One of the most significant advantages of cloud-based data pipelines is the reduced burden on internal IT teams.
Cloud providers manage the maintenance, security, and scalability of infrastructure, freeing up resources for more strategic activities.
Organisations can focus on extracting value from their data rather than managing the systems that store it.
Data teams are now expected to manage many more data sources, typically accessed via APIs provided by their source application vendors.
Leveraging those APIs requires writing code to extract data from that source and maintaining the code over time, as APIs tend to evolve and change frequently.
Even the smallest of changes can break data pipelines, leading to missing, inaccurate, or incomplete data.
Data teams often find themselves overwhelmed by the sheer number of tools required to work with data, including those for extraction, ingestion, transformation, and orchestration.
This makes it difficult for teams to demonstrate their ROI, leaving them constantly playing catch-up with business demands.
Hybrid approach: bridging the gap
While cloud-based solutions offer numerous benefits, many organisations are not in a position to fully transition away from on-premises systems due to the sensitive nature of the data or for other logistical reasons.
For these companies, a hybrid approach, which combines the control of on-premises systems with the scalability of the cloud, offers an interesting alternative.
Hybrid data pipelines enable businesses to maintain sensitive or mission-critical data on-premises while utilising the cloud for less sensitive workloads and more dynamic scaling.
This approach offers the best of both worlds: the security and control of on-premises infrastructure and the flexibility and cost-efficiency of the cloud.
Read more: How cloud observability is transforming the finance sector
A hybrid model also allows for a more gradual transition to the cloud. Rather than a disruptive full-scale migration, businesses can move specific workloads to the cloud at their own pace.
This flexibility is particularly valuable for larger enterprises with significant investments in legacy systems.
It allows them to modernise their data infrastructure without risking operational continuity.
AI and the transformation of data pipelines
As organisations continue to refine their data strategies, AI plays an increasingly prominent role in the evolution of data pipelines.
AI-driven automation can streamline many complex tasks associated with data management, from integration to transformation and analysis.
In data pipelines, AI is particularly valuable for its ability to simplify and accelerate the creation of customised data integrations.
For example, AI can automate the process of parsing API documentation, identifying key parameters, and generating YAML configuration files, significantly reducing the burden on data engineers and allowing them to focus on more high-level tasks.
Data warehousing in hybrid and cloud environments
Another significant trend in data pipeline management is the centralisation of data in data warehouses or data lakes, which serve as a hub for analytics, operational, and AI processes.
The modern data warehouse and data lake have become increasingly important aspects of a business’s data strategy.
They act as a central repository that allows organisations to consolidate data from various sources, providing a single source of truth that can power a wide range of use cases, from traditional business intelligence to advanced AI models.
The concept of reverse ETL (Extract, Transform, Load) further illustrates the growing importance of the data warehouse.
In this process, data from the warehouse is fed back into operational systems, enabling more informed decision-making and tighter integration between data insights and business operations.
For example, a CRM platform can collect event data on how users interact with their product features.
It can then segment users based on their product usage and send targeted emails to educate them about the benefits of the features they haven’t utilised to be proactive with their customer base.
Additionally, it can leverage the same data to trigger alerts for the account owner to notify them that their accounts are starting to explore new features that could lead to potential upsell opportunities.
This trend illustrates the hybrid nature of modern data pipelines, where data flows into a central repository and back out to support real-time business needs.
The future of data pipelines: a blurred line
Looking toward the future, the distinctions between on-premises, hybrid, and cloud-based data pipelines will likely blur as AI and other technologies evolve.
The near future of data pipelines will likely be characterised by flexibility, with organisations adopting the combination of systems that best meets their unique needs.
When AI becomes more deeply integrated into data management, the role of data pipelines will expand beyond simple data transfer to encompass more sophisticated functions, such as real-time data processing, automated decision-making, advanced analytics, and retrieval-augmented generation (RAG).
The challenge for organisations will be to harness these capabilities in a strategic and sustainable way.
Harnessing the power of hybrid cloud
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