Estuary

Debezium + Kafka VS Talend

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

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Comparison between Debezium + Kafka and Talend
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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 Debezium + Kafka 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

Debezium + Kafka logo
Debezium + Kafka
Talend logo
Talend
Estuary Flow logo
Estuary Flow
Use cases
Database replication (CDC) - sourcesDebezium + KafkaCommon databases supported Real-time replication (sub-second to seconds)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 ODSDebezium + Kafka

real-time only

Talend
Estuary Flow

Requires re-extraction of sources for new destinations

Op. data integrationDebezium + Kafka

no integration features

Talend

(but limited real-time scale)

Estuary Flow

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

Data migrationDebezium + Kafka

Well suited for ongoing replication.

Handles schema changes.

Talend
Estuary Flow

Great schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Stream processingDebezium + Kafka

kSQL, SMTs

Talend
Estuary Flow

Real-time ETL in Typescript and SQL

Operational AnalyticsDebezium + Kafka
Talend
Estuary Flow

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

AI PipelinesDebezium + Kafka

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

Talend

(OpenAI component)

Estuary Flow

Pinecone support for real-time data vectorization.

Transformations can call ChatGPT & other AI APIs.

Connectors
Number of connectorsDebezium + Kafka100+ Kafka sources and destinations (via Confluent, vendors)Talend50+ managed connectors 1000 connections (API based)Estuary Flow150+ high performance connectors built by Estuary
Streaming connectorsDebezium + KafkaMost common OLTP databases supported for CDC Community-maintained connectorsTalendYes. CDC, Kafka, Kinesis, Azure Storage Queue, PubSub, RabbitMQ, AMQP, JMS, MQTTEstuary FlowCDC, Kafka, Kinesis, Pub/Sub
Support for 3rd party connectorsDebezium + Kafka

Kafka ecosystem

Talend
Estuary Flow

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

Custom SDKDebezium + Kafka

Kafka Connect

Talend

Talend Component Kit

Estuary Flow

SDK for source and destination connector development.

API (for admin)Debezium + Kafka

Kafka API

Talend
Estuary Flow

API and CLI support

Core features
Batch and streamingDebezium + KafkaStreaming-centric (subscribers and pick up in intervals)TalendStreaming and batch supportEstuary FlowBatch and streaming
Delivery guaranteeDebezium + KafkaAt least once for most destinationsTalendExactly once.Estuary FlowExactly once (streaming, batch, mixed)
Load write methodDebezium + KafkaYes (identical data by topic)TalendSoft and hard deletes, append and update in place (with work)Estuary FlowAppend only or update in place (soft or hard deletes)
DataOps supportDebezium + Kafka

CLI, API

Talend

(CLI, API)

Estuary Flow

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

ELT transformsDebezium + Kafka
Talend

Dbt only.

Estuary Flow

dbt integration

ETL transformsDebezium + Kafka

Minimal via SMTs

Talend

tMaps transformations. SQL function. Works with dbt

Estuary Flow

Real-time, SQL and Typescript

Schema inference and driftDebezium + Kafka

Support for message-level schema evolution (Kafka Schema Registry) with limits by source and destination

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 replayDebezium + Kafka

requires re-extract for each destination

Talend
Estuary Flow

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Time travelDebezium + Kafka
Talend
Estuary Flow

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

SnapshotsDebezium + Kafka

Supports incremental and full snapshots

Talend

N/A

Estuary Flow

Full or incremental

Ease of useDebezium + Kafka

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

Talend
Estuary Flow

streaming transforms may take learning

Deployment options
Deployment optionsDebezium + KafkaOpen source, Confluent Cloud (Public)TalendOn premises (self-hosted), private cloud, public cloudEstuary FlowOpen source, public cloud, private cloud
The abilities
Performance (minimum latency)Debezium + Kafka< 100 msTalendSub-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.
ReliabilityDebezium + KafkaHigh (Kafka); Medium (Debezium)TalendHighEstuary FlowHigh
ScalabilityDebezium + KafkaHigh (GB/sec)TalendHigh but requires bulk-mode loadingEstuary FlowHigh 5-10x scalability of others in production
Security
Data Source AuthenticationDebezium + KafkaSSL/SSHTalendOAuth / HTTPS / SSH / SSL / API TokensEstuary FlowOAuth 2.0 / API Tokens SSH/SSL
EncryptionDebezium + KafkaEncryption in-motion (Kafka for topic security)TalendEncryption at rest, in-motionEstuary FlowEncryption at rest, in-motion
Support
SupportDebezium + Kafka

Low (Debezium community)

Talend
Estuary Flow

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

Slack community.

Cost
Vendor costsDebezium + Kafka

Low for OSS

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 costsDebezium + Kafka

OSS infrastructure

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 costsDebezium + Kafka

OSS infrastructure

Talend
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.

Debezium + Kafka

Debezium started within Red Hat following the release of Kafka, and Kafka Connect. It was inspired in part by Martin Kleppmann’s presentations on CDC and turning the database inside out.

Debezium is the open-source option for general-purpose replication, and it does many things right for replication, from scaling to incremental snapshots (make sure you use DDD-3 and not whole snapshotting.) If you are committed to open source, have the specialized resources needed, and need to build your own pipeline infrastructure for scalability or other reasons, Debezium is a great choice.

Otherwise, think twice about using Debezium because it will be a big investment in specialized data engineering and admin resources. While the core CDC connectors are solid, you will need to build the rest of your data pipeline including:

  • The many non-CDC source connectors you will eventually need. You can leverage all the Kafka Connect-based connectors to over 100 different sources and destinations. But they have a long list of limits (see confluent docs on limits).
  • Data schema management and evolution - while the Kafka Schema Registry does support message-level schema evolution, the number of limitations on destinations and the translation from sources to message makes this much harder to manage.
  • Kafka does not save your data indefinitely. There is no replay/backfilling service that manages previous snapshots and allows you to reuse them, or do time travel. You will need to build those services.
  • Backfilling and CDC happens on the same topic. So if you need to redo a snapshot, all destinations will get it. If you want to change this behavior you need to have separate source connectors and topics for each destination, which adds costs and source loads.
  • You will need to maintain to main your Kafka cluster(s), which is no small task.

If you are already invested in Kafka as your backbone, it does make good sense to evaluate Debezium.

Pros

  • Real-Time CDC: Kafka + Debezium captures database changes in real-time.
  • Flexibility: Debezium supports multiple databases and allows for flexible configuration options for filtering and handling database changes.
  • Scalable Data Streams: Kafka’s distributed architecture ensures that even high-velocity data streams are processed efficiently and can scale horizontally.

Cons

  • Complex Setup: Managing a self-hosted Kafka cluster alongside Debezium requires significant operational effort, including scaling, monitoring, and ensuring fault tolerance.
  • At-Least-Once Delivery: Debezium guarantees at-least-once delivery, meaning duplicate records may need to be handled at the consumer level. This adds complexity to building exactly-once data pipelines.
  • High Infrastructure Costs: Running a Kafka cluster and Debezium connectors, especially at scale, can require substantial infrastructure resources, making it more costly than other CDC alternatives.

Pricing

Kafka itself is open-source and free to use, but the costs associated with deploying and maintaining a Kafka cluster can vary depending on cloud or on-premise infrastructure. Managed Kafka services such as Confluent Cloud can provide a more streamlined, albeit pricier, solution. Debezium is open-source, but operational costs come from the Kafka infrastructure and any associated storage, processing, and egress costs.

Talend

Talend introductory image

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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