Addressing Big Complexity Through Strategic Orchestration

Within the past decade, the conversation around data has pivoted from the sheer volume of “Big Data” to the intricate web of “Big Complexity” that organizations navigate daily. As the available technology solutions continue to expand with specialized tools and platforms, data professionals find themselves in a labyrinth of fragmented operations.

Heterogeneous
Technologies

Heterogeneous Teams
and Stakeholders

In this blog post, I’ll dive into the core issues at the heart of today’s data environments, challenge the traditional views on data orchestration, and advocate for a shift in perspective on overall data engineering strategies.

The Challenges of Fragmentation

The modern data ecosystem is a double-edge sword.

On the positive side, the diversity of tools has democratized data access and management, enabling specialized solutions for specific needs, such as analytics tools specific to marketing, finance and product, or specific data tools used in data science teams. Within local teams, these tools have boosted performance.

On the other hand, this specialization has led to siloed data practices, a lack of interoperability between tools, and an increased maintenance burden that can stifle innovation. It also often leads to a lack of compromise, each team favoring their specialized tool set.

Resultantly, the data practitioners that sit at the center of the organizations practices—the data engineers—often find themselves grappling with the Sisyphean task of ensuring data reliability and quality across disparate systems, with no single source of truth or unified operational oversight.

The impacts affect all stakeholders:

• For the end users of data, such as business analysts and data scientists, fragmentation can lead to confusion and mistrust in data. These professionals may encounter inconsistent metrics or reports that lead to conflicting insights. Additionally, the time spent reconciling data from various sources can impede decision-making and reduce the overall agility of the business.
• The people responsible for the overall platform design face challenges with governance, security, and compliance. Fragmentation makes it difficult to enforce policies and monitor data usage across different tools and systems. This can expose the organization to risks and make it harder to comply with data protection regulations.
• The data engineers and developers responsible for building and maintaining data pipelines must navigate a complex web of APIs and interfaces. Fragmentation can lead to increased complexity in pipeline design, making it harder to ensure data quality and pipeline reliability. It also complicates debugging and increases the likelihood of data processing errors.
• The stakeholders responsible for the financial investment in data tools and infrastructure, such as your CTOs and CFOs, have to deal with increased costs resulting from fragmentation. The overhead of licensing multiple tools, integrating them, and training staff on their use can be substantial. Couple this with the lack of a unified view and you have a very difficult task in terms of assessing the return on investment for data initiatives.

Addressing these challenges would enable organizations to create a more cohesive, efficient, and trustworthy data environment that better serves the needs of all stakeholders involved.

Rethinking the scope of data orchestration helps us address these issues.

The Evolution of Data Orchestration

Historically, data orchestration has played a backseat role in data operations, largely confined to the scheduling and mechanical operations of tasks.

A few examples of these more mundane tasks include—but aren’t limited to:

• Batch Processing Schedules: Managing nightly batch jobs that processed and moved large amounts of data. This process too often relies on a dumb schedule rather than the workflows’ adaptability or real-time data processing needs.
• ETL Job Sequencing: Sequentially executing Extract, Transform, Load (ETL) tasks. The emphasis here was on ensuring that extracted data from source systems was correctly transformed, and loaded into data warehouses without necessarily considering the broader context of the data’s use, lifecycle, or downstream dependencies.
• Workflow Dependency Management: This was probably the most common orchestration function. For example, ensuring that a data cleaning job completed successfully before a dependent data analysis job began. While, yes, this is crucial, it doesn’t account for more complex dependencies or the dynamic nature of modern data workflows.

This narrow view underestimates the potential of orchestration as a backbone in the data value chain.

As data environments grow in complexity, this need for orchestration to evolve beyond its operational confines becomes more and more obvious.

At Dagster Labs, we believe it’s time for data orchestration to come to center stage as a strategic function that not only unifies but streamlines data operations - a shift in perspective that opens up new possibilities.

