Fivetran VS Meltano

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.

• 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.

• 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.

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.

Fivetran was founded in 2012 by data scientists who wanted an integrated stack to capture and analyze data. The name was a play on Fortran and meant to refer to a programming language for big data. After a few years the focus shifted to providing just the data integration part because that’s what so many prospects wanted. Fivetran was designed as an ELT (Extract, Load, and Transform) architecture because in data science you don’t usually know what you’re looking for, so you want the raw data. 

In 2018, Fivetran raised their series A, and then added more transformation capabilities in 2020 when it released Data Build Tool (dbt) support. That year Fivetran also started to support CDC. Fivetran has since continued to invest more in CDC with its HVR acquisition.

Fivetran’s design worked well for many companies adopting cloud data warehouses starting a decade ago. While all ETL vendors also supported “EL” and it was occasionally used that way, Fivetran was cloud-native, which helped make it much easier to use. The “EL” is mostly configured, not coded, and the transformations are built on dbt core (SQL and Jinja), which many data engineers are comfortable using.

• Ease of Use: Fivetran is modern SaaS ELT with an easy-to-use UI, especially in comparison to more traditional eTL tools. It allows you to set up a data pipeline without coding.
• Pre-built Connectors: Fivetran offers nearly 300 native connectors and an additional 300+ “lite” connectors based on APIs.
• Scalability: Fivetran is known for scaling better than many of its competitors.
• Integration with dbt: Fivetran has done a good job of integrating dbt core into the Fivetran platform.
• Focus on replication: Fivetran is good at data extraction and loading (EL), even if it is batch only, making it a strong choice if your primary goal is to efficiently move data into your warehouse for analysis.
• Advanced schema evolution: Fivetran and Estuary are the two leading vendors with support for automating how changes in sources are passed through to destinations.

• Latency: While Fivetran uses change data capture at the source, it is batch CDC, not streaming. Enterprise-level is guaranteed to be 15 minutes of latency. Business critical is 1 minute of latency, but costs more than 2x the standard edition. Its ELT architecture can also be slowed down by the target load and transformation times.
• Costs: Another major complaint are Fivetran’s high vendor costs, which have been 5x the cost of Estuary as stated by customers. Fivetran costs are based on monthly active rows (MAR) that change at least once per month. This may seem low, but for several reasons (see below and the pricing section) it can quickly add up.
• Unpredictable costs: Another major reason for high costs is that MARs are based on Fivetran’s internal representation of rows, not rows as you see them in the source.
  For some data sources you have to extract all the data across tables, which can mean many more rows. Fivetran also converts data from non-relational sources such as SaaS apps into highly normalized relational data. Both make MARs and costs unexpectedly soar. This also does not account for the initial load where all rows count.
• Reliability: Another reason for replacing Fivetran is reliability. Customers have struggled with a combination of alerts of load failures, and subsequent support calls that result in a longer time to resolution. There have been several complaints about reliability with MySQL and Postgres CDC, which is due in part because Fivetran uses batch CDC. Fivetran also had a 2.5 day outage in 2022. Make sure you understand Fivetran’s current SLA in detail. Fivetran has had an “allowed downtime interval” of 12 hours before downtime SLAs start to go into effect on the downtime of connectors. They also do not include any downtime from their cloud provider.
• Deployment options: while Fivetran claims private cloud as an option, it’s not really an option. Its private cloud deployment requires some installation work and only supports 8 sources and 5 destinations. There is also a self-hosted option for HVR only.
• Support: Customers also complain about Fivetran support being slow to respond. Combined with reliability issues, this can lead to a substantial amount of data engineering time being lost to troubleshooting and administration.
• DataOps: Fivetran does not provide much control or transparency into what they do with data and schema. They alter field names and change data structures and do not allow you to rename columns. This can make it harder to migrate to other technologies. Fivetran also doesn’t always bring in all the data depending on the data structure, and does not explain why.
• Roadmap: Customers frequently comment Fivetran does not reveal as much of a future direction or roadmap compared to the others in this comparison, and do not adequately address many of the above points.

Fivetran's pricing is based on monthly active rows (MAR). This can be very unpredictable because MARs are based on Fivetran’s internal representation of data, not yours. Any non-relational or nested data gets turned into highly normalized rows that raise costs.

Lower latency is also very expensive. To reduce latency from 1 hour to 15 minutes can cost you 33-50% more (1.5x) per million MAR, and 100% (2x) or more to reduce latency to 1 minute, which is rarely deployed. Some connectors require all data to be extracted each time, which also becomes more expensive as you lower latency and increase the number of extracts.

Even then, you still have the latency of the data warehouse load and transformations. The additional costs of frequent ingestions and transformations in the data warehouse can also be expensive and take time. Companies often keep latency high to save money.

While a small deployment (2M MARs/month) can cost $700-$2667, 10M MARs/month get you into $10K a month. It is not unheard of for Fivetran costs to reach 6 digits annually, especially with certain high-cost connectors that end up having many more MARs.

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.

• 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.

• 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.

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.