10 Best Azure Data Factory Alternatives to Consider in 2026

If you are here, your current setup probably feels heavier than it should. Azure Data Factory gets the job done, but it often feels complex, expensive, or too tightly tied to the Azure ecosystem.

Choosing the right alternative to Azure Data Factory is not easy, especially when every tool promises scale and flexibility. This guide will help you compare the top Azure Data Factory alternatives with clear breakdowns of features, pricing, pros, cons, and practical decision points.

We will cover solutions that offer better scalability, flexibility, and value for teams looking to build reliable data workflows. Among these options, Hevo stands out as a fully managed and cost-effective data integration platform specifically for growing teams.

Want to explore it? Sign up for a 14-day free trial today.

What Is Azure Data Factory?

You likely already know Azure Data Factory (ADF) as the primary ETL engine for the Microsoft cloud. Most teams rely on it for data ingestion across databases, SaaS tools, and storage systems. It orchestrates large-scale data integration and backup workflows within Azure environments.

Enterprises with deep Microsoft adoption use it to manage complex pipelines, while engineers value its power and flexibility at scale.

At the same time, it demands strong technical expertise and close cost oversight. Recent moves toward Microsoft Fabric signal closer analytics and AI alignment for modern users. Still, Azure Data Factory remains a core option for traditional enterprise ETL, especially in hybrid setups.

Overview of the Azure Data Factory Alternatives

Here’s a comparison of leading Azure Data Factory alternatives:

Why Are People Moving Away From Azure Data Factory?

Although Azure Data Factory offers strong features, numerous limitations compel many organizations to look for substitutes.

1. High technical overhead

Operating Azure Data Factory usually requires dedicated data engineers. Building pipelines is only part of the work. You also spend time on monitoring, debugging, failures, and performance tuning. For smaller teams or time-sensitive use cases, this ongoing effort can slow down delivery and increase operational load.

The things I dislike most about ADF are usually around its complexity and limitations. Debugging pipelines is often tricky and can be frustrating, especially when something fails, there aren’t many tools for step-by-step troubleshooting. If my workflow is complex, managing and understanding everything can get overwhelming with so many moving pieces and screens to keep track of.

Dinesh G

Lead Digital Cloud Architect - Hybrid Cloud

View Review

2. Complex pricing structure

ADF has a multi-dimensional pricing model, where you pay for orchestration, data movement, transformation compute, and monitoring individually. This structure starts affordable, but costs become unpredictable and rise quickly, especially when pipelines stay active around the clock.

The one aspect that could be improved is the cost management, if not carefully monitored, the expenses can add up quickly, particularly when dealing with large volumes of data or frequent pipeline runs.

Sowjanya G.

View Review

3. Slower pace for modern data requirements

Many teams now want faster setup, simpler transformations, and clearer visibility into data flows. Azure Data Factory handles enterprise workflows well, but it feels rigid when teams need to move quickly. This pushes modern analytics teams toward tools built for speed and simplicity.

The debugging experience on the portal can be slow especially for large data flows. Also data flow mapping UI could be more responsive.

Shirisha D

Infra transformation sr analyst

View Review

4. Tight Azure dependency

ADF works best when most systems already live inside Azure. Once teams start using tools across other clouds or SaaS platforms, integrations become less straightforward. This makes it harder to stay flexible or change tooling without adding complexity to the overall data stack.

Microsoft done a great job to create this tool and there are nothing much but if the can provide more support to other different vendor connectors, databases for its transfromation activities as most of it only support to azure related storage. I mean there are few crucial activites like get metadata, until, stored procedure etc, although they are keep adding and supporting to new connectors in each update.

Mohan N

Programming Analyst - AIA Data. (Azure Data Engineer)

View Review

Detailed Breakdown of the Top 10 Azure Data Factory Alternatives

1. Hevo Data: Best for simple, reliable, and transparent pipelines

G2 rating: 4.4 (273)

Capterra rating: 4.7 (110)

Hevo Data simplifies data integration by offering a no-code platform that gets data moving in minutes. With a minimal learning curve, it helps you create reliable and scalable pipelines that are fully managed by the platform.

The 150+ pre-built connectors handle everything from databases to SaaS apps, while automatic schema mapping eliminates manual configuration. With real-time pipeline visibility and enterprise-grade security, Hevo is an ideal choice to build efficient workflows with consistent, analytics-ready data.

