Tech-native businesses manage an ever-growing data universe. According to Forbes, data generation will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025. There is more data available than ever before, but more data also means more complexity, creating a soaring demand for data orchestration.
If organizations want to truly operationalize the data they are gathering, they must build a strong foundation that prevents growing pains and is scalable to continuously run dataflows smoothly. However, simply introducing new tools could cause system sprawl, and building DIY orchestration solutions will quickly become irrelevant. In this article, we define data orchestration and discuss how a modern data orchestration framework will help companies automate their workflows and truly operationalize their data to drive real-time business decisions.
What Is Data Orchestration?
Data orchestration is the step in the data management process that leverages software to optimally and efficiently move and process data across the data ecosystem of a company. The core of a data orchestration system is the creation of data pipelines and workflows that move data from one place to another, in addition to combining, verifying, and storing that data to make it useful. Other frequently used terms are big data orchestration and ETL or ELT orchestration.
Why Is Data Orchestration Important?
There are times when data orchestration may not be required, especially for teams working with data sets that are relatively static. In these cases, they can often use raw data to achieve their business goals without the need for pipelines. This approach is usually sufficient so long as cost and performance remain acceptable.
However, as teams introduce new data into the process, this approach becomes expensive or slow. Companies might have data coming from hundreds of different sources—ranging from cloud APIs to cloud warehouses, or on-prem databases to data lakes. Successful data orchestration enables more stakeholders to work on refining and using data—adding tremendous business value without exploding infrastructure costs or generating demands on data teams’ time.
In more detail, here are some of the main reasons data orchestration is important:
- Data Collection: A complete data orchestration process manages the data ingestion step to collect the necessary data and organize it.
- Data Transformation: After the data has been collected, orchestration services allow you to standardize and validate values and attributes. You can alter these values, which include names, times, and events, to comply with a preset schema.
- Data Consolidation: Data orchestration services help integrate data from several input streams into a single location—allowing you to create a unique, single image of your data.
- Data Delivery: Data orchestration services deliver or make the processed data available to business intelligence systems and data analytics tools that regularly require it in order to provide insights.
How Data Orchestration Has Evolved
Data orchestration technologies, like any other type of technology, undergo frequent changes to meet the evolving data management requirements of companies.
In the past, data teams used cron, a utility in Linux systems, to schedule data jobs. The foundation was a timer-based model, running jobs at regular intervals based on a simple workflow. For example, once a night, count the number of website visitors by browser and geography and write it into a database table that powers a BI tool for the marketing team. This type of timer-based data orchestration is still common, useful, and relatively efficient, especially when companies receive big blocks of data at once or when they have simple questions that need answering.
However, this approach is not scalable. As the number of data sources grows, systems get more complicated, and stakeholders want curated data faster to make far better business moves, creating complex cron jobs got more and more difficult:
- As the number of cron jobs grew, managing dependencies between jobs became difficult, time-consuming, and error-prone.
- An on-call engineer had to handle alerting and failure manually, which made errors often fatal.
- Manually auditing logs to assess job performance on a certain day was a time sink.
Data Orchestration and DAGs
As complexity increased, data teams start to introduce DAGs, or directed acyclic graphs (graphs that do not loop back on themselves). DAGs are a collection of the tasks you want to run, arranged to reflect their relationships and dependencies. Each node in a DAG represents a task in the process.
The structure of a DAG (its tasks and dependencies) is often represented as code in a Python script. In their simplest form, DAGs could consist of three steps. For example, ingestion, transformation, and delivery. Tasks can be triggered by different mechanisms like sequence or a specific time. Data orchestration systems have a scheduler to manage the tasks, executors to initiate tasks, and a metadata store to record the pipeline’s status.
A common pattern emerges as a wider variety of teams embrace DAGs: they want to share their data and findings with each other, and subsequently build their own ETL (or ELT) logic that depends on the other teams’ data and logic. This spurs the need to start building more sophisticated data pipelines—and the business value then begins to grow exponentially.
Only a data orchestration platform that supports concurrent DAG orchestration and “DAG of DAGs” can handle this level of complexity. It can create an output of cleaned, transformed data in one pipeline and turn around to use that as input into another. Any change made to the transformation upstream will automatically flow through to the downstream pipeline.
By now, you might be asking yourself: doesn’t orchestrating multiple DAGs at once lead to potential resource contention for your underlying infrastructure? Yes, it can. As volume increases, multiple DAGs may end up competing for the same processing power of your underlying infrastructure. That’s why a modern data orchestration platform should support an increasing number of pipelines by aligning to a few key principles:
- Allocate resources to workflows based on their assigned priorities, including inferring priorities of workloads by traversing DAGs.
- Parallelize workloads whenever possible. If you do have extra capacity, run workloads if at all possible, even if those workloads are lower priority (such as when high-priority workloads are waiting for other data to become available). This reduces the likelihood of resource contention later on as you traverse the DAGs.
- Allow for resource isolation. At times you may still want dedicated resources for different workloads and should be able to provide reserved and/or dedicated resources for separate workloads.
Automated data orchestration hasn’t become the standard yet. Data engineers and data teams still rely on more manual ways of orchestrating pipelines by creating a set of instructions: read data from x source, run y code on it, and push the results to z. However, once a pipeline is codified, it quickly becomes brittle. Everything is forever changing: the data, the workloads and resources, and the assumptions. This can lead to what no data engineer wants to hear—alerts going off at 3 a.m. about the pipeline breaking and part of crucial data infrastructure going down.
The more data teams scale, the more they need automation to streamline their day-to-day tasks, from ingestion and transformation to delivery, maintenance, and governance.
Final Thoughts on What Is Data Orchestration and Next Steps
Automated data orchestration not only saves data engineering time, but it also ensures data governance and visibility, fresher data, and data privacy compliance. The Ascend platform leverages a sophisticated Control Plane that is non-existent in imperative systems. It takes care of mundane tasks, so the only thing developers need to do is create and curate the high-level spec. There’s no need to worry about how the dataflow automation system manages the legion tasks required to implement the spec.
Using this declarative model, orchestration requires less code and less maintenance; it accelerates development cycles and makes pipelines more adaptive to change.
- DataAware: Ascend’s Data Automation Cloud tracks all the data, for all time, including its profile, where it came from, how it got there, where it went to, how it was used, and why.
- Autonomous: Ascend continuously watches for changes to data and code and dynamically generates tasks and parameters to ensure the right data arrives, always.
- Scalable: Ascend can track trillions of records and billions of data partitions and react to changes within seconds—with single- or multi-DAG orchestration.
- Self-Optimizing: Ascend leverages data collected to drive greater efficiencies than can reasonably be done manually, such as avoiding duplicate and unnecessary computations with mid-pipeline checkpointing, automated backfills, and automated rollbacks.
The result? Data teams can focus more on doing things better and faster and focus less on the monotony and mechanics of how they get done.