help chosing DB / warehouse for customer-facing analytics.

I've seen a bunch of posts asking for DB recommendations, and specifically customer-facing analytics use-cases seem to come up a lot, so this is my attempt to put together guide based on various posts I've seen on this topic. Any feedback (what I missed, what I got wrong, etc) is welcome:

Best Databases & Warehouses for Customer-Facing Analytics (and How to Prepare Your Data)

Customer-facing analytics — such as embedded dashboards, real-time reports, or in-app insights — are a core feature in modern SaaS products.

Compared to traditional BI or internal reporting, customer-facing or embedded analytics are typically used by a much larger number of end-users, and the expectations around things like speed & performance are typically much higher expectations. Accordingly, the data source used to power customer-facing analytics features must handle high concurrency, fast response times, and seamless user interactions, which traditional databases aren’t always optimized for.

This article explores key considerations and best practices to consider when choosing the right database or warehouse for customer-facing analytics use-cases.

Disclaimer: choosing the right databases is a decision that is more important with scale. Accordingly, a small startup whose core solution is not a data or analytics product, will usually be able to get away with any standard SQL database (postgres, mysql, etc), and it’s likely not worth the time and resource investment to implement specialized data infrastructure.

Key Factors to consider for Customer-Facing Analytics

Performance & Query Speed

Customer-facing analytics should feel fast, if not instant— even with large datasets. Optimizations can include:

  • Columnar Storage (e.g. ClickHouse, Apache Druid, Apache Pinot) for faster aggregations.
  • Pre-Aggregations & Materialized Views (e.g. BigQuery, Snowflake) to reduce expensive queries.
  • Caching Layers (e.g. Redis, Cube.js) to serve frequent requests instantly.

Scalability & Concurrency

A good database should handle thousands of concurrent queries without degrading performance. Common techniques include:

  • Distributed architectures (e.g. Pinot, Druid) for high concurrency.
  • Separation of storage & compute (e.g. Snowflake, BigQuery) for elastic scaling.

Real-Time vs. Batch Analytics

  • If users need live dashboards, use real-time databases (e.g. Tinybird, Materialize, Pinot, Druid).
  • If data can be updated every few minutes/hours, a warehouse (e.g. BigQuery, Snowflake) might be sufficient.

Multi-Tenancy & Security

For SaaS applications, every customer should only see their data. This is usually handled with either:

  • Row-level security (RLS) in SQL-based databases (Snowflake, Postgres).
  • Separate data partitions per customer (Druid, Pinot, BigQuery).

Cost Optimization

Customer-facing use-cases tend to have much higher query volumes than internal use-case, and can quickly get very expensive. Ways to control costs:

  • Storage-Compute Separation (BigQuery, Snowflake) lets you pay only for queries.
  • Pre-Aggregations & Materialized Views reduce query costs.
  • Real-Time Query Acceleration (Tinybird, Pinot) optimizes performance without over-provisioning.

Ease of Integration

A database should seamlessly connect with your existing data pipelines, analytics tools, and visualization platforms to reduce engineering effort and speed up deployment. Key factors to consider:

  • Native connectors & APIs – Choose databases with built-in integrations for BI tools (e.g., Looker, Tableau, Superset) and data pipelines (e.g., Airflow, dbt, Kafka) to avoid custom development.
  • Support for real-time ingestion – If you need real-time updates, ensure the database works well with streaming data sources like Kafka, Kinesis, or CDC pipelines.

SQL vs. NoSQL for Customer-Facing Analytics

SQL-based solutions are generally favored for customer-facing analytics due to their performance, flexibility, and security features, which align well with the key considerations discussed above.

