ETL as a service

Companies prefer ETLaaS (ETL as a Service) because traditional ETL pipelines require heavy maintenance. They demand in-house experts and local hardware setup, which is both complex and expensive.

Traditional ETL can scale, but it usually takes manual provisioning and tuning. To get built-in auto-scaling and adapt to schema changes, many companies switch to ETLaaS.

In this blog, let’s explore how ETLaaS simplifies building, managing, and scaling data integration pipelines in the cloud.

What is ETL as a Service?

ETL is the process of extracting, transforming, and loading data from source to destination. Data engineers build on-prem traditional ETL pipelines to perform these tasks. 

But this process becomes simpler when moved to the cloud. Cloud providers are third-party services that handle the infrastructure, scalability, and monitoring behind these ETL processes, making it a full-fledged service called ETLaaS (ETL as a Service).

These tools come with drag-and-drop interfaces for easy building, built-in connectors for familiar data sources, real-time runs or scheduled jobs, and built-in error handling and logging.

ETLaaS integrates well with cloud-native architecture, making it a good choice for modern data stacks. 

ETL as a Service vs. traditional ETL tools

Traditional ETL tools and ETLaaS share the same end goal: integrating diverse data sources into a single warehouse. However, they differ in handling scalability, flexibility, security, and automation. Let’s discuss the key differences in depth.

1. Cloud-based

Traditional ETL tools are installed on-prem and require local setup, while ETLaaS is a cloud-native tool. These cloud-based tools offer pre-built connectors for popular sources and destinations. 

Building data integration pipelines with these drag-and-drop connectors is simple. You just fill in the source details in the connector template, define any transformation steps, and link it to the destination.

These cloud-native platforms integrate well with modern data stacks and automatically trigger jobs on a schedule.

2. Automation

Modern ETL tools automate extraction, transformation, loading, and scheduled runs. Once you create the ETL pipeline, you can schedule it to run at set intervals, and it will update automatically based on that frequency.

Traditional ETL tools require more maintenance compared to cloud tools. Since they’re deployed on-prem, teams must manage the infrastructure and hardware themselves. ETL as a service removes this burden by automatically handling the resources and backend infrastructure that pipelines require.

3. Scalability

Scalability is another key differentiator between cloud-based ETL and traditional ETL tools. 

Say the data volumes at the source suddenly surge, this won’t affect the cloud-based integration pipelines because cloud resources scale up automatically to handle the growing source data. Similarly, those resources scale down when not in use. These auto-scaling features are commonly found in certain ETL solutions like AWS Glue, Azure Data Factory, and Hevo. This keeps pipelines available, resilient, and low-maintenance. 

4. Flexibility

Cloud ETL platforms handle diverse data sources and formats, allowing you to quickly adapt to changing business needs. They connect to various sources (databases, APIs, SaaS applications, and streaming data), supporting structured and unstructured data. These tools also offer flexibility in target destinations, supporting various storage systems like data warehouses, data lakes, and on-prem databases.

5. Cost efficiency

Traditional ETL tools are relatively more expensive than modern cloud platforms because on-prem tools require your own hardware setup and ongoing IT maintenance. That includes upfront costs for physical servers and dedicated IT staff.

Cloud tools, on the other hand, offer a pay-as-you-go pricing model so you only pay for the resources you actually use. Thanks to auto-scaling capabilities, costs stay more manageable, as resources scale up or down based on demand.

What are the benefits of ETL as a service?

Everyday use cases and ideal scenarios

ETL processes enable various business functions, especially those that rely on data for decision-making. Other common use cases include compliance reporting and auditing purposes.

1. Real-time streaming & analytics

Sometimes, ETL processes load data into warehouses that are directly connected to end-user dashboards. When the source data updates, the ETL provides the real-time data to end-user analytics via CDC.

Cloud ETL tools also support streaming data sources, moving data to the destination in real time. Some tools use batch processing with minimal latency, enabling near real-time data movement, while others (like Hevo) offer true real-time job execution. Either way, you can deliver low-latency data downstream.

