Data Management Trends for 2025 (That Aren’t Cloud or AI)

While cloud and AI get much of the attention, there are other ongoing advancements that database administrators should be aware of

In recent years, data management and database administration have experienced significant advancements, reflecting the evolving needs of organizations to handle and analyze vast amounts of data efficiently. Of course, the biggest trends in data management, as with most IT subjects, focus on AI and cloud computing. But there are many other trends that impact the way in which data and databases are accessed, managed and administered. Let’s take a look at some of them.

Emergence and Adoption of Vector Database Systems

Vector database systems are a specialized type of DBMS designed to handle and process vectorized data. Instead of querying traditional structured data (rows and columns), vector databases allow efficient nearest neighbor searches in massive datasets.

Because vector database systems are designed to store, index and retrieve high-dimensional vector embeddings, they are useful for applications involving:

• Similarity search
• Anomaly and fraud detection
• Drug discovery and genomics
• Image and video recognition
• IOT and sensor data analysis
• Recommendation systems
• Multi-modal searches (that is, searching across different types of data)

Of course, vector databases are also ideal for AI uses cases such as natural language processing (NLP), machine learning (ML), and AI-driven analytics.

Although vector database implementation is still nascent, several technology companies and startups have integrated vector databases into their workflows. Companies such as Pinecone, Weaviate and Milvus have developed specialized vector database solutions, and MongoDB has integrated vector search capabilities into its MongoDB Atlas platform.

Integration of Property Graph Queries in SQL:2023

The SQL:2023 standard, adopted in June 2023, introduced several enhancements, perhaps most notably the incorporation of property graph queries (SQL/PGQ). This feature allows users to perform graph-based queries directly within SQL, bridging the gap between relational databases and native graph databases.

Traditionally, relational database systems use tables and joins, while graph database systems use nodes, edges and properties to model relationships. With SQL:2023, relational databases can now natively query graphs using SQL, reducing the need for separate graph-specific query languages like Cypher (Neo4j) or Gremlin.

The new SQL/PCG standard allows a read-only graph query to be called inside a SQL SELECT statement, using syntax similar to PGQL. The result returned is a table of data values. There is also a DDL aspect to SQL/PGQ which enables tables to be mapped to a graph view schema object with nodes and edges associated to sets of labels and data properties.

The SQL/PGQ standard should help to minimize the fragmented approach that was being taken by relational vendors as they implemented their own proprietary extension. As the standard gets adopted it will improve interoperability across databases, simplify portability of graph queries and reduce vendor lock-in for organizations.

As organizations increasingly rely on graph analytics for applications like fraud detection, social network analysis, supply chain optimization, and knowledge graphs for AI-powered recommendations, the integration of PGQ into SQL should help to simplify the complex data relationships that underly these use cases.

Adoption of Data Mesh and Data Fabric Architectures

Data mesh and data fabric are two competing but complementary frameworks (or concepts) for data management. The goal of both is to enable organizations to better manage and use data, wherever it resides, on-premises and in the cloud. Both focus on the delivery of self-service data and are built on modern technologies (including AI and machine learning).

Data fabric includes an architecture combined with services that enable orchestration and management of data. With a data fabric approach, there is typically a single unified data architecture with an integrated set of technologies and services on top of that architecture. Data may be all over the place, but it is integrated by the technologies and services of data fabric.

These technologies and services exist to define, describe and enrich the data with the goal of ensuring its quality and accessibility. Data fabric provides the capabilities for data management and data governance, as well as self-service to data across the organization. Usually, Data Fabric provides a data catalog, data pipeline management, and other key aspects of data management, all accessible via a unified architecture.

Data mesh grew more out of the data warehousing and data lake world, and it is more of an API-driven approach. It focuses more on people than technology, relying on subject matter experts who administer domains within the data mesh. A domain, in this context, is a group of micro-services that facilitate access to data via APIs.

Experts with a core understanding of the data within their domain are responsible for establishing and ensuring all the ongoing management needs of their data, including data standards, data governance and all things associated with data. The mesh is intended to extend across all data sources, locations and types, delivering access to consumers of the data.
Of course, there are additional concepts behind all this, for example, continuous data quality improvement, populating and maintaining the data catalog, and so on. But there are beneficial qualities to both approaches. And the two can interact with and augment each other.

Adopting a data fabric and/or data mesh approach promotes scalability and agility by treating data as a product and implementing self-serve data platforms. Organizations like Netflix and PayPal have adopted data mesh principles to enhance their data infrastructure.

Evolution of Data Engineering Practices

Data engineering continues to evolve with the adoption of advanced tools and methodologies. Data engineering is the discipline of designing, building and maintaining the infrastructure that enables the collection, storage, processing and analysis of data at scale. It focuses on ensuring data is accessible, reliable and efficiently processed for various applications, including analytics, machine learning and business intelligence. Key elements of a data engineering practice include:

• Reliable data pipelines
• Data quality and governance
• Automation and monitoring
• DataOps practices
• Data security
• Infrastructure management and scalability
• Data modeling
• Documentation

Data engineering is important because it helps to promote more scalable systems for efficiently handling massive amounts of data, improving data-driven decision making, optimizing the performance of real-time applications and improving data accuracy and reliability.

High-performance computing frameworks like Apache Spark and TensorFlow are widely used for processing large datasets. DataOps products are gaining wide acceptance, too, for example Keboola (self-service data management), Castordoc (automated data discovery), and DataBricks (data intelligence platform). Additionally, the use of workflow management systems, such as Apache Airflow, has become common to orchestrate complex data pipelines effectively.

Data Lakehouse Adoption

Another significant trend is the rise of the data lakehouse, which bridges the gap between data warehouses and data lakes, combining their best features into a unified platform. A data lakehouse provides the flexibility and scalability of a data lake, allowing for the storage of structured, semi-structured, and unstructured data at low cost, while incorporating the performance, reliability and transactional consistency of a data warehouse.

This hybrid approach eliminates the need for complex ETL (extract, transform, load) processes that traditionally move data between lakes and warehouses, enabling real-time analytics to drive insights directly on raw data. Data lakehouses can simplify data management, improve query performance with ACID-compliant transactions and reduce data duplication and silos, leading to faster decision-making and cost efficiency.

Popular technologies for data lakehouse implementation include Apache Iceberg, Delta Lake and Apache Hudi.

Summary

So, as you can plainly see, AI and cloud are not the only interesting trends impacting the realm of data and database management. Vector databases, SQL/PGM, data mesh and data fabric, data engineering and data lakehouses are part of the dynamic landscape that is transforming data management and database administration. Indeed, the ever-growing demand for efficient data processing and analysis continues to impact the industry, driving technological advancements to better enable the way we manage data.

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