What is Data Orchestration? A Comprehensive Guide

In a digital data landscape, data keeps changing due to evolving user requirements. Manually managing such dynamic data from different sources is no longer sufficient to get the latest information. You need a solution that can coordinate and seamlessly integrate data in a centralized storage system. It should also be able to handle the current load and scale as your business continues to expand. This is where data orchestration tools come into play. They offer an all-in-one solution to manage your data efficiently. 

In this article, we’ll go over the basics of data orchestration and its phases, the benefits of implementing data orchestration tools, the difference between data orchestration and ETL, and the popular data orchestration software available in the market.

What is Data Orchestration?

Data orchestration aims to ensure data is efficiently collected, processed, transformed, and made available to support a wide range of business operations and analytics. It automates how the data moves across multiple systems and applications within an organization. Data orchestration is a crucial component of data management and plays a key role in modern data-driven businesses.

Let’s understand data orchestration with an example. 

Say your marketing team wants to customize product and service recommendations based on customers’ browsing and purchase history. Here, data orchestration involves:

• Collecting data from different sources, such as user engagement on websites and mobile applications. This can also include data from sources like social media reviews, customer purchase history, and more.
• Transforming and enriching this data to create a unified customer profile.
• Automating this process to continuously update this profile in real-time.
• Using this data to deliver personalized product recommendations.

In this example, data orchestration allows the marketing team to gather customer data from various sources. By updating a unified customer profile with real-time information, the team can offer customized product recommendations based on purchase history. This personalized approach helps customers buy interested products quickly, leading to an increase in sales.

Phases of Data Orchestration Platform

Data orchestration, when automated, streamlines the intricate process of transferring data from source to destination. This can include scheduling workflow orchestration by specifying the order of tasks, setting dependencies, and automating the workflow. You can select which parts of your data pipelines you want to automate and then set up a schedule. Here are the data orchestration phases that can be automated for moving data from source to storage. This is done by configuring multiple pipeline tasks in a sequence to create a single end-to-end process:

1. Data Ingestion: The first step involves extracting data from multiple sources, which can include databases, cloud services, or APIs. You can automate the data extraction steps by scheduling tasks to be done at specific times, when events trigger, or in real time. Automation in this phase extracts data in its raw form, ready for further processing.
2. Data Transformation: After data ingestion, the next step is data transformation and processing. In this phase, you can automate the tasks required to clean, enrich, and transform the raw data into a format suitable for analysis or storage. This includes data cleaning, normalization, aggregation, format conversions, and more. Automating these tasks allows you to streamline repeatable data transformations and eliminate the need for manual intervention.
3. Data Loading: This step involves loading data into a destination, which could be a data warehouse, data lake, or cloud storage. Data orchestration tools help ensure data is loaded efficiently. You can schedule this step based on specific conditions. This step often includes data quality checks and validation before the data is made available for analysis or reporting.

Apart from the above steps, you can also orchestrate the following steps.

• You can set the order of data processing tasks, their dependencies, and triggers for automation. 
• Implement automatic error detection and handling mechanisms within the data workflows.
• You can configure automated alerts and notifications for critical tasks or issues.
• Set up automatic monitoring of data orchestration pipelines to track their performance and progress.
• Implement automated data security measures, such as encryption or access control.
• Enable automatic scaling of data orchestration processes to accommodate increasing data volumes.
• You can specify validation rules for orchestration tools to maintain data quality.

These steps minimize manual intervention and reduce errors in the data transfer process. They also ensure reliable data movement from source to destination, making data available for analysis and decision-making.

What are DAGs?

Many data orchestration tools use Directed Acyclic Graphs (DAGs) to represent and manage complex data workflows. A DAG is a graph structure where nodes represent tasks or processes, and directed edges represent dependencies between tasks. Generally, it describes how you want to carry out your workflow.

