Estuary

Airbyte VS Confluent

Read this detailed 2024 comparison of Airbyte vs Confluent. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.

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Comparison between Airbyte and Confluent
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Introduction

Do you need to load a cloud data warehouse? Synchronize data in real-time across apps or databases? Support real-time analytics? Use generative AI?

This guide is designed to help you compare Airbyte vs Confluent across nearly 40 criteria for these use cases and more, and choose the best option for you based on your current and future needs.

Comparison Matrix

Airbyte logo
Airbyte
Confluent logo
Confluent
Estuary Flow logo
Estuary Flow
Use cases
Database replication (CDC) - sourcesAirbyteMySQL, SQL Server, Postgres, etc. ELT load onlyConfluentDebezium database sources supported, real-timeEstuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch
Replication to ODSAirbyte

batch ELT only

Confluent
Estuary Flow

Requires re-extraction of sources for new destinations

Op. data integrationAirbyte

batch ELT only

Confluent

With Kafka Connect

Estuary Flow

Real-time ETL data flows ready for operational use cases.

Data migrationAirbyte

batch ELT, support for schema change management

Confluent

Accelerator program available to migrate from Kafka to Confluent.

Kafka Connect required for database migrations

Estuary Flow

Great schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Stream processingAirbyte

batch ELT only

Confluent

Flink, kSQL

Estuary Flow

Real-time ETL in Typescript and SQL

Operational AnalyticsAirbyte

Higher latency batch ELT only

Confluent

Through Kafka Connect or other integrations only

Estuary Flow

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

AI PipelinesAirbyte

Pinecone, Weaviate support (ELT only)

Confluent

Kafka support by vector database vendors, custom coding (API calls to LLMS, etc.)

Estuary Flow

Pinecone support for real-time data vectorization.

Transformations can call ChatGPT & other AI APIs.

Connectors
Number of connectorsAirbyte400+Confluent100+Estuary Flow150+ high performance connectors built by Estuary
Streaming connectorsAirbyteBatch CDC only. Batch Kafka, Kinesis (destination only)ConfluentDebezium connectorsEstuary FlowCDC, Kafka, Kinesis, Pub/Sub
Support for 3rd party connectorsAirbyte
Confluent

Many OSS Kafka Connect connectors

Estuary Flow

Support for 500+ Airbyte, Stitch, and Meltano connectors.

Custom SDKAirbyte

Extensive connector development kit

Confluent

OSS Kafka API and Kafka Connect framework

Estuary Flow

SDK for source and destination connector development.

API (for admin)Airbyte
Confluent
Estuary Flow

API and CLI support

Core features
Batch and streamingAirbyteBatch onlyConfluentStreaming-centric; supports incremental batchEstuary FlowBatch and streaming
Delivery guaranteeAirbyteExactly once batch, at least once (batch) CDCConfluentExactly once; strong consistency for streaming dataEstuary FlowExactly once (streaming, batch, mixed)
Load write methodAirbyteAppend only (soft deletes)ConfluentAppend-onlyEstuary FlowAppend only or update in place (soft or hard deletes)
DataOps supportAirbyte

Scheduling, monitoring, reporting, version control, and schema evolution support.

Confluent

CLI, API support for automations

Estuary Flow

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

ELT transformsAirbyte

Only lightweight data-cleaning transformations are supported.

Confluent
Estuary Flow

dbt integration

ETL transformsAirbyte
Confluent

Flink and kSQL

Estuary Flow

Real-time, SQL and Typescript

Schema inference and driftAirbyte

Unreliable source sampling

Confluent

Inference depends on Kafka Connect connector implementation.

Supports schema evolution through Kafka Schema Registry.

Estuary Flow

Real-time schema inference support for all connectors based on source data structures, not just sampling.

Store and replayAirbyte

Only point-to-point replication. No in-flight transformations or storage.

Confluent

Requires re-extract for new destinations.

Tiered storage requires engineering efforts to operate.

Estuary Flow

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Time travelAirbyte
Confluent

Allows time travel with Kafka topics

Estuary Flow

Can restrict the data materialization process to a specific date range.

