Estuary

Estuary Flow VS Striim

Read this detailed 2024 comparison of Estuary Flow vs Striim. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.

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Comparison between Estuary Flow and Striim
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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 Estuary Flow vs Striim 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

Estuary Flow logo
Estuary Flow
Striim logo
Striim
Use cases
Database replication (CDC) - sourcesEstuary FlowNative CDC MySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce Many-to-many ETL and ELTStriimReal-time (and batch) replication (sub-second to hours)
Replication to ODSEstuary Flow
Striim
Historical AnalyticsEstuary FlowMany-to-many ELT/ETLStriimYes (but complex, real-time only)
Op. data integrationEstuary Flow

No support for restricting load process based on time interval

Striim

Real-time replication

Transforms via TQL

Data migrationEstuary Flow

Support for type inference.

Striim

(too complex)

Stream processingEstuary Flow

(real-time ETL)

Striim

(using TQL)

Operational AnalyticsEstuary Flow

Microbatch

Striim

(TQL transforms)

Data science and MLEstuary Flow

Support for SQL, Typescript (Python Q2 24)

Striim

Not used

Connectors
Number of connectorsEstuary Flow150+ high performance connectors built by EstuaryStriim100+
Streaming connectorsEstuary FlowStreaming CDC, Kafka, Kinesis (source only)StriimCosmosDB, MariaDB, MongoDB, MySQL, Oracle, Postgres, SQL Server
Support for 3rd party connectorsEstuary Flow

Support for 500+ Airbyte, Stitch, and Meltano connectors

Striim
Custom SDKEstuary Flow

(adds new 3rd party connector support fast)

Striim
API (for admin)Estuary Flow

Estuary API docs

Striim
Core features
Batch and streamingEstuary FlowStreaming to batch Batch to streamingStriimStreaming-centric but can do incremental batch
Delivery guaranteeEstuary FlowExactly once (streaming, batch, mixed)StriimAt least once
Load write methodEstuary FlowAppend only or update in place (soft or hard deletes)StriimAppend-only
DataOps supportEstuary Flow

No.

Only Batch ELT

Striim

CLI, API

ELT transformsEstuary Flow

Dbt. Integrated orchestration.

Striim

dbt Cloud integration

ETL transformsEstuary Flow

No.

Striim

TQL transforms

Schema inference and driftEstuary Flow

Not ideal. Schema evolution depends on tap/target implementation.

Striim

With some limits by destination

Store and replayEstuary Flow

Can backfill multiple targets and times without requiring new extract.

Striim

(requires re-extract for new destinations)

Time travelEstuary Flow
Striim
SnapshotsEstuary Flow

Full or incremental

Striim

N/A

Ease of useEstuary Flow

(streaming transforms may take learning)

Striim

Takes time to learn flows, especially TQL

Deployment options
Deployment optionsEstuary FlowOpen source, Public cloud, private cloudStriimOn prem, Private cloud, Public cloud
The abilities
Performance (minimum latency)Estuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.Striim< 100 ms
ReliabilityEstuary FlowHighStriimHigh
ScalabilityEstuary FlowHigh 5-10x scalability of others in productionStriimHigh (GB/sec)
Security
Data Source AuthenticationEstuary FlowOAuth 2.0 / API Tokens SSH/SSLStriimSAML, RBAC, SSH/SSL, VPN
EncryptionEstuary FlowEncryption at rest, in-motionStriimEncryption in-motion
Support
SupportEstuary Flow

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

Striim
Cost
Vendor costsEstuary 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

Striim
Data engineering costsEstuary Flow

2-4x greater productivity, dbt or derivations

Good schema inference, evolution automation

Striim

Requires proprietary SQL-like language (TQL)

Admin costsEstuary Flow

“It just works”

Striim

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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. 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.

Striim

Striim-Logo-Dark.png

Striim is a real-time data integration and streaming platform that simplifies the movement of data from various sources, including databases, cloud services, and messaging systems. Striim offers out-of-the-box connectors for real-time data capture, replication, and stream processing, making it a competitive option for enterprise-grade streaming architectures.

Pros

  • Low-Latency Streaming: Striim specializes in low-latency data movement.
  • Enterprise-Grade Features: Striim offers built-in support for exactly-once processing, data transformations, in-flight processing, and scalability.
  • Comprehensive Integration: Striim provides pre-built connectors to a wide array of databases (including Oracle and SQL Server), cloud storage systems, messaging platforms like Kafka, and more.

Cons

  • Complex Pricing Model: Striim’s pricing model can be complex, with costs depending on factors such as data volume, number of sources, and the specific features used. It may not be as cost-effective for smaller businesses with modest data needs.
  • Vendor Lock-In: Like other managed streaming solutions, Striim can create a dependency on its platform, making migration to alternative solutions or self-hosted setups more challenging.
  • Limited Open Source: While Striim provides a wide range of features, it is not an open-source platform, meaning users have less flexibility and control over the code and architecture compared to open-source options like Kafka and Debezium.

Pricing

Striim operates on a subscription model with pricing tiers based on the number of data sources, targets, and data volumes. Pricing is typically custom-quoted based on the organization’s specific needs. Tiers start at $1,000/mo + Compute $0.75 /vcpu/hr & Data Transfer $0.10/GB in, $0.10/GB out.

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

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    Make sure you read through the documentation, especially the get started section.

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