Confluent VS Meltano
Read this detailed 2024 comparison of Confluent vs Meltano. 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 Confluent vs Meltano 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 | ConfluentDebezium database sources supported, real-time | MeltanoMariaDB, MySQL, Oracle, Postgres, SQL Server (Airbyte) Batch only. | Estuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch |
Replication to ODS | Confluent | Meltano Batch pipelines only. | Estuary Flow Requires re-extraction of sources for new destinations |
Op. data integration | Confluent With Kafka Connect | Meltano Batch pipelines only. | Estuary Flow Real-time ETL data flows ready for operational use cases. |
Data migration | Confluent Accelerator program available to migrate from Kafka to Confluent. Kafka Connect required for database migrations | Meltano Has issues with large scale data and doesn't support continuous streaming replication | Estuary Flow Great schema inference and evolution support. Support for most relational databases. Continuous replication reliability. |
Stream processing | Confluent Flink, kSQL | Meltano | Estuary Flow Real-time ETL in Typescript and SQL |
Operational Analytics | Confluent Through Kafka Connect or other integrations only | Meltano Only Batch ELT | Estuary Flow Integration with real-time analytics tools. Real-time transformations in Typescript and SQL. Kafka compatibility. |
AI Pipelines | Confluent Kafka support by vector database vendors, custom coding (API calls to LLMS, etc.) | Meltano Not ideal. Supports Pinecone destination (batch ELT only) | Estuary Flow Pinecone support for real-time data vectorization. Transformations can call ChatGPT & other AI APIs. |
Connectors | |||
Number of connectors | Confluent100+ | Meltano200+ Singer tap connectors | Estuary Flow150+ high performance connectors built by Estuary |
Streaming connectors | ConfluentDebezium connectors | MeltanoBatch CDC, Batch Kafka source, Batch Kinesis destination | Estuary FlowCDC, Kafka, Kinesis, Pub/Sub |
Support for 3rd party connectors | Confluent Many OSS Kafka Connect connectors | Meltano Higher latency batch ELT only. | Estuary Flow Support for 500+ Airbyte, Stitch, and Meltano connectors. |
Custom SDK | Confluent OSS Kafka API and Kafka Connect framework | Meltano Great SDK for connector development. | Estuary Flow SDK for source and destination connector development. |
API (for admin) | Confluent | Meltano None. | Estuary Flow API and CLI support |
Core features | |||
Batch and streaming | ConfluentStreaming-centric; supports incremental batch | MeltanoBatch only | Estuary FlowBatch and streaming |
Delivery guarantee | ConfluentExactly once; strong consistency for streaming data | MeltanoAt least once (Singer-based) | Estuary FlowExactly once (streaming, batch, mixed) |
Load write method | ConfluentAppend-only | MeltanoMostly append-only with soft deletes, depends on connector. | Estuary FlowAppend only or update in place (soft or hard deletes) |
DataOps support | Confluent CLI, API support for automations | Meltano CLI support | Estuary Flow API and CLI support for operations. Declarative definitions for version control and CI/CD pipelines. |
ELT transforms | Confluent | Meltano dbt support for destinations | Estuary Flow dbt integration |
ETL transforms | Confluent Flink and kSQL | Meltano | Estuary Flow Real-time, SQL and Typescript |
Schema inference and drift | Confluent Inference depends on Kafka Connect connector implementation. Supports schema evolution through Kafka Schema Registry. | Meltano Sampling-based discovery step for databases which don't provide schemas | Estuary Flow Real-time schema inference support for all connectors based on source data structures, not just sampling. |
Store and replay | Confluent Requires re-extract for new destinations. Tiered storage requires engineering efforts to operate. | Meltano | Estuary Flow Can backfill multiple targets and times without requiring new extract. User-supplied cheap, scalable object storage. |
Time travel | Confluent Allows time travel with Kafka topics | Meltano | Estuary Flow Can restrict the data materialization process to a specific date range. |
Snapshots | Confluent Supports snapshots | Meltano N/A | Estuary Flow Full or incremental |
Ease of use | Confluent Requires knowledge of internals to operate optimally | Meltano Takes time to learn, set up, implement, and maintain (OSS) Python knowledge is required. | Estuary Flow streaming transforms may take learning |
Deployment options | |||
