This guide compares modern ELT tools and alternatives to help teams choose the right approach for cloud data warehouses, real-time data movement, and broader integration needs.

This guide to ELT alternatives is continuously evolving. Conversations can be had on the Estuary Slack workspace and Estuary LinkedIn pages.

This guide is meant for two audiences: 

• Teams primarily focused on loading data into a cloud data warehouse
• Teams that also need broader data integration, including operational systems, real-time pipelines, or multiple destinations

Key Data Integration Alternatives to Consider

There are three data integration alternatives to consider:

• ELT (Extract, Load, and Transform) - Most ELT technologies are newer, built within the last 10-15 years. ELT is focused on running transforms using dbt or SQL in cloud data warehouses. It’s mostly multi-tenant SaaS, with some open source options as well. Comparing ELT vendors is the focus of this guide.
• ETL (Extract, Transform, and Load) - ETL started to become popular 30 years ago as the preferred way to load data warehouses. Over time it was also used for data integration and data migration as well. For more read The Data Engineer’s Guide to ETL Alternatives.
• CDC (Change Data Capture) - CDC is really a subset of ETL and ELT, but technologies like Debezium can be used to build your own custom data pipeline. If you’re interested in CDC you can read The Data Engineer’s Guide to CDC for Analytics, Ops, and AI Pipelines.

ELT has been widely adopted for loading cloud data warehouses. That’s in part because modern ELT was built specifically for loading BigQuery, Snowflake, and Redshift originally. Pay-as-you-go cloud data warehouses, and pay-as-you-go ELT with data warehouses running the “T” as dbt or SQL, made it easy to get started.

But ELT SaaS has rarely been used for other projects. Data integration with other destinations usually requires ETL and real-time data movement. Companies who have regulatory and security requirements require private cloud or on premises deployments. Most have historically opted for ETL for these reasons. This gap between warehouse-focused ELT and broader integration needs is why many teams now evaluate ELT and ETL tools together.

Top ELT Vendors to Consider in 2026

This guide evaluates vendors who support ELT in alphabetical order (so as not to bias.)

• Airbyte (ELT)
• Estuary (ELT and ETL)
• Fivetran (ELT)
• Hevo (ELT)
• Meltano (ELT)

The ETL guide will compare the following ETL vendors:

• Estuary
• Informatica
• Matillion
• Rivery (ELT/ETL)
• Talend

While there is a summary of each vendor, an overview of their architecture, and when to consider them, the detailed comparison is best seen in the comparison matrix, which covers the following categories:

• 9 Use cases - database replication, replication for operational data stores (ODS),  historical analytics, data integration, data migration, stream processing, operational analytics, data science and ML, and AI
• Connectors - packaged, CDC, streaming, 3rd party support, SDK, and APIs
• Core features - full/incremental snapshots, backfilling, transforms, languages, streaming+batch modes, delivery guarantees, backfilling, time travel, data types, schema evolution, DataOps 
• Deployment options - public (multi-tenant) cloud, private cloud, and on-premises
• The “abilities” - performance, scalability, reliability, and availability
• Security - including authentication, authorization, and encryption
• Costs - Ease of use, vendor and other costs you need to consider

Over time, we will also be adding more information about other vendors that at the very least deserve honorable mention, and at the very best should be options you consider for your specific needs. Please speak up if there are others to consider.

It’s OK to choose a vendor that’s good enough. The biggest mistakes you can make are:

• Choosing a good vendor for your current project but the wrong vendor for future needs
• Not understanding your future needs
• Not insulating yourself from a vendor

Make sure to understand and evaluate your future needs and design your pipeline for modularity so that you can replace components, including your ELT/ETL vendor, if necessary.

Hopefully by the end of this guide you will understand the relative strengths and weaknesses of each vendor, and how to evaluate these vendors based on your current and future needs.

Comparison Criteria

This guide starts with the detailed comparison before moving into an analysis of each vendor’s strengths, weaknesses, and when to use them.

The following comparison matrix covers the following categories:

Use cases

Over time, you will end up using data integration for most of these use cases. Make sure you look across your organization for current and future needs. Otherwise you might end up with multiple data integration technologies, and a painful migration project.