SnapshotsAirbyte

N/A

Confluent

Supports snapshots

Estuary Flow

Full or incremental

Ease of useAirbyte

Takes time to learn, set up, implement, and maintain (OSS)

Confluent

Requires knowledge of internals to operate optimally

Estuary Flow

streaming transforms may take learning

Deployment options
Deployment optionsAirbyteOpen source, public cloudConfluentOn prem, Private cloud, Public cloudEstuary FlowOpen source, public cloud, private cloud
The abilities
Performance (minimum latency)Airbyte1 hour min for Airbyte Cloud, one source at a time. 5 minutes (CDC and batch connectors) for open source.Confluent< 100 msEstuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.
ReliabilityAirbyteMediumConfluentHighEstuary FlowHigh
ScalabilityAirbyteLow-Medium Lack of source scaleoutConfluentHigh (GB/sec)Estuary FlowHigh 5-10x scalability of others in production
Security
Data Source AuthenticationAirbyteOAuth / HTTPS / SSH / SSL / API TokensConfluentOAuth / HTTPS / SSH / SSL / API TokensEstuary FlowOAuth 2.0 / API Tokens SSH/SSL
EncryptionAirbyteEncryption at rest, in-motionConfluentEncryption at rest, in-motionEstuary FlowEncryption at rest, in-motion
Support
SupportAirbyte

Had limited support (forums only). Added premium support mid-2023.

Confluent

Responsive account team

Estuary Flow

Fast support, engagement, time to resolution, including fixes.

Slack community.

Cost
Vendor costsAirbyte
Confluent
Estuary Flow

2-5x lower than the others, becomes even lower with higher data volumes. Also lowers cost of destinations by doing in place writes efficiently and supporting scheduling

Data engineering costsAirbyte

Requires engineering and operational efforts to provision and maintain OSS version.

Requires dbt for transformations

Confluent

Even with the managed offering, requires engineer efforts to optimally operate.

Estuary Flow

Focus on DevEx, up-to-date docs, and easy-to-use platform.

Admin costsAirbyte

Some admin and troubleshooting, frequent upgrades

Confluent
Estuary Flow

“It just works”

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Estuary Flow

Estuary introductory image

Estuary was founded in 2019. But the core technology, the Gazette open source project, has been evolving for a decade within the Ad Tech space, which is where many other real-time data technologies have started.

Estuary Flow is the only real-time and ETL data pipeline vendor in this comparison. There are some other ETL and real-time vendors in the honorable mention section, but those are not as viable a replacement for Fivetran.

While Estuary Flow is also a great option for batch sources and targets, where it really shines is any combination change data capture (CDC), real-time and batch ETL or ELT, and loading multiple destinations with the same pipeline. Estuary Flow currently is the only vendor to offer a private cloud deployment, which is the combination of a dedicated data plane deployed in a private customer account that is managed as SaaS by a shared control plane. It combines the security and dedicated compute of on prem with the simplicity of SaaS.

CDC works by reading record changes written to the write-ahead log (WAL) that records each record change exactly once as part of each database transaction. It is the easiest, lowest latency, and lowest-load for extracting all changes, including deletes, which otherwise are not captured by default from sources. Unfortunately ELT vendors like Airbyte, Fivetran, Meltano, and Hevo all rely on batch mode for CDC. This puts a load on a CDC source by requiring the write-ahead log to hold onto older data. This is not the intended use of CDC and can put a source in distress, or lead to failures.

Estuary Flow has a unique architecture where it streams and stores streaming or batch data as collections of data, which are transactionally guaranteed to deliver exactly once from each source to the target. With CDC it means any (record) change is immediately captured once for multiple targets or later use. Estuary Flow uses collections for transactional guarantees and for later backfilling, restreaming, transforms, or other compute. The result is the lowest load and latency for any source, and the ability to reuse the same data for multiple real-time or batch targets across analytics, apps, and AI, or for other workloads such as stream processing, or monitoring and alerting.

Estuary Flow also has broad packaged and custom connectivity, making it one of the top ETL tools. It has 150+ native connectors that are built for low latency and/or scale. While may seem low, these are connectors built for low latency and scale. In addition, Estuary is the only vendor to support Airbyte, Meltano, and Stitch connectors as well, which easily adds 500+ more connectors. Getting official support for the connector is a quick “request-and-test” with Estuary to make sure it supports the use case in production. Most of these connectors are not as scalable as Estuary-native, Fivetran, or some ETL connectors, so it’s important to confirm they will work for you. Flow’s support for TypeScript and SQL also enables ETL.

