Benchmarks

PostgreSQL → Apache Iceberg Connector Benchmark

(OLake vs. Popular Data-Movement Tools)

1. Speed Comparison – Full-Load Performance

Tool	Rows Synced	Throughput (rows / sec)	Relative to OLake
OLake	4.01 B	46,262 RPS	–
Fivetran	4.01 B	46,395 RPS	Parity (≤1 % faster)
Debezium (memiiso)	1.28 B	14,839 RPS	3.1 × slower
Estuary	0.34 B	3,982 RPS	11.6 × slower¹
Airbyte Cloud	12.7 M	457 RPS	101 × slower

¹ Estuary ran the same 24-hour window but processed a ~10× smaller dataset, so its throughput looks even lower when normalized.

info

1. The time elapsed for all the tools was 24 hours, but OLake, Debezium, Estuary and Fivetran were able to process the entire dataset in that time. Airbyte failed with a sync after 7.5 hours, so we only have throughput for the first part of the test.

Key takeaway: OLake sustains the same top-tier bulk-load speed as Fivetran while outpacing every other open-source option by 3-to-100×.

2. Speed Comparison – Change-Data-Capture (CDC)

Tool	CDC Window	Throughput (rows / sec)	Relative to OLake
OLake	22.5 min	36 982 RPS	–
Fivetran	31 min	26,910 RPS	1.4 × slower
Debezium (memiiso)	60 min	13,808 RPS	2.7 × slower
Estuary	4.5 h	3,085 RPS	12 × slower
Airbyte Cloud	23 h	585 RPS	63 × slower

info

The rows synced in the CDC test were the same 50 million changes that OLake processed in 22.5 minutes. The other tools were tested on the same dataset, but they had different CDC windows (timings).

Key takeaway: For incremental workloads OLake leads the pack, moving 50 million PostgreSQL changes into Iceberg 40 % faster than Fivetran and 10-60× faster than other OSS connectors.

3. Cost Comparison (Vendor List Prices)

Tool	Scenario	Spend (USD)	Rows Synced
OLake	Full Load / CDC	Cost of a Standard D64ls v5 (64 vcpus, 128 GiB memory) running for 24 hours < $75	4.01 B / 50M
Fivetran	Full Load	$ 0 (free full sync)	4.01 B
Estuary	Full Load	$ 1,668	0.34 B
Airbyte Cloud	Full Load	$ 5,560	12.7 M
Fivetran	CDC	$ 2, 375.80	50 M
Estuary	CDC	$ 17.63	50 M
Airbyte Cloud	CDC	$ 148.95	50 M

• OLake is open-source and can be deployed on your own Kubernetes cluster or cloud VMs; you pay only for the compute and storage you provision.

Footnotes

1. Airbyte: Please find attached data for the Airbyte issues we faced during the test - here.

Dataset and Table Schemas

Please refer to this GitHub repository for the dataset we used to conduct these benchmarks.

trips table (used for Full Load)

CREATE TABLE trips (
   trip_id                 BIGINT,
   vendor_id               VARCHAR,
   pickup_datetime         TIMESTAMP,
   dropoff_datetime        TIMESTAMP,
   store_and_fwd_flag      VARCHAR(1),
   rate_code_id            BIGINT,
   pickup_longitude        DOUBLE,
   pickup_latitude         DOUBLE,
   dropoff_longitude       DOUBLE,
   dropoff_latitude        DOUBLE,
   passenger_count         BIGINT,
   trip_distance           DOUBLE,
   fare_amount             DOUBLE,
   extra                   DOUBLE,
   mta_tax                 DOUBLE,
   tip_amount              DOUBLE,
   tolls_amount            DOUBLE,
   ehail_fee               DOUBLE,
   improvement_surcharge   DOUBLE,
   total_amount            DOUBLE,
   payment_type            VARCHAR,
   trip_type               VARCHAR,
   pickup                  VARCHAR,
   dropoff                 VARCHAR,
   cab_type                VARCHAR,
   precipitation           BIGINT,
   snow_depth              BIGINT,
   snowfall                BIGINT,
   max_temperature         BIGINT,
   min_temperature         BIGINT,
   average_wind_speed      BIGINT,
   pickup_nyct2010_gid     BIGINT,
   pickup_ctlabel          VARCHAR,
   pickup_borocode         BIGINT,
   pickup_boroname         VARCHAR,
   pickup_ct2010           VARCHAR,
   pickup_boroct2010       BIGINT,
   pickup_cdeligibil       VARCHAR(1),
   pickup_ntacode          VARCHAR,
   pickup_ntaname          VARCHAR,
   pickup_puma             VARCHAR,
   dropoff_nyct2010_gid    BIGINT,
   dropoff_ctlabel         VARCHAR,
   dropoff_borocode        BIGINT,
   dropoff_boroname        VARCHAR,
   dropoff_ct2010          VARCHAR,
   dropoff_boroct2010      BIGINT,
   dropoff_cdeligibil      VARCHAR(1),
   dropoff_ntacode         VARCHAR,
   dropoff_ntaname         VARCHAR,
   dropoff_puma            VARCHAR
);

• Column count: 51
• Type mix: 26 × VARCHAR (labels & categorical codes), 18 × numeric (DOUBLE or BIGINT), 5 × TIMESTAMP, 2 × single-byte flags.
• Why so wide? Weather columns (precipitation → wind) and geography lookup columns (borough/census codes) are pre-joined to make BI queries single-table.

fhv_trips table (used for CDC)

#	Column	Type	Note
1	dispatching_base_num	VARCHAR(6)	TLC base licence
2	pickup_datetime	TIMESTAMP	ISO 8601
3	dropoff_datetime	TIMESTAMP	"
4	PUlocationID	INT	TLC zone ID
5	DOlocationID	INT	"
6	SR_Flag	SMALLINT NULL	1 = shared-ride
7	affiliated_base_number	VARCHAR(6)	vehicle’s own base

• Column count: 7
• All types present: timestamp, integer, varchar, boolean-as-tinyint.

Average row size & storage footprint

Table	Rows	Raw CSV size	Size ÷ rows
trips	≈ 3.96 B	≈ 518 GB un-compressed	≈ 505 B/row
fhv_trips	≈ 50 M	≈ 2.8 GB un-compressed	≈ 56 B/row

note

We used AWS Glue as Iceberg catalog and AWS S3 as the storage layer on the destination side for this benchmarks.

What These Numbers Mean for You

• Peak throughput without lock-in: OLake matches or beats proprietary SaaS speeds while letting you keep data and infrastructure in your own account.
• Superior CDC latency: Faster change propagation means fresher downstream analytics and near-real-time feature generation for ML.
• Predictable TCO: Because OLake is self-hosted, you scale resources up or down to hit your desired SLA at the lowest cloud cost—no opaque credit systems.
• Resource profile: In these tests OLake used 57.6 GB RAM (roughly one Standard D64ls v5 (64 vcpus, 128 GiB memory) VM) for both full-load and CDC runs; adjust sizing linearly with your workload.

Bottom line: If you need to land terabytes of PostgreSQL data into Apache Iceberg quickly—and keep it continually up-to-date—OLake delivers enterprise-grade speed without the enterprise-grade bill.