Key features

• Log-based Change Data Capture (CDC): Syncs data in real-time and captures incremental changes directly from source systems rather than reloading entire tables.
• Flexible transformations: Helps you clean, enrich, and standardize data before and after loading it using an intuitive interface or through Python and SQL.
• Built-in fault tolerance: Retries failed loads and resumes pipelines from the point of failure to prevent partial data loads.
• Data quality monitoring: Provides detailed metrics on throughput, latency, and failures with real-time alerts for faster identification of bottlenecks.
• Dedicated customer support: Offers 24/7 human assistance for setup, optimization, and troubleshooting. This is especially useful for smaller teams that rely on the platform for engineering.

Why choose Hevo over Azure Data Factory

• Warehouse-agnostic support: Hevo works across major cloud data warehouses without locking you into a single ecosystem. This flexibility is valuable for teams operating outside a strictly Azure-first environment.
• Predictable subscription plan: Unlike ADF, Hevo offers a straightforward subscription model that is easier to forecast and is affordable for both small and large businesses.
• Built for analytics-first SaaS teams: Hevo features a variety of SaaS connectors and aligns well with modern BI and analytics workflows due to its speed and reliability. Azure Data Factory fits broader enterprise data movement, but can feel heavy for analytics-driven use cases.

Pricing

• Free: Includes up to 1M events each month for five users.
• Starter: From $239/month, supports 5M events up to 50M, with SSH and SSL security for 10 users.
• Professional: From $679/month for 20M events up to 100M events, with reverse SSH access with no user limits.
• Business Critical: Tailored pricing for data volumes that exceed one hundred million events monthly.

A 14-day trial is offered across all paid tiers. Sign up now!

Customer review

Thanks to Hevo, we have successfully managed to set up a cutting-edge pipelining system, saving 6 months of development time. Maintenance has a marginal impact, and it requires an average of 10h/month, allowing us to avoid allocating full-time resources to this task and refocusing them on where we need the most. Overall, on a yearly basis, we estimate that our savings are close to €120K.

Marco Rossi

Head of Business Intelligence, Playtomic

View the full case study

2. Fivetran: Built for low-maintenance enterprise data movement

G2 rating: 4.2 (445)

Capterra rating: 4.4 (25)

Fivetran is an automated data movement platform that excels at replicating data from over 700 sources, including SaaS applications and databases, into cloud warehouses. It automatically handles API changes, schema drift, and connection issues so your pipelines run even when source systems are updated.

Its batch-focused and idempotent architecture efficiently manages large volumes of data and ensures data integrity by preventing duplicates even during network interruptions. This makes Fivetran well-suited for those seeking a standardized and highly reliable data movement layer.

Pros

• Supports hybrid and on-premise deployments.
• Reverse ETL lets you send processed data back to operational tools for activation.
• Native dbt integration orchestrates complex warehouse-side transformations.

Cons

• Usage-based pricing can escalate significantly with millions of active rows.
• 1-minute syncs are only available in Enterprise plans.
• Limited pre-load transformation options compared to traditional ETL tools

Advantages of Fivetran over Azure Data Factory

• Consistent pipeline behavior: Fivetran enforces standardized replication logic across all connectors. Azure Data Factory allows flexibility, but that often results in uneven pipeline behavior across teams and projects.
• Automated schema normalization: Fivetran delivers data in normalized schemas close to third normal form for SaaS connectors, with consistent type mapping and structure. ADF typically requires more manual schema handling and delivers data in formats that need additional modeling before analysis.

Pricing

Fivetran uses a consumption-based pricing approach based on Monthly Active Rows. A free tier is available with limited data usage and model runs.

Paid plans start at $499.99/MAR and increase with higher data volumes. New users can explore the platform with a 14-day free trial.

Customer review

Fivetran performs as expected. Once the destinations and connectors are set-up, we typically don't have to worry about them. They just work. If there are issues, the Fivetran team supporting our account is very responsive.

IT Associate

Software

3. Integrate.io: Built for operational ETL and rapid API generation

G2 rating: 4.3 (208)

Capterra rating: 4.6 (17)

Integrate.io is an API-first integration platform with strong support for ETL, ELT, and reverse ETL workflows. You can visually build pipelines with over 150 native connectors, write Python transformations, or use SQL-based approaches depending on your team’s skills.

If you operate in time-sensitive workflows, Integrate.io’s sub-60-second latency in database replication consistently delivers updated data. It automates pipeline orchestration and dependency management across complex workflows.

