Data Orchestration vs ETL: Key Differences

December 5, 2024
20 min read

Data is considered an important asset, so data management is a critical task for your organization. Data orchestration and data integration through ETL are important processes in data management. While both may seem similar and are used interchangeably, they are two different concepts that need to be understood thoroughly.

This guide dives into the data orchestration vs ETL comparison, providing you with a complete and detailed understanding of both methods. Let’s get started!

What is Data Orchestration?

Components of Data Orchestration

Data orchestration is the process of streamlining and optimizing various data management tasks, such as data integration and transformation, governance, quality assurance, and more. By systematically managing data flows, you can make datasets more accessible throughout the organization, empowering your teams to develop effective strategies. To make data orchestration more effective, you can adopt data orchestration tools that can help you remove data silos, leading to better data-driven decisions.

Pros & Cons of Data Orchestration

Take a look at some of the benefits and disadvantages of data orchestration to understand this process better.

Pros:

  • Improved Data Quality: Data orchestration allows your organization to reduce manual intervention in data processing. This decreases the possibility of human errors, enhancing data quality and reliability.
  • Operational Efficiency: Streamlining repetitive data management tasks enables you to free resources for more important strategic tasks. With data orchestration, your teams can monitor and oversee data integration and governance processes more effectively.
  • Enhanced Scalability and Adaptability: Using data orchestration tools, you can manage increasing amounts of data from multiple sources. Centralized control allows you to handle data consistently and securely, helping your organization respond to market changes promptly.
  • Better Data Governance: Data orchestration helps you enforce data governance and regulatory requirements across all organization datasets. This helps you comply with all data privacy and security laws, ensuring safe data management.

Cons:

  • Complex Implementation: While data orchestration tools can ease the burden of managing data workflows, integrating and operating them still requires significant technical expertise. The team that manages all data orchestration processes must have in-depth knowledge of data pipeline management, governance procedures, and workflow systems.
  • Integration Difficulties: Combining data from diverse sources requires a thorough understanding of various structures and formats. Issues such as incompatible schemas, departmental data standardization processes, and conflicting data models may be challenging to handle.
  • Higher Costs: For startups or small businesses, deploying comprehensive data orchestration tools and processes may prove to be expensive.

Practical Use Cases of Data Orchestration

  • Leveraging data orchestration can improve the efficiency of your machine learning workflows. Tools like Kuberflow automate data preprocessing and cleaning steps, allowing you to input accurate and reliable data into your ML models.
  • With data orchestration, you can create personalized marketing campaigns. By integrating customer data from various touchpoints, you can gain a holistic view of consumer behavior and preferences.

Understand data orchestration better with the following use case:

Graniterock is an American corporation that has been supplying California’s construction industry with quality materials for major infrastructure projects for years. With the advancements in cloud computing, Graniterock leveraged a complex data infrastructure to optimize operations. However, the team faced challenges with expensive, hard-to-maintain tools that often caused data integrity issues, slowing workflows and increasing maintenance costs.

Graniterock then turned to Airbyte, a robust data movement platform that offers over 550+ pre-built connectors. Airbye also offers the flexibility to build custom connectors through its no-code Connector Builder with an AI-assistant feature and its low-code Connector Development Kit.

Airbyte

You can easily integrate Airbyte with Prefect, a data orchestration and workflow automation tool. Graniterock adopted Airbyte and Prefect to streamline data integration and scheduling of their data pipelines.

Airbyte’s extensive library of connectors helped them reduce their need for custom connector development, allowing them to readily build data pipelines within minutes. Prefect enabled Graniterock to integrate dbt for transforming data and Great Expectations to test and troubleshoot data pipeline metrics.

With this new framework, Graniterock standardized its data processes, achieved greater visibility into data flows, and drastically reduced its development time and costs. According to Cody Kaiser, the Enterprise Data Manager at Graniterock, the internal development efforts and data tool costs dropped by 50% and 25%, respectively.

With proper data orchestration, Graniterock could set up pipelines faster, build advanced dashboards, and identify and address data errors. This empowered them to stay agile and meet business requirements without any downtime.

What is ETL?

ETL: Extract, Transform, and Load

ETL stands for Extract, Transform, and Load. It is a popular data integration approach designed to help you move large volumes of data from source systems to destination. Take a look at the three steps involved:

  • Extract: In the first step, you must extract data from various sources, such as local files, CRM databases, or APIs.
  • Transform: In the transformation stage, you must clean, enrich, and standardize the raw data to ensure it is compatible with the target system. This includes rectifying inconsistencies, such as missing values and outliers, and validating formats to match the destination schemas.
  • Load: In the final stage, you can move the transformed data into the target system, such as a data warehouse. 

Pros & Cons of ETL

ETL has been a reliable method for several years. However, it is important to assess its pros and cons before adopting the process for your organization:

Pros:

  • Data Consolidation: ETL helps you consolidate data from multiple sources into a single pipeline. It is the most effective way to process large datasets in near real-time and unify them at a central repository.
  • Data Enrichment and Cleansing: During ETL, the data transformation happens within the staging area of the pipeline. This saves the time required to clean and prepare data in the data warehouse, ensuring your datasets are ready for immediate analysis.
  • Removes Data Silos: Data integration through ETL allows you to eliminate data silos. Combining all different datasets into one single destination gives you a comprehensive view of relevant and accurate data. You can also remove redundant and duplicate data for better results.

