Moving and transforming data without a well-defined execution order is asking for incomplete data, mysterious errors, and broken analytics. You need a well-supported data orchestration setup to coordinate the smooth transfer of data into your dashboards and analytics engines. 

In this guide, we explain what data orchestration is and why it’s important for your business, and share a few best practices to guide your efforts.

What is data orchestration? 

Data orchestration is the automated management and coordination of tasks and workflows across your ecosystem. Typical jobs include unifying various data sources, combining and transforming data, and preparing it for analytics. Orchestration ensures that each task happens in the appropriate order. By automating these processes, you won’t need to spend time manually collecting and organizing your data or sequencing the transformations you need to perform on it.

Data orchestration vs. ETL

Extract, transform, and load (ETL) is a data ingestion process that moves data from source systems, transforms it, and then stores it in warehouses or lakes.

Data orchestration is a more expansive form of task and workflow management. Rather than just covering ETL, it also includes the coordination of different pipelines and transformation tools, oversees data activation or reverse ETL workflows, and monitors for failures and irregularities. 

Many companies combine ETL and data orchestration, meaning the two aren’t competing processes. They’re often part of a larger data movement strategy, with orchestration ensuring that each step of ELT — and every downstream process that depends on it — happens in the right order.

Why is data orchestration important?

Effective data orchestration moves content to where it needs to be quickly and automatically. Here are a few of the most notable benefits:

• Improved efficiency: Automating repetitive and laborious tasks reduces engineering demand, allowing your team to focus on more pressing matters.
• Higher data quality: Processing and standardizing data automatically reduces the potential for human error in your data ecosystem. Orchestration also simplifies the process of validating transformations.
• Increased scalability: Automation allows you to scale your systems without increasing engineering burden.
• Real-time processing: Build out real-time and change data capture (CDC) event pipelines using ordered workflows to create real-time ingestion and processing.

The data orchestration process

While data orchestration is made up of many moving parts, the process broadly falls into these three steps.

Step 1. Organizing data

First, data needs to be extracted from sources and centralized into a single destination. Orchestration tools connect to SaaS platforms and other source databases, sync event streams, and load raw data into storage automatically. Most businesses choose to store this content in a cloud warehouse or data lake.

Automating data ingestion and schema management ensures integration jobs run reliably.

Any data that needs cleaning or normalizing arrives in your warehouse without additional steps required. 

Step 2. Transforming data

Once you’ve centralized your data, the next step is transformation. This is the process of cleaning and standardizing data and turning it into analytics-ready datasets. The exact transformations you apply will depend on your data and its purpose.

Orchestration engines automate the order in which these transformation tasks occur, searching and mapping out dependencies between datasets to reduce the likelihood of a processing failure. If any errors do occur, they log them and rerun any related workloads. Warehouse-native tools like dbt allow you to structure the logic you want your transformation engines to follow.

Step 3. Data activation

Data activation is the use of transformed datasets in operational analytics. Orchestration platforms take analytics-ready data and use reverse ETL to move it into operational systems where end-users can use insights to power data-driven decision-making. 

Orchestration ensures dashboards update regularly after ingestion and facilitates clearly defined refresh schedules and dependency checks to prevent incomplete data from showing up. 

Top 3 data orchestration tools

Data orchestration tools can streamline the automation process. Here are three of the best platforms available on the market.

1. Apache Airflow

Apache Airflow is an open-source orchestration platform that allows you to schedule and monitor data pipelines at scale. Its modular architecture makes it easy to create complex workflows, define new pipelines in Python, and build scalable data systems. Airflow uses a web-based user interface that provides full visibility over data tasks, logs, and scheduling. It also features an ample range of cloud platform integrations.

2. Dagster

Dagster is a unified control plane for building, scaling, and observing data pipelines. Specializing in AI visibility, the platform supports ETL and ELT workflows, full-scale data transformations, and embedding data into AI training systems. Dagster focuses on boosting observability, with data lineage and monitoring features that offer a compliance-first orchestration solution, and access controls and audit logs to keep visibility as clear as possible. 

3. Prefect

Prefect is a Python-native system that allows you to write scripts for scalable data workflow orchestration. Since the platform uses open-source material, you’re able to integrate new features with ease and minimal refactoring. Prefect offers self-hosted and managed options, and includes a range of observability tools that give you an overview of how data moves through your systems. 

Data orchestration challenges 

While data orchestration comes with a range of benefits, it’s an expansive change that also presents challenges. Here’s what to look out for when orchestrating your data workflows:

• Security vulnerabilities: Orchestration tools need access to multiple important systems across your ecosystem. Without strict management and visibility over these tools, you could inadvertently expose your data to vulnerabilities.
• Resource management constraints: If you don’t properly optimize your workflows, orchestration can consume a large amount of resources. For example, incorrect scheduling or unnecessary reruns can create performance bottlenecks and increase costs if you’re using scalable cloud resources.
• Persistent data silos: Before you can enact data orchestration, you’ll need to perform extensive system discovery to understand which components need connections to where. Orchestration without full visibility can lead to inconsistent data, duplicated information, or reduced functionality. 

How Fivetran simplifies data orchestration

Fivetran’s fully automated ETL pipelines make it easier than ever to ingest high-quality data at scale. With over 700 pre-built connectors, automatic schema mapping, and reliable synchronization, Fivetran handles the entire ingestion orchestration process. The platform helps to reduce manual maintenance and improve scalability, while providing you with access to consistently high-quality data for analytics.

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