Pros

• Compliant with HIPAA, SOC 2, and GDPR.
• White-glove customer support offers 24/7 assistance.
• Standardizes and aggregates datasets before they reach the warehouse.

Cons

• The initial investment can be expensive for smaller teams.
• Error handling can be less intuitive than competitors.
• Custom connector development requires professional services engagement.

Advantages of Integrate.io over Azure Data Factory

• Visual in-flight transformations: Azure Data Factory often relies on heavy Spark clusters or external Azure Functions for complex logic. Integrate.io offers 220+ pre-built transformation components in a drag-and-drop interface, which helps you join, filter, and aggregate data before it reaches the warehouse.
• Instant API generation: Integrate.io can convert any database or file system into a secure, fully documented REST API endpoint within minutes. It exposes that data to custom applications or third-party services without writing backend code.

Pricing

Integrate.io offers clear pricing and a 14-day free trial. Subscription plans begin at $1,999/month, with custom pricing available for enterprise solutions.

Customer review

Integrate.io simplifies data tranformation by allowing you to build and reuse processes. In addition to this, you can set up schedules to automate your workflow. It has many features that allow you to get exactly what you need from the data. To top things off, the customer service is fantastic.

Verified User

Insurance

4. Airbyte: Built for open-source flexibility

G2 rating: 4.4 (75)

Capterra rating: NA

Airbyte is an open-source data integration engine designed for complete flexibility. It excels at moving data between various sources and destinations through its massive community-driven library of 600+ connectors.

The platform is best for technical teams that want to avoid vendor lock-in while maintaining full control over their data infrastructure.  Airbyte provides both a managed cloud service and a self-hosted option, bridging the gap between pre-built automation and custom-coded solutions for modern data workflows.

Pros

• Custom connector creation with simple SDK.
• Flexible orchestration through APIs and cron scheduling.
• Native PyAirbyte support helps you run syncs directly in Python notebooks for fast prototyping.

Cons

• Self-hosted instances require significant engineering time for updates and scaling.
• Community-contributed connectors vary in stability compared to Airbyte-managed options.
• Relies heavily on external dbt integrations for complex logic during syncs.

Advantages of Airbyte over Azure Data Factory

• Native vector database: Airbyte supports modern AI workloads with built-in destinations for vector databases, like Pinecone and Weaviate. With ADF, you must link pipelines to Azure AI Search and Azure Document Intelligence to enable retrieval-augmented workflows. Airbyte simplifies this by allowing you to configure chunking and embedding settings directly inside the destination connector.
• Pricing transparency: Airbyte’s higher-tier cloud plan offers a clear capacity-based model based on Data Workers. Costs align with actual compute effort rather than opaque activity charges. This allows high-throughput pipelines to scale without the bill surprises common in traditional consumption-based pricing.

Pricing

Aibyte provides a free open-source version for self-hosted setups. Its Standard cloud plan starts at $10/month, with extra credits priced at $2.50 each.

The higher-tier plans are capacity-based and customized according to your requirements.

Customer review

We absolutely love Airbyte. It makes a process that normally has several steps (extract, move the data, format the data, ingest the data, process the data) a simple point and click process. It\'s multiple connectors and ease of use is unparallelled.

IT ASSOCIATE

Banking

5. Matillion: Built for warehouse-centric data flow

G2 rating: 4.4 (81)

Capterra rating: 4.3 (111)

Matillion is a cloud-native data integration platform that uses the power of your warehouse for high-speed processing. It operates natively inside warehouses like Snowflake, BigQuery, Redshift, and Databricks rather than as a separate ETL layer. This push-down architecture delivers faster performance for large-scale transformations.

The platform also offers an AI-powered assistant called Maia that helps you create connectors and script pipeline logic using natural language prompts. This assists engineers in delivering ready-to-use data without the manual overhead of traditional coding.

Pros

• Native orchestration for complex dependencies across multi-step data workflows.
• Strong support for CI/CD through Git-based deployment workflows.
• Converts any OpenAPI specification into a functional connector without manual coding.

Cons

• Enterprise pricing can be expensive for teams running many concurrent pipelines.
• Limited support for real-time or streaming ingestion use cases.
• UI can feel dense for users new to ELT tooling.

