Understanding the Difference Between Data Orchestration and ETL

Data orchestration and ETL can both be important processes for businesses that use data. Discover the differences between data orchestration and ETL here.
Last updated
May 2, 2024

Data orchestration and ETL can both be important processes for businesses that use data. While these two processes have quite a few similarities, they’re not necessarily the same thing. Rather than using these terms interchangeably, it’s best to get familiar with the differences so you can decide what should be used in your business. With that being said, let’s dive into the differences between data orchestration and ETL!

A brief overview

Data management has become increasingly complex in recent years. Big data is essential for many companies, but that data needs to be managed, processed and analyzed properly to make the most of your data investments.

Data orchestration and ETL are both popular approaches for handling large amounts of data, though these approaches differ in several ways. First, let’s define ETL and go over some pros and cons of this approach.

What Is ETL? (Extract, Transform, Load)

ETL, or Extract, Transform, Load, is a process that retrieves data from different data sources and gets it ready for analysis and reporting. As evidenced by the name, the simplified version of this process happens in three steps:

  • Extract — The extract state pulls data from various data sources. These sources might include databases, web APIs or anywhere else a company derives its data.
  • Transform — The transform stage of the process cleans and standardizes the data. In other words, the data is transformed into the desired format. This step ensures the data is complete and accurate.
  • Load — Finally, data goes through the load stage, where it is loaded into a data warehouse or other storage system where it can be pulled for reporting and analysis.

ETL is widely used, especially in large organizations. It is known for its efficiency in processing large batches of data. Of course, ETL isn’t without its drawbacks. Overall, it’s a tried-and-true data management approach, but it may not be the perfect solution for some organizations. Let’s take a look at some of the main pros and cons of ETL to better understand where it should be used.

Pros and Cons

There’s a reason that Extract, Transform, Load has been used for many years. It’s a largely reliable method for many businesses that deal with large volumes of data. Here are some of the main pros of using ETL:

Pros of ETL:

  • Enables diverse data sources — Since ETL standardizes data, it can pull useful data from numerous different data sources and formats, unifying and making it easier to analyze.
  • Fewer redundancies — ETL helps to eliminate data redundancy by bringing together an organization’s data under one umbrella.
  • Handles large volumes efficiently — ETL can batch process large volumes of data, reducing data processing demands and getting organizations the data they need efficiently
  • Improved data quality — ETL cleanses and validates data, helping to ensure data accuracy and consistency.

It’s important to remember that ETL isn’t perfect for every situation. Next, let’s get into some of the drawbacks of ETL.

Cons of ETL:

  • Time-consuming — While ETL is much faster than cleaning data manually, it can sometimes take time to process large batches of data.
  • Complex setup — ETL requires a dedicated data stack and infrastructure, which some businesses may not be equipped to handle.
  • Data silos — If a business is set up where departments manage their data, this may lead to data silos when using the ETL method.
  • Data delays — ETL processing is usually done on a fixed schedule, so there may sometimes be delays in getting the most up-to-date data.

In short, organizations that need real-time data or need a more simplified infrastructure may need a different solution. But ETL is a great approach for managing and integrating data overall.

What Is Data Orchestration?

Data orchestration, similarly to ETL, is a process that involves integrating data from multiple sources. Data orchestration takes this a little further, with data management and multiple other techniques like data mapping, data modeling and more to centralize an organization’s data and give users better access.

Unlike ETL, data orchestration involves more than just the extracting, transforming and loading process. Data orchestration also includes data management tasks such as data governance, data quality management, data access and more. With data orchestration, the goal is to get business users quality data whenever and wherever they need it.

Many modern data-driven businesses have embraced data orchestration processes and tools to make data more accessible, gain better insights and empower more data-driven decisions. Overall, data orchestration can streamline and improve data management, which can be beneficial for businesses, both big and small.

Pros and Cons

Now that we have a better idea of what data orchestration encompasses let’s go over some of its pros and cons. 

