Data Analyst Monitoring Trajectory of Novel Phase in Data Coordination
Data traverses continuously. It constantly moves between applications, data repositories, via the channels created by application programming interfaces, and more... Yet, it's no longer confined to being confined to a single database or stagnating in the rigid rows of a data warehouse. This shift has increased its value.
These data assets now consist of source data, metadata, transformation logic, documentation, tests, and access policies. They serve as the foundation for a multitude of use cases - from analytics and AI to operational applications that provide real-time insights and actions. However, with this shift comes complexity. This is according to Julian LaNeve, the CTO of Astronomer, a company known for its platform that provides a single location for software developers and data science teams to schedule data pipelines and monitor their performance, allowing adjustments.
“Even though we can now easily define data assets today, delivering consistent data packages at the enterprise level requires collaboration across intricate systems involving interconnected pipelines and dependencies. Challenges such as fragmented orchestration, inefficient resource allocation, reactive problem-solving, and custom tooling have become all too common, hindering progress. As a result, data and platform engineering teams often find themselves overwhelmed, responding to failures only after issues have impacted business operations,” LaNeve explained.
Misaligned Orchestration Layers
Modern data orchestration involves three main layers:
- The data layer, where data is stored and managed.
- The workflow layer, which defines how data is processed and moved.
- The infrastructure layer, which offers the computing resources to run the entire operation.
These layers often operate in silos, which leads to inefficiencies. A single schema change in the data layer, for example, can trigger failures across workflows and infrastructure. Without a unified system, teams are left scrambling to fix problems after they’ve cascaded through the entire system.
“Infrastructure provisioning creates more challenges. Teams often over-provision resources, wasting money, or under-provision, missing deadlines,” LaNeve said. “Custom-built solutions further complicate the system, slowing collaboration and limiting scalability. There is also a people problem, particularly in the data layer, i.e., data teams have to deal with poor data quality from upstream sources like Salesforce and need to handle ad hoc requests all day long. Rather than focusing on creative, needle-moving solutions, data engineers spend far too much time trying to clean up their data sources.”
Shifting to Unified Orchestration
To address these challenges, LaNeve recommends that data orchestration must evolve from a fragmented approach to a comprehensive, end-to-end strategy. This means unifying orchestration across data, workflows, and infrastructure while incorporating observability into the entire system. Such a strategy allows data teams to detect and resolve potential issues before they impact the end product, thus promoting proactive management rather than reactive firefighting.
“A unified orchestration system improves the reliability and trustworthiness of data assets. It provides teams with the visibility to understand which tasks ran, their sequence, and whether they met the prescribed service level agreements. This transparency boosts confidence in the quality of data assets and reduces the operational burden on engineering teams, allowing them to focus on building and optimizing new products,” he said.
A comprehensive orchestration approach is argued to offer measurable advantages across multiple dimensions. It allows data engineering teams to enhance reliability and build trust, reducing unexpected failures and making it easier for teams to manage dependencies effectively. Apart from that, standardized processes and streamlined workflows also improve development speed.
Team Speed Boost
“With the orchestrated approach in place, teams can work faster and collaborate more effectively, avoiding the inefficiencies that come with fragmented systems. Real-time insights into resource requirements help optimize infrastructure management, cutting unnecessary expenses and ensuring resources are allocated where they’re most needed,” LaNeve explained. At the same time, governance and security are fortified, with a comprehensive platform ensuring compliance with policies across the entire data stack and strengthening data protection and regulatory adherence.
Automation plays a key role in increasing productivity. It's a message that has been pervasive across the disciplines of robotic process automation, generative AI functions, and every automation accelerator benefit that exists in between. The suggestion here is that by handling orchestration and observability tasks automatically, engineering teams can refocus their attention from keeping the lights on maintenance to driving new projects and creating more value for the business.
“There is no doubt that enterprises in every field need a unified orchestration platform to take advantage of the best practices from software engineering, including automation and self-healing capabilities, to manage the complexities of data pipelines. The technologies that work in this space will help companies predict and prevent disruptions, aligning with an organization's agility and innovation goals. Full-stack orchestration is not just about making current systems more efficient - it transforms data management, enabling data to serve as a strategic asset that drives growth,” LaNeve concluded.
He believes that by focusing on holistic, proactive orchestration, companies can turn data assets into reliable, scalable, and secure resources that support their most ambitious initiatives.
The Yin-Yang of Data
There’s a push-pull yin-yang effect occurring in the data world to a degree - we’re working diligently to ensure we can provide disaggregated componentization so that we can separate certain computing and data processing layers from each other to provide more interoperability, more composability, and more granular control over what data we put where and what task we assign it.
Similarly, we're putting in a great deal of effort to harmonize and coordinate data (anyone mention Kubernetes lately?). This allows us to monitor and manage data streams with pinpoint accuracy and control. The transformation from conventional to unified coordination could potentially shape the future success of data-driven enterprises.
The shift towards unified data orchestration is essential to address the challenges of inconsistent data packages at the enterprise level. This involves coordinating data layers, workflow layers, and infrastructure layers, promoting proactive management and reducing operational burden.
Misaligned orchestration layers often result in inefficiencies, such as over-provisioning of resources, undershooting deadlines, or dealing with poor data quality from upstream sources. A unified orchestration system can improve the reliability and trustworthiness of data assets, allowing teams to focus on building and optimizing new products.
