Last updated:9/3/2025|... min read

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Benchmarks

Use the tabs below to view detailed benchmarks per connector. Each tab has a unique URL you can copy/share.

Postgres
MongoDB
MySQL

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

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.

Dataset and Table Schemas

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

note

We first performed a full-load sync of empty dummy tables. Afterwards, we inserted the top 25 million records from both trips and fhv_trips into these tables and ran a CDC sync.

`trips` table

CREATE TABLE trips (
	id bigserial NOT NULL,
	cab_type_id int4 NULL,
	vendor_id int4 NULL,
	pickup_datetime timestamp NULL,
	dropoff_datetime timestamp NULL,
	store_and_fwd_flag bool NULL,
	rate_code_id int4 NULL,
	pickup_longitude numeric NULL,
	pickup_latitude numeric NULL,
	dropoff_longitude numeric NULL,
	dropoff_latitude numeric NULL,
	passenger_count int4 NULL,
	trip_distance numeric NULL,
	fare_amount numeric NULL,
	extra numeric NULL,
	mta_tax numeric NULL,
	tip_amount numeric NULL,
	tolls_amount numeric NULL,
	ehail_fee numeric NULL,
	improvement_surcharge numeric NULL,
	congestion_surcharge numeric NULL,
	airport_fee numeric NULL,
	total_amount numeric NULL,
	payment_type int4 NULL,
	trip_type int4 NULL,
	pickup_nyct2010_gid int4 NULL,
	dropoff_nyct2010_gid int4 NULL,
	pickup_location_id int4 NULL,
	dropoff_location_id int4 NULL,
	CONSTRAINT trips_pkey PRIMARY KEY (id)
);

`fhv_trips` table

CREATE TABLE fhv_trips (
	id bigserial NOT NULL,
	hvfhs_license_num text NULL,
	dispatching_base_num text NULL,
	originating_base_num text NULL,
	request_datetime timestamp NULL,
	on_scene_datetime timestamp NULL,
	pickup_datetime timestamp NULL,
	dropoff_datetime timestamp NULL,
	pickup_location_id int4 NULL,
	dropoff_location_id int4 NULL,
	trip_miles numeric NULL,
	trip_time numeric NULL,
	base_passenger_fare numeric NULL,
	tolls numeric NULL,
	black_car_fund numeric NULL,
	sales_tax numeric NULL,
	congestion_surcharge numeric NULL,
	airport_fee numeric NULL,
	tips numeric NULL,
	driver_pay numeric NULL,
	shared_request bool NULL,
	shared_match bool NULL,
	access_a_ride bool NULL,
	wav_request bool NULL,
	wav_match bool NULL,
	legacy_shared_ride int4 NULL,
	affiliated_base_num text NULL,
	CONSTRAINT fhv_trips_pkey PRIMARY KEY (id)
);

note

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

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.

MongoDB Benchmarks

In the fast-paced world of data management, every second counts. When it comes to syncing massive datasets from MongoDB into a data warehouse or even a lakehouse, you need a tool that is not just reliable but also blazing fast and cost-effective.

This is where OLake comes into picture.

Speed Comparison: Full Load Performance

For a collection of 230 million rows (664.81GB) from Twitter data*, here's how OLake compares to leading competitors:

Tool	Full Load Time	Performance
OLake	46 mins	X times
Fivetran	4 hours 39 mins (279 mins)	6x slower
Airbyte	16 hours (960 mins)	20x slower
Debezium (Embedded)	11.65 hours (699 mins)	15x slower

OLake is up to 20x faster than competitors like Airbyte, significantly reducing the time and resources required for full data syncs.

No more waiting for hours or even days for your data to be loaded into your warehouse. 46 minutes is all you need to process 230 million rows with OLake.

note

We used Debezium server version v2.6.2 for carrying out these benchmarks.

CDC Sync Performance

Testing with 1 Million Rows (2.88GB, 999450 records) across 10 collections showed how efficiently we do it:

Tool	CDC Sync Time	Records per Second (r/s)	Performance
OLake	28.3 sec	35,694 r/s	X times
Fivetran	3 min 10 sec	5,260 r/s	6.7x slower
Airbyte	12 min 44 sec	1,308 r/s	27.3x slower
Debezium (Embedded)	12 min 44 sec	1,308 r/s	27.3x slower

OLake processes 1 million records in just 28.3 seconds, achieving 35,694 records per second (r/s), which is 6.7x faster than Fivetran and a 27.3x faster than Airbyte and Debezium (Embedded).