2. Data warehousing

Organizations use a centralized data repository (a data warehouse) to store data from diverse sources for easy accessibility and usability. To bring data from these sources into the warehouse, ETL processes are essential. They connect each source to the warehouse and enable smoother data movement between them. 

3. Business intelligence & dashboarding

ETL processes can deliver cleaned and processed datasets ready for business intelligence and dashboarding. During the transformation step in ETL, you apply robust cleaning and processing logic to prepare the data for final analysis. This refined data can then be fed directly to the BI dashboards for insights. ETL pipelines automate this entire procedure.

4. Machine learning & AI

ETL processes provide the necessary data infrastructure for machine learning and AI use cases. They deliver clean, reliable data for training and developing AI/ML models.

You can also include feature engineering tasks (like creating new features, standardizing formats, and handling numerical values) within the ETL step. This prepares the final dataset to be fed into ML models.

5. Data migration & compliance reporting 

Companies are moving away from on-prem databases like Oracle and Microsoft SQL to platforms like Snowflake, BigQuery, or Databricks. This migration takes up time and resources, and adds unnecessary complexity when done manually.

Cloud ETL tools automate this process. They handle field mapping, format conversion, and error checks, making the data compatible with destination warehouses and streamlining the migration.

Which are the top ETL as a Service providers?

Now that we’ve covered what ETL as a Service is and why it matters, let’s look at some of the top ETLaaS providers leading the space.

1. Hevo Data

Hevo Data is a top modern cloud tool that provides an intuitive interface for building ETL pipelines. Its drag-and-drop interface and 150+ pre-built connectors make it easy to connect and schedule data integration pipelines.

Hevo stands out in the market with three primary offerings: a user-friendly interface, affordable pricing, and supports both ETL and reverse ETL pipelines. 

It has a Hevo transformer that provides native support with dbt Core. Inside the platform, you can write dbt transformation queries and run them on the data stored in the warehouse.

Even if you move raw data straight into the warehouse using Hevo, you can still use the same platform (Hevo) to transform that data inside the warehouse and deliver it to end-user use cases, like dashboards, analytics, reports, and more.

Key features

• Hevo’s autoscaling capabilities automatically manage resources based on traffic. That means scaling up during spikes and shrinking when idle. 
• Hevo’s log-based CDC (identifying and capturing data changes using transactional logs) delivers updated data to your warehouse.
• Hevo automatically detects the format changes at source and adapts the schema changes to match the destination. This is possible via its auto schema mapping capabilities. 

Pros

• No-code platform with easy setup.
• Affordable pricing tiers. 
• Automated error handling
• Supports both stream and real-time batch processing.

Cons

• Limited advanced customization capabilities
• Limited on-prem deployment features 

2. Fivetran

Fivetran streamlines building data pipelines that automate the ETL — extract, transform, and load activities. 

It offers all the competitive features you’d expect from modern data tools, like automatic schema management, flexible sync intervals, and pre-built connectors.

Fivetran’s MAR pricing model charges are based on monthly active rows (MAR). That means users are charged per number of unique rows added, updated, or deleted in their data warehouse each month. 

Key features

• Fivetran offers over 500 pre-built connectors to different data sources, including SaaS applications, databases, and APIs. 
• Sync frequency and scheduled runs can be customized based on your needs — hourly, daily, monthly, or even every few minutes.
• Supports CDC updates, without requiring to move all data from scratch with every run.
• Customize sync frequency to balance cost and latency.

Pros

• Offers in-warehouse transformations, enabling teams to apply SQL logic directly within their destination systems.
• Supports both full historical loads and efficient incremental syncs
• Provides automatic drift handling to adapt to changing source schemas
• Fivetran HVR for enterprise data movements

Cons

• Can become expensive at high volumes
• Offers limited flexibility beyond SQL

3. Matillion

Matillion is a cloud-native data integration platform with powerful AI capabilities built in. It provides a quick and easy way to build and manage data pipelines across low-code, Co-pilot, SQL, Python, and dbt environments. It’s optimized to work best with cloud data warehouses such as Snowflake, BigQuery, Redshift, and Databricks. 