Advantages of Matillion over Azure Data Factory

• AI-driven auto-documentation: Matillion uses AI to scan component metadata and automatically generate clear, human-readable Markdown documentation. Joins, filters, and transformations are automatically documented by default, which helps new team members understand complex workflows without digging through configuration panels.
• Warehouse-specific SQL optimization: ADF Mapping Data Flows translate visual logic into generic Spark code, which can increase execution time and cluster costs. Matillion applies push-down optimization to compile pipelines into the native SQL dialect of the target warehouse. This approach fully utilizes micro-partitioning and clustering for efficient large-scale transformations.

Pricing

Matillion offers a compute-based pricing structure tied to vCore usage. Entry-level Developer access begins at $2.50 per credit, while higher-tier plans require a monthly minimum and are priced based on deployment needs. A 14-day trial is available.

Customer review

Matillion is one of the most used full-fledged ETL tools, which provides all types of ETL components for Extract, load, and transform components, which helps people who are less familiar with SQL or code to build the whole data pipeline.

Verified Author

View Review

Check out this Matillion vs Azure Data Factory guide for a deeper understanding of how both tools compare.

6. Keboola: Built for AI-augmented data ecosystems

G2 rating: 4.6 (137)

Capterra rating: 4.9 (12)

Keboola is a modular, cloud-based data integration and automation platform that unifies ingestion, transformation, orchestration, governance, and analytics. It connects to more than 700 sources and destinations and helps you centralize, enrich, and share data without managing complex infrastructure.

The platform also provides a powerful AI agent called the Keboola Data Agent that generates pipelines and troubleshoots errors based on your specific project context. Keboola suits teams that need agility with strong governance in modern data stacks.

Pros

• SQL, Python, Julia, and R transformations are supported within governed environments.
• Simplifies dev, staging, and production workflows through Development Branches.
• Granular role-based access control across projects and components.

Cons

• Interface complexity can overwhelm users new to modular data platforms.
• Advanced governance features require upfront configuration effort.
• Debugging custom components may require deeper knowledge of the platform.

Advantages of Keboola over Azure Data Factory

• Native Data App hosting: Keboola goes beyond simple data delivery by allowing you to host interactive Data Apps, such as Streamlit or custom internal tools, directly on its infrastructure. This allows you to move from raw data to a user-facing application within the same governed environment and significantly accelerates the time-to-value for business users.
• Context-aware automation and lineage: Keboola treats telemetry and lineage as a core platform feature rather than an add-on. Every action, from the first extraction to the final transformation, is automatically logged and visualized. This ensures that you have an instant audit trail for every data point without any configuration.

Pricing

Keboola offers a hybrid pricing approach that blends a base subscription with consumption-based charges tied to compute usage.

The free tier offers 120 processing minutes for the first month, after which usage is billed per 60 minutes every subsequent month. Additional minutes are billed at $0.14.

Enterprise plans are custom-priced for advanced architecture.

Customer review

I have to say that coming across Keboola at the beginning of my career as a Data Analyst was the best experience I could have had. The integration with Snowflake works flawlessly, making the workspaces intuitive. Keboola works even better when a company has strong Data Governance. The Keboola Academy helped me the most at the start, but I still return to it—and as I gain more experience, I continue to get even deeper context from it. I use Keboola almost on a daily basis. I haven’t needed to contact customer support, as my supervisor is able to answer all of my questions.

Zbyněk V.

Data Quality Specialist

7. Qlik Talend Cloud: Built for multi-cloud integration agility

G2 rating: NA

Capterra rating: NA

Qlik Talend Cloud is a unified cloud-based data integration platform that combines Qlik Talend Data Integration, Talend Cloud, and Talend Studio. It enables data movement across cloud, on-premises, and hybrid environments.

The platform offers a modular set of tools that handle real-time change data capture, advanced transformations, and automated data profiling. These features help technical teams maintain compliance and security standards while accelerating time-to-insight.

Pros

• Automates complex workflow orchestration and dependency management with Qlik Automate.
• Centralized metadata management across multiple environments.
• Offers unlimited data movement to Qlik Cloud Analytics.

Cons

• Configuring remote engines and governance policies requires deep platform expertise.
• Advanced features, like Spark processing, are locked behind high-cost plans.
• Pricing can become costly for high-volume workloads.

