Debezium + Kafka VS Hevo Data
Read this detailed 2024 comparison of Debezium + Kafka vs Hevo Data. 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 Hevo Data 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) | Hevo DataMySQL, SQL Server, Postgres, MongoDB, Oracle (ELT load only) Single target only | Estuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch |
Replication to ODS | Debezium + Kafka real-time only | Hevo Data Real-time ETL in Typescript and SQL | Estuary Flow Requires re-extraction of sources for new destinations |
Op. data integration | Debezium + Kafka no integration features | Hevo Data Integration with real-time analytics tools. Real-time transformations in Typescript and SQL. Kafka compatibility. | Estuary Flow Real-time ETL data flows ready for operational use cases. |
Data migration | Debezium + Kafka Well suited for ongoing replication. Handles schema changes. | Hevo Data Support for SQL and Typescript | Estuary Flow Great schema inference and evolution support. Support for most relational databases. Continuous replication reliability. |
Stream processing | Debezium + Kafka kSQL, SMTs | Hevo Data Pinecone (ETL) support. Transformations can call ChatGPT & other AI APIs. | Estuary Flow Real-time ETL in Typescript and SQL |
Operational Analytics | Debezium + Kafka | Hevo Data Microbatch integrations only. | Estuary Flow Integration with real-time analytics tools. Real-time transformations in Typescript and SQL. Kafka compatibility. |
AI Pipelines | Debezium + Kafka Kafka support by vector database vendors, custom coding (API calls to LLMS, etc.) | Hevo Data (batch ELT only) | Estuary Flow Pinecone support for real-time data vectorization. Transformations can call ChatGPT & other AI APIs. |
Connectors | |||
Number of connectors | Debezium + Kafka100+ Kafka sources and destinations (via Confluent, vendors) | Hevo Data150+ connectors built by Hevo | Estuary Flow150+ high performance connectors built by Estuary |
Streaming connectors | Debezium + KafkaMost common OLTP databases supported for CDC Community-maintained connectors | Hevo DataBatch CDC, Kafka batch (source only). | Estuary FlowCDC, Kafka, Kinesis, Pub/Sub |
Support for 3rd party connectors | Debezium + Kafka Kafka ecosystem | Hevo Data Support for 500+ Airbyte, Stitch, and Meltano connectors. | Estuary Flow Support for 500+ Airbyte, Stitch, and Meltano connectors. |
Custom SDK | Debezium + Kafka Kafka Connect | Hevo Data (adds new 3rd party connector support fast) | Estuary Flow SDK for source and destination connector development. |
API (for admin) | Debezium + Kafka Kafka API | Hevo Data | Estuary Flow API and CLI support |
Core features | |||
Batch and streaming | Debezium + KafkaStreaming-centric (subscribers and pick up in intervals) | Hevo DataBatch only | Estuary FlowBatch and streaming |
Delivery guarantee | Debezium + KafkaAt least once for most destinations | Hevo DataExactly once (batch only) | Estuary FlowExactly once (streaming, batch, mixed) |
Load write method | Debezium + KafkaYes (identical data by topic) | Hevo DataAppend only (soft deletes) | Estuary FlowAppend only or update in place (soft or hard deletes) |
DataOps support | Debezium + Kafka CLI, API | Hevo Data No CLI, API | Estuary Flow API and CLI support for operations. Declarative definitions for version control and CI/CD pipelines. |
ELT transforms | Debezium + Kafka | Hevo Data Dbt. Separate orchestration | Estuary Flow dbt integration |
ETL transforms | Debezium + Kafka Minimal via SMTs | Hevo Data Python scripts. Drag-and-drop row-level transforms in beta. | Estuary Flow Real-time, SQL and Typescript |
Schema inference and drift | Debezium + Kafka Support for message-level schema evolution (Kafka Schema Registry) with limits by source and destination | Hevo Data dbt integration | Estuary Flow Real-time schema inference support for all connectors based on source data structures, not just sampling. |
Store and replay | Debezium + Kafka requires re-extract for each destination | Hevo Data Real-time, SQL and Typescript | Estuary Flow Can backfill multiple targets and times without requiring new extract. User-supplied cheap, scalable object storage. |
Time travel | Debezium + Kafka | Hevo Data Many-to-many pub-sub ETL | Estuary Flow Can restrict the data materialization process to a specific date range. |
Snapshots | Debezium + Kafka Supports incremental and full snapshots | Hevo Data N/A | Estuary Flow Full or incremental |
Ease of use | Debezium + Kafka Takes time to learn, set up, implement (OSS) | Hevo Data Easy to use connectors | Estuary Flow streaming transforms may take learning |
Deployment options | |||
Deployment options | Debezium + KafkaOpen source, Confluent Cloud (Public) | Hevo DataPublic cloud | Estuary FlowOpen source, public cloud, private cloud |
The abilities | |||
Performance (minimum latency) | Debezium + Kafka< 100 ms | Hevo Data5 minutes (CDC and batch) in theory. In reality intervals are similar to others - 10s or minutes to 1 hour+ intervals. | 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) | Hevo DataMedium | Estuary FlowHigh |
Scalability | Debezium + KafkaHigh (GB/sec) | Hevo DataLow-Medium Row ingestion limits | Estuary FlowHigh 5-10x scalability of others in production |
Security | |||
Data Source Authentication | Debezium + KafkaSSL/SSH | Hevo DataOAuth / API Keys | Estuary FlowOAuth 2.0 / API Tokens SSH/SSL |
Encryption | Debezium + KafkaEncryption in-motion (Kafka for topic security) | Hevo DataEncryption at rest, in-motion | Estuary FlowEncryption at rest, in-motion |
Support | |||
Support | Debezium + Kafka Low (Debezium community) | Hevo Data Slow to fix issues when discovered | Estuary Flow Fast support, engagement, time to resolution, including fixes. Slack community. |
Cost | |||
Vendor costs | Debezium + Kafka Low for OSS | Hevo Data Higher than Airbyte, 5x per GB on avg compared to Estuary | 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 | Hevo Data Requires dbt Limited schema evolution (reversioning) | Estuary Flow Focus on DevEx, up-to-date docs, and easy-to-use platform. |
Admin costs | Debezium + Kafka OSS infrastructure | Hevo Data Less admin and troubleshooting | 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.