Here’s how DAGs are used in data orchestration:

• Task Representation: For instance, a data validation task depends on the successful completion of a data transformation task. This ensures that data is validated only after its proper transformation.
• Visualization: Using DAGs, you can see the visual representation of data workflows. This simplifies task sequencing and managing dependencies. The graphical view helps in workflow design, troubleshooting, and monitoring. 
• Scheduling: With data orchestration tools, you can schedule task execution within DAG using predefined event triggers, time intervals, or mentioned conditions. This ensures the execution of data workflows when required.
• Parallel Execution: DAGs parallelly execute tasks with no dependencies on each other. This will improve the speed and efficiency of data processing workflows.
• Error Handling: DAG tools facilitate error detection and handling within the workflow. They identify failed tasks, take alternate actions, and notify you about the issue.

Why use Data Orchestration Tools?

As the company’s operations expand, you face several challenges, including data fragmentation, data quality, scalability, timely insights, and data governance. Manually handling these processes can be a burdensome and time-consuming task. However, data orchestration tools address these challenges and offer several advantages:

• Integration: You can use the data orchestration tool to seamlessly integrate data from various sources. This integration provides a holistic view of your business processes.
• Cost Efficiency: By using an orchestration tool, you can automate the repetitive data workflow. This reduces the need for manual interventions and leads to cost savings in terms of time, labor, and operational costs.
• Scalability: These tools are designed to handle large and growing data volumes. They provide infrastructure capabilities that ensure your data workflow scales effectively to meet changing demands.
• Real-Time and Batch Time Processing: Data orchestration tools support both real-time and batch processing. This allows you to work with data in near real-time or analyze historical data as needed.
• Data Quality: These tools often include data validation and quality checks. This feature helps you to maintain high-quality data, which is essential for accurate analytics and reporting.

Data Orchestration vs. ETL

Data orchestration and Extract, Transform, and Load (ETL) are both important processes in managing and processing data, but they serve different purposes.

	Data Orchestration	ETL
Purpose	Manages complex workflows with multiple sources, data quality checks, and dependencies between tasks.	Focuses only on extracting, transforming, and loading data from source to target.
Processing	Ensures efficient real-time processing.	Often used for batch processing but can support real-time with better ETL management.
Data Modelling	Defines the data models according to the use cases.	Helps prepare the data according to the data models.
Governance	Regulates access to the data for better data governance.	Doesn’t focus on data governance or data access.

Top 3 Data Orchestration Tools 

Hevo Data

Hevo Data is a robust cloud-based data integration and replication software that automates and manages data extraction, loading, and transformation. With Hevo, you can seamlessly connect and integrate data from over 150+ sources to destination connectors. This extensive set of connectors eliminates the need for coding skills, making it an accessible solution for both technical as well as non-technical users.

In addition to its extensive connector library, Hevo enhances its data integration capabilities with DAGs. You can leverage Hevo’s Workflow feature to create DAGs that define dependencies between data models and transformations.

Apache Airflow

Apache Airflow is a free data orchestration platform that simplifies data pipelines using Python. It provides both a web-based and a CLI interface for simple workflow management. Airflow DAGs allow you to organize, monitor, and schedule complex workflows. 

Airflow offers extensive support for task scheduling, dependency management, and monitoring, making it a powerful tool for orchestrating data.

Dagster

Dagster is a cloud-based data orchestration tool that helps you build, schedule, and monitor the ETL pipeline. It offers both a user-friendly web-based dashboard to visualize and monitor your pipeline and CLI for programmatic interaction and automation. 

While Dagster doesn’t use DAGs to represent workflow, it offers its approach to workflow management. Dagster pipelines are organized into Ops. Each Ops represented as a Python function encapsulates a specific unit of task within a pipeline. You can use these Ops to build complex workflows with explicitly defined dependencies. This ensures tasks are executed in the correct order.

Conclusion

Data orchestration serves as a backbone of the modern data-driven organizations and analytics ecosystem. It plays a crucial role in using data efficiently in areas like business intelligence, data warehousing, machine learning, and more. By using data orchestration, you can automate and streamline your data workflows and get quick insights. This allows you to deliver timely insights, improve operations, and make informed decisions.

If you’re in the market for a real-time data replication tool, try Hevo. Hevo Data can also help you set up a near-real-time data transfer pipeline between any two platforms. With an intuitive interface and data transformation capabilities, Hevo is an effective solution for your data integration needs.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Schedule a demo to see if Hevo would be a good fit for you, today!