Debezium + Kafka VS Estuary Flow
Read this detailed 2024 comparison of Debezium + Kafka vs Estuary Flow. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.
View all comparisonsIntroduction
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 Estuary Flow 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 | Debezium + KafkaCommon databases supported Real-time replication (sub-second to seconds) | Estuary FlowNative CDC MySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce Many-to-many ETL and ELT | |
Replication to ODS | Debezium + Kafka (real-time only) | Estuary Flow | |
Historical Analytics | Debezium + KafkaReplication only, Limited sources | Estuary FlowMany-to-many ELT/ETL | |
Op. data integration | Debezium + Kafka (no integration features) | Estuary Flow No support for restricting load process based on time interval | |
Data migration | Debezium + Kafka (no integration features) | Estuary Flow Support for type inference. | |
Stream processing | Debezium + Kafka (via Kafka and coding or streaming into destination) | Estuary Flow (real-time ETL) | |
Operational Analytics | Debezium + Kafka | Estuary Flow Microbatch | |
Data science and ML | Debezium + Kafka | Estuary Flow Support for SQL, Typescript (Python Q2 24) | |
Connectors | |||
Number of connectors | Debezium + Kafka100+ Kafka sources and destinations (via Confluent, vendors) | Estuary Flow150+ high performance connectors built by Estuary | |
Streaming connectors | Debezium + KafkaMost common OLTP databases supported for CDC Community-maintained connectors | Estuary FlowStreaming CDC, Kafka, Kinesis (source only) | |
Support for 3rd party connectors | Debezium + Kafka Kafka ecosystem | Estuary Flow Support for 500+ Airbyte, Stitch, and Meltano connectors | |
Custom SDK | Debezium + Kafka Kafka Connect | Estuary Flow (adds new 3rd party connector support fast) | |
API (for admin) | Debezium + Kafka Kafka API | Estuary Flow Estuary API docs | |
Core features | |||
Batch and streaming | Debezium + KafkaStreaming-centric (subscribers and pick up in intervals) | Estuary FlowStreaming to batch Batch to streaming | |
Delivery guarantee | Debezium + KafkaAt least once for most destinations | Estuary FlowExactly once (streaming, batch, mixed) | |
Load write method | Debezium + KafkaYes (identical data by topic) | Estuary FlowAppend only or update in place (soft or hard deletes) | |
DataOps support | Debezium + Kafka CLI, API | Estuary Flow No. Only Batch ELT | |
ELT transforms | Debezium + Kafka | Estuary Flow Dbt. Integrated orchestration. | |
ETL transforms | Debezium + Kafka Coding (SMT) | Estuary Flow No. | |
Schema inference and drift | Debezium + Kafka Support for message-level schema evolution (Kafka Schema Registry) with limits by source and destination | Estuary Flow Not ideal. Schema evolution depends on tap/target implementation. | |
Store and replay | Debezium + Kafka (requires re-extract for each destination) | Estuary Flow Can backfill multiple targets and times without requiring new extract. | |
Time travel | Debezium + Kafka | Estuary Flow | |
Snapshots | Debezium + Kafka Supports incremental and full snapshots | Estuary Flow Full or incremental | |
Ease of use | Debezium + Kafka Takes time to learn, set up, implement (OSS) | Estuary Flow (streaming transforms may take learning) | |
Deployment options | |||
Deployment options | Debezium + KafkaOpen source, Confluent Cloud (Public) | Estuary FlowOpen source, Public cloud, private cloud | |
The abilities | |||
Performance (minimum latency) | Debezium + Kafka< 100 ms | Estuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline. | |
Reliability | Debezium + KafkaHigh (Kafka); Medium (Debezium) | Estuary FlowHigh | |
Scalability | Debezium + KafkaHigh (GB/sec) | Estuary FlowHigh 5-10x scalability of others in production | |
Security | |||
Data Source Authentication | Debezium + KafkaSSL/SSH | Estuary FlowOAuth 2.0 / API Tokens SSH/SSL | |
Encryption | Debezium + KafkaEncryption in-motion (Kafka for topic security) | Estuary FlowEncryption at rest, in-motion | |
Support | |||
Support | Debezium + Kafka Low (Debezium community) High (Confluent Cloud) | Estuary Flow Fast support, engagement, time to resolution, including fixes. | |
Cost | |||
Vendor costs | Debezium + Kafka Low for OSS | 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 | Debezium + Kafka OSS infrastructure | Estuary Flow 2-4x greater productivity, dbt or derivations Good schema inference, evolution automation | |
Admin costs | Debezium + Kafka OSS infrastructure | Estuary Flow “It just works” |
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. Using Confluent Cloud does simplify your deployment, but at a cost.
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.
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. 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.
Of the various ELT vendors, Estuary is the lowest total cost option. ETL vendors are more expensive.
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.
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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