By Saurabh Dutta, Senior Solutions Architect, Gathr Data Inc
As data volumes continue to grow, organisations are always seeking better ways to make data work for them. Driving business outcomes from data requires multiple data quality variables to be in place along with the responsibilities of managing the data as per different availability, security, and compliance requirements. Using a combination of people, processes, and technology, the data-to-outcome ecosystem needs to be built on a holistic and integrated platform, instead of a “best of breed” approach for each stage in the data journey.
There is an increased demand for self-service platforms, that can serve different teams, irrespective of their skills and proficiency over certain tools and proprietary languages. This is where no-code or low-code data platforms come into the picture. Further, organisations need their data stack and processing capabilities to be able to handle massive-scale data in near real-time along with innovative capabilities.
We will discuss these emerging trends in data processing flows and how they are shaping modern data platforms:
AI-driven data pipelines
The incorporation of AI in processing workflows helps keep pace with the increasing complexity, variety, and volume of data. AI-powered pipelines help developers to build their pipelines quickly by assisting them with generating and writing expressions, queries, code snippets, etc., and leading to an accelerated time-to-insight and improved decision-making capabilities. Such AI-powered operators reduce configuration hassles and simplify custom logic and scripting within pipelines. Such pipelines are typically free from errors and offer easy maintenance to help teams focus better on their strategic initiatives.
AI also assists in expediting the development process by providing boilerplate codes or interpreting existing codes.
Native cloud execution
Native cloud execution involves the deployment and execution of data processing jobs directly within the cloud environment, rather than relying on external or proprietary execution engines. It aligns with the broader industry trend towards cloud-centric operations, capitalising on the scalability and flexibility offered by cloud platforms.
This trend signifies a fundamental shift in not just the way enterprise data is stored and processed, but also in executing critical operations directly within the cloud infrastructure. The cloud’s inherent advantages, such as parallel processing and distributed computing, contribute to enhanced performance compared to traditional on-premises solutions.
Optimising performance is a core benefit of native cloud execution. Cloud-native services dynamically allocate resources in response to varying workloads, ensuring optimal performance during peak demands while avoiding unnecessary costs during periods of lower activity. This elasticity not only enhances the efficiency of data processing operations but also contributes to cost optimisation.
Native cloud execution additionally opens avenues for seamless integration with advanced cloud services. A myriad of cloud-based tools like machine learning, artificial intelligence, and advanced analytics services open up new possibilities to innovate when used with data pipelines.
Emergence of data apps
Data Apps empower organisations by providing targeted and efficient solutions to industry-specific challenges. Rather than employing generic applications, businesses use data apps as specialised tools that understand and address their unique operational requirements.
The hallmark of Data Apps lies in their specificity. These applications are designed to cater to distinctive needs. Examples include pre-canned apps for FinOps, specifically addressing cloud cost optimisations. Likewise, Apps tailored for DevOps and SecOps offer targeted solutions in the realms of Development and Security operations, addressing very specific challenges.
Marking a departure from one-size-fits-all solutions, Data Apps are finely tuned to the nuances of their industry. As these apps are purpose-built for specific areas, they can yield more impactful and precise results, ultimately advancing overall operational efficiency.
Further, these Apps are designed to seamlessly integrate with existing data workflows. This ensures a smooth transition into operational processes, with minimal disruption, enabling organisations to adopt them without re-wiring their entire infrastructure.
Towards intelligent data pipelines
As we herald a new year and take a look at the forces driving the evolution of data-to-insight pipelines, we perceive a huge promise and transformative potential. The trends we’ve explored — from AI-driven data pipelines and native cloud execution to the emergence of Data Apps — collectively paint a picture of data pipelines that are dynamic, intelligent and purpose-driven.
Imbibing AI represents not just a technological evolution, but a paradigm shift in how organisations approach the orchestration of data. The promise of seamless configuration, automated custom logic and accelerated time-to-insight promises a future where AI serves as a powerful ally in navigating the complexities of an ever-expanding data universe.
Native cloud execution emerges as another cornerstone, aligning data processing with the broader industry shift towards cloud-centric operations. The scalability, flexibility, cost, and performance optimisation inherent in this approach plays a pivotal role in shaping the future of data processing.
The ever-increasing need for specialisation is going to drive the emergence of more Data Apps in 2024 and beyond. These shall be finely tuned for specific industries, helping organisations tailor their data strategies to meet the unique challenges of their domains. Data Apps usher a promise of being catalysts for innovation and efficiency gains.
Innovation thrives in the synergy of human expertise and technological prowess. Those who navigate emerging trends with insight and agility will not only harness the power of their data but will define the very contours of success. The future is not just data-driven, it is data-empowered, and the journey has only just begun.
