Data Orchestration Explained

Organizations with multiple data systems often face challenges with inconsistent and unusable data formats. Data can also be siloed in different locations — making it difficult to access and analyze.

Data Orchestration is a solution to these problems, helping to streamline and organize this data, ensuring it is consistent, accessible, and ready for analysis.

What is Data Orchestration?

Data orchestration is an automated process that gathers data from various sources and silos, ensuring the data is consistent and available in a usable format for analysis. This process often spans multiple departments and systems, making it easier to locate and utilize data effectively.

Data is then sent to data analysis tools, which collect and analyze it to provide insights into the factors affecting the business. The primary goal of data orchestration is to organize complex data, often in a cloud environment, to make it readily accessible.

This process relies on software that connects different storage systems, enabling easy access to data analysis tools, rather than requiring manual intervention by data scientists or data engineers.

(Know the difference: automation vs. orchestration.)

Key components of Data Orchestration

The process of Data orchestration goes through multiple phases to effectively move data from source to storage. The steps include organizing data, transforming data, and activating the data.

Step 1. Organizing data

This first step of the process is to identify and collect data from different sources, locations, and formats. This includes:

• Databases
• Logs
• Spreadsheets
• CRMs
• And other sources

The data is then organized into a comprehensive inventory, with information like its purpose, format, and access permissions.

This creates a unified view of the data, which makes it easier to get insights into the organization, allowing for better data-driven decisions.

Step 2. Transforming data

Once the data is organized, it is time to transform it to continue the orchestration. Transforming data involves identifying and correcting it to meet the necessary quality — also known as data normalization. This includes fixing…

• Errors
• Inconsistencies
• Missing values

Transforming data also involves combining data from different sources to create better datasets. This means giving the data context and, in some cases, merging it to make more sense together.

Once all the data is transformed, it should be validated to ensure it meets the standards needed to support accurate analyses.

Step 3. Activating data

The final step ensures that the data is accessible for data analysis tools. Transformed data is sent to its destination — a data warehouse, data lake, or any other application, depending on the need.

Once the data is sent to downstream tools, it can be used for:

• Visualizing data in dashboards (to best understand it)
• Creating campaigns or projects.
• Making important business decisions.

This step also involves continuously tracking the performance of data pipelines to identify potential issues. Doing so ensures that data quality and availability are up to standard.

You can get the best out of data orchestration by ensuring that these three steps are properly managed and implemented. This will maximize the use of your data, allowing for strategic data-driven decisions.

Benefits of data orchestration

By executing the data orchestration steps mentioned above properly, you can reap the benefits of quality data and much more.

Improved data visibility. Data scattered in different silos is a common issue many organizations face. These isolated silos hinder your teams' efforts as your data team would have to painstakingly search for data themselves.

Data orchestration gathers data from every nook and cranny of your organization, providing visibility of all your data so teams can conduct cross-domain analyses with ease.

(Related reading: data observability.)

Enhanced data quality. Data orchestration automates the cleansing and transformation processes, reducing issues like human error and inconsistent data quality. This results in a more reliable collection of data, which is substantially more useful for business processes.

(Related reading: ETL, ELT, and data quality management.)

Time and cost savings. Manually performing these processes would require employees to work long hours with a supervisor overseeing them. By automating time-consuming manual tasks, data orchestration saves time and money in the long run.

Consistency & standardization. Data orchestration ensures a consistent process every single time, enabling teams to:

• Standardize on workflows and processes.
• Maintain the same level of quality with every output and iteration.

Compliance with data governance policies. Proper execution of data orchestration allows your organization to:

• Implement data governance policies to adhere to regulations.
• Comply with data privacy laws.

(Related reading: regulatory compliance & continuous compliance.)

Challenges with data orchestration

Data orchestration can significantly improve data analysis operations within an organization, reducing costs and time wastage. However, this process does come with its challenges.

Data complexity. Although the primary function of data orchestration is to reduce the complexity of data within the data pipeline, the increasingly complicated data ecosystems of modern organizations can pose challenges even for the most advanced tools. To overcome this, a data map can help: it documents all data sources and their respective details, such as:

• Format
• Location
• Purpose

(Related reading: data dictionaries.)

Intentional implementation. Implementing data orchestration means adjusting the current flow of data in your organization’s system — this may disrupt processes temporarily.

There is also the possibility that the orchestration tools might not properly integrate with older systems. To avoid this issue, it is best to gradually implement the process and ease data orchestration into your system. You can also use API connectors to help with a smooth integration.

As you can see, data pipelines can be quite complex, so there’s always a possibility that issues can arise unexpectedly. If these threats aren’t mitigated in time, they might snowball into more significant issues in the system. These issues can be overcome by following appropriate troubleshooting tactics, such as constantly overseeing a centralized view of the data flow to spot errors and setting up automated alerts to notify you when errors occur.

Data orchestration tools

A collection of tools can be used for the proper execution and maintenance of data orchestration. These orchestration tools help data teams implement the process with ease. Here are some of the popular tools for data orchestration among data engineers and other professionals.

Prefect

Prefect is an open-source data orchestration tool designed for data engineering. It is popular due to its range of features, including scheduling, logging, observability, notifications, and event-based orchestration.

Prefect is a Python-based tool, allowing developers to easily schedule and monitor their data pipelines thoroughly and frequently.

Apache Airflow

When it comes to data orchestration, Apache Airflow has quite the reputation! This open-source tool specializes in orchestrating complex workflows and data pipelines. It helps data teams program, schedule, and monitor workflows, ensuring you always know how your data pipeline is performing.

Airflow allows users to leverage standard Python features, such as loops to generate dynamic tasks and date-time formats for scheduling. This tool is also praised for its scaling capabilities, as it can integrate easily with most systems and technologies.

Dagster

Dagster is a cloud-agnostic tool that can be deployed on-premises or in cloud environments to maximize flexibility. It is an open-source tool designed for developing and maintaining data assets.

AWS step functions

This tool is specifically designed to run in the popular Amazon Web Services ecosystem. The AWS Step Functions data orchestration tool is different from its peers in that it is serverless, meaning there is no need to manage any infrastructure to operate.

It is very popular among users because it has a user-friendly interface that allows you to build workflows through drag and drop. AWS Step Functions can also handle errors and retries, monitor execution status, and schedule workflows to start when required. It has quite a reputation for orchestrating large-scale data workloads simultaneously.

By using tools like these, data orchestration can be implemented into an organization's system with ease. Most of these tools offer free and paid versions, so if you’re just getting started, you can slowly integrate these technologies according to the needs of your organization.

Orchestrate data for better outcomes

Data orchestration is crucial in the context of data analytics. It allows data teams to organize, transform, and prepare data for analysis. While it does come with challenges, using appropriate tools and techniques can make overcoming them manageable.

Proper implementation of data orchestration will reward your organization with accurate and valuable data-driven insights, maximizing business operations.

FAQs about Data Orchestration