Top 8 Data Pipeline Tools in 2025

It’s hard to wrap your head around it, but the world generates about 328.77 terabytes of data every single day. That’s not just numbers in the cloud; it’s everything from sales transactions and app activity to social media interactions and sensor data. And if your team doesn’t have a good data pipeline in place, most of that valuable information ends up underused or stuck in silos.

In this guide, we’ll walk you through the best data pipeline tools to consider in 2025, break down the key features that matter, compare different types of tools, and help you find the right fit for your team.

If you don’t have time to read through our research, here’s a quick comparison table of the best data pipeline tools to check out:

Tool Name	Best For	Key Strengths	Ideal Users
Hevo Data	No-code real-time pipelines	Automated schema mapping, real-time sync	Business analysts, cloud teams
Apache NiFi	Visual batch and streaming flows	Fine flow control, data lineage, and scalability	Data engineers, regulated industries
Talend Data Management	Enterprise ETL and data governance	Strong data quality and governance features	Compliance teams, large enterprises
Apache Kafka	High-throughput real-time streaming	Durable, scalable, open-source	Developers, event-driven apps
Redpanda Data	Low-latency streaming	Kafka-compatible, high-performance	Developers, real-time analytics
Stitch	Simple ETL pipelines	Easy setup, broad integrations	SMBs, data analysts
AWS Glue	Serverless big data ETL	Scalable, cross-cloud integration	Cloud engineers, analysts
Google Cloud Dataflow	Real-time stream and batch processing	Fully managed, autoscaling, flexible	Cloud engineers, data scientists
Apache Airflow	Workflow orchestration	Highly extensible, Python-based	Data engineers, DevOps teams
IBM DataStage	Complex enterprise data integration	Scalable, AI-powered, reliable	Large enterprises, data architects

What is a Data Pipeline Tool?

A data pipeline tool helps you move data from one place to another. It connects different systems, pulls in raw data, organizes it, and delivers it to where your team needs it, like a data warehouse or analytics dashboard. This makes it easier to work with your data and trust that it’s accurate and up to date.

Most tools follow the ETL (Extract, Transform, and Load) approach. First, they extract the data from multiple sources. Then they clean it up and format it. Finally, they load it into a destination where it can be analyzed or used in reports.

Key Features and Benefits of a Data Pipeline Tool

Monitoring and Alerting

From my experience, integrated monitoring and alert systems are crucial. They notify you immediately when issues arise, helping prevent downtime and ensuring smooth data flow. This feature saves valuable time by reducing troubleshooting efforts.

Cross-Functional Collaboration

Modern data pipeline software provides centralized platforms that enable different teams, such as engineers and analysts, to securely access, manage, and share data. This breaks down silos and improves communication, which is essential for effective decision-making.

Reliable Delivery and Scalable Performance

Whether handling batch data or real-time streams, a data pipeline tool must deliver data accurately and on time. Scalability is key as data volumes grow, and in my view, choosing tools that grow with your business prevents future bottlenecks.

Data Quality and Consistency

Built-in transformation and validation processes ensure data is clean, consistent, and ready for analysis before reaching storage or analytics platforms. This guarantees that business decisions are based on reliable and trustworthy data.

What are the Types of Data Pipeline Tools

• ETL/ELT Tools
• Batch Data Pipeline Tools
• Streaming Data Pipeline Tools
• Cloud-Native Data Pipeline Tools

1. ETL/ELT Tools

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) tools automate moving data from multiple sources to destinations for analysis. They organize data, making it easier for teams to concentrate on understanding what the data means. In my view, you need these tools to set up reliable data pipelines quickly, without needing to use complicated programming.

Businesses of all sizes use ETL/ELT tools to accelerate scalable data integration. They simplify connecting diverse data sources and help avoid reinventing the wheel every time you onboard new data.

Tool	Use Cases
Hevo Data	Enabling seamless data synchronization for marketing analytics dashboards. Consolidating e-commerce transaction data in real time for inventory management.
Stitch	Migrating legacy CRM data into a modern cloud data warehouse. Automating periodic backups of financial data from multiple SaaS platforms.

A. Hevo Data

At Hevo, we built a cloud-based no-code ELT platform to make building data pipelines quick and easy. With over 150 connectors, you can effortlessly pull data from databases and SaaS apps and load it into your destination.

What I am most proud of is our automation, which handles schema mapping, error detection, and recovery automatically. This means your pipelines run smoothly with minimal effort. Our immediate data replication and extensive tracking allow you to monitor and control everything.

From my experience, Hevo is perfect for teams that want reliable and scalable pipelines without complex coding. It helps you spend less time managing data and more time turning it into insights.