• Replication - Read-only and read-write load balancing of data from a source to a target for operational use cases. CDC vendors are often used in cases where built-in database replication does not work.
• Operational data store (ODS) - Real-time replication of data from many sources into an ODS for offloading reads or staging data for analytics.
• Historical analytics - The use of data warehouses for dashboards, analytics, and reporting. CDC-based ETL or ELT is used to feed the data warehouse. 
• Operational data integration - Synchronizing operational data across apps and systems, such as master data or transactional data, to support business processes and transactions.
  NOTE: none of the vendors in this evaluation support many apps as destinations. They only support data synchronization for the underlying databases.
• Data migration - This usually involves extracting data from multiple sources, building the rules for merging data and data quality, testing it out side-by-side with the old app, and migrating users over time. The data integration vendor used becomes the new operational data integration vendor.
  NOTE: ELT vendors only support database destinations for data migration.
• Stream processing - Using streams to capture and respond to specific events.
• Operational analytics - The use of data in real-time to make operational decisions. It requires specialized databases with sub-second query times, and usually also requires low end-to-end latency with sub-second ingestion times as well. For this reason the data pipelines usually need to support real-time ETL with streaming transformations.
• Data science and machine learning - This generally involves loading raw data into a data lake that is used for data mining, statistics and machine learning, or data exploration including some ad hoc analytics. For data integration vendors this is very similar to loading data lakes/lakehouses/warehouses. 
• AI - the use of large language models (LLM) or other artificial intelligence and machine learning models to do anything from generating new content to automating decisions. This usually involves different data pipelines for model training and model execution including Retrieval Augmented Generation (RAG).

Connectors

The ability to connect to sources and destinations in batch and real-time for different use cases. Most vendors have so many connectors that the best way to evaluate vendors is to pick your connectors and evaluate them directly in detail.

• Number of connectors - The number of source and destination connectors. What’s important is the number of high-quality and real-time connectors, and that the connectors you need are included. Make sure to evaluate each vendor’s specific connectors and their capabilities for your projects. The devil is in the details.
• Streaming (CDC, Kafka) - All vendors support batch. The biggest difference is how much each vendor supports CDC and streaming sources and destinations.
• Destinations - Does the vendor support all the destinations that need the source data, or will you need to find another way to load for select projects?
• Support for 3rd party connectors - Is there an option to use 3rd party connectors?
• CDK - Can you build your own connectors using a connector development kit (CDK)?
• API - Is an admin API available to help integrate and automate pipelines?

Core features 

How well does each vendor support core data features required to support different use cases? Source and target connectivity are covered in the Connectors section.

• Batch and streaming support - Can the product support streaming, batch, and both together in the same pipeline?
• Transformations - What level of support is there for streaming and batch ETL and ELT? This includes streaming transforms, and incremental and batch dbt support in ELT mode. What languages are supported? How do you test?
• Delivery guarantees - Is delivery guaranteed to be exactly once and in order?
• Data types - Support for structured, semi-structured, and unstructured data types.
• Store and replay - The ability to add historical data during integration, or later additions of new data in targets.
• Time travel - The ability to review or reuse historical data without going back to sources.
• Schema evolution - support for tracking schema changes over time, and handling it automatically.
• DataOps - Does the vendor support multi-stage pipeline automation?

Deployment options 

Does the vendor support public (multi-tenant) cloud, private cloud, and on-premises (self-deployed)?

The “abilities” - How does each vendor rank on performance, scalability, reliability, and availability? 

• Performance (latency) - what is the end-to-end latency in real-time and batch mode?
• Scalability - Does the product provide elastic, linear scalability
• Reliability - How does the product ensure reliability for real-time and batch modes? One of the biggest challenges, especially with CDC, is ensuring reliability.

Security

Does the vendor implement strong authentication, authorization, RBAC, and end-to-end encryption from sources to targets?

Costs

The vendor costs, and total cost of ownership associated with data pipelines

• Ease of use - The degree to which the product is intuitive and straightforward for users to learn, build, and operate data pipelines.
• Vendor costs - including total costs and cost predictability
• Labor costs - Amount of resources required and relative productivity
• Other costs - Including additional source, pipeline infrastructure or destination costs

Comparison Matrix

Below is a detailed comparison of the top ELT and ETL vendors across various categories, including use cases, connectors, core features, deployment options, and more

Comparison by Use Cases

Comparison by Connectors
 

 Comparison by Core Features

 Comparison by Deployment Options, Abilities & Security

 Comparison of Costs

1. Airbyte

Airbyte was founded in 2020 as an open source data integration company, announced Airbyte Cloud in 2021, and expanded Cloud availability in 2022.

