Airbyte VS Talend
Read this detailed 2024 comparison of Airbyte vs Talend. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.
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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 Talend 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
Use cases | |||
---|---|---|---|
Database replication (CDC) - sources | AirbyteMySQL, SQL Server, Postgres, etc. ELT load only | TalendDB2 (i Series), MariaDB, MySQL, Oracle, Postgres, Progress, SQL Server, Sybase, (Custom) | Estuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch |
Replication to ODS | Airbyte batch ELT only | Talend | Estuary Flow Requires re-extraction of sources for new destinations |
Op. data integration | Airbyte batch ELT only | Talend (but limited real-time scale) | Estuary Flow Real-time ETL data flows ready for operational use cases. |
Data migration | Airbyte batch ELT, support for schema change management | Talend | Estuary Flow Great schema inference and evolution support. Support for most relational databases. Continuous replication reliability. |
Stream processing | Airbyte batch ELT only | Talend | Estuary Flow Real-time ETL in Typescript and SQL |
Operational Analytics | Airbyte Higher latency batch ELT only | Talend | Estuary Flow Integration with real-time analytics tools. Real-time transformations in Typescript and SQL. Kafka compatibility. |
AI Pipelines | Airbyte Pinecone, Weaviate support (ELT only) | Talend (OpenAI component) | Estuary Flow Pinecone support for real-time data vectorization. Transformations can call ChatGPT & other AI APIs. |
Connectors | |||
Number of connectors | Airbyte400+ | Talend50+ managed connectors 1000 connections (API based) | Estuary Flow150+ high performance connectors built by Estuary |
Streaming connectors | AirbyteBatch CDC only. Batch Kafka, Kinesis (destination only) | TalendYes. CDC, Kafka, Kinesis, Azure Storage Queue, PubSub, RabbitMQ, AMQP, JMS, MQTT | Estuary FlowCDC, Kafka, Kinesis, Pub/Sub |
Support for 3rd party connectors | Airbyte | Talend | Estuary Flow Support for 500+ Airbyte, Stitch, and Meltano connectors. |
Custom SDK | Airbyte Extensive connector development kit | Talend Talend Component Kit | Estuary Flow SDK for source and destination connector development. |
API (for admin) | Airbyte | Talend | Estuary Flow API and CLI support |
Core features | |||
Batch and streaming | AirbyteBatch only | TalendStreaming and batch support | Estuary FlowBatch and streaming |
Delivery guarantee | AirbyteExactly once batch, at least once (batch) CDC | TalendExactly once. | Estuary FlowExactly once (streaming, batch, mixed) |
Load write method | AirbyteAppend only (soft deletes) | TalendSoft and hard deletes, append and update in place (with work) | Estuary FlowAppend only or update in place (soft or hard deletes) |
DataOps support | Airbyte Scheduling, monitoring, reporting, version control, and schema evolution support. | Talend (CLI, API) | Estuary Flow API and CLI support for operations. Declarative definitions for version control and CI/CD pipelines. |
ELT transforms | Airbyte Only lightweight data-cleaning transformations are supported. | Talend Dbt only. | Estuary Flow dbt integration |
ETL transforms | Airbyte | Talend tMaps transformations. SQL function. Works with dbt | Estuary Flow Real-time, SQL and Typescript |
Schema inference and drift | Airbyte Unreliable source sampling | Talend Not without coding, but can be done. | Estuary Flow Real-time schema inference support for all connectors based on source data structures, not just sampling. |
Store and replay | Airbyte Only point-to-point replication. No in-flight transformations or storage. | Talend | Estuary Flow Can backfill multiple targets and times without requiring new extract. User-supplied cheap, scalable object storage. |
Time travel | Airbyte | Talend | Estuary Flow Can restrict the data materialization process to a specific date range. |
Snapshots | Airbyte N/A | Talend N/A | Estuary Flow Full or incremental |
Ease of use | Airbyte Takes time to learn, set up, implement, and maintain (OSS) | Talend | Estuary Flow streaming transforms may take learning |
Deployment options | |||
Deployment options | AirbyteOpen source, public cloud | TalendOn premises (self-hosted), private cloud, public cloud | Estuary 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. | TalendSub-second loading at low volumes. Requires bulk mode to scale. | Estuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline. |
Reliability | AirbyteMedium | TalendHigh | Estuary FlowHigh |
Scalability | AirbyteLow-Medium Lack of source scaleout | TalendHigh but requires bulk-mode loading | Estuary FlowHigh 5-10x scalability of others in production |
Security | |||
Data Source Authentication | AirbyteOAuth / HTTPS / SSH / SSL / API Tokens | TalendOAuth / HTTPS / SSH / SSL / API Tokens | Estuary FlowOAuth 2.0 / API Tokens SSH/SSL |
Encryption | AirbyteEncryption at rest, in-motion | TalendEncryption at rest, in-motion | Estuary FlowEncryption at rest, in-motion |
Support | |||
Support | Airbyte Had limited support (forums only). Added premium support mid-2023. | Talend | Estuary Flow Fast support, engagement, time to resolution, including fixes. Slack community. |
Cost | |||
Vendor costs | Airbyte | Talend | 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 costs | Airbyte Requires engineering and operational efforts to provision and maintain OSS version. Requires dbt for transformations | Talend Steep learning curve and requires work to implement features like upserts. | Estuary Flow Focus on DevEx, up-to-date docs, and easy-to-use platform. |
Admin costs | Airbyte Some admin and troubleshooting, frequent upgrades | Talend | Estuary Flow “It just works” |
Estuary Flow
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
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.
Talend
Talend, also now part of Qlik, has two main products—Talend Data Fabric and Stitch, which is ELT. Talend Data Fabric is a data integration platform that, like Informatica, is broader than ETL. It also includes data quality and data governance capabilities.
Talend also had an open-source solution, Talend Open Studio, that could help you kickstart your first data integration and ETL projects. It has been discontinued by Qlik in 2024.
You could use Talend Open Studio for data processes that require lightweight workflows. For the majority of enterprise data pipelines should consider Data Fabric.
Pros
- ETL platform: Data Fabric has rich transformation, data mapping, and data quality features that help with building data pipelines.
- Real-time and batch: Real-time support includes streaming CDC. While it’s mature technology, it is still real-time.
- Strong monitoring and analytics: Like Informatica, Talend has built up good visibility for operations.
Cons
- Learning curve: Talend has an older UI that takes time to learn, just like some other ETL tools. Building transforms can take time.
- Limited Open Studio features: while Open Studio is free, it’s also limited. Other open source options are less limited in their capabilities.
- Limited connectors: Talend claims 1000+ connectors. But it lists 50 or so databases, file systems, applications, messaging, and other systems it supports. The rest are Talend Cloud Connectors, which you create as reusable objects.
- High costs: there is no pricing listed, but it costs more than most pay-as-you-go tools, as well as Stitch.
Pricing
Outside of open source Open Studio and Singer, pricing quotes are available upon request. This should give you a sense that it’s going to be higher cost than Estuary, Rivery, and several of the pay-as-you-go ELT vendors, maybe with the exception of Fivetran.
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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