Advantages of Qlik Talend Cloud over Azure Data Factory

• Active data quality checks: Qlik Talend Cloud stands out for its focus on data health. It assigns a Qlik Trust Score to every dataset, measuring factors like accuracy, diversity, and usage. While ADF requires separate services like Purview for cataloging, Qlik builds data quality directly into the pipeline.
• Secure and agnostic architecture: Qlik Talend Cloud streams data directly from sources to targets, avoiding intermediate hubs. The architecture-agnostic structure lets you deploy pipelines across hub, fabric, or mesh models without being tied to a single cloud provider, unlike ADF’s Azure-centric approach.

Pricing

Qlik Talend Cloud uses a subscription-based tiered model with a 14-day free trial. It offers custom-priced plans based on features, connectivity, and automation needs.

Customer review

Talend Data Integration helps to collaborate between different services and helps in data ingestion from various sources like Azure, AWS, on-premises, etc. It supports almost all kinds of file types and there are very good data quality check features available in Talend.

Arijit C.

Data Engineer

Compare Talend and Azure Data Factory in detail here.

8. AWS Glue: Built for serverless data lake processing

G2 Rating: 4.3 (194)

Capterra rating: 4.1 (10)

AWS Glue is Amazon’s serverless integration service that simplifies data discovery and preparation for analytics or machine learning. It excels at handling massive datasets within a data lake architecture and performs best within the Amazon ecosystem.

The platform supports both visual interfaces and code-based environments for maximum flexibility. Its natural integration with services like S3, Redshift, and Athena makes it a strong choice for native AWS users.

Pros

• Supports Python, Scala, and Spark for flexible transformations.
• Integrates with AWS Lake Formation for security.
• Features native ACID-compliant transaction support for Iceberg, Delta, and Hudi.

Cons

• Costs can grow with frequent large ETL jobs.
• Limited support for non-AWS cloud sources.
• Debugging complex jobs requires expertise with AWS and Spark.

Advantages of AWS Glue over Azure Data Factory

• Automated metadata stewardship: While Azure Data Factory requires dataset configuration for each pipeline, AWS Glue uses intelligent crawlers that automatically discover and catalog schemas across your S3 data lake. These crawlers scan buckets, identify formats, and keep your Data Catalog synchronized. This eliminates manual maintenance when managing thousands of files in growing data lakes.
• Performance-optimized runtime: AWS Glue 5.0 uses a performance-optimized Spark runtime that is up to 3.9x faster than standard open-source Spark. This directly translates to lower costs and shorter execution windows for batch transformations.

Pricing

AWS Glue uses a pay-as-you-go pricing model based on ETL jobs, crawlers, DataBrew, and data quality tasks. A free tier is available for select components, and pricing scales with usage.

Customer review

I love how simple data management and organization are with it. AWS Glue saves a ton of time by automating most of the data integration and preparation process. Even for novices the visual interface is easy to use, and because it's serverless, I don't have to worry about infrastructure. The user interface is simple to use and navigate, making tasks straightforward.

Milan S.

Senior Data Analyst

9. Informatica: Built for enterprise‑grade, AI‑powered data management

G2 Rating: 4.4 (85)

Capterra rating: 4.1 (18)

Informatica is an AI-powered platform built for high-scale enterprise data across multi-cloud and hybrid landscapes. As part of the Intelligent Data Management Cloud (IDMC), it combines data integration, quality, governance, API management, and master data services.

The CLAIRE AI engine helps you automate labor-intensive tasks like data discovery and mapping. Data engineers can use AI-guided assistance embedded throughout pipeline design to accelerate development of advanced workflows.

Pros

• Creates a unified 360-degree view of customers directly during integration.
• Offers strong change management and version control capabilities.
• Auto-scalable in multi-cloud environments.

Cons

• Steep licensing costs for small or mid-sized teams.
• Complex setup demands experienced platform specialists.
• Slower innovation cycles than cloud-native competitors.

Advantages of Informatica over Azure Data Factory

• Governance-native integration: Informatica IDMC is governance-native, meaning data quality rules and privacy policies are embedded inside the integration engine. Data is automatically profiled and validated against corporate standards as it flows to ensure trustworthiness.
• Self-service data marketplace: While ADF relies on separate Azure services for data discovery, Informatica features a Data Marketplace that functions like a storefront for business teams. You can browse a catalog of certified, governed datasets and request access through a simplified shopping experience.

Pricing

Informatica offers a volume-based custom pricing model that varies by deployment and workload. New users can try the Cloud Data Integration service with a 30-day free trial.