While this section is about Airbyte, you could include Stitch and Meltano here because they all support the Singer framework. Stitch created the Singer open source ETL project and built their offering around it. Stitch then got acquired by Talend, which in turn was acquired by Qlik. This left Singer without a major company driving its innovation. Instead, there are several companies who use the connectors. Meltano is one of those. They have built on the Stitch taps (connectors) and other open source projects.

Airbyte started as a Singer-based ELT tool, but has since changed their protocol and connectors to be different. Airbyte has kept Singer compatibility so that it can support Singer taps as needed. Airbyte has also kept many of the same principles, including being batch-based. This is eventually where Airbyte’s limitations come from as well.

If you go by pricing calculators and customers, Airbyte is the second lowest cost vendor in the evaluation after Estuary. Most of the companies we’ve talked to were considering cloud options, so we’ll focus on Airbyte cloud. 

• Latency: Airbyte supports both batch syncs and log-based CDC for several databases. In Airbyte Cloud, scheduled syncs can run at most every 60 minutes (contact Sales if you need more frequent replication). End-to-end freshness also depends on destination load behavior and any downstream transformations (for example, dbt runs after the load).
• Reliability: There are some issues with reliability you will need to manage. Most CDC sources, because they’re built on Debezium, only ensure at-least-once delivery. It means you will need to deduplicate (dedup) at the target. Airbyte does have both incremental and deduped modes you can use though. You just need to remember to turn them on. Also, Debezium does put less of a load on a source because it uses Kafka. This does make it less of a load on a source than Fivetran CDC. A bigger reliability issue is failure of under-sized workers. There is no scale-out option. Once a worker gets overloaded you will have reliability issues (see scalability.) There is also no staging or storage within an Airbyte pipeline to preserve state. If you need the data again, you’ll have to re-extract from the source.
• Scalability: Airbyte Cloud runs one sync per connection at a time, and scheduled syncs can run at most every 60 minutes by default. For large tables or high-change sources, the practical limit is often how long each sync takes, because the next scheduled sync will only start after the previous one finishes. For higher scale requirements, evaluate resource controls, connector-specific throughput, and whether your use case requires near real-time replication rather than periodic batch syncs.
• ELT only: Airbyte cloud supports dbt cloud. This is different from dbt core used by Fivetran. If you have implemented on dbt core in a way that makes it portable (which you should) the move can be relatively straightforward. But if you want to implement transforms outside of the data warehouse, Airbyte does not support that.
• DataOps: Airbyte provides UI-based replication, and also supports automation through its API and Terraform provider for configuration as code. Pipeline testing and schema governance still typically live outside Airbyte (for example, in dbt, CI, or your orchestration and observability stack).

2. Fivetran

Fivetran was founded in 2012 by data scientists who wanted an integrated stack to capture and analyze data. It’s best known for managed connectors that move data from common sources into cloud destinations, and for an ELT-style workflow where transformations typically run after loading.

Fivetran added built-in transformation support via dbt (hosted dbt Core) and other transformation integrations. In 2021, Fivetran signed an agreement to acquire HVR (now positioned as its self-hosted deployment option for database replication use cases). 

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.

But today Fivetran often comes in conversations as a vendor customers are trying to replace. Understanding why can help you understand Fivetran’s limitations. 