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.
Hevo Data
Hevo is a cloud-based ETL/ELT service for building data pipelines that, unlike Fivetran, started as a cloud service in 2017, which makes it more mature than Airbyte. Like Fivetran, Hevo is designed for “low code”, though it does provide a little more control to map sources to targets, or add simple transformations using Python scripts or a new drag-and-drop editor (currently in Beta) in ETL mode. Stateful transformations such as joins or aggregations, like Fivetran, should be done using ELT with SQL or dbt.
While Hevo is a good option for someone getting started with ELT, as one user put it, “Hevo has its limits”.
Pros
- Ease of use: Like several other modern ELT tools, Hevo is intuitive and easy to use, especially compared to traditional ETL tools.
- ELT and ETL: Hevo has started to add ETL support including Python scripts and a new drag-and-drop editor (in Beta.) This is limited mostly to row-level transformations. Hevo’s main transformation support is dbt (ELT).
- Reverse ETL: Hevo supports the ability to insert source data back into the source once it’s been cleansed. This might be good for you if you’re looking for this feature. It is a very specific use case where you write modified data back directly into the source. A more general-purpose solution is to have a pipeline write back to the sources, which is not supported by most modern ETL/ELT vendors. It is supported by iPaaS vendors.
Cons
- Connectivity: Hevo has the lowest number of connectors at slightly over 150. While this is a lot, it means you need to think about what sources and destinations you need for your current and future projects to make sure it will support your needs.
- Latency: Hevo is still mostly batch-based connectors on a streaming Kafka backbone. While data is converted into “events” that are streamed, and streams can be processed if scripts are written for any basic row-level transforms, Hevo connectors to sources, even when CDC is used, is batch. There are starting to be a few exceptions. For example, you can use the streaming API in BigQuery, not just the Google Cloud Storage staging area. But you still have a 5 minute or more delay at the source. Also, there is currently no common scheduler. Each source and target frequency is different. So latency can be longer than the source or target when they operate at different intervals.
- Costs: Hevo can be comparable to Estuary for low data volumes in the low GBs per month. But it becomes more expensive than Estuary and Airbyte as you reach 10s of GBs a month. Costs will also be much more as you lower latency because several Hevo connectors do not fully support incremental extraction. As you reduce your extract interval you capture more events multiple times, which can make costs soar.
- Reliability: CDC is batch mode only, with the minimum interval being 5 minutes. This can load the source and even cause failures. Customers have complained about Hevo bugs that make it into production and cause downtime.
- Scalability: Hevo has several limitations around scale. Some are adjustable. For example, you can get the 50MB Excel, and 5GB CSV/TSV file limits increased by contacting support.
But most limitations are not adjustable, like column limits. MongoDB can hit limits more often than others. A standalone MongoDB instance without replicas is not supported. You need 72 hours or more of OpsLog retention. And there is a 4090 columns limit that is more easily hit with MongoDB documents.
There are ingestion limits that cause issues, like a 25 million row limit per table on initial ingestion. In addition there are scheduling limits that customers hit, like not being able to have more than 24 custom times.
For API calls, you cannot make more than 100 API calls per minute. - DataOps: Like Airbyte, Hevo is not a great option for those trying to automate data pipelines. There is no CLI or “as code” automation support with Hevo. You can map to a destination table manually, which can help. But while there is some built-in schema evolution that happens when you turn on auto mapping, you cannot fully automate schema evolution or control the rules. There is no schema testing or evolution control. New tables can be passed through, but many column changes can lead to data not getting loaded in destinations and moved to a failed events table that must be fixed within 30 days or the data is permanently lost. Hevo used to support a concept of internal workflows, but it has been discontinued for new users. You cannot modify folder names for the same “events”.
Pricing
Hevo is more expensive than Airbyte and Estuary, but still less expensive than Fivetran and various ETL vendors.
- Free: Limited to 1 million free events per month with free initial load, 50+ connectors, and unlimited models
- Starter ($239/mo for 5M rows): Offers 150+ connectors, on-demand events, and 12 hours of support as an SLA. Additional rows are $10 or more per million (~1GB)
- Business (Custom Pricing): HIPAA compliance with a dedicated data architect and dedicated account manager
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.
GETTING STARTED WITH ESTUARY
Free account
Getting started with Estuary is simple. Sign up for a free account.
Sign upDocs
Make sure you read through the documentation, especially the get started section.
Learn moreCommunity
I highly recommend you also join the Slack community. It's the easiest way to get support while you're getting started.
Join Slack CommunityEstuary 101
I highly recommend you also join the Slack community. It's the easiest way to get support while you're getting started.
Watch