  • Improved data quality — One of the biggest and most obvious advantages of data orchestration is that it automates data management tasks and improves data quality.
  • Flexibility — Data orchestration tools can handle both structured and unstructured data and pull from numerous data sources, making it a flexible approach for many different industries.
  • Scalability — Data orchestration is a scalable process, able to handle large volumes of data as a business grows. 
  • Real-time processing — One of the biggest benefits is that data orchestration can process data in real time, which can be crucial for businesses that need timely reporting and analysis.

While data orchestration can be a great approach to data management, it does have some drawbacks. Let’s take a look at some of the cons.

Cons of Data Orchestration

  • Complexity — Data orchestration can be complex to implement, requiring technical expertise and knowledge. This can be helped with data orchestration tools and customer support for these tools.
  • Cost — For smaller businesses or startups, the cost of implementing expansive data orchestration processes can sometimes be cost-prohibitive.

Generally, data orchestration is known for providing significant benefits to businesses. Once implemented, it can make data more accessible to stakeholders and improve data-driven decisions.

Data Orchestration vs. ETL

Now that we understand more about each of these data management approaches let’s specifically dive into the differences and the factors a business needs to consider when choosing which method works best for them.

Key Differences

The key differences between ETL and data orchestration include the following:

  • Data processing — Generally, ETL is a process that takes a batch-based approach. It pre-processes and transforms data before loading it into a data warehouse or other data storage destination. In contrast, data orchestration tools can process data insights in real time.
  • Data sources — ETL is primarily designed to extract data from multiple sources, transform it into a predefined format and load it into the destination database. In contrast, data orchestration can be more flexible, connecting to multiple sources and handling structured, unstructured and semi-structured data. 
  • Data accessibility — Data orchestration is focused on centralizing and making data highly accessible to an entire organization. With just ETL in place, the departments managing their data may not have an easy way to automatically share data from their warehouses, leading to silos.

Understanding these key differences is essential to choosing the right data approach for your business. Now that we understand some of the main differences let’s look at the factors to consider when choosing the right method for your business.

Factors To Consider

When it comes to managing data, it's important to consider several factors to determine which approach is right for your business. These factors may include:

  • Data Sources — Consider the sources of data you have. ETL is great for multiple structured data sources, but data orchestration might be the better option for more complex sources or sources with both structured and unstructured data.
  • Data Volume — Both methods can handle large volumes of data, but remember that ETL does batch processing. This is usually done at an interval, so it may not be as agile as orchestration.
  • Data Latency — As mentioned, ETL processes data in batches, which means updated data may be delayed. If you work in an industry that requires real-time data insights, orchestration may be the better choice for you.
  • Budget — You will always need to consider budget as a major factor in how you build your data management stack. Expenses for ETL and data orchestration can vary based on the tools and infrastructure you may need. Make sure to compare prices on these tools to find the ones that will fit best for your business needs.

By considering these factors, you can choose the data approach that meets your business needs and ensure your team can achieve the best possible data outcomes.

When To Use Each

Now that we've covered the basics of ETL and data orchestration let’s sum up some ideal scenarios for each approach:

Use ETL if:

  • Your data is mostly structured
  • You have the resources to manage and maintain your ETL pipeline
  • You need to store data in a specific data warehouse

Use data orchestration if:

  • You have a lot of unstructured data or data from different sources
  • You want to streamline your data pipelines and reduce data silos
  • You need to perform real-time data analysis

Overall, it’s worth keeping in mind that many businesses use a combination of both these approaches. ETL can often be a component of the more comprehensive data orchestration approach. For example, ETL can be used for the initial extraction and transformation of data while data orchestration streamlines integration and analysis. Ultimately, you should take a look at the ETL and data orchestration resources available to decide what is right for you.

Try Secoda for Free

Secoda is a robust and useful addition to any data stack. With Secoda, you get an all-in-one data management tool with solutions for data lineage, data dictionaries, data access management, data analysis, data sharing and more. With Secoda, you can enable data discovery across your organization. Your team will be empowered to query and find data-driven insights in one collaborative, intuitive and searchable platform. Book a demo or try Secoda for free today.

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