Oracle to Databricks Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from Oracle to Databricks. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the Oracle database for Fivetran to replicate to Databricks. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.

Postgres to Snowflake Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from Postgrest to Snowflake. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the Postgres database for Fivetran to replicate to Snowflake. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.

SQL Server to Snowflake Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from SQL Server to Snowflake. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the SQL Server database for Fivetran to replicate to Snowflake. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.

MySQL to Snowflake Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from MySQL to Snowflake. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the MySQL database for Fivetran to replicate to Snowflake. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.

Oracle to Snowflake Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from Oracle to Snowflake. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the Oracle database for Fivetran to replicate to Snowflake. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.

Oracle to BigQuery Benchmark

Consistent high volume throughput across historical syncs

Fivetran efficiently handles the historical sync of large data volumes with a throughput greater than 500 GB/hour. For the best performance and to ensure a database can release additional transactional data while an import is running, Fivetran breaks large tables into consumable chunks, reducing the duration of our transaction during a historical sync. The data below shows Fivetran consistently replicates the data at high throughput levels, saving our users time and ensuring their data is readily available for downstream workflows. 

To understand the impacts of a historical data sync. Fivetran runs a performance benchmark roughly once per week to see the throughput performance of replicating data from Oracle to BigQuery. The throughput values are calculated with the following formula: Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

Throughput (GB/hr) = Data Volume (GB) / Historical Sync Time (hr)

High-performance pipelines for high-volume workloads

With even some of the largest volumes of change data that customers replicate (greater than 16,000 transactions per second), Fivetran keeps up with incremental syncs in near real-time. Fivetran uses change data capture replication to incrementally update large volumes of data. The benchmark measures the latency and throughput of incremental syncs to highlight how performant Fivetran is even under intense load – 250+ GB/hr throughput and 15min or less latency. Many enterprises desire a 30 minute to 1 hour replication SLA and Fivetran successfully meets these needs when replicating transactional data into data warehouses or data lakes.

This workload represents the largest real-world databases with HammerDB. We see similar fluctuations in the benchmarking data volumes of a real-world database. Understand our benchmark tests further.

Over the course of 2 hours, 16,000+ new records are created per second on the Oracle database for Fivetran to replicate to BigQuery. Given the high volume of change data, Fivetran measures the latency and throughput to ensure all changed data is written to the destination in a timely manner. The above graph shows latency percentiles at P50, P80, and P95 confidence levels for full transparency ensuring we are quickly and efficiently replicating all changes regardless of the data volumes.