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Considering Apache Druid?

  • ▸ Time-series OLAP at scale — observability or ad-tech workload with billions of events / day and the roll-up storage savings are the reason you're looking past ClickHouse and Pinot.
  • ▸ Kafka indexing topology — supervisor tasks aren't auto-balancing the way you expected and segment compaction is becoming the operational bottleneck.
  • ▸ Imply Polaris vs self-managed — the TCO model needs real numbers, and the team is debating whether the operational savings justify the managed-service premium.

JusDB Apache Druid specialists design, deploy, and operate real-time OLAP at scale. See Druid consulting →

Real-Time OLAP, Time-Series-First

Apache Druid Platform Services

In short: Apache Druid is an open-source, real-time OLAP datastore built for time-series-heavy analytical workloads. It separates ingestion, storage, and query into independently scaling tiers, uses roll-up pre-aggregation and time-partitioned segments, and ingests streaming data from Kafka for sub-second queries over event data.

Kafka indexing supervisors, roll-up pre-aggregation, time-partitioned segments, and the Coordinator + Overlord + Historical + Broker + Router topology — purpose-built for time-series OLAP at billions-of-events scale.

Apache Druid service paths

Druid Consulting

Kafka indexing topology, roll-up strategy, segment granularity, Coordinator / Historical tier sizing, Imply Polaris vs self-managed economics — written advisory deliverables.

Druid vs Pinot

Side-by-side comparison — roll-up vs star-tree, Kafka indexing vs LLRT, multi-tenancy models, Imply Polaris vs StarTree Cloud, when each one wins.

What we build with Apache Druid

From cluster design to production query tuning — end-to-end Druid expertise.

Real-Time Kafka Ingestion
Kafka indexing service auto-scales supervisor tasks across MiddleManagers; second-level freshness from topic to query-ready segments.
Roll-Up Pre-Aggregation
Destructive aggregation at ingestion time — 10-100x storage reduction for time-series workloads where raw rows aren't needed downstream.
Time-Partitioned Segments
Segments natively partitioned by time interval — query pruning, retention policies, and compaction all operate on time-aligned units.
Tier-Decoupled Architecture
Coordinator + Overlord + Historical + Broker + Router tiers scale independently — match infrastructure to actual workload shape.
Deep Storage + Hot Tiers
Deep storage on S3/HDFS/GCS plus hot Historical-node caching — predictable retention with cost-aware tiering.
Imply Polaris Operations
Managed-Druid SaaS with Pivot visualisation included — fast time-to-value when operational burden is the dominant cost.

Apache Druid — common questions

Ready to evaluate Druid?

Book a 30-minute scoping call. We'll review your workload shape, the roll-up strategy, and the managed-vs-self-managed decision before any statement of work.