Introduction🔊
If you have ever been to an orchestra then you could find the people swinging violin in a synchronous manner to produce loud music. This makes sure that everything is intact and you could receive the best possible output. Similar is the case with Data Orchestration where we need to consistently update the data to get the latest insights and stay in tact with the current trend.
What is Data Orchestration🔁
To define in layman's terms, it is a process that allows correlate and automate data workflows(set of small tasks). We can either automate the entire workflow or choose only a part of workflow that needs to be automated. We can schedule the frequency at which this needs automation say every minute, hour, day, or week and so on. This scheduling needs to be done only once as per the needs and never worry about as this process is automated. It reduces time, saves energy, more control over data pipeline, and better outline of what is happening to the data.
To establish data orchestration the logic that runs behind the scenes is Direct Acyclic Graphs(DAGs). These are a collection of all tasks(instructions) that needs to be completed in a specific order following the relationships and dependencies between individual tasks.
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Why Data Orchestration❓
Let's consider a few hypothetical situations:
- Say you have forget to update the latest data manually.
- You haven't run the script that fetches the latest update.
- Have done a analysis on old data but the current data produces different insights.
All these issues may look small but once in industry standards or while presenting the analytics to higher official if any disparity is found due to datasets then we are into deep trouble. Also most recommendation systems requires continuous data updated to produce the relevant results. In most daily life products knowingly or unknowingly data orchestration is involved.
Different tools available⚒️
Flyte ML
It is a Kubernetes-native workflow automation platform to unify data and ML processes. This makes easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing. We write code, test locally, ship to cluster, execute, visualize, and retrieve valuable insights.
Astronomer
It is a cloud-based solution that helps you focus on your data pipelines and spend less time managing Apache Airflow, with capabilities enabling you to build, run and observe data all in one place. It focusses on pipelines and eases steps like data integration, analytics, and orchestration.
y42
It is a full stack data platform that anyone can run. It removes the complexity of managing multiple tools. We can easily integrate with databases, build model, orchestrate data, leverage built-in visualizations, and finally export data which helps in automating workflows.
Prefect
An open-source workflow orchestration and management tool that helps us integrate small tasks into workflows that can be monitored with the help of Prefect UI or API. We can schedule, trigger, execute, and visualize data workflows.
Conclusion🔚
Hope this blog gives a brief introduction to Data Orchestration. It highlights the key terms, process happening inside the black box, and different tools to elevate your data orchestration workflow. The importance of data orchestration holds true and will still remain true for decades as importance, size, and relevance of dataset is growing exponentially.
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