Companies need a reliable method to collect data from multiple sources, standardize and clean up data sets and load them into a data store. A standard method to do this is through an ETL (Extract, Transform, Load) data pipeline. 

ETL is a data integration method that enables businesses to unify data and make it available for analysis. Given how ingrained ETL has been in the data integration sphere, the term is often used interchangeably with “data pipeline.” But the two terms are very different.

ETL is also comparable to another data integration framework called ELT (Extract, Load, Transform) — each method has its advantages and disadvantages.

In this article, we’ll dive into the components of an ETL data pipeline, explain how it differs from a data pipeline and compare it with the ELT method.

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What is an ETL pipeline?

An ETL pipeline outlines the processes involved in extracting data from various sources, transforming it and then loading it into a destination. 

Using the ETL data integration method, analysts get access to a central repository of cleaned and formatted data. They can use this data for analytics and business intelligence.

There are three stages in the ETL process:

Extract

In the extraction stage, data from multiple sources is collected and loaded onto a staging area or intermediate destination. Common data sources include:

• SaaS applications
• CRM platforms
• Sales and marketing tools
• Event streams
• SQL or NoSQL databases 

Data from these sources can be synced synchronously or asynchronously, depending on data analysis requirements. For example, data from a CRM tool can be updated twice a week, whereas customer app data can be collected daily. 

Developers can build manual APIs for each source, but this isn’t always feasible for companies with innumerable sources. Instead, they can use solutions like Fivetran to implement pre-built connectors to link to all their sources easily.

Transform

Raw data from every source must be cleaned, structured and formatted to be used in data models for analysis. This process is data transformation.

Transformation involves:

• Normalization
• Cleansing
• Restructuring
• Deduplication
• Data validation

Depending on the use case, it can also include summarization, sorting, ordering and indexing. Instead of developers and data teams manually coding transformations for each dataset, they can use pre-built transformations to speed up this process.

Load

The loading process moves transformed data to a centralized destination, like a database, data warehouse, data lake or cloud data warehouse. Data can also be directly sent to business intelligence tools for faster analysis.

ETL pipeline use cases

ETL tools are typically used for:

• Centralizing and standardizing data: ETL pipelines can consolidate data from numerous sources into a central database, like a target data warehouse, where analysts can access and use cleaned data for analysis.
• Data migration: ETL data pipelines can help organizations move from older, legacy storage systems to modern ones that drive faster and more comprehensive analysis.
• In-depth analytics: A structured data set can be fed into analytics tools to gain insights quickly without losing any contextual data. Context is crucial for analysis as it shows how certain results or conclusions were obtained and helps analysts solve and fix any errors.

ETL pipeline challenges

While ETL has its merits for certain analytical use cases, organizations and data teams struggle in three key areas.

Scaling

ETL pipeline architecture relies on servers for data processing. As the volume and variety of data increase, the setup must also be scaled. Doing this involves a significant investment, especially if it requires on-premise hardware and takes time. 

This system might work for batch processing. But, scaling an ETL pipeline becomes unfeasible as the need for real-time data grows.

Increasing labor needs

Engineers must build a new pipeline for every data source, adding to their workload of managing existing pipelines. Building and applying transformations adds another challenge.

Since ETL pipelines are built for specific use cases, qualified engineers with special training might be required to manage the system.

These factors cost companies thousands of dollars in additional workers, extra working hours and the facilities required to house these employees.

Workflow reliability

ETL pipelines must be rebuilt when there are changes to the upstream data schema or downstream data needs. The first scenario requires engineers to modify or rebuild all downstream transformation code, while the second requires them to build new transformations.

Pipeline changes are fairly constant as business logic evolves continuously. This adds to the ever growing workload of developers and engineers, leading to further expenses and potentially delayed data collection.

ETL pipeline vs. data pipeline

Many users confuse the terms “ETL pipeline” and “data pipeline.” However, the two terms are used for different data integration solutions. 

Before we delve into the differences, let’s understand what a data pipeline is.

Data pipeline

A data pipeline is a collection of processes that move data between the source system and the target repository. It is an umbrella term for the steps used to collect and load data.

An ETL pipeline is a type of data pipeline. It refers to a specific way in which data is gathered, transformed and loaded into destination systems.

A data pipeline has six key components:

• Source: A source is any system that data is collected from.
• Destination: A destination is a central repository where the consolidated data is stored for analysis.
• Dataflow: Dataflow defines how the data will move from one system to another. 
• Processing: Processing is where data integration occurs. Raw data is turned into consumable information using transformations. Developers and managers determine the parameters of these transformations. 
• Monitoring: Data pipelines rely on complex processes to be completed consecutively without any hitches. This is unreasonable as errors are unavoidable. Monitoring involves constantly checking the pipeline to see if it’s working as intended.
• Workflow: Workflow determines the order in which each step in the pipeline will take place.

