FAQ
What is the best OLAP database
There is no single best OLAP database — the right choice depends on your latency, scale, and operational constraints:
ClickHouse — best raw query speed on a single node or small cluster; ideal for user-facing analytics, logs, and event data.
Apache Druid / Apache Pinot — best for sub-second queries at high concurrency over streaming-ingested data (ad tech, real-time dashboards).
StarRocks — strong alternative to ClickHouse/Druid for hybrid batch+streaming with a MySQL-compatible interface.
DuckDB — best for local or embedded analytics on files (Parquet, CSV); no server required.
Trino / PrestoDB — best for federated queries across heterogeneous sources (S3, Hive, RDBMS) without moving data.
Apache Spark — best for large-scale batch ETL and ML pipelines where latency is not critical.
Snowflake / BigQuery / Redshift — best when you want fully managed infrastructure with elastic scaling and no ops overhead.
OLAP vs OLTP
| OLAP | OLTP | |
|---|---|---|
| Workload | Complex analytical queries (aggregations, scans) | Simple transactional queries (reads/writes by key) |
| Storage | Columnar | Row-oriented |
| Typical query | SELECT sum(revenue) GROUP BY region |
SELECT * FROM orders WHERE id = 42 |
| Scale | Billions of rows, read-heavy | Millions of rows, write-heavy |
| Examples | ClickHouse, Druid, BigQuery | PostgreSQL, MySQL, DynamoDB |
What is a data lakehouse
A data lakehouse combines the low-cost scalable storage of a data lake (files on S3/GCS/ADLS) with the ACID transactions, schema enforcement, and query performance of a data warehouse. Open table formats like Apache Iceberg, Delta Lake, and Apache Hudi implement the lakehouse pattern on top of Parquet files.
Kafka vs Pulsar
Apache Kafka is the de facto standard with the largest ecosystem, best tooling support, and widest operator knowledge. Apache Pulsar offers multi-tenancy, geo-replication, and a decoupled storage layer (via BookKeeper) out of the box — useful when those features are required from day one. Most teams should start with Kafka.
Open table formats: Iceberg vs Delta Lake vs Hudi
| Iceberg | Delta Lake | Hudi | |
|---|---|---|---|
| Best for | Large-scale analytics, multi-engine | Spark-native workloads, Databricks | CDC / upsert-heavy pipelines |
| Engine support | Spark, Flink, Trino, Hive, Dremio | Spark (best), Flink, Trino | Spark, Flink |
| Upserts | Merge-on-read or copy-on-write | Copy-on-write (merge-on-read in progress) | First-class, optimized |
| Governance | Apache Foundation | Linux Foundation | Apache Foundation |
See the comparison links in the Open table formats section for detailed benchmarks.
ClickHouse vs Apache Druid vs Apache Pinot vs StarRocks
All four are real-time OLAP databases with sub-second query latency. Key differences:
| ClickHouse | Apache Druid | Apache Pinot | StarRocks | |
|---|---|---|---|---|
| Best for | Log/event analytics, ad-hoc queries | Streaming-ingested time-series data | User-facing analytics, high concurrency | Hybrid batch+streaming, flexible schema |
| Architecture | Shared-nothing, columnar MergeTree | Segment-based, time-partitioned | Segment-based, real-time + offline tables | MPP with vectorized execution engine |
| Ingestion | Kafka, files, HTTP push | Kafka, Kinesis, native streaming | Kafka, Kinesis, files | Kafka, files, Flink, Spark |
| Upserts | Limited (ReplacingMergeTree) | No | No (append-only) | Yes (primary key tables) |
| Query concurrency | Medium | High | Very high (user-facing) | High |
| SQL dialect | ClickHouse SQL (mostly ANSI) | Druid SQL (ANSI subset) | PQL + Druid-compatible SQL | MySQL-compatible SQL |
| Written in | C++ | Java | Java | C++ / Java |
| Managed cloud | ClickHouse Cloud | Imply Polaris | StarTree Cloud | CelerData |
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 (Elastic for some features) |
When to pick which
ClickHouse — highest raw throughput for analytics on a single cluster; ideal for logs, metrics, and BI queries.
Apache Druid — best when data arrives via Kafka and you need time-partitioned rollups with guaranteed low latency.
Apache Pinot — best for user-facing products where thousands of end-users hit the DB concurrently (dashboards, embedded analytics).
StarRocks — best when you need upserts, a MySQL-compatible interface, or a single engine for both batch and streaming.
See the Benchmark section for query performance comparisons across engines.