What is Data Orchestration?[+Steps, Components, Tools]

This guide dives into data orchestration, its components, tools, importance, and best practices in data. It can be defined as the process or a tool that manages data-related activities. It automates the process of coordinating, integrating, and managing data from various sources instead of manually handling each task.

An essential part of driving a successful business involves gathering data, handling data, identifying inconsistencies, storing them, and then using them to make informed decisions. Businesses steadily rely on data-driven insights. Therefore, its importance has increased. Data orchestration enhances decision-making enabling organizations to streamline operations.

However, managing complex data workflows can be challenging. Examples of these challenges include data silos, robust governance, and incompatible format. These challenges include effective orchestration strategies to ensure data is accessible and accurate across the organization.

What is Data Orchestration?

Data orchestration is the process or a tool that manages data-related activities. It automates the process of coordinating, integrating, and managing data from various sources instead of manually handling each task. It filters, organizes, sorts, and publishes complex data within a data-storing cloud. Lastly, prepared data is delivered to the analytics tools, enabling decision-making.

Although it shares similarities with ETL (Extract, Transform, Load) and data integration, there are important differences.  ETL is a data integration process that combines, cleans, and organizes data from multiple sources into a consistent data set for storage in a target system. On the other hand, data integration is combining data from various sources into a single and unified view across an organization, including ETL and data integration processes. It manages complex workflows across multiple systems, handling real-time integration for structured and unstructured data allowing organizations to avoid and overcome challenges such as inefficient workflows and data silos.

Why Data Orchestration Matters?

According to a recent study, 95% of businesses manually pull unstructured data from spreadsheets, APIs, and databases. Then, clean the data, standardize the format, and send it to target systems and they find these processes difficult. It is important because:

• Automation & Efficiency: Orchestrating data workflows reduces manual effort, automates processes, and enhances scalability and efficiency.
• Improved Data Quality & Integration: Streamlined workflows lead to higher data quality and seamless integration across systems.
• Enhanced Governance & Security: Ensures robust data governance, strengthens data security, and supports better decision-making.
• Elimination of Data Silos: Enables data engineers to break down data silos without relying on manual migrations, increasing data accessibility.
• Ideal for Large Organizations: This is especially beneficial for companies managing extensive datasets and multiple data warehouse systems.

Core Component of Data Orchestration

Core components include:

• Data Governance: Data governance maintains the quality, security, and availability of an organization’s data by implementing policies and standards to protect data throughout its lifecycle. It differs according to domain and location.
• Automation: Its main goal is automating several tasks involved in managing voluminous data. Generally, it automates tasks that involve bringing data together, cleaning it for analysis, and maintaining accurate data.
• Data Integration: Integration is using data pipelines to ingest data from different data locations into a centralized storage system. Data orchestration makes this process easy collecting and integrating data automatically in regular intervals.
• Data Validation: Data validation is important in orchestration for regularly validating data quality and accuracy. Data validation processes include data profiling to identify anomalies to enforce data standards.
• Data Transformation: Data usually comes in different standards and formats. Data transformation is the converting and standardizing of data into a usable format for data analysis by cleaning, deduplicating, and enriching it.

How Data Orchestration Works?

There are three main steps in orchestration and they include;

• Data Collection: The first stage of orchestration involves collecting data from disparate sources, including APIs, cloud storage, and databases, and performing accuracy and integrity checks.
•  Data Transformation: Orchestration tools employ transformers that standardize data format, ensuring reliable, accurate, and consistent data.
• Data Activation: Data activation is the last phase and puts the data to operational use. Processed data is ready to use by analytics teams or tools for real-time decision-making.

Data Orchestration Tools

• Prefect: Prefect is a Python-based orchestration tool offering a modern workflow management approach between data pipelines.
• Dagster: Dagster is an open-source orchestrator that creates reliable and maintainable data pipelines with strong data quality controls. It ensures data types are consistent across tasks.
• Apache Airflow: Apache Airflow is a widely-used open-source orchestration tool that is used to create, monitor, and schedule workflow using Python. It utilizes Directed Acyclic Graphs (DAGs).
• Mage: Mage is designed to make pipeline management accessible and easy providing an interface for monitoring, scheduling, and building data workflows.
• Luigi: Luigi is a Python framework for building batch-processing pipelines. It is straightforward and focuses on task dependencies and workflow management.
• Google Cloud Dataflow: A fully managed service for stream and batch processing that integrates seamlessly with other Google Cloud services, offering automatic resource management.

To know more about data orchestration tools, check out our detailed blog.

Best Practices

• Cost Management: Keep an eye on the costs, and optimize resource utilization associated with orchestration of your data.
• Monitoring: Regularly monitoring data workflows, using monitoring tools and alerts to stay informed on the health of data pipelines to detect issues or failures.
• Data Security: Implementing strict access controls, governance policies, and data encryption to maintain data integrity and security.
• Scalability: Creating scalable workflows for growing data volumes and evolving business needs which includes monitoring, ensuring data quality, and implementing robust error handling.
• Collaboration: Fostering collaboration between data engineers, business stakeholders, and data scientists encouraging communication and feedback to ensure that workflows meet the organization’s needs.
• Documentation: Thoroughly document your workflows and any changes made to the workflow.
• Modular Workflows: Design modular workflows that can easily be modified to adapt to changing requirements.
• Tool Selection: Choosing a flexible that allows easy updates. A user-friendly tool with built-in editors helps you easily design workflows, set across controls, and more.

Conclusion

Data orchestration is a game-changer for organizations looking to streamline data workflows, enhance efficiency, and drive better decision-making. By automating complex data processes, it eliminates manual tasks, reduces data silos, and ensures data consistency across systems. As businesses continue to generate and handle vast amounts of data, adopting the proper tools and strategies will be crucial to maintaining scalability, improving data quality, and staying competitive. Embracing orchestration not only optimizes operations but also empowers organizations to harness the full potential of their data assets.

With Hevo, you can automate and streamline data integration across multiple sources without any manual effort. Experience seamless data migration with Hevo’s no-code platform—Try Hevo for free today!

FAQs