The Command Center Perspective

Let’s take a moment to visualize data orchestration not as a background utility or something you tag on at the end to keep things running on time, but as the strategic command center of an organization’s data operations. Instead of perceiving it as a post-facto plug-in, picture what it'd be like with data orchestration at the forefront of your planning processes. It can look like whatever you want - personally, I think the bridge of a ship in something like Star Wars or Star Trek - but it’d essentially be your nexus - the place from where you monitor, coordinate, and govern data flows to provide a panoramic view of the data lifecycle. This means that every piece of data is accounted for, every policy enforced, and every stakeholder equpiied with the visibility they need.

Let's illustrate how your operations could benefit with concrete examples:

Unified Interface

A unified orchestration platform could provide a single pane of glass for monitoring and managing all data workflows. For instance, a data engineer could quickly assess the health of all active pipelines, identify bottlenecks, and address them before they impact downstream processes, all from one dashboard.

Governance and Compliance

The platform could enforce data governance policies and/or facilitate the integration of such logic across different tools and systems within data workflows. Imagine a scenario where data engineers define custom policies that automatically enforce data retention rules, manage permissions, and log all data access, ensuring that every step of the data pipeline is compliant with industry regulations and company policies.

Data Quality and Reliability

The platform could have automatic data quality checks and alerts built into the orchestration process. Consider a data pipeline that ingests user-generated content. The platform could automatically verify the integrity of the data, flagging and quarantining any records that fail to meet predefined quality thresholds.

Scalable and Adaptive Execution

The platform would offer the ability to dynamically scale computing resources to meet the demands of varying workloads. For example, during times of high data throughput, such as a product launch or a major marketing campaign, the system would automatically allocate additional resources to handle the increased load. Conversely, during periods of low activity, it would scale down to conserve resources. This ensures that performance remains consistent and cost-effective, regardless of the workload intensity.

This shift from afterthought to forethought not only transforms your approach to managing data but also reinforces the importance of orchestration in enabling a seamless, efficient, and scalable data ecosystem.

The Importance of a Central Data Platform

Centralizing data operations on a unified platform can be transformative. It fosters collaboration by breaking down silos, streamlining accountability by providing clear ownership, and enhancing efficiency by reducing redundant efforts. A central data platform can serve as the foundation for a data-driven culture, where decisions are informed by reliable data insights and where teams can focus on innovation rather than being encumbered by the complexities of operational details.

Real-World Translations: A Hypothetical Example

Imagine a tech company. More specifically, a game company. I like video games, so this is naturally where my mind goes when thinking of this stuff.

For the sake of this example, we’ll call them “Extraboring Gaming”.

Now let’s say Extraboring specializes in online multiplayer games and that their success hinges on understanding player behavior, optimizing game features, and ensuring seamless gaming experiences. They face the challenge of orchestrating a complex data ecosystem that includes real-time game event streams, player feedback from forums, in-game purchase transactions, and server performance metrics.

Here's how a strategic orchestration platform could address their challenges:

• Declarative Workflows: By using a platform that simplifies pipeline creation with clear definitions and automatic triggers for analyzing new game features, Extraboring can improve the maintainability and insight generation of their systems.
• Asset-Centric Management: The platform would ensure that data assets like engagement metrics are tracked, versioned, and updated, enhancing trust in data used for their modeling and forecasting.
• Automated Quality Checks: By incorporating automatic data validation against quality standards, the platform would ensure that only high quality data is used for decision-making.
• Policy Enforcement: Their platform would automate governance, including data retention and privacy compliance, securing their data integrity and ensuring legal adherence.
• Cross-Platform Data Integration: The platform would ensure that data from various platforms, such as consoles, PCs, and mobile devices, is standardized and integrated for a unified view of the player experience.
• Scalability and Flexibility: The platform would support growth and new features with scalable infrastructure and adaptable workflows, aligning Extraboring's data operations with company innovation.
• Collaboration and Self-Service: The platform would promote teamwork and efficient decision-making through a shared platform for data access and manipulation across roles.