The most common points that come up in these conversations and online forums are about needing lower latency, improved reliability, and lower, more predictable costs:

• Latency: Fivetran is primarily schedule-based. Depending on plan, connectors can run as frequently as 1 minute (Enterprise or Business Critical) or at longer intervals (5, 15, 30 minutes and up to daily), but not all connectors support 1-minute syncs. Real end-to-end latency also depends on connector behavior (for example, some connectors use periodic reimports or rollback windows) and destination load time.
• Costs: Fivetran pricing is based on Monthly Active Rows (MAR), which are rows that change within a billing period. In March 2025, Fivetran changed pricing tiering from account-level to connection-level, which can increase costs for accounts with many similarly sized connections and decrease costs for accounts with one dominant connection. Fivetran also introduced improved “free re-sync” detection for identical rows. Because billing is usage-based, cost predictability depends heavily on source change rates, connector sync behavior, and how many connections you operate at high frequency.
• Reliability: Like any managed data movement service, reliability is influenced by connector-specific limitations (API rate limits, schema drift, reimports/rollback windows) and operational incidents. For example, an incident on June 17, 2022 impacted core Fivetran services for about 3 hours. When evaluating Fivetran for critical workloads, review plan-specific support/SLA terms, connector failure modes, and how quickly you can detect and recover from sync issues.
• Deployment options: Fivetran documents three main deployment models: SaaS Deployment (fully managed), Hybrid Deployment (process data inside your network while using Fivetran as the control plane), and Self-Hosted Deployment via HVR for organizations that require full infrastructure control. Availability of certain models and connectors depends on plan (for example, Hybrid Deployment is tied to higher-tier plans). 
• 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.

3. Hevo Data

Hevo is a cloud-based ETL and ELT service for building data pipelines. Hevo began in 2017 and is delivered as a managed cloud platform. 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 drag-and-drop transformation blocks 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.”

• Connectivity: Hevo supports 150+ sources and destinations, so you should confirm early that it includes the connectors you need for both current and future projects.
• Latency: Hevo is primarily schedule-driven. By default, Hevo syncs data every hour, and you can configure shorter intervals such as 30 or 45 minutes. A 5-minute sync frequency is available by request through Hevo Support, and schedules can extend up to 48 hours. Hevo also separates ingestion frequency from destination load scheduling, so end-to-end freshness depends on both the source sync schedule and the destination load schedule.
• 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: Hevo reliability depends on the Source type and ingestion mode. For several databases, Hevo supports log-based ingestion modes (for example using database logs such as OpLog or BinLog), where incremental changes can be captured with one-to-one replication. For scheduled, pull-based Sources, freshness and reliability are more sensitive to polling schedules, API limits, and schema changes. For MongoDB log-based replication, Hevo recommends maintaining sufficient OpLog retention so the pipeline does not fall behind and miss events.
• Scalability: Hevo scaling limits are often connector-specific. For file ingestion, Hevo documents limits such as 50MB for Excel sheets and 5GB for CSV and TSV files (limits can be increased by contacting support). Some destinations also impose column limits, and Hevo documents practical limits such as replicating up to 4090 columns per table for certain destinations due to destination constraints and Hevo metadata columns. For PostgreSQL full loads, Hevo documents ingestion row limits that may require support to raise (for example, increasing the default limit to 25 million rows for some PostgreSQL pipeline variants). For MongoDB log-based ingestion, OpLog retention should be sized so the pipeline does not fall behind.
• DataOps: Hevo supports pipeline automation through a public REST API, including creating and updating pipelines, managing schedules, and operating pipeline actions (resume, pause, restart). Most teams still handle testing, schema governance, and broader orchestration outside Hevo, but the API enables “configuration and operations automation” for common pipeline workflows.

4. Meltano

Meltano was founded in 2018 as an open source project within GitLab to support their data and analytics team. It’s a CLI-first, configuration-driven EL tool that primarily runs on the Singer specification (Singer taps and targets). 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 designed for teams who prefer pipelines as code: you define plugins and jobs in YAML, run pipelines via the CLI, and manage changes through standard software workflows like version control and CI.

• Connectivity: Meltano Hub positions itself as “connectors for 600+ sources and destinations,” spanning Singer taps and targets with a mix of official, partner, and community-maintained plugins.
• Latency: Meltano is primarily batch and schedule-driven: it runs pipelines repeatedly and uses incremental replication state so each run can resume where the previous run left off. It is not a streaming runtime for sub-second delivery; it’s best when minute-plus latency is acceptable and pipelines are run on a cadence.
• Reliability: Some will say Meltano has less issues when compared to Airbyte. 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 commonly pairs EL with dbt for transformations, and Meltano pipelines can include dbt steps as part of a single run command.
• Deployment options: Self-managed open source is the default. Meltano has also offered a managed option over time (including branding changes like Meltano Cloud and Arch), so it’s worth verifying the current managed offering directly before you assume it’s available for your evaluation.
• DataOps: Meltano is well-suited to “pipelines as code” workflows: YAML configuration, CLI execution, and automation via standard engineering practices.

