Right engine for each workload
Transactional systems need consistency and low-latency writes; analytics needs columnar storage and fast scans. We separate the two so reporting never slows down checkout.
OLTP & OLAP Data Platforms
Keep transactions fast and analytics fresh. We design OLTP databases for consistency and speed, and OLAP platforms that answer questions over billions of rows in seconds.
Transactions that never wait. Analytics that never lag.
OLTP and OLAP, designed as one data platform.
Who it's for
Teams usually come to us in one of these situations.
Reports slowing production
Analytics queries competing with customer transactions on one database.
Real-time product analytics
Products that need dashboards updated in seconds, not overnight.
Growing event data
Logs, events and telemetry measured in billions of rows.
What we build
OLTP design
Schemas, constraints, indexing and isolation levels for fast, safe transactions.
Columnar analytics
ClickHouse clusters for sub-second dashboards over billions of rows.
Change data capture
Stream changes from production into analytics without heavy batch jobs.
Event streaming
Kafka pipelines with schemas, ordering and replay built in.
Data modeling
Star schemas, materialized views and rollups shaped around real questions.
Lifecycle & cost
Partitioning, TTLs and tiered storage to control growth.
Engines and tools
OLTP
- PostgreSQL
- MySQL
- SQL Server
- MongoDB
- Aurora
OLAP
- ClickHouse
- BigQuery
- Amazon Redshift
- Materialized views
Streaming & CDC
- Apache Kafka
- Debezium
- Redis Streams
Fresh numbers, straight from production
Dashboards update within seconds of a transaction, while the production database keeps serving customers at full speed.
- Change data capture from OLTP
- Streaming through Kafka
- Columnar storage in ClickHouse
- Sub-second dashboards
Common situations we solve
Real problems we are asked to fix, what we do about them and the result to expect.
The situation
Dashboards are run straight against the production database
We stream changes into a dedicated analytical store with change data capture, so dashboards stop competing with transactions.
Fast analytics with a calmer production database
The situation
Event tables have grown to billions of rows
We design partitioning, compression, rollups and retention in ClickHouse or a similar engine so queries stay fast as data grows.
Sub-second queries on very large tables
The situation
Data in analytics is a day behind
Nightly batch jobs are replaced with streaming pipelines and monitored lag, so reports reflect what happened minutes ago.
Fresh data for real-time decisions
The situation
Every team has its own version of the numbers
We model shared metrics and dimensions once, document them and expose them to BI tools from a single source.
One trusted set of numbers
What you get
Fast transactions
OLTP workloads isolated from heavy reporting.
Sub-second analytics
Columnar stores that aggregate billions of rows quickly.
Fresh data
Change data capture keeps analytics seconds behind production.
Principles we design by
Real-time without the fragility
Change data capture and streaming keep analytical copies seconds behind production, with schemas, retries and monitoring designed in from the start.
Built to grow
Partitioning, retention, tiered storage and materialized views keep query times flat as data volumes grow.
Schema and model design
We design transactional schemas for integrity and concurrency, and analytical models such as star schemas, wide tables and materialized views around the questions your teams actually ask.
Operating the platform
Replication, backups, monitoring, upgrades and cost control are part of the design, so the platform stays reliable after launch and can be run by your team or by ours.
Frequently asked questions
What is the difference between OLTP and OLAP?
OLTP systems handle many small, concurrent transactions such as orders and payments. OLAP systems handle large analytical queries across historical data, such as dashboards and reports.
Why ClickHouse for analytics?
ClickHouse is a columnar database built for fast aggregation over very large datasets. We also work with other engines where they suit the workload better.
Can analytics run on our existing database?
Sometimes, with read replicas and careful indexing. When reporting starts to affect production, a dedicated analytical store is usually the safer path.
Can we keep using PostgreSQL or MySQL for analytics?
For moderate volumes, read replicas and good indexing can be enough. We help you decide when a dedicated analytical engine is worth it.
Do you work with cloud data warehouses?
Yes. Alongside ClickHouse we work with BigQuery, Amazon Redshift and other engines where they fit the workload and budget.
How do you keep data consistent between systems?
Change data capture with ordering, retries and reconciliation checks, plus monitoring on lag and row counts.
Talk to us about OLTP & OLAP Data Platforms.
Request a scoped cloud assessment, or start with a free DevOps and SRE consultation. Talk to us about services, AI solutions or enterprise development.