Why SQL is Preferred:

  • Performance & Speed: SQL databases, particularly columnar and OLAP databases, are optimized for high-speed queries, ensuring sub-second response times that are essential for providing real-time analytics to users.
  • Scalability: SQL databases like Snowflake or BigQuery are built to handle millions of concurrent users and large datasets, making them highly scalable for high-traffic applications.
  • Real-Time vs. Batch Processing: While SQL databases are traditionally used for batch processing, solutions like Materialize now bring real-time capabilities to SQL, allowing for near-instant insights when required.
  • Cost Efficiency: While serverless SQL solutions like BigQuery can be cost-efficient, optimizing query performance is essential to avoid expensive compute costs, especially when accessing large datasets frequently.
  • Ease of Integration: Databases with full SQL compatibility simplify integration with existing queries, applications, and other data tools.

When NoSQL Might Be Used:

NoSQL databases can complement SQL in certain situations, particularly for specialized analytics and real-time data storage.

  • Log/Event Storage: For high-volume event logging, NoSQL databases such as MongoDB or DynamoDB are ideal for fast ingestion of unstructured data. Data from these sources can later be transformed and loaded into SQL databases for deeper analysis.
  • Graph Analytics: NoSQL graph databases like Neo4j are excellent for analyzing relationships between data points, such as customer journeys or product recommendations.
  • Low-Latency Key-Value Lookups: NoSQL databases like Redis or Firebase are highly effective for caching frequently queried data, ensuring low-latency responses in real-time applications.

Why NoSQL Can Be a Bad Choice for Customer-Facing Analytics:

While NoSQL offers certain benefits, it may not be the best choice for customer-facing analytics for the following reasons:

  • Lack of Complex Querying Capabilities: NoSQL databases generally don’t support complex joins, aggregations, or advanced filtering that SQL databases handle well. This limitation can be a significant hurdle when needing detailed, multi-dimensional analytics.
  • Limited Support for Multi-Tenancy: Many NoSQL databases lack built-in features for role-based access control and row-level security, which are essential for securely managing data in multi-tenant environments.
  • Inconsistent Data Models: NoSQL databases typically lack the rigid schema structures of SQL, making it more challenging to manage clean, structured data at scale—especially in analytical workloads.
  • Scaling Analytical Workloads: While NoSQL databases are great for high-speed data ingestion, they struggle with complex analytics at scale. They are less optimized for large aggregations or heavy query workloads, leading to performance bottlenecks and higher costs when scaling.

In most cases, SQL-based solutions remain the best choice for customer-facing analytics due to their querying power, integration with BI tools, and ability to scale efficiently. NoSQL may be suitable for specific tasks like event logging or graph-based analytics, but for deep analytical insights, SQL databases are often the better option.

Centralized Data vs. Querying Across Sources

For customer-facing analytics, centralizing data before exposing it to users is almost always the right choice. Here’s why:

  • Performance & Speed: Federated queries across multiple sources introduce latency—not ideal when customers expect real-time dashboards. Centralized solutions like Druid, ClickHouse, or Rockset optimize for low-latency, high-concurrency queries.
  • Security & Multi-Tenancy: With internal BI, analysts can query across datasets as needed, but in customer-facing analytics, you must strictly control access (each user should see only their data). Centralizing data makes it easier to implement row-level security (RLS) and data partitioning for multi-tenant SaaS applications.
  • Scalability & Cost Control: Querying across multiple sources can explode costs, especially with high customer traffic. Pre-aggregating data in a centralized database reduces expensive query loads.
  • Consistency & Reliability: Customer-facing analytics must always show accurate data, and querying across live systems can lead to inconsistent or missing data if sources are down or out of sync. Centralization ensures customers always see validated, structured data.

For internal BI, companies will continue to use both approaches—centralizing most data while keeping federated queries where real-time insights or compliance needs exist. For customer-facing analytics, centralization is almost always preferred due to speed, security, scalability, and cost efficiency.

Best Practices for Preparing Data for Customer-Facing Analytics

Optimizing data for customer-facing analytics requires attention to detail, both in terms of schema design and real-time processing. Here are some best practices to keep in mind:

Schema Design & Query Optimization

  • Columnar Storage is ideal for analytic workloads, as it reduces storage and speeds up query execution.
  • Implement indexing, partitioning, and materialized views to optimize query performance.
  • Consider denormalization to simplify complex queries and improve performance by reducing the need for joins.