Deployment options | ConfluentOn prem, Private cloud, Public cloud | MeltanoOpen source | Estuary FlowOpen source, public cloud, private cloud |
The abilities | |||
Performance (minimum latency) | Confluent< 100 ms | MeltanoCan be reduced to seconds. But it is batch by design, scales better with longer intervals. Typically 10s of 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 | ConfluentHigh | MeltanoMedium | Estuary FlowHigh |
Scalability | ConfluentHigh (GB/sec) | MeltanoLow-medium | Estuary FlowHigh 5-10x scalability of others in production |
Security | |||
Data Source Authentication | ConfluentOAuth / HTTPS / SSH / SSL / API Tokens | MeltanoOAuth / API Keys | Estuary FlowOAuth 2.0 / API Tokens SSH/SSL |
Encryption | ConfluentEncryption at rest, in-motion | MeltanoNone | Estuary FlowEncryption at rest, in-motion |
Support | |||
Support | Confluent Responsive account team | Meltano Open source support | Estuary Flow Fast support, engagement, time to resolution, including fixes. Slack community. |
Cost | |||
Vendor costs | Confluent | Meltano Requires self-hosting open source | 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 | Confluent Even with the managed offering, requires engineer efforts to optimally operate. | Meltano Everything needs to be self-hosted. Requires dbt for transformations. No automated schema evolution. | Estuary Flow Focus on DevEx, up-to-date docs, and easy-to-use platform. |
Admin costs | Confluent | Meltano (self-managed open source) | 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.
Confluent
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.
Meltano
Meltano was founded in 2018 as an open source project within GitLab to support their data and analytics team. It’s a Python framework built on the Singler protocol. The Singer framework was originally created by the founders of Stitch, but their contribution slowly declined following the acquisition of Stitch by Talend (which in turn was later acquired by Qlik.).
Meltano is focused on configuration based ELT using YAML and the CLI.
Pros
- Open source ELT: Meltano is the main successor to Stitch if you’re looking for a Singer-based framework.
- Configure-driven: If you are looking for a configure-driven approach to ELT, Meltano may be a great option for you.
- Connectivity: Meltano and Airbyte collectively have the most containers, which makes sense given their open source history with Singer. Meltano supports Singer and has an SDK wrapper for Airbyte, giving it 600+ open source connectors in total. Open source connectors have their limits, so it’s important to test out carefully based on your needs.
Cons
- Configure not low-code: If you’re looking for a more graphical, low-code approach to integration, Meltano is not a good choice.
- Latency: Meltano is batch-only. It does not support streaming. While you can reduce polling intervals down to seconds, there is no staging area. The extract and load intervals need to be the same. Meltano is best suited for supporting historical analytics for this reason.
- Reliability: Some will say Meltano has less issues when compared to Airybte. But it is open source. If you have issues you can only rely on the open source community for support.
- Scalability: There isn’t as much documentation to help with scaling Meltano, and it’s not generally known for scalability, especially if you need low latency. Various benchmarks show that larger batch sizes deliver much better throughput. But it’s still not the level of throughput of Estuary or Fivetran. It’s generally minutes even in batch mode for 100K rows.
- ELT only: Meltano supports open source dbt and can import existing dbt projects. Its support for dbt is considered good. It also has the ability to extract data from dbt cloud. Meltano does not support ETL.
- Deployment options: Meltano is deployed as self-hosted open source. There is no Meltano Cloud, though Arch is offering a broader service with consulting.
- DataOps: Data engineers generally automate using the CLI or the Meltano API. While it is straightforward to automate pipelines, there isn’t much support for schema evolution and automating responses to schema changes.
Pricing
Meltano is open source. There is no pricing. But it’s not really free. You’ll need to spend more on data engineering resources to stand up, build, and maintain Meltano. If you need scalability, there isn’t a lot of documentation on how to scale. Make sure you evaluate carefully and find some Meltano expertise.
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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