Overall, if you are focused on open source, Airbyte and Meltano are two good ELT options. If you prefer simplicity you might consider Airbyte. Estuary is source-available, and many connectors are open source, but most teams choose the managed public deployment for simplicity.

5. Estuary

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.

Within this vendor set, Estuary is the option designed for real-time CDC plus ETL and ELT across both streaming and batch pipelines.

While Estuary is also a strong option for batch sources and targets, it shines in CDC, real-time pipelines, and loading multiple destinations with the same pipeline. Deployment options include public deployment (managed SaaS), private deployment, and BYOC, which support stricter security, compliance, and networking requirements.

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 Airbyte, Fivetran, 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 streams and stores data as collections, which enables replay, backfills, and reuse across multiple destinations without re-extracting from the source. This architecture supports low-latency pipelines and makes it easier to feed analytics, operational systems, and AI workloads from the same governed data streams.

Estuary supports broad packaged and custom connectivity. It has 200+ native connectors built for low latency and scale. In addition, Estuary can also support Airbyte, Meltano, and Stitch connectors, which can add 500+ more connectors when you need coverage beyond the native set. Getting official support for a connector is typically a request-and-test process with Estuary to confirm it meets your production requirements. Some community connectors may not match the performance of Estuary-native connectors, so it’s important to validate them for scale and reliability. Estuary also supports SQL and TypeScript transformations, which enables ETL.

Estuary is typically a low and predictable TCO option because pricing is primarily based on data volume moved, rather than row-based metering. Validate your exact estimate based on connector count, data volume, and deployment type.

While open source is free, and for many it can make sense, you need to add in the cost of support from the vendor, specialized skill sets, implementation, and maintenance to your total costs to compare. Without these skill sets, you will put your time to market and reliability at risk. Many companies will not have access to the right skill sets to make open source work for them.

How to Choose the Right ELT/ETL Solution for Your Business

Start by deciding whether your core need is scheduled warehouse loading (ELT) or broader data movement across systems (ETL plus streaming). If your requirements include real-time CDC, multiple destinations, and the ability to replay or backfill without re-extracting, you should evaluate platforms designed for both streaming and batch, including Estuary. Estuary is the right-time data platform, meaning teams can choose when data moves (sub-second, near real-time, or batch).

Key Takeaways

• Use a batch ELT tool when your main goal is scheduled loading into a cloud data warehouse with transformations in dbt.
• Use a streaming-capable platform when you need CDC, lower latency, or you need to serve multiple downstream systems beyond a warehouse.
• Evaluate tools on connector quality, recovery behavior, schema change handling, deployment options, and total cost under realistic change rates.

Signals you need more than batch ELT

• Real-time or near real-time requirements: If the business needs fresh operational data or rapid analytics updates, batch polling is often the limiting factor.
• Multiple destinations: If the same data must feed a warehouse plus operational systems or AI workloads, tools that support many-to-many pipelines can reduce duplicated extraction and operational overhead.
• Replay and backfills: If you expect to add new destinations later or need fast recovery after downstream failures, prefer systems that can replay from durable storage rather than re-extract from sources.
• Deployment constraints: If you have strict security, compliance, or networking requirements, shortlist vendors that support private deployment or BYOC in addition to managed SaaS.

When a simpler ELT tool may be enough

If you only need batch loads into one warehouse on a 15-minute to daily schedule and you run transformations in dbt, a batch ELT product may be sufficient.

How to evaluate quickly

Pick 5 to 10 connectors you actually need, test them with production-like data volume, and compare latency, failure recovery, schema drift handling, and total cost. Then validate that the vendor’s deployment model and support meet your operational requirements.

Getting Started with Estuary

Getting started with Estuary is simple. Sign up for a free account.

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

I highly recommend you also join the Slack community. It’s the easiest way to get support while you’re getting started.

If you want an introduction and walk-through of Estuary you can watch the Estuary 101 Webinar.

Questions? Feel free to contact us any time!