Data teams can modify these components to build data pipelines that align with business intelligence requirements.

Types of data pipelines

Data pipelines can be broadly classified into four categories:

• Batch: A batch pipeline runs at predetermined intervals and loads a high volume of data. For example, a pipeline that collects data once a month from a CRM tool is a batch-processing pipeline.
• Real-time: Streaming or real-time pipelines continuously sync data as and when the source is updated.
• Cloud-based: These pipelines are hosted in the cloud by a third-party vendor and are a great solution for saving on infrastructure expenses. Some cloud-based platforms offer advanced features that can streamline your entire data management.
• Open-source: Open-source database software is usually free and gives complete access to use and modify the source code. Using these tools requires significant expertise and time.

A data pipeline can have characteristics of more than one of the types mentioned above. Many organizations use hybrid pipelines to enable different functions.

Differences between an ETL pipeline and a data pipeline

An ETL data pipeline and a data pipeline have three major differences.

Data pipelines don’t have to include transformations

An ETL pipeline must include transformation, whereas a generic data pipeline may not have transformations.

ETL pipelines end after loading

ETL pipelines are completed after the data is loaded into the target repository. Any other data pipeline could use loading as part of the pipeline that triggers further action. An ELT pipeline is a good example of this.

ETL pipelines move data in batches

Most ETL pipelines are batch-processing pipelines. Data is synced from sources in batches on a predetermined schedule. A data pipeline can be configured to collect data in batches or in real-time.

ETL pipeline vs. ELT pipeline

ETL is often compared to ELT, a newer method of data integration. Before we dive into which one is better, let’s understand the ELT framework.

ELT 

ELT is a data integration method where data is collected from a source, loaded onto a destination and then transformed by analysts when needed.

Essentially, it inverts the latter two processes of ETL. Doing this decouples the loading and transformation processes, allowing for faster data syncing and preventing the two main threats to an ETL pipeline — changes to upstream schemas and downstream data models.

An ELT data pipeline gives analysts and data engineers complete control of how their source data is modified. They can build and apply transformations to create specific data models without interrupting the extraction process.

ELT pipelines are customizable and used for advanced analytics, real-time data and event streams, machine learning and artificial intelligence. 

Using a fully-managed ELT platform like Fivetran is cheaper, easier to implement and speeds up your time to insights compared to building and maintaining ETL pipelines manually.

Why ELT is the way of the future

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ELT is the ideal data integration architecture for modern business needs.

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Organizations are switching to fully-managed ELT for five crucial reasons:

ELT is simpler

ELT simplifies data integration by populating databases directly from the source. This ensures data integrity and is easier for analysts, who can create better models to suit their analytical needs.

It also streamlines data engineering and development by enabling a no-code data pipeline set up in minutes. This reduces their workload and allows them to focus on more mission-critical tasks.

ELT is more reliable

ELT pipelines eliminate the need for constant pipeline rebuilding because the loading and transformation processes are independent. Data teams have faster access to data and can build and modify data models based on business use cases without interrupting data movement from the source.

If you choose a fully-managed solution like Fivetran, maintenance and troubleshooting are handled by the tool’s developers rather than yours. The platform is regularly updated to boost security and compliance and add more features.

ELT supports automation

ELT pipelines that third-party vendors handle automate mundane pipeline maintenance tasks like modifying data extraction scripts, keeping schema up-to-date and normalizing data source extracts.

Automation can also be powered by integration with other data management tools. 

ELT facilitates outsourcing

Organizations using automated ELT can create standardized data models that can facilitate outsourcing. Outsourcing data pipeline management can save time and money while freeing up your data teams to focus on their core tasks.

Platforms like Fivetran allow role-based access so companies can control how contractors or third parties interact with their data. An easy-to-use interface also means pipeline modification can be done in a few clicks without needing specially-trained experts.

ELT is flexible and scalable

ELT pipelines don’t have to stick to specific transformations. Instead, they let data scientists and analysts decide what to do with the data and implement their own processes for analysis.

Moreover, cloud platforms are easy to scale. Fivetran, for example, uses a payment model where you only pay for what you use. Companies can unlock new resources by upgrading their subscription plan. 

This eliminates the need for expensive on-premise upgrades or buying other systems to support a fast-growing ETL pipeline.

Conclusion

ETL data pipelines are ideal for organizations that handle a smaller data volume and can rely on slower insights powered by batch data processing. Other types of data pipelines are needed for real-time, quick analysis.

A fully-managed ELT pipeline is the solution for companies looking for real-time data loading, fast analysis and easier scaling.

Fivetran can help data teams set up and manage their data pipelines with ease. Sign up to uncover how our platform can elevate your data integration.

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