Cost Comparison (Considering 230M first full load & 50M CDC rows per month as of 30th Sep)

When it comes to pricing, OLake is not just faster; it's cost-efficient. Here's the breakdown based on a typical use case involving a 230 million-row first full sync and 50 million CDC rows per month:

Tool	First Full Sync Cost	CDC Sync Cost (Monthly)	Total Monthly Cost	Info	Factor
OLake	10-50 USD	250 USD	300 USD	Heavier instance required only for 1-2 hours	X times
Fivetran	Free	6000 USD	6000 USD	15 min sync frequency, pricing for 50M rows & standard plan	20x costlier
Airbyte	6000 USD	1408 USD	7400 USD	First Load - 1.15 TB data synced	24.6x costlier
Debezium MSK connect + AWS MSK serverless	-	-	100 USD + 800 USD = 900 USD	1.2 TB total data (CDC & first full sync)	3x costlier

OLake offers a total cost of just 300 USD per month, compared to 6000 USD for Fivetran and a staggering 7400 USD for Airbyte.

That's 20x more cost-effective than Fivetran and 24x cheaper than Airbyte.

Why Choose OLake?

Speed: OLake is up to 20x faster than competitors for full data syncs and 27.3x faster for CDC syncs.
Stability: No failed syncs, no downtime. OLake delivers a reliable experience even for the largest datasets.
Cost-Effective: At 300 USD per month, OLake is 20x cheaper than Fivetran and 24x more affordable than Airbyte, with 3x savings against Debezium MSK connect + AWS MSK serverless setup without sacrificing performance.

Testing Infrastructure

The impressive performance metrics of OLake were achieved using a robust infrastructure setup, which included:

Virtual Machine: Standard_D64as_v5
CPU: 64 vCPUs
Memory: 256 GiB RAM
Storage: 250 GB of shared storage

MongoDB Setup:

3 Nodes running in a replica set configuration:
- 1 Primary Node (Master) that handles all write operations.
- 2 Secondary Nodes (Replicas) that replicate data from the primary node.

*Twitter dataset - Archive.org (This JSON dataset has 4 levels of complex nesting).

Your success with OLake is our priority. Don’t hesitate to contact us if you need any help or further clarification!

Benchmarks

PostgreSQL → Apache Iceberg Connector Benchmark

1. Speed Comparison – Full-Load Performance

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

3. Cost Comparison (Vendor List Prices)

Dataset and Table Schemas

`trips` table

`fhv_trips` table

MongoDB Benchmarks

Speed Comparison: Full Load Performance

CDC Sync Performance

Cost Comparison (Considering 230M first full load & 50M CDC rows per month as of 30th Sep)

Why Choose OLake?

Testing Infrastructure

MongoDB Setup:

MySQL Benchmarks

💡 Join the OLake Community!

Join our growing community

GitHub

Slack

Twitter

LinkedIn

YouTube

PostgreSQL → Apache Iceberg Connector Benchmark​

1. Speed Comparison – Full-Load Performance​

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

3. Cost Comparison (Vendor List Prices)​

Dataset and Table Schemas​

trips table​

fhv_trips table​

MongoDB Benchmarks​

Speed Comparison: Full Load Performance​

CDC Sync Performance​

Cost Comparison (Considering 230M first full load & 50M CDC rows per month as of 30th Sep)​

Why Choose OLake?​

Testing Infrastructure​

MongoDB Setup:​

MySQL Benchmarks​

💡 Join the OLake Community!

Join our growing community

GitHub

Slack

Twitter

LinkedIn

YouTube

PostgreSQL → Apache Iceberg Connector Benchmark

1. Speed Comparison – Full-Load Performance

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

3. Cost Comparison (Vendor List Prices)

Dataset and Table Schemas

`trips` table

`fhv_trips` table

MongoDB Benchmarks

Speed Comparison: Full Load Performance

CDC Sync Performance

Cost Comparison (Considering 230M first full load & 50M CDC rows per month as of 30th Sep)

Why Choose OLake?

Testing Infrastructure

MongoDB Setup:

MySQL Benchmarks