Data engineering - Camunda: Defining data orchestration's role in data engineering
This is a guest post for the Computer Weekly Developer Network written by Daniel Meyer, CTO at Camunda.
Camunda is known for its open source platform that automates business processes by helping users model, execute and monitor them.
Meyer writes as follows…
Companies continue to amass data from countless sources at a vast rate. From operational metrics to customer interactions, the sheer volume and variety of available data is eye-watering, but making it work efficiently and effectively is the holy grail for many businesses.
As a result, data engineering has become pivotal in today’s data-driven enterprise as businesses need to design, build and maintain systems that enable the effective collection, storage, processing and analysis of large-scale data. With businesses becoming increasingly reliant on data analytics to inform decisions, innovate and drive automation, the ability to orchestrate data efficiently becomes critical.
This has seen the emergence of data orchestration as a key element of today’s data engineering arsenal.
In short, data orchestration is the methodical and automated process of managing and directing data from multiple sources to the appropriate destination, ensuring it’s in the correct format and available at the right time. This process harmonises disparate datasets, enabling cohesive and seamless data processing across different platforms.
Beyond data integration & ETL
Data orchestration extends beyond the basic premise of data integration and traditional ETL (Extract, Transform, Load) processes and takes these concepts to the next level. It is not just about moving data from point A to point B; it’s about managing and optimising the entire data journey.
The process of managing data flows often involves multiple steps beyond traditional ETL tasks, including real-time processing, complex workflows and interactions with diverse processing frameworks. Data orchestration serves as the coordinator of these activities, akin to how an air traffic controller ensures planes take off and land at a busy airport.
At its core, as a core data engineering practice, data orchestration focuses on optimising and streamlining processes. It oversees task dependencies, handles errors, dynamically adjusts resource allocation and mitigates against bottlenecks ensuring the seamless data flow across systems and platforms. Many data orchestration tools offer visual interfaces for designing and monitoring workflows, making the process manageable and transparent. By directing data movement across various systems and platforms, data orchestration ensures that data is not only transferred but also processed according to specific business rules and logic. This enables data to be cleansed, enriched and transformed during transit or while at rest.
The result is a flexible and responsive data infrastructure, capable of supporting complex, real-time, data-driven decision-making.
Data pipeline orchestration
Data pipeline orchestration also plays a vital role in data management by automating and coordinating the movement of data from its source to its destination.
This process involves a sequence of automated tasks that work in unison to move, transform and prepare data for analysis or other business applications. This eliminates the need for manual intervention and ensures that tasks are performed consistently and without error. Data pipeline orchestration extends beyond managing a linear sequence of tasks. It often involves coordinating multiple interconnected processes that may run in parallel or follow a specific sequence based on dependencies. For instance, one stream of data might be undergoing processing while another set of data flows through a separate pipeline for a different analysis. A pipeline orchestrator oversees these simultaneous operations, ensuring each task is executed accurately and efficiently. It manages task execution, tracks their progress and, when necessary, reroutes data or initiates additional processes.
This level of control is essential for handling complex data workflows that demand precise timing and coordination.
Engineering data orchestration success
Incorporating data orchestration into a business’ data engineering strategy offers numerous benefits, such as improved data quality, more agile operations, better compliance, increased efficiency and reduced operational costs.
As the world becomes increasingly AI-driven, the importance of reliable data continues to grow. Data orchestration will become ever more important, not just in collecting data, but in helping companies channel it effectively to inform decisions and drive business success.