Key Features

• Monitoring and Alerting: You can monitor your CDC pipeline health with intuitive dashboards showing every pipeline stat and data flow. You also get real-time visibility into your CDC pipeline with alerts and activity logs.
• Automated Schema Management: Whenever there’s a change in the schema of the source database, Hevo automatically picks it up and updates the schema in the destination to match.
• Seamless Integration with dbt Core: Create, test, and run dbt models directly on Hevo’s platform, leveraging dbt Core’s full capabilities like version control and modular SQL, while potentially avoiding the licensing fees associated with dbt Cloud.
• Pre and Post-Load Transformations: Offers flexibility to clean, enrich, and transform data both before and after loading, using Python or dbt.
• Security: Hevo complies with major security certifications such as HIPAA, GDPR, and SOC-2, ensuring data is securely encrypted end-to-end.
• 24×7 Customer Support: Live chat with around-the-clock assistance and thorough support documentation is available.

Pros

• Designed to manage updates, patches, and scaling effectively, ensuring consistent uptime and optimal performance.
• Smart Assist feature in Hevo proactively displays any error or condition that could affect the data ingestion in your CDC Pipelines.
• No Technical Expertise Required
• Supports data transformations through a drag-and-drop interface and Python code-based transformations as well.

Cons

• Limited on-premise support: Primarily built for cloud-based integrations.
• No custom connectors

Customer Testimonial:

“Huge lift with minimal effort: We had numerous files that needed rapid ingestion and a pipeline established without being hindered by server infrastructure or schema complexities. Hevo enabled us to achieve this efficiently.”

B. Stitch

With a main goal of being simple to get started, Stitch is a powerful tool for developers. Having many integrations allows you to easily transfer data from several sources into your data warehouse or lake.

The scalability of Stitch is valuable because it means companies can expand without having to adjust their pipeline setup. It makes it easy for teams to start using the platform right away.

I think Stitch is perfect for developers and data teams who want a simple, straightforward ETL system. Since it is simple to use and has clear prices, it attracts businesses of all types.

Key Features

• Efficient Extract and Load Core: Stitch Data prioritizes streamlined data extraction and loading processes. 
• Integration with Open-Source Standards: The platform integrates with open-source tools, notably Singer.io, providing a foundation for advanced users and developers.
• Automated Data Synchronization: Stitch Data facilitates automated data updates through scheduled syncing. Users can define frequencies (e.g., hourly, daily) to ensure data in target systems remains current without manual intervention.
• SOC 2 Certified: Ensures data security and compliance.
• Comprehensive documentation: The Stitch team provides very detailed documentation of their product, and they release every change log on time.

Pros

• Simple interface and very quick onboarding process.
• Transparent and relatively affordable pricing for lower data volumes.
• Good for familiar SaaS sources and databases.

Cons

• Limited number of connectors compared to Fivetran or Hevo.
• Customer support has received mixed reviews, especially for complex issues.
• Less suitable for high data volumes or complex transformation needs.

Customer Testimonial:

“Code free and easy integration. Also free limit for new integrations which help in testing.”

2. Batch Data Pipeline Tools

Batch data pipeline tools process data at scheduled intervals instead of continuous processing. They extract, transform, and load large volumes of data in batches, ensuring accuracy and consistency over time. These tools work well when real-time processing isn’t essential, but reliable data delivery is critical.

This type of pipeline is common in industries like finance, healthcare, and other regulated sectors. They rely on batch tools to maintain data integrity, support auditing, and meet compliance requirements while efficiently handling large datasets.

Tool	Use Cases
Apache NiFi	Moving data from IoT devices to databases quickly and reliably.Automating data flow between different business applications in real time.
Talend Data Management Platform	Combining data from different cloud apps into one clean system.Ensuring data quality and consistency during system upgrades.

C. Apache NiFi

Apache NiFi is a visual, flow-based batch data pipeline tool that allows teams to design and control data workflows with ease. It offers detailed data provenance tracking, which is critical for compliance and auditing in regulated industries. NiFi supports hybrid cloud environments and integrates well with Microsoft Azure services.

It is best for organizations needing strong data lineage and secure data transfer within complex batch workflows. NiFi helps data engineers build reliable pipelines with visual tools and robust security features.

What makes NiFi unique is its user-friendly drag-and-drop interface combined with fine-grained control over data flows, supporting a variety of data formats and protocols.

Key Features: 

• Data Lineage Tracking: NiFi meticulously records the journey of your data, showing where it came from, how it changed, and where it went, which is great for troubleshooting and meeting rules.
• Robust Security Features: It offers secure ways for data to travel, options for who can access what, and methods to protect sensitive information.
• Customizable Functionality: If you need something specific, you can build your own data handling tools in Java to expand what NiFi can do.
• Reliable Data Transfer: It employs smart techniques like logging changes first and using holding queues to make sure data doesn’t get lost if something goes wrong.
• Flow Control Management: NiFi can dynamically adjust how fast data moves to prevent systems from getting swamped, ensuring a smooth flow between different parts.