Customer review

Informatica is easy to use, great documentation explaining exact features, and great support. It\'s easy to implement business logic via the transformations offered.

Kritika

Data Engineer

Want to explore how Informatica compares with other tools? Here’s a comprehensive Informatica vs Matillion guide.

10. Pentaho: Built for hybrid data orchestration

G2 rating: 4.1 (50)

Capterra rating: 4.3 (76)

Pentaho, now part of Hitachi Vantara, is an ETL option with enterprise features that support hybrid environments spanning on-premises and cloud systems. It handles data processing across multiple deployment models and excels at both batch workloads and real-time stream processing.

The tool is best-suited if you require a highly customizable solution with integrated analytics capabilities. Pentaho simplifies sophisticated data pipelines through its drag-and-drop interface, which makes it convenient for teams to collaborate.

Pros

• Ingests high-velocity IoT data using the MQTT protocol integration through plugins.
• Provides built-in reporting and dashboards without external tools.
• Runs identical pipelines across on-prem, private cloud, and public cloud.

Cons

• Production workloads require paid licensing beyond the Developer Edition.
• Consumes more memory and CPU than serverless cloud-native platforms.
• Advanced customization demands strong Java expertise.

Advantages of Pentaho over Azure Data Factory

• Unified BI and analytics: ADF is purely an integration tool, requiring you to move data to Power BI or Synapse for visualization. Pentaho includes Business Analytics (PBA) directly within the platform. This allows you to build reports and interactive dashboards in one environment and visualize insights immediately during the transformation phase of ETL.
• Advanced Metadata Injection: Pentaho features ETL Metadata Injection, a capability that allows you to create a single transformation, and the platform injects the specific metadata for different sources at runtime. This helps you manage hundreds of unique files with varying structures using one reusable transformation.

Pricing

Pentaho uses a subscription and licensing model that varies by edition and deployment needs. The Developer edition is free for non‑production use, while higher tiers offer progressively more support, features, and enterprise capabilities.

It offers a 30-day free trial for its enterprise plan.

Customer review

Pentaho is one of the best etl tool to extract ,transform and load the data among various sources ,it just requires connections of the database and transfers data very fast.

Sandeep C.

Data Analyst

What Factors Should You Consider When Choosing an Azure Data Factory Alternative?

Team’s technical expertise

The skills and size of your team play a crucial role in selecting the right ETL tool. No-code platforms are ideal for smaller or less technical teams because they allow them to build pipelines quickly. More complex or open-source options require engineers familiar with coding, orchestration, and infrastructure management. Choose a tool that balances the control you need with your team’s skillset.

Sync frequency and latency

Define your sync requirements early. Some tools support real-time syncs while others operate in intervals of several minutes or hours. Consider how critical fresh data is for your reporting, dashboards, or analytics workflows, and pick a solution that meets these requirements. Sync latency can vary by plan, so consider this when reviewing pricing.

Pricing predictability

ETL costs can escalate depending on data volume, pipeline frequency, and transformations. Transparent pricing models allow you to predict your monthly spending and tool migrations later. If you are going for a self-hosted platform, calculate how much you’ll spend on hiring a team, ongoing maintenance, hardware costs, software licenses, etc.

Ongoing maintenance

Some platforms demand regular monitoring, debugging, and performance tuning to keep pipelines running smoothly. Assess your team’s capacity to handle these operational tasks. Fully managed solutions can reduce this burden and minimize the risk associated with pipeline failures or downtime.

Connector coverage

Your ETL platform should support all key sources and warehouses your team uses. Look for tools that offer custom connector options to handle niche or proprietary systems. Wide connector coverage ensures future flexibility as your data ecosystem grows or you adopt new SaaS, databases, or storage solutions.

Why Hevo Is the Smart Choice for Teams Growing in Data Capacity

Hevo gives growing teams the balance they need: enterprise reliability without complexity or unpredictable costs. It scales seamlessly from hundreds to millions of events and provides built-in monitoring to keep every pipeline visible and error-free. 

With a no-code setup plus optional scripting, teams can focus on analysis instead of maintenance, while event-based pricing keeps spend predictable. Combined with SOC 2 Type II, GDPR, HIPAA, DORA, and CPRA compliance, Hevo ensures your data workflows stay efficient, secure, and future-ready.

Explore how Hevo can simplify your pipelines if you’re looking beyond Azure Data Factory. Schedule a free demo with an expert today!

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