Have you ever wondered about the different technology powers how everything from banking apps to social networks store and organize their data? Welcome to the world of database systems!
In this comprehensive guide, we will explore 6 database models that each serve distinct needs:
- Relational databases – The stalwart workhorses powering most business systems today
- NoSQL databases – Flexible and scalable foundations for modern web/mobile apps
- Graph databases – Specialized for mapping complex relationship networks
- Columnar databases – Optimized for high-performance analytics
- Object-oriented databases – For working natively with complex data types
- Time series databases – Purpose-built for massive streams of time-indexed data
Let‘s delve into what makes each tick!
Relational Databases
Relational databases have provided the foundation for managing structured business data since they were first conceived in 1970 by IBM researcher Edgar Codd in his influential paper on the relational model for database management.
| Tables | Advantages | Disadvantages |
|---|---|---|
| ACID compliant transactions | Ensures data integrity | Limits horizontal scalability |
| SQL querying | Flexible data analysis | Rigid predefined schemas |
| Referential integrity | Robust data consistency | Joins impact performance |
| Enterprise maturity | Stable, supported | Perceived complexity |
Although innovative Big Data technologies have captured mindshare recently, stalwart relational databases from Oracle, Microsoft, MySQL and PostgeSQL still power most business applications today – a testament to the enduring usefulness of normalization, foreign keys, referential integrity and ACID compliance principles for managing core enterprise data.
Relational databases enable robust data governance – but strict table schemas inhibits their ability to cost-effectively scale across commodity servers to handle web-size workloads.
NoSQL Databases
Seeking to overcome such limitations to support modern cloud-first, web-scale applications, a new breed of non-relational “NoSQL” databases emerged in the mid-2000s as viable alternatives.
NoSQL databases embrace a BASE rather than ACID consistency model that relaxes atomicity guarantees while still maintaining reliable “eventual consistency” at global scale. By tapping partition tolerance and replication across key-value stores, column-oriented databases, document databases or graph databases, NoSQL databases trade absolute transactional integrity for massive horizontal scalability and developer flexibility.
Leading examples include:
- MongoDB – The popular document-oriented database platform
- Apache Cassandra – A distributed wide column store database
- Redis – An ultra-fast in-memory key-value store
- Neo4J – A native graph database management system
As web and mobile ecosystems increasingly generate semi-structured data from apps, IoT devices and other endpoints – the versatility of NoSQL databases will only continue gaining traction.
Graph Databases
Graph databases take a completely different approach from more traditional tabular formats – representing data as networks of entities ("vertices") connected by relationships ("edges") instead of rigid rows and columns.
This powerful graph-oriented model traces its roots to graph theory – the mathematical study of networks and relationships. By encoding metadata directly into elements and connections within the graph itself, associations and commonalities that might be impractical to model in a relational system can suddenly become explicitly accessible.
Queries against graph databases (like Facebook‘s GraphQL) often involve traversing relationships between vertices – an operation with native graph algorithms will always outperform equivalent relational JOINs against.
Leading native graph database platforms include Neo4j, TigerGraph and Amazon Neptune. Look for graph-powered use cases to continue rising across recommendation engines, fraud detection and master data management initiatives in the years ahead.
Columnar Databases
Seeking to simultaneously deliver versatility and high analytical performance led to another important database innovation – the column-oriented database architecture.
Unlike row-oriented relational database tables, columnar databases organize data sequentially by column rather than row on the physical storage layer. This counterintuitive design optimizes query performance by:
- Minimizing I/O – Analytics workloads frequently target specific columns
- Reducing storage through compression – Column data contains more repetitive values
- Accelerating vectorized query processing – Column methods apply across all values
Various database platforms now integrate columnar technologies – from analytic relational OLAP databases to high-performance massively parallel processing (MPP) data warehouses like Google BigQuery, Snowflake and Amazon Redshift that leverage columnar storage and clustering to enable stunning petabyte-scale analytics.
Object-Oriented Databases
Object-oriented databases take yet another approach – persistently storing data in native application object formats to avoid painful object-relational mapping.
Whereas relational databases require flattening rich application objects like a Customer class with nested Address properties down to thin rows and columns, object-oriented databases allow directly persisting objects relations like:
Customer -> Address
Order -> Customer
Order -> OrderItemBy natively working with application objects, OODBMS removes the “impedance mismatch” that can bog down performance and complicate software development.
Leading object-oriented databases include Versant and Objectivity/DB – capable of directly persisting rich data models for multimedia content, geospatial data and more at extreme scale.
Time Series Databases
Driving today‘s real-time analytics are time series databases purpose-built to ingest, store and process massive streams of continuously generated timestamped data from sources like application metrics, IoT sensors, and financial trading systems.
Time series data shares common characteristics:
- Time indexed – Enriches context for analysis
- Immutable – Values encode what happened at a specific point in time
- Sequential – Values continuously appended as time advances
Special time series databases like InfluxDB and TimescaleDB optimize storage, analysis and retention policies for these time-structured workloads through:
- Columnar storage: Optimized compression + query acceleration
- Data rollups: Summarize across time windows
- Downsampling: Strategically reduce resolution of historical data
- Continuous processing: Stream analytics integrated with storage
Purpose-built time series databases now provide the backend for real-time analytics across industries from IT/OT monitoring to algorithmic trading.
Looking to the Future
As data environments grow more complex, the innovations explored in this guide aim to empower software developers and architects to embrace the right data foundations. Just as understanding relational principles has long served application builders – familiarity with these modern database categories will prove invaluable for creatively harnessing data in the years ahead!
What database questions do you have? Ping me below!
