Apache Pinot Consulting
Build user-facing analytics with sub-second OLAP query latency at billions of rows. Real-time Kafka ingestion, StarTree indexes, and hybrid batch+streaming tables — designed and operated by experts.
What We Build with Apache Pinot
From cluster design to production query tuning — end-to-end Pinot expertise.
StarTree indexes, inverted indexes, and sorted columns deliver <1s query latency on billions of rows — even at thousands of concurrent queries.
LLRT consumers ingest from Kafka partitions with seconds-level freshness. We configure partition alignment, flush thresholds, and offset checkpointing.
Combine offline batch segments from S3/HDFS with live real-time Kafka segments in a single hybrid table — queries span both transparently.
Serve dashboard and analytics API queries with p99 latency under 1 second at thousands of QPS — purpose-built for end-user-facing workloads.
Schema design for time-series OLAP, dimension/metric column selection, StarTree pre-aggregation, inverted indexes, and range indexes for fast filters.
Controller HA, broker load balancing, server tier scaling, Minion task management, and Prometheus/Grafana dashboards for segment health and query latency.
Apache Pinot Use Cases We Deliver
Real-world analytics workloads we implement with Pinot in production.
User-Facing Dashboards
Power internal and customer-facing analytics dashboards with sub-second query response — even under thousands of concurrent users.
Real-Time Metrics API
Serve aggregated metrics APIs (totals, percentiles, top-N) with seconds-level data freshness from Kafka event streams.
Event Analytics at Scale
Analyze clickstream, app events, and log data at billions-of-rows scale with time-series partitioning and retention policies.
Ad Tech & Attribution
High-frequency impression, click, and conversion analytics with real-time ingestion and aggregation for campaign reporting.
Financial Reporting
Near-real-time trade, transaction, and risk reporting with ACID-like consistency across batch and streaming ingestion paths.
IoT & Time-Series Analytics
Ingest sensor and telemetry data from Kafka at high volume and serve aggregated time-window queries at millisecond latency.
Apache Pinot Cluster Architecture
We design and deploy every component of the Pinot cluster for production reliability.
Controller
Cluster metadata, schema/table management, segment assignment
Broker
Query routing, scatter-gather, result aggregation
Server
Segment storage and query execution (offline + real-time)
Minion
Background tasks: segment merge, purge, conversion
ZooKeeper
Cluster coordination and leader election
Deep Storage
Segment persistence on S3, GCS, HDFS, or Azure Blob
Pinot Index Types We Configure
The right index combination is critical for sub-second query performance at scale.
Apache Pinot vs Other OLAP Databases
When Pinot is the right choice over ClickHouse, Druid, or BigQuery.
| Capability | Apache Pinot | ClickHouse | Apache Druid |
|---|---|---|---|
| Query Latency P99 | <1s at 1000s QPS | 1–10s under load | <1s (similar) |
| Real-Time Ingestion | ✅ Native Kafka LLRT | ⚠️ Via Kafka engine | ✅ Native Kafka |
| Pre-Aggregation | ✅ StarTree index | ✅ Materialized views | ✅ Rollup |
| SQL Support | ✅ Full SQL | ✅ Full SQL | ⚠️ Limited SQL |
| User-Facing Scale | ✅ Purpose-built | ⚠️ Ad-hoc focus | ✅ Good |
| Operational Complexity | Medium | Low | High |
Our Apache Pinot Delivery Process
From workload analysis to production-grade cluster with ongoing tuning.
Workload Assessment
Analyze query patterns, data volumes, ingestion rates, concurrency requirements, and latency SLAs.
Schema Design
Design Pinot schema: time column, dimension columns, metric columns, and multi-value fields for optimal query performance.
Cluster Architecture
Size Controller, Broker, and Server tiers. Design real-time vs offline server split, ZooKeeper quorum, and deep storage.
Index Configuration
Configure StarTree indexes, inverted indexes, sorted columns, and range indexes based on query access patterns.
Ingestion Setup
Configure Kafka LLRT consumers (real-time) and batch segment generation jobs (offline) with retention and compaction policies.
Monitoring & Tuning
Deploy Prometheus metrics, Grafana dashboards for query latency/throughput, segment health alerts, and ongoing query tuning.
Apache Pinot FAQs
Build Sub-Second Analytics with Apache Pinot
Get a free Pinot assessment — we'll review your query patterns, data volumes, and latency requirements, then design the optimal cluster architecture and index strategy.