • Cloud-based, so requires fewer resources and infrastructure

  • Offers more flexibility for adapting to changing data needs

  • Provides more scalability for complex data integration requirements

iPaaS Cons:

  • Limited control and customization over data flow compared to traditional ETL

  • May not provide the specific data transformation capabilities of ETL

ETL vs. CDPs

Customer Data Platforms (CDPs) can be considered an alternative to traditional ETL solutions for managing and integrating customer data. While ETL solutions focus on extracting, transforming, and loading data from various sources, CDPs provide a unified view of customer data from different sources and platforms. CDPs also offer built-in analytics and segmentation capabilities, enabling personalized customer experiences and marketing campaigns.

Pros of CDPs:

  • Provide a unified view of customer data across different sources and platforms

  • Enable personalized customer experiences and marketing campaigns

  • Often have built-in analytics and segmentation capabilities

Cons of CDPs:

  • May not provide as much control over data flow and transformation as traditional ETL solutions

  • Can be more expensive than traditional ETL solutions

  • Require some level of technical expertise for implementation and management

When to use CDPs

CDPs are ideal for organizations that prioritize a unified view of customer data and require personalized customer experiences and marketing campaigns.

ETL vs. Reverse ETL

Reverse ETL is a newer approach to data integration that provides an alternative to traditional ETL solutions. While ETL solutions focus on extracting, transforming, and loading data from different sources, reverse ETL focuses on extracting data from a central source and delivering it to various destinations.

Pros of Reverse ETL over ETL

  • Provides greater control over data delivery and management

  • Can be more efficient for specific data integration needs

  • Offers real-time data delivery for faster insights and decision-making

Cons of Reverse ETL

  • May not be as scalable as ETL for handling complex data integration needs

  • Requires a centralized data platform for data delivery
    When to use Reverse ETL

Reverse ETL is more suitable for organizations that need to deliver data to multiple destinations from a central source, such as delivering real-time data to business applications or customer-facing systems. Reverse ETL can also be used for specific data integration needs that require greater control over data delivery and management.

ETL: The Bottom Line

The ETL process is critical for organizations that rely on accurate and timely data to make informed decisions. It involves extracting data from multiple sources, transforming and cleaning it to a consistent format, and loading it into a destination system for analysis.

When considering an ETL solution, there are several factors to keep in mind, such as the data sources and destinations, scalability, reliability, monitoring, and alerting. Organizations can either develop their ETL pipelines in-house, purchase ETL software, or hire ETL consultants to handle the process.

There are numerous ETL tools available, ranging from top-paid software to free open-source options. Cloud providers such as AWS and GCP also offer their own ETL tools.

Ultimately, selecting the right ETL solution will depend on an organization's specific needs, such as data volumes, sources, and the level of automation required. By keeping these factors in mind, organizations can choose an ETL solution that best suits their requirements, leading to better insights and informed business decisions.

Using Portable for ETL

Portable is a versatile and user-friendly data integration tool. It is specifically suitable for teams dealing with long-tail data sources. Its vast collection of specialized data connectors is a standout feature. It provides access to over 300 unique data sources.

Moreover, Portable offers hands-on assistance 24/7, as well as custom data source connectors on request, without extra charge.

Portable has very affordable pricing plans. This includes a free plan for manual data processing and a monthly flat fee of $200 for automatic data transfers.

Overall, Portable is an excellent choice for companies that need to connect multiple data sources and want to focus on extracting insights from data rather than constructing and managing data pipelines. With Portable, teams can quickly and easily transport their data integration tools between contexts. This ensures seamless integration across different devices or platforms.