Pros

• Can handle almost any data routing/transformation with its extensive processor library.
• Free, open-source software with an engaged and supportive community
• Provides broad frameworks for ingesting and governing varied data sources and formats

Cons

• Requires significant, and tunable, computer resources (CPU, memory)
• Complex transformations could require many granular, chained processors.
• Primarily focused on data flow/orchestration, not deep or singular transformations.

Customer Testimonial:

“Value for money!: The user interface is quite good and easy to navigate to every area.”

D. Talend Data Management Platform

Talend Data Management Platform combines batch ETL, data quality, and governance into a scalable platform. It offers robust data cleansing, validation, and compliance features, making it suitable for enterprises with stringent regulatory needs. Talend integrates natively with Azure Data Lake and Azure Synapse Analytics, ensuring smooth cloud adoption.

Talend is ideal for organizations that need an all-in-one solution for managing large batch jobs while enforcing data governance policies. It helps teams automate and secure their data workflows efficiently.

Talend’s strength lies in its blend of powerful ETL capabilities and governance controls, tailored for enterprise-scale deployments.

Key Features

• Visual Transformation Studio (Talend Studio): Talend Studio provides a graphical, drag-and-drop interface where developers design data integration “jobs” by connecting a vast library of pre-built components.
• Integrated Data Quality Management: A key strength is the tight integration of data quality tools directly within transformation workflows.
• Code Generation and Extensibility: While primarily visual, Talend generates underlying Java or Spark code that can be inspected, customized, or extended by developers needing to implement highly specific logic or integrate custom routines.

Pros

• End-to-end solution for complex, enterprise-wide data needs.
• Strong data governance, quality, and compliance features.
• Versatile deployment options catering to various infrastructures.

Cons

• Steep learning curve and may require dedicated IT resources.
• Pricing can be high, making it less suitable for smaller organizations.
• The interface can feel complex compared to newer, more streamlined tools.

Customer Testimonial:

“Worked as a data engineer on tools like talent and abinitio : Easy to use, less coding and more automated.”

3. Streaming Data Pipeline Tools

Streaming data pipeline tools process data instantly as it arrives, enabling real-time analysis and action. They are vital for businesses that need to detect fraud, track user activity, or monitor IoT devices immediately. Unlike batch processing, streaming pipelines provide a continuous flow of fresh data, allowing quick responses to changes.

For companies relying on live data to stay competitive, streaming pipelines are the best choice. They enable faster decisions and better customer experiences by delivering timely insights without delay.

Tool	Use Cases
Apache Kafka	Handling financial transactions with reliable, fault-tolerant message streams.Delivering real-time player stats and matchmaking in online games.
Redpanda Data	Streaming vehicle telemetry for real-time fleet monitoring.Managing smart city sensor data for traffic and environment tracking.

E. Apache Kafka

Apache Kafka is a leading real-time streaming platform known for handling large data volumes reliably and quickly. It integrates smoothly with Azure Event Hubs, supporting seamless Azure cloud use.

Kafka is best for companies building event-driven systems that require durability and scalability. It helps developers and data engineers build robust streaming applications that manage millions of events per second.

Kafka’s strengths include its distributed architecture, fault tolerance, and strong community support.

Key Features

• Acts as a Protective Buffer: Apache Kafka functions as an intermediary, receiving data from various source systems. This means your target systems aren’t directly hit with overwhelming data, as Kafka sits in its own dedicated set of servers (a cluster), preventing crashes.
• Simplifies Integrations: Instead of your developers writing code to connect every source system to every target system, Kafka centralizes this. You create one integration from each source to Kafka, and one integration from Kafka to each target, significantly reducing the number of custom connections needed.
• Achieves Low Latency and High Throughput: Kafka allows systems to consume data when they’re ready, effectively decoupling them. This drastically reduces the delay (latency) in data delivery, often down to just 10 milliseconds for real-time speed. It can also scale out to handle massive data volumes by adding more servers to its cluster.
• Provides Centralized and Easy Data Access: Since all data flows through Apache Kafka, it becomes a central hub. This makes it much simpler for any team in your organization to access the data they need directly from Kafka, without having to go through multiple other teams or systems.

Pros

• High availability and fault tolerance: It achieves high availability and fault tolerance through replication at the partition level.
• Flexibility: The platform allows customers to configure brokers and topics according to their unique requirements.
• Support for different consumption needs: The pull-based approach enables apps to read data at individual rates and choose how to consume information, in real-time or in batch mode. Kafka also lets you set data retention rules for a particular topic.