By leveraging strategic data orchestration, Extraboring can manage the complexity of their data ecosystem and gain a competitive edge by rapidly iterating on game features and improving player satisfaction. This would also lead to things like better risk mitigation, increased cross-functional collaboration, and the ability to future-proof their business as they develop their title(s).

While the gaming industry presents its unique set of data challenges, the strategic orchestration approach is versatile enough to be applied across a multitude of sectors, including but not limited to:

• Ecommerce, where orchestrating customer interactions, inventory levels, and logistics data is vital for personalized shopping experiences.
• Healthcare, where patient data and treatment outcomes must be meticulously managed.
• Financial Services, which require real-time transaction processing and fraud detection.
• Manufacturing, where supply chain optimization and predictive maintenance are critical, can all benefit from this robust data management strategy.
• Energy, where integrating sensor data across vast geographical networks is crucial for efficient resource management.
• Space Exploration, where orchestrating vast amounts of telemetry and scientific data can lead to groundbreaking discoveries and be the essential element in mission successes.
• Geospatial Analysis, where orchestrating spatial data from multiple sources enables critical insights for urban planning, environmental conservation, and emergency responses.

Moving Forward: Adopting the Mindset

It’s no secret: if your organization is looking to thrive in the era of Big Complexity, it’s time to reassess the strategic role of data orchestration. What this reassessment means is elevating data orchestration to be the cornerstone of your data infrastructure.

How do you do this?

You start by taking the following steps to adopt an orchestration platform that embodies these recommended principles and functionalities:

• Adopt an orchestration tool that allows you to define data pipelines declaratively, capturing the intent and dependencies of your workflows and making them easier to understand, maintain, and scale. Ideally, this tool enables progressive disclosure of complexity, meaning you can do simple implementations for straightforward tasks while providing the depth needed for more complex scenarios.
• Implement a solution that offers comprehensive monitoring capabilities, giving you insights into data lineage, job performance, and system health - a level of observability crucial for proactively managing data operations and ensuring data quality.
• Choose a platform that provides a consistent experience and visualization from development to production, including local testability of data pipelines. This consistency reduces the iteration time and increases developer productivity.
• Considering a platform that models data as assets, which can be tracked, versioned, and managed throughout their lifecycle. This asset-centric approach ensures that you treat your data with the utmost care, with clear lineage and metadata.
• Using an orchestration tool that is highly configurable and extensible, ultimately allowing you to tailor it to your specific needs without being overly prescriptive. It should support custom logic and third-party tool integration, giving you the best strategy approaches for your data stack.

By integrating these functionalities into your data orchestration strategy, your organization can create a robust framework capable of addressing the issues of Big Complexity. A shift like this not only would streamline your data operations but empower your teams to deliver reliable, high-quality data products faster, more cost-effectively, and more efficiently.

Conclusion

Addressing Big Complexity demands a strategic approach to data orchestration.

By reimagining orchestration as a command center equipped with the right functionalities, organizations can navigate the intricacies of their data environments with confidence. The result is a data operation that turns complexity into a strategic advantage, ensuring that data assets are reliable, scalable, and primed for delivering value.

The strategic role of data orchestration is undoubtedly pivotal in the era of Big Complexity. It's not just about managing workflows; it's about creating a robust framework that empowers organizations to harness the full potential of their data. By adopting a command center perspective, organizations can ensure that their data operations are not only efficient and reliable but also aligned with their strategic objectives.

With data orchestration as the cornerstone of your data infrastructure you can transform your data practices, reduce the friction caused by fragmentation, and unlock new opportunities for innovation and growth.

We're always happy to hear your feedback, so please reach out to us! If you have any questions, ask them in the Dagster community Slack (join here!) or start a Github discussion. If you run into any bugs, let us know with a Github issue. And if you're interested in working with us, check out our open roles!

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