Data Pipeline Orchestration
In any Enterprise, the journey of data is long and complicated as it goes through complex processing before it becomes an information asset. This journey starts from several sources, structured and unstructured (including semi-structured), being ingested, integrated, transformed, enriched, and persisted into a central data repository (Delta lake or Lakehouse) for enabling self-service data consumption for Analytics and Predictive needs.
As part of the System Development Life Cycle (SDLC) practices during the Discovery and Design phases, Data Modeling occurs which establishes a data architecture blueprint that outlines what standards and approaches need adherence to develop robust & fail safe engineering pipelines to make a data product fit for purpose for downstream applications enabling further end-user exploration. Though it may sound simple, at every stage, as data gets processed based on the complex business rules, it is imperative to have visibility over the end-to-end data value chain with a unilateral goal for providing reliable and trustworthy data on a timely basis.
Data volumes are growing tremendously and the importance of real-time availability of data for analytics is also growing. To make this entire process run in an automated fashion, a stable Orchestration Mechanism is needed. Orchestration helps streamline and logically sequence data engineering operations on an on-going basis.
Expectations from Orchestration
For every project a Data engineering team handles to cater to the needs of business teams, the team builds numerous complex pipelines. The right Orchestration tool should be able to -
- Schedule the pipelines to run at desired frequency without any interruption
- Give flexibility to data engineers to configure and maintain dependencies appropriately
- Send timely actionable alerts and notifications to the stakeholders teams in case of success or failure of a pipeline run
- Help the engineering teams through a easy to adopt interface to enable orchestration, so that efficient scheduling of jobs can be achieved faster and quicker
- Provide fail-safe options for planned system maintenance or for any other unplanned events
Benefits of Orchestration
Pipeline orchestration tools are integral to faster implementation of data pipelines. Organizations need a solid approach to manage a variety of complex datasets with large-scale storage and migration
needs. An orchestration strategy is key to maintaining data integrity and minimizing time and cost to market.
With the proper capabilities, your data team can create optimized data management processes
sooner. Data orchestration helps you move seamlessly toward an optimized data systems and pipelines while requiring less investment of time and expenses.
By selecting the right tool for analytics orchestration your team can reap key advantages such as:
- Increased productivity: Companies can rely on data orchestration tools to maintain and manage data through automation and strategic organization.
- Improved cost-efficiency: Enabling end-user self-service, this solution can help business manage data more efficiently at a limited cost through higher trust in data.
- Optimized standardization: Analytics tools standardized all data, eliminating manual transformation requirements thereby minimizing any human error.
- Reliable compliance: Through data orchestration, companies gain additional tools to support compliance and privacy laws. They can also demonstrate ethical data gathering via these tools.
- Streamlined processes: Overall, data orchestration optimizes and enforces best practices for data governance, credibility and effectiveness.
In conclusion an Orchestration solution needs to enable speed to market, while putting a higher emphasis on lowered TCO (total cost of ownership) where data teams spend less time integrating discrete pipelines and instead lend focus to other high priority engineering tasks (like building a data pipeline).
How Dextrus Can Help?
Dextrus is a comprehensive data platform with several built-in solutions to cater all the activities in data engineering process. Job Scheduler is one of the components that can be leveraged to orchestrate the data movement from the source to the target platforms with a user-friendly interface and can help you achieve the objective with simple drag and drop functionality.