Cons: 

• No managing and monitoring tools
• Steep learning curve and technological complexity

Customer Testimonial:

“Kafka: The Ultimate Choice for Event-Driven Architectures : Best option when you want to build and control a huge event driven system.”

F. Redpanda Data

Redpanda is a modern streaming platform designed as a faster, simpler alternative to Apache Kafka. It offers low latency and high throughput with easier deployment and management.

Because Redpanda is API-compatible with Kafka, it allows organizations to easily switch without needing to adjust existing applications. It is best for companies that aim for strong streaming and want to keep things simple when operating.

Redpanda stands out for its simplified architecture and resource efficiency.

Key Features

• Single Binary Deployment: A single binary with built-in schema registry, HTTP proxy, and message broker simplifies deployment and scaling in self-managed environments.
• Kafka API-Compatible: Migrate from Kafka without application code changes or use the entire Kafka ecosystem out-of-the-box if new to streaming.
• Redpanda Console: Provides a complete web UI for visibility into data streams, featuring time travel debugging and cluster administration.
• Developer-Friendly (Local Dev, CI/CD, CLI): Deploys quickly and runs efficiently anywhere (containers, laptops, cloud), with a developer-first CLI (RPK) for managing the entire cluster.
• No External Dependencies (No ZooKeeper, No JVM): Offers freedom from ZooKeeper and JVM, simplifying deployments in modern environments like Kubernetes and edge.

Pros

• Deploys Anywhere: Runs easily in cloud, private cloud, bare metal, or at the edge, supporting existing identity and access management.
• Inline Data Transforms: Built-in data transforms enrich data or apply business logic without needing additional processing infrastructure.
• Redpanda Connect: Offers 290+ pre-built connectors for easy data integration, available for free or with paid support for enterprise features.
• Scalable Cluster Operations: Provides built-in operational workflows like Maintenance Mode, diagnostics, and seamless rolling upgrades for cluster health.

Cons

• It’s newer, so it has a smaller track record and community.
• Its super-fast internal design is complex, which might be a lot for teams not deeply into tech details.

Customer Testimonial: 

“Scalability, Usability, Stability – Pick all : Red Panda is substantlially easier to get up and running than a traditional Kafka queueing solution; while it’s also more scalable and performance.”

4. Cloud Native Data Pipeline Tools

Cloud-native data pipeline tools fully use cloud infrastructure, offering great scalability and ease of use. From my experience, they help businesses avoid infrastructure hassles and focus on building efficient data workflows. With Azure integration, deploying and scaling these applications is streamlined.

These tools suit organizations moving to cloud-first strategies. They handle both batch and streaming data, reduce costs, and automate resource management while connecting smoothly with cloud storage and analytics.

Tool	Use Cases
AWS Glue	Automating ETL from S3 to Redshift for analytics.Cleaning and cataloging data for compliance across AWS.
Google Cloud Dataflow	Processing real-time clickstream data for marketing.Running batch transformations for ML training on GCP.

G. AWS Glue

AWS Glue is a serverless ETL service that automates data discovery and preparation, helping teams reduce manual work. I’ve found it especially useful for businesses already using AWS, as it integrates well with services like S3 and Redshift.

It handles both batch and streaming data, enabling it to support multiple types of data processing. Using serverless technology, the system can automatically handle a growing workload, making infrastructure issues nonexistent.

What is notable is that teams can focus on setting up pipelines rather than handling servers, making it much quicker and simpler to scale.

Key Features

• Natural Language ETL Code Generation (Amazon Q): Create ETL jobs using natural language with Amazon Q Data Integration, which auto-generates Apache Spark code.
• Schema Management for Data Streams: AWS Glue Schema Registry validates and controls the evolution of streaming data using registered Apache Avro schemas for data quality.
• In-Flight Streaming Data Transformation: Serverless streaming ETL jobs continuously consume, clean, and transform streaming data from sources like Kinesis and MSK.
• Apache Iceberg Table Optimization: AWS Glue Data Catalog supports optimization of Apache Iceberg tables through compaction, snapshot retention, and unreferenced file deletion.
• Query Performance Optimization with Column Statistics: Supports column-level statistics for various data formats, enabling services like Athena and Redshift to optimize query plans.

Pros

• Sensitive Data Detection & Remediation: Helps define, identify, and process sensitive data like PII in data pipelines by redacting, replacing, or reporting on it.
• Python Code Scaling with Ray: AWS Glue for Ray enables data engineers to process large datasets using familiar Python and its libraries on the Ray.io framework serverlessly.
• Serverless Data Integration: A serverless, scalable service for discovering, preparing, moving, and integrating data without managing