Pros

  • Modern data pipeline: Estuary Flow has the best support for schema drift, evolution, and automation, as well as modern DataOps.
  • Modern transforms: Flow is also both low-code and code-friendly with support for SQL, TypeScript (and Python coming) for ETL, and dbt for ELT.
  • Lowest latency: Several ETL vendors support low latency. But of these Estuary can achieve the lowest, with sub-100ms latency. ELT vendors generally are batch only. 
  • High scale: Unlike most ELT vendors, leading ETL vendors do scale. Estuary is proven to scale with one production pipeline moving 7GB+/sec at sub-second latency.
  • Most efficient: Estuary alone has the fastest and most efficient CDC connectors. It is also the only vendor to enable exactly-and-only-once capture, which puts the least load on a system, especially when you’re supporting multiple destinations including a data warehouse, high performance analytics database, and AI engine or vector database.
  • Deployment options: Of the ETL and ELT vendors, Estuary is currently the only vendor to offer open source, private cloud, and public multi-tenant SaaS.
  • Reliability: Estuary’s exactly-once transactional delivery and durable stream storage makes it very reliable.
  • Ease of use: Estuary is one of the easiest to use tools. Most customers are able to get their first pipelines running in hours and generally improve productivity 4x over time. 
  • Lowest cost: for data at any volume, Estuary is the clear low-cost winner in this evaluation. Rivery is second.
  • Great support: Customers consistently cite great support as one of the reasons for adopting Estuary.

Cons

  • On premises connectors: Estuary has 150+ native connectors and supports 500+ Airbyte, Meltano, and Stitch open source connectors. But if you need on premises app or data warehouse connectivity make sure you have all the connectivity you need.
  • Graphical ETL: Estuary has been more focused on SQL and dbt than graphical transformations. While it does infer data types and convert between sources and targets, there is currently no graphical transformation UI.

Pricing

Of the various ELT and ETL vendors, Estuary is the lowest total cost option. Estuary only charges $0.50 per GB of data moved from each source or to each target, and $100 per connector per month. So you can expect to pay a minimum of a few thousand per year. But it quickly becomes the lowest cost pricing. Rivery, the next lowest cost option, is the only other vendor that publishes pricing of 1 RPU per 100MB, which is $7.50 to $12.50 per GB depending on the plan you choose. Estuary becomes the lowest cost option by the time you reach the 10s of GB/month. By the time you reach 1TB a month Estuary is 10x lower cost than the rest.

Airbyte

Introduction image - Airbyte

Airbyte was founded in 2020 as an open-source data integration company, and launched its cloud service in 2022.

Airbyte started as a Singer-based ELT tool, but has since changed their protocol and connectors to be different. Airbyte has kept Singer compatibility so that it can support Singer taps as needed. Airbyte has also kept many of the same principles, including being batch-based. This is eventually where Airbyte’s limitations come from as well.

If you go by pricing calculators and customers, Airbyte is the second-lowest-cost vendor in the evaluation after Estuary.

Pros

  • Ease of use: Airbyte is an easy-to-use (harder to operate), modern ELT product.
  • Open source: Airbyte is open source, which means you can self-host. In addition, open source has fewer limits, such as being able to run more frequent batch intervals.
  • Low cost: Airbyte Cloud is one of the lowest-cost options for batch ETL.
  • Widely used: Even though Airbyte is only 4 years old, it is widely used. Most of the customers use the open-source version. The official 1.0 product launch, the big milestone for any open-source project, was September, 2024.

Cons

  • Only 50+ managed connectors: While Airbyte lists 300+ connectors, only 50+ of these are connectors actively developed by Airbyte. The rest are open source connectors listed as Marketplace connectors for Airbyte Cloud. Make sure you evaluate the connectors based on your needs.
  • High Latency: While Airbyte has CDC source connectors mostly built on Debezium (except for a new Postgres CDC connector), and also has Kafka and Kinesis source connectors, everything is loaded in intervals of 5 minutes or more with the open source version. Airbyte Cloud is much worse. It only supports 1+ hour intervals and one source connector at a time. There is no staging or storage, so if something goes wrong with either source or target the pipeline stops. 
    Also, Airbyte is pulling from source connectors in batch intervals. When using CDC, this can put a load on the source databases. In addition, because all Airbyte CDC connectors (other than the new Postgres connector) use Debezium, it is not exactly-once, but at-least-once guaranteed delivery. Latency is also made worse with batch ELT because you need to wait for loads and transforms in the target data warehouse.
  • Reliability: There are some issues with reliability you will need to manage. Most CDC sources, because they’re built on Debezium, only ensure at-least-once delivery. It means you will need to deduplicate (dedup) at the target. Airbyte does have both incremental and deduped modes you can use though. You just need to remember to turn them on. Also, Debezium does put less of a load on a source because it uses Kafka. This does make it less of a load on a source than Fivetran CDC. A bigger reliability issue is failure of under-sized workers. There is no scale-out option. Once a worker gets overloaded you will have reliability issues (see scalability.) There is also no staging or storage within an Airbyte pipeline to preserve state. If you need the data again, you’ll have to re-extract from the source.
  • Scalability: Airbyte is not known for scalability. It has scalability issues that may not make it suitable for your larger workloads. For example, each airbyte operation of extracting from a source or loading into a target is done by one worker. The source worker is generally the most important component, and its most important component is memory. The source worker will read up to 10,000 records into memory, which could lead to GBs of RAM. By default only 25% of each instance’s memory is allocated to the worker container, which you have little control over in Airbyte Cloud.
    Airbyte is working on scalability. The new PostgreSQL CDC connector does have improved performance. Its latest benchmark as of the time of this writing produced 9MB/sec throughput, higher than Fivetran’s (non HVR) connector. But this is still only 0.5TB a day or so depending on how loads vary throughout the day.
  • ELT only: Airbyte cloud supports dbt cloud. This is different from dbt core used by Fivetran. If you have implemented on dbt core in a way that makes it portable (which you should) the move can be relatively straightforward. But if you want to implement transforms outside of the data warehouse, Airbyte does not support that.
  • DataOps: Airbyte provides UI-based replication designed for ease of use. It does not give you an “as code” mode that helps with automating end-to-end pipelines, adding tests, or managing schema evolution. But there is Octavia which acts as a CLI for Airbyte.