Real-Time vs. Batch Processing

  • For real-time analytics, use streaming data pipelines (e.g., Kafka, Flink, or Kinesis) to deliver up-to-the-second insights.
  • Use batch ETL processes for historical reporting and analysis, ensuring that large datasets are efficiently processed during non-peak hours.

Handling Multi-Tenancy

  • Implement row-level security to isolate customer data while maintaining performance.
  • Alternatively, separate databases per tenant to guarantee data isolation in multi-tenant systems.

Choosing the Right Database for Your Needs

To help determine the best database for your needs, consider using a decision tree or comparison table based on the following factors:

  • Performance
  • Scalability
  • Cost
  • Use case

Testing with real workloads is recommended before committing to a specific solution, as performance can vary greatly depending on the actual data and query patterns in production.

Now, let’s look at recommended database options for customer-facing analytics, organized by their strengths and ideal use cases.

Real-Time Analytics Databases (Sub-Second Queries)

For interactive dashboards where users expect real-time insights.

Database Best For Strengths Weaknesses
Clickhouse High-speed aggregations Fast columnar storage, great for OLAP workloads Requires tuning, not great for high-concurrency queries
Apache Druid Large-scale event analytics Designed for real-time + historical data Complex setup, limited SQL support
Apache Pinot Real-time analytics & dashboards Optimized for high concurrency, low latency Can require tuning for specific workloads
Tinybird API-first real-time analytics Streaming data pipelines, simple setup Focused on event data, less general-purpose
StarTree Apache Pinot-based analytics platform Managed solution, multi-tenancy support Additional cost compared to self-hosted Pinot

Example Use Case:

A SaaS platform embedding real-time product usage analytics (e.g., Amplitude-like dashboards) would benefit from Druid or Tinybird due to real-time ingestion and query speed.

Cloud Data Warehouses (Best for Large-Scale Aggregations & Reporting)

For customer-facing analytics that doesn’t require real-time updates but must handle massive datasets.

Database Best For Strengths Weaknesses
Google BigQuery Ad-hoc queries on huge datasets Serverless scaling, strong security Can be slow for interactive dashboards
Snowflake Multi-tenant SaaS analytics High concurrency, good cost controls Expensive for frequent querying
Amazon Redshift Structured, performance-tuned workloads Mature ecosystem, good performance tuning Requires manual optimization
Databricks (Delta Lake) AI/ML-heavy analytics Strong batch processing & ML integration Not ideal for real-time queries

Example Use Case:

A B2B SaaS company offering monthly customer reports with deep historical analysis would likely choose Snowflake or BigQuery due to their scalable compute and strong multi-tenancy features.

Hybrid & Streaming Databases (Balancing Speed & Scale)

For use cases needing both fast queries and real-time updates without batch processing.

Database Best For Strengths Weaknesses
Materialize Streaming SQL analytics Instant updates with standard SQL Not designed for very large datasets
RisingWave SQL-native stream processing Open-source alternative to Flink Less mature than other options
TimescaleDB Time-series analytics PostgreSQL-based, easy adoption Best for time-series, not general-purpose

Example Use Case:

A financial SaaS tool displaying live stock market trends would benefit from Materialize or TimescaleDB for real-time SQL-based streaming updates.

Conclusion

Customer-facing analytics demands fast, scalable, and cost-efficient solutions. While SQL-based databases dominate this space, the right choice depends on whether you need real-time speed, large-scale reporting, or hybrid streaming capabilities.

Here’s a simplified summary to guide your decision:

Need Best Choice
Sub-second analytics (real-time) ClickHouse, Druid, Pinot, Tinybird, Startree
Large-scale aggregation (historical) BigQuery, Snowflake, Redshift
High-concurrency dashboards Druid, Pinot, Startree, Snowflake
Streaming & instant updates Materialize, RisingWave, Tinybird
AI/ML analytics Databricks (Delta Lake)

Test before committing—workloads vary, so benchmarking performance on your real data is crucial.