The data industry is rising and the main reason is that many companies are built around data. Lot’s of data-driven applications are built yearly that solve different issues. Having accurate data helps to improve products, keep customers happy and therefore focus organization goals more strategically.
The more data-driven applications the more need for data orchestration as lots of innovative frameworks are being developed so the need for orchestration increases. The goal is to abstracts data access from storage systems, virtualizes it, and present via APIs with a global namespace to data-driven applications.
Data orchestration handles tones of flows that enable the data to be consumed by the applications easily, at the end of the day the application interacts with the data orchestration platform that handles data regardless where it is stored.
What is Data Orchestration?
A simple definition of data orchestration
Data orchestration is a technology that extracts data from all different kinds of technologies, collects and stores in a system which helps to present it in a more accurate way.
The technical definition of data orchestration
Data orchestration virtualises the data from different types of storage deployments and presents the data to all the data applications through the industry common API to achieve the goal.
Simply saying the applications consume the data by interacting with the data orchestration platform which easily communicates with the data from all over the place, it doesn’t matter where or how it is stored.
When to use data orchestration?
When the data silos across data centres, regions and clouds create a mess and the data infrastructures becomes a huge mess. It becomes complex and time consuming to maintain.
To solve the chaos in the data infrastructure there are 3 places to do it:
- compute side (new innovations of frameworks could come up),
- storage side (tones of new solutions are emerging)
- orchestration from different types of storages
The most cost-effective and fast solution is to orchestrate the data so that the organisations will focus on the business logic instead of thinking how to add another framework. Currently, the data industry is missing the data orchestration layer between the computer systems and storage systems.
Data Orchestration Requirements
- Global Namespace – abstract the data silos to make data elastic for data-driven application
- Intelligent Caching – enable data locality for fast performance regardless of where the data is stored
- Data Management – seamless data movement across data lakes without app changes
- Structured Data Catalog – Provide a structured data service to greatly optimize SQL engines
- Data Transformation – Transform data to compute-optimized representations for easy consumption
Data orchestration solutions
The concept of data orchestration is relatively new and this is why there is not much software that can do data orchestration. Take a look at the following solutions for data orchestrations:
- Orchestrate a Data Symphony https://www.youtube.com/watch?time_continue=123&v=p_H1lQ1Ejys&feature=emb_title
- Data Orchestration: What Is it, Why Is it Important? https://dzone.com/articles/data-orchestration-its-open-source-but-what-is-it
- Big data orchestration https://www.activeeon.com/resources/whitepaper-big-data-orchestration/