Pricing

Airbyte starts at $10 per GB of data moved from a database, and $15 per million rows of data moved via an API (or custom source.) There are volume-based discounts. You do pay for backfills as well. While this is solely volume-based, Estuary becomes less expensive for 10s of GB per month.

Confluent

confluent-logo.png

Confluent Cloud is a fully managed service built on top of Apache Kafka, the distributed streaming platform. Confluent Cloud abstracts away some of Kafka’s operational complexity, making it easier for organizations to leverage real-time data streaming. Confluent also offers tools such as ksqlDB and Schema Registry to simplify stream processing and schema management.

Pros

  • Managed Service: Confluent Cloud eliminates the need for operational management, including Kafka cluster setup, scaling, and maintenance.
  • Wide Ecosystem: Confluent integrates seamlessly with a variety of cloud services, databases, and messaging systems.
  • Enterprise Features: Confluent Cloud offers additional enterprise features, including Confluent Schema Registry, ksqlDB for stream processing, and connectors.
  • Scalability: As a managed Kafka offering, Confluent scales elastically to accommodate high throughput with little manual intervention.

Cons

  • Cost Complexity: While Confluent Cloud provides a simplified pricing model, usage-based billing can quickly become expensive as data volumes increase, especially for organizations that have high-throughput streaming needs.
  • Vendor Lock-In: Relying on Confluent Cloud can lead to vendor lock-in, as migrating to a different Kafka provider or setting up self-hosted Kafka clusters could require significant effort.
  • Operational Limits: Although much of the Kafka infrastructure is managed, some complex Kafka configurations and optimizations may not be accessible or customizable in Confluent Cloud.

Pricing

Confluent Cloud's pricing is usage-based, with separate charges for data ingress, egress, storage, and additional services like the Schema Registry and ksqlDB. Data streaming is priced at $0.12 per GB of data ingested or $0.14 per GB of data egressed. Additional costs apply for partitions, connectors, and the use of advanced features. For small to mid-sized use cases, the cost is manageable, but at scale, expenses can rise quickly.

How to choose the best option

For the most part, if you are interested in a cloud option, and the connectivity options exist, you may choose to evaluate Estuary.

Modern data pipeline: Estuary has the broadest support for schema evolution and modern DataOps.

Lowest latency: If low latency matters, Estuary will be the best option, especially at scale.

Highest data engineering productivity: Estuary is among the easiest to use, on par with the best ELT vendors. But it also has delivered up to 5x greater productivity than the alternatives.

Connectivity: If you're more concerned about cloud services, Estuary or another modern ELT vendor may be your best option. If you need more on-premises connectivity, you might consider more traditional ETL vendors.

Lowest cost: Estuary is the clear low-cost winner for medium and larger deployments.

Streaming support: Estuary has a modern approach to CDC that is built for reliability and scale, and great Kafka support as well. It's real-time CDC is arguably the best of all the options here. Some ETL vendors like Informatica and Talend also have real-time CDC. ELT-only vendors only support batch CDC.

Ultimately the best approach for evaluating your options is to identify your future and current needs for connectivity, key data integration features, and performance, scalability, reliability, and security needs, and use this information to a good short-term and long-term solution for you.

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