The prolific growth of structured and unstructured data presents tremendous opportunities for businesses to outperform rivals by extracting insights faster. Every year new developments increase capabilities to handle data at a massive scale and unlock its value via intelligence. Here we explore major innovations likely to disrupt the big data landscape moving forward.
Key trends of big data like moving to cloud, machine learning integration, real-time processing, automation and responsible data practices are shaping big data’s path to mainstream adoption across more industries.
Moving to the Cloud
More companies are moving their big data infrastructure to the cloud. The cloud provides benefits like flexibility, scalability, and cost savings.
Cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud Platform offer ready-to-use big data services. Companies can spin up Hadoop clusters, Spark workloads, data warehouses, and analytics tools quickly without having to maintain on-premises hardware.
According to surveys, 80% of enterprises will move their big data workload to the cloud by 2025. The availability, economics, and performance of cloud infrastructure are accelerating this migration.
Real-Time Data and Streaming Analytics
Real-time data processing is becoming critical for businesses. To leverage insights from big data in real-time, companies are adopting streaming analytics platforms like Apache Kafka, Amazon Kinesis, Azure Event Hubs.
Streaming analytics helps companies respond instantly to customer behavior, detect fraud immediately, improve real-time decision making, and more. Areas like the Internet of Things, machine telemetry also heavily use streaming analytics.
Industry experts forecast the real-time streaming analytics market to grow 26.5% CAGR over the next few years. The need for instant big data insights will further increase streaming adoption.
Artificial Intelligence and Machine Learning
AI and machine learning are becoming integral parts of big data systems. From data processing to analytics and interpretation, machine learning augments big data capabilities.
As big data platforms integrate tightly with machine learning services on the cloud, applying AI to drive data-driven decisions will gain more adoption.
Managing large data volumes requires extensive human resources and effort. From infrastructure to analytics, humans add delays and costs.
Hence industries will aim to automate big data processes aggressively. Automation in security, monitoring, optimization, metadata management reduces errors and drives efficiency.
Machine learning also plays a key role here by handling tasks humans performed manually earlier. Things like query performance tuning, cube design, semantic optimization of text can be automated using AI.
The big data automation market is predicted to reach over USD 28.58 billion by 2032.
Relational databases traditionally managed most enterprise data. But they cannot effectively manage relationships between connected data.
Graph databases and analytics, with nodes and edges, can map relationships way better. This drives patterns, dependencies, and insights not visible earlier.
Areas like customer journey analysis, fraud patterns, recommendation engines benefit hugely from graph data stores. Expert estimates indicate the graph database market will expand at over 18% CAGR from 2023 to 2032.
Containers and Microservices Gain Traction
Monolithic applications limit the performance, scaling, and agility of big data infrastructures. Containers and microservices solve these problems by modularizing applications.
Containers package applications with dependencies to isolate resources. Microservices break down applications into independent modular services interacting via APIs. Both these approaches improve flexibility and resource utilization.
Leading big data platforms now integrate natively with Kubernetes container orchestration. Use of containers and microservices in on-prem and cloud data lakes will thus grow exponentially.
Similar to DevOps, DataOps aims to improve collaboration between data engineers and consumers. The goal is delivering business value faster from data and analytics.
DataOps requires organizational and process changes across data warehousing, business intelligence, machine learning teams. Adopting agile frameworks leads to continuous processes from data collection to business insights.
Gartner predicts DataOps will reach mainstream adoption by 2025. Smooth data flow between producers and consumers will enable data-driven organizations.
Customers and stakeholders demand more transparency in data sourcing, processing, and usage lately. Ethics around privacy, explainability are also gaining focus.
Big data platforms thus aim at cataloging fully data flows, lineage to improve visibility. Explainable and interpretable AI helps stakeholders understand model behavior and decisions better.
Best practices like data minimization, localized processing, and user consent storage also address privacy concerns. More chief data officers prioritize data transparency, quality, and compliance today.
Over the next few years, responsible data collection, processing, and auditing will become non-negotiable.
Skills and Resource Shortage
As big data expands across industries, demand for related technical skills sees a huge supply gap. Resources for engineering, science, visualization are severely inadequate today.
Surveys estimate over 2 million big data job vacancies in the US alone by 2025. Skill sets like AI, machine learning, cloud platforms are especially scarce. Even IoT, cybersecurity require trained talent.
Educational institutions thus need updating curriculums to address emerging areas. Retraining employees in latest data tech should also help protect industries better.
Attracting and growing big data talent will be mission-critical this decade for businesses keen to tap analytics.