Cons:

  • Time-Intensive: ETL processes can be time-consuming, especially when you are processing extensive datasets or dealing with complex transformations. It may cause data delays that are urgently required for analysis and visualization.
  • Margin for Errors: Conducting data transformation in ETL can be challenging for team members who are not well-versed in data pipeline management. Any errors made during the transformation phase can greatly affect the data quality and lead to poor decision-making.

Practical Use Cases of ETL

  • In the retail sector, real-time ETL pipelines help update datasets containing customer orders, inventory levels, and shipping information. These datasets help organizations determine demand patterns and consumer preferences.
  • ETL pipelines play a significant role in the finance industry, where teams can mask and encrypt sensitive information in the staging area to avoid data breaches. With these pipelines, datasets remain up-to-date, helping banks and financial institutions detect fraudulent transactions and take timely actions.
  • In the manufacturing sector, ETL pipelines help organizations understand when machines require predictive maintenance. The pipelines can collect data from IoT devices, sensors, and operational logs. Examining them allows manufacturers to gauge equipment performance and avoid machine failure and downtime.

Data Orchestration vs ETL

When deciding between data orchestration vs ETL, it is crucial to evaluate both methods to see which one aligns with your business. Here’s a breakdown of their differences:

Scope of Functions

ETL does not have a broad scope of functions, as it simply focuses on moving data from source to destination. However, with data orchestration, the number of functions increases. The process encompasses data integration, end-to-end workflow management, task scheduling, and error handling.

Flexibility

Traditional ETL tools had a fixed pattern of data extraction, basic transformation, and loading. Modern ETL tools are more flexible, allowing you to integrate data from multiple sources with the help of pre-built connector libraries. On the other hand, data orchestration tools are much more adaptable as they enable you to handle multiple tasks simultaneously and integrate with data integration platforms.

Granular Control

With ETL processes, you have granular control over each step. You can specify which sources to select, what transformations to apply, and which database the data should be stored. If required, making any changes or modifications is much easier.

In the case of data orchestration, the number of functions is greater, and having granular control over each step becomes difficult. This process is more focused on overseeing all workflows and handling dependencies. Managing changes can be cumbersome since tasks are interdependent.

Operational Scale

ETL can be used for both small and large datasets as it operates on a linear process. This makes it suitable for organizations of all sizes. Conversely, data orchestration is ideal for large-scale organizations with complex data infrastructure. The process is essential when you have multiple interconnected operations.

Cost

Building and maintaining data pipelines can be financially draining if you do not have a strong IT team. However, choosing data movement platforms like Airbyte to build data pipelines can be quite cost-effective. You can integrate Airbyte with several data orchestration tools, such as Dagster, Kubernetes, and Prefect, and create ETL pipelines through PyAirbyte. This can be a budget-friendly solution, allowing you to handle all data orchestration tasks from a central location.

Choosing between Data Orchestration and ETL: Main Factors to Consider

Data orchestration and ETL processes have some common functions but converge in different areas. Here are some main factors that influence them:

Factor Data Orchestration ETL

Automation

Easy to automate and schedule pipelines through advanced data orchestration tools or techniques.

It can be automated after you configure the source and destination. However, adding and configuring new sources or making changes would require manual efforts.

Security You can design comprehensive security frameworks to protect data and comply with regulations like GDPR. You can include end-to-end encryption and role-based authentication to control who can access the pipelines.
Integration with Emerging Technologies Data orchestration platforms can be integrated with ML and AI tools for swift data analysis and decision-making. ETL pipelines are vital for ensuring the accuracy of ML models. Some ETL tools even have cloud-native solutions that support easy cloud integration.
Supported Data Sources and Formats Can support multiple data sources and formats, such as structured, semi-structured, and unstructured data. Can support several data sources and formats. However, transformation for structured datasets is most easy to carry out.

When to Consider Data Orchestration?

You can consider applying data orchestration processes in the following situations:

  • Data orchestration can be immensely useful when managing data throughout its lifecycle. With orchestration processes, you can view, modify, and understand data at each stage, from collection to visualization.
  • Inculcating data orchestration tasks is vital if you are building an agile and technologically advanced data landscape for your organization. It provides a flexible framework that can constantly adapt to shifting market trends, enabling you to remain competitive and responsive.
  • If you handle large amounts of personally identifiable information, you must comply with privacy laws and regulations. Data orchestration helps you achieve this, as data governance is an important step in the process.
  • For essential yet repetitive tasks, such as troubleshooting pipeline code or scheduling pipeline events, you must consider data orchestration. It allows you to automate these tasks, ensuring more time is spent on strategy and decision-making.

When to Consider ETL?

Here are a few instances when you must consider adopting ETL processes for your organization:

  • When you need to access data from multiple systems, such as databases, APIs, and websites, you should consider building an ETL pipeline. This will help you standardize raw data and analyze it faster.
  • ETL pipelines are ideal for datasets that require complex transformations, especially when your existing destination data warehouse does not offer transforming features.
  • If you are processing healthcare or financial data, it is best to use an ETL pipeline. You can conduct real-time operations and secure sensitive information before loading datasets into the target repository.
  • Having an ETL pipeline helps when new data sources are added to your database. Different teams can add more sources to the same pipeline without disrupting the current workflows.

The Final Word

This data orchestration vs ETL guide clearly explains both processes and when you should use them. The core functionality of ETL is to collect, transform, and load data. In contrast, data orchestration can be used to oversee all data workflow projects. Both approaches complement each other within a more extensive data management strategy, so you must leverage their strengths for optimal efficiency.

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