What is Data Services Orchestration?


Although businesses have been debating the best way to perform data integration for decades, data orchestration is a relatively recent concept—particularly as cloud computing and storage become increasingly intertwined. But what is data services orchestration, exactly, and why is it important? Below, we'll go over everything you need to know about data services orchestration.

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What Is Data Services Orchestration?

Data orchestration refers to the automation of various processes in the data management pipeline: from gathering data from multiple sources to combining it and preparing it for analysis. It can also include tasks like resource provisioning and monitoring.

A data orchestration system has multiple foundational tasks: first, the authoring of data pipelines and workflows to transport data from one location to another; second, merging, verifying, and storing this data to enable meaningful data analytics.

Why Is Data Orchestration Important?

Before the rise of modern data orchestration, it was clear that prior methods were failing to address one of the most significant constraints to optimal data use: data silos.

"Data silos" aren't real silos like you'd find on a farm; rather, they're a metaphor for the problem of data being imprisoned in a single location, organization, or application with no obvious way to access or use it. In many respects, orchestration is the discipline of dismantling silos by making data accessible.

Modern data orchestration services have been tasked with making data more available and valuable across a company. To do this, data orchestration practices entail identifying the fundamental task or tasks within a data system and graphing them to reveal their relationships.

Data orchestration breaks through the silos that divide your data stack and cause your data to grow stale over time. Orchestration not only saves you time with data engineering, but also improves data governance and visibility, allows you to use more recent consumer data, and ensures privacy compliance. Orchestration also eases many of the growing pains that many businesses face by providing a scalable mechanism to keep their stacks connected and data flowing seamlessly.

As such, data orchestration is ideal for businesses who have a lot of data systems to connect, have begun to integrate the contemporary data stack and want to get more out of it, or have just started constructing their first stack and want to lay a strong basis for future growth.

The Five Components of Data Orchestration

Although data orchestration jobs are part of bigger workflows, the work they do varies from system to system. These tasks, on the whole, can be divided into five categories:

  1. Data collection and preparation: Before entering or moving through a system, data must typically be formatted and prepared. This includes checking for integrity and correctness, adding labels and designations and enhancing new third-party data with existing database information.
  2. Data transformation: Not all data is ready for analysis out of the box. Data orchestration also involves applying the appropriate modifications to data so that it can be integrated and used in analytics tools.
  3. Data enrichment and stitching: Orchestration systems can perform tasks like recording and reporting on data, cleaning up duplicated data and so on based on data conditions.
  4. Data decision-making: Data orchestration systems can use rule-based criteria to weight, rank, organize or curate data. They may also use artificial intelligence to drive smarter decision-making.
  5. Data syncing: Finally, depending on where the data must go, a system will write it to a data store, data warehouse, or data lake.

Five Challenges in Data Orchestration

Data orchestration, like any other complex IT process, has its own set of implementation issues:

  1. Complexity: Even with the most cutting-edge technologies, orchestration methods can become challenging. Software developers and data analysts can devote their entire careers to creating comprehensive data workflow management solutions.
  2. Heterogeneous architectures: The numerous storage and compute infrastructures available for use add to the complexity of data orchestration. This involves not just multiple data platforms, but also cloud (public, private or hybrid) and infrastructure configurations (SaaS, PaaS, IaaS, etc.).
  3. Cleansing and stitching automation: Having an omnichannel view of data requires accurate and sound cleaning and stitching of data from a variety of locations and collection sources, each with its own limitations and configurations.
  4. Regulatory compliance: As data is moved from one location to another, orchestration systems must pay attention to security and compliance. Companies operating in the EU, for example, are required by GDPR to document consent for marketing and requests for data erasure, documents that must be kept indefinitely. Similarly, US standards like FedRAMP and HIPAA contain stringent regulations for the security, encryption, and usage of sensitive data that leave no space for error.
  5. Data governance: Good data governance is crucial for a data orchestration system to remain effective. Clear governance standards are commonly required as part of compliance frameworks, but they also assist businesses in determining the extent, scalability, and efficiency of data gathering and integrity management.

How Integrate.io Can Help With Data Orchestration

Data orchestration is a lot easier when you have a mature, feature-rich data integration solution like Integrate.io. The Integrate.io platform takes the challenges out of data orchestration by working with your data no matter where it is: on-premises or in the public and private cloud. Integrate.io's low-code, customizable platform lets you quickly and effectively see the benefits of big data without expensive investments in hardware, software, or personnel.

With a visual drag-and-drop interface and more than 140 pre-built integrations, Integrate.io makes data orchestration easy for users of any technical skill level. Ready to orchestrate your modern data stack? Get in touch with our team of data integration experts today for a chat about your situation, or for a seven-day pilot of the Integrate.io platform.

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Glossary of Terms

A guide to the nomenclature of data integration technology.