We are witnessing an unprecedented data explosion from technologies like 5G, IoT sensors, social feeds, satellites and autonomous devices. IDC predicts global data volumes to almost double yearly till 2025. To gain timely value from data, the fundamental databases powering applications need a radical transformation in their design philosophy and architecture. Let us examine six evolutionary trends that aim to database development.
Cloud Becomes the Defacto Platform By 2024, almost 80% of new database deployments are forecasted to run on public or private cloud platforms. Cloud infrastructure offers automatically scalable capacity, built-in availability and lower TCO which are key to handling the incoming deluge of data. But beyond lift and shift migration, databases need to be intrinsically designed for cloud native attributes like distributed caching, concurrent user access, container packaging and infrastructure-as-code paradigms to realize the complete value potential.
Accelerating with Specialized Hardware Innovations in semiconductor technologies, GPUs and memory hardware hold exciting performance improvement prospects for databases through parallelized processing. Dedicated hardware like AWS Graviton chips, Oracle Exadata platforms, IBM Power Systems and startups like Cerebras promise 10x or greater acceleration in transaction throughputs, query responses and data load speeds. Though still evolving, combining specialized hardware with database platform optimizations will be key to achieving speeds necessary to extract value from highly transient data.
Smarter Systems through AI infusion Making databases self-driving and intelligent will be instrumental in simplifying management while also enabling predictive capabilities on Big Data. Advances like Amazon Redshift ML, Oracle Autonomous Databases, CockroachDB prediction services, CosmosDB analytics and similar augment capabilities to spot anomalies, forecast emerging trends and uncover hidden insights. Core considerations around trustworthiness, explainability and observability need to be factored in while building intelligence to ensure user acceptance and accountability.
Specialized Data Stores for Diverse Needs The common one-size-fits-all relational database is no longer adequate for the variety of data formats and access types emerging. Graph databases better model connections, key-value stores allow simpler distributed access, while document databases work best for schema flexibility required by semi-structured data. Future systems embrace this polyglot persistence through multi-model designs that integrate transactional, analytical and Big Data workloads across relational, distributed and NoSQL databases. Declarative frameworks will simplify building such integrated hybrid data landscapes.
Event-driven Architectures with Streaming For today’s highly connected world, having static data batches processed periodically is inadequate to uncover insights. Database architectures must evolve to ingest and analyze streaming data feeds in real time to support mission-critical decisions. Enabling capabilities like change data capture, event triggers, transient staging and stream processing integrations will be key. Support for streaming SQL, persistent queries and temporal history retention moves databases from serving just systems of record to becoming systems of insight.
Governance and Sustainability Built-in Data compliance needs for privacy, sovereignty and transparency will necessitate stronger governance capabilities like granular audit histories, access controls, surveillance systems and data values oversight through blockchain-based mechanisms. Estimates show database systems consuming over 5% of global energy accentuating the need for inbuilt sensors to monitor usage, identify optimization opportunities and track carbon emissions. Future database design will need to enable trustworthiness alongside performance and scalability.
Paths to Progress The advancements databases need to incorporate by 2024 seem extensive to handle the data avalanche ahead. But incremental evolution is already underway through innovative platforms, cloud infrastructure and hardware technologies. The bigger challenge is holistic thinking to connect these disparate capabilities into an integrated, scalable and intelligent data fabric that can unleash value from data at scale. Using declarative and model-driven techniques that span data architectures, infrastructure and insights delivery accelerates this data platform transformation. The biggest upside lies in progressing from collection constraints to unlocking cognition through data.