Along with its simple UI and low-code tools, Matillion also offers AI capabilities to transform your data for analytics usecases. 

Key features

• Matillion’s intuitive interface streamlines work for technical users and allows non-technical users to set up pipelines easily.
• Provides Gen AI capabilities to enhance business workflows and analytics.
• Offers deep integration with cloud warehouses like AWS, Azure, and Google Cloud.

Pros

• Native integration with Git for best DataOps practices
• Matillion is highly scalable due to its cloud-native design

Cons

• Supports post-load transformations only.
• It doesn’t offer much for on-premises or mixed setups.

Build scalable, no-code data pipelines with Hevo BOOK A DEMO

Comparing ETL as a Service Tools: how to choose the right one

With several strong ETLaaS platforms available, the next step is figuring out which one fits your needs best. Let’s break down how to choose the right one for your business.

1. Source connectors

ETL tools are designed to automate data movement between the source and the destination. This is typically achieved through pre-built source connectors. You simply fill in the required source and destination details in these templates, and the tool automatically establishes the connection.

These connectors are built for popular sources like SaaS applications and cloud warehouses. For less common sources, you may need custom connectors to connect source APIs with your destination warehouse.

Since connectors play a crucial role in simplifying source-to-destination integration, it’s important to review a tool’s connector list before choosing it. Ideally, it should include pre-built connectors for all your data sources and destinations. If not, check how easily it supports building custom API connectors.

2. User interface

User interface plays a critical role in tool adoption and usage across an organization. With an intuitive UI, more users can easily adopt the tool with a minimal learning curve.

Low-code tools speed up data engineers’ workflows, allowing them to focus more on complex tasks and less on maintenance and repetitive tasks.

A no-code interface with built-in drag-and-drop functionality and AI capabilities also empowers business users to build the routine data pipelines they need, reducing dependency on IT teams and increasing accessibility across the organization.

That’s why choosing a tool with minimal complexity and an easy-to-use interface like Hevo is essential. 

3. Pricing

Each platform has its own pricing structure. For example, Hevo has a tier-based model where you can choose a plan based on your requirements and features offered, paying a fixed monthly fee. This model offers highly predictable pricing and doesn’t increase significantly as data volumes grow. You can voluntarily move to a higher tier as your data scales.

Fivetran charges based on Monthly Active Rows (MAR), which makes pricing less predictable and can add up quickly as usage increases. Similarly, Matillion uses a usage-based pricing model, providing a fixed monthly amount of credits.

Choose the pricing model that best suits your needs. According to user reviews, Hevo tends to offer more predictable and affordable pricing than other tools.

Excellent tool for ETL

Siddhartha

Director- analytics in India

4. Real-time data replication  

Real-time data replication is the process of continuously (or nearly continuously) syncing data from source to destination, so your analytics or ML models always work with the freshest data. ETL tools support real-time replication using log-based CDC and micro-batch syncs.

Hevo wins among the top tools for its real-time data replication capabilities. Its streaming architecture uses Kafka-like internal message queues to process, and load the data in real-time. This enables near real-time sync with latency as low as a few seconds to a few minutes.

Fivetran also offers micro-batch syncs every 1–5 minutes. While not true streaming, it gets close enough for many use cases.

This is an important factor when working with streaming data, so it’s worth considering it when selecting the tool.

5. Customer support

Premium support makes onboarding smoother and helps resolve issues quickly when they arise. Hevo offers live chat, email, and 24/7 support across all plans. 

Fivetran provides a web support portal, email support, Slack for enterprise customers, and phone support for premium users. 

Other tools also have their own customer support models. At a minimum, make sure the tool and plan you choose offer solid support when pipelines break. You can also look for extras like dedicated tool experts or onboarding assistance to further streamline your workflows.

It’s also worth checking the tool’s help docs, knowledge base, and community forums so you can quickly troubleshoot common issues on your own. 

FAQs about ETL as a service