The Evolution of Data Orchestration: From Fragmentation to Composable Collaboration

Introduction

Data engineering and software engineering, once distinct disciplines with their own tools and workflows, are converging thanks to advancements in data orchestration. This key capability enables organizations to unify knowledge, integrate complex systems, and deliver powerful data products. However, as data ecosystems grow, orchestration must evolve from merely coordinating processes to fostering collaborative data sharing.

This article explores the current state of data orchestration, its components, tools, and the emerging trends that are transforming how businesses manage and leverage their data.

The Current Landscape of Data Orchestration

Data orchestration ensures that data moves smoothly from source systems to actionable insights, tackling challenges such as:

• Diverse data sources: Each with unique semantics and constraints.

• Multiple destinations and use cases: Catering to varied stakeholders.

• Complex workflows: Involving a mix of tools and processes.

At its core, orchestration enables the ingestion, transformation, and serving of data while coordinating between these stages.

Key Components of Data Orchestration

1. Ingestion

Data ingestion involves moving data from source systems (e.g., databases, APIs) to storage systems like data lakes or warehouses. Orchestration at this stage focuses on scheduling tasks or responding to triggers when data becomes available.

2. Transformation

Transformations shape raw data into structured formats that align with business domains. This is typically achieved using SQL or Python scripts. Orchestration ensures these transformations are executed efficiently and in the correct sequence to produce desired models.

3. Serving

Data serving involves making curated datasets available to downstream applications or end-users. Orchestrators synchronize data pipelines to ensure up-to-date, secure, and governed access to the processed data.

Tools of the Trade: From Scheduling to Streaming

Simple Schedulers (e.g., cron)

• Suitable for straightforward, loosely coupled tasks.

• Challenges arise with complex workflows, hidden dependencies, and delays.

Workflow Engines (e.g., Apache Airflow)

• Enable explicit task dependencies and robust pipeline orchestration.

• Offer visibility and governance controls, enhancing reliability.

Streaming Frameworks (e.g., Apache Flink)

• Built for continuous data flows, replacing batch-oriented tasks with real-time computations.

• Enable fine-grained, low-latency processing in dynamic environments.

The Shift from Orchestration to Composition

The next frontier for data orchestration is composable systems, which simplify workflows and reduce reliance on complex “glue” code. This shift is powered by open standards and a deconstructed data stack.

Composable Data Systems: The Future of Orchestration

1. Open Standards

Open formats like Apache Parquet (for columnar data) and Apache Arrow (for in-memory data) eliminate the need for costly conversions and enable zero-copy data sharing. For example:

• Ingestion processes can store data as Parquet files in object storage.

• Downstream services access these files directly without creating duplicates.

Open table formats like Apache Iceberg build on these standards to add governance and enable seamless integration across tools.

2. The Deconstructed Stack

Traditional closed systems often lock businesses into proprietary APIs, limiting flexibility and scalability. In contrast, open systems separate storage, processing, and serving, enabling:

• Choice of best-of-breed tools for each function.

• Simplified governance through standardized data formats.

• Efficient orchestration by sharing references to a single authoritative data source.

Benefits of Composable Collaboration

• Simplified Workflows: Reduce orchestration complexity by leveraging shared data formats.

• Improved Governance: Centralize data management while maintaining flexibility.

• Future-Ready Systems: Adopt emerging technologies without vendor lock-in.

Conclusion

Data orchestration is evolving from a coordination-focused layer into a foundation for collaborative data ecosystems. By adopting open standards and embracing composability, organizations can reduce complexity, improve governance, and unlock new opportunities for innovation.

The future of data orchestration lies in systems designed not just to move data but to share it seamlessly—paving the way for more efficient, adaptable, and innovative data products.