Analytical databases are no longer confined to OLAP cubes and MDX. Today, they encompass a broader ecosystem of tools that enable fast, complex, and multidimensional querying across diverse data structures. This post traces my first-hand account of the evolution of analytical databases from traditional multidimensional OLAP to more modern paradigms, including graph and vector databases. I provide my views on selecting the right engine for the analytical job based on structure, purpose, and performance.
OLAP: Where Analytical Databases Took Off
Relational databases are excellent at managing transactional data. Still, their structure can be limiting when users need to slice, dice, and drill across multiple dimensions quickly. That’s where Online Analytical Processing (OLAP) emerged.
OLAP databases are built for fast, interactive analysis across hierarchies. They typically store pre-aggregated data to enable high-speed retrieval and pivoting, often visualized as multidimensional cubes. These engines optimize performance for business users, querying sales by region or profits by product category over time.
While the acronym OLAP stuck, Pendse’s FASMI test (Fast Analysis of Shared Multidimensional Information) better captures what analytical databases aim to deliver: fast, secure, multidimensional access to summarized data.
One of the defining aspects of OLAP was the emergence of MDX (Multidimensional Expressions), a language designed specifically for navigating cube-based data. MDX introduced more intuitive ways to access hierarchies, dimensions, and sets, making it easier for analysts to query complex structures.
While MDX is powerful, it is limited to the OLAP world. The rise of in-memory engines, big data frameworks, and semantic layers has since diversified the languages and interfaces used for analytical querying.
Different OLAP storage strategies have emerged:
- MOLAP (Multidimensional OLAP): Stores data in proprietary multidimensional formats. Extremely fast, ideal for high-performance dashboards.
- ROLAP (Relational OLAP): Stores data in relational tables and relies on SQL. Useful for real-time or near-real-time use cases.
- HOLAP (Hybrid OLAP): Combines MOLAP and ROLAP. Stores aggregated data multidimensionally and atomic data relationally.
Today, many analytics platforms take a hybrid approach, using caching, in-memory computation, and distributed processing to reduce the need for rigid storage decisions upfront.
Graph Databases: From Hierarchies to Networks
While OLAP databases excel at hierarchies, they struggle with networks. Enter the graph database.
Graph databases model relationships as first-class citizens, storing data as nodes and edges. This is a better fit for:
- Fraud detection
- Customer segmentation
- Knowledge graphs
- Supply chain relationships
Where OLAP helps you analyze “sales by product,” a graph database lets you ask, “Who influences whom?”
They are optimized for traversals, pathfinding, and centrality. These types of queries are painful in traditional SQL and nearly impossible in MDX.
Columnar Databases: High-Speed Querying for Wide Datasets
Columnar databases store data by columns rather than rows, making them extremely efficient for analytical queries that touch only a subset of columns.
Popular examples include:
- Amazon Redshift
- Google BigQuery
- ClickHouse
- Vertica
These databases shine in scenarios with massive datasets where aggregations and scans need to run quickly without reading irrelevant data. They underpin many cloud data warehouses and BI tools today.
Time Series Databases: Patterning the Pulse of Data
Time series databases are specialized for storing and analyzing time-stamped data, such as system metrics, sensor logs, or financial market ticks.
Top choices include:
- InfluxDB
- TimescaleDB
- OpenTSDB
They excel at time-based aggregations, retention policies, and windowed calculations—making them a go-to for monitoring, forecasting, and anomaly detection.
In-Memory Analytical Engines: Speed Without the Disk
In-memory databases keep data resident in RAM, enabling ultra-low latency for complex analytical queries.
Leaders in this space include:
- SAP HANA
- Apache Druid
- SingleStore (formerly MemSQL)
They are best for real-time dashboards, operational intelligence, and use cases requiring simultaneous write-read access at speed.
Search-First Databases: When Text Meets Metrics
Originally designed for full-text search, these platforms have evolved to support analytical functions as well.
Common tools include:
- Elasticsearch
- Apache Solr
They combine inverted indexing with aggregation capabilities, making them ideal for log analytics, observability platforms, and user-facing search experiences with built-in analytics.
Vector Databases: When Context Is Everything
Another modern frontier is the vector database, designed to store and search high-dimensional embeddings from machine learning models.
These are essential for:
- Semantic search
- Recommendation engines
- Natural language processing (NLP)
- AI-powered assistants
Vector databases let you search for “similar meaning” rather than exact matches, using vector distance (like cosine similarity) instead of equality filters. They are analytical in nature but live in an entirely different mathematical space than cubes or graphs.
How to Choose the Right Analytical Engine
Choosing an analytical database is no longer just MOLAP vs ROLAP. Instead, ask:
- Is the data hierarchical, relational, networked, or semantic?
- Do I need speed, explainability, or flexibility?
- Is my query language SQL, MDX, Cypher, or something else entirely?
You may find that a composite architecture works best:
- OLAP cubes for financial reporting
- Graph for operational intelligence
- Vector search for enterprise search
- Columnar engines for dashboards
- Time series for forecasting
Final Thoughts
Analytical databases are definitely evolving. What began as hypercubes for accountants and quants has blossomed into a multi-paradigm field of smart engines tuned for context, complexity, and compute.
Your job isn’t to pick a winner. It’s to understand the nature of your questions and align the database architecture accordingly.
Disclaimer: All views are my own and do not reflect those of my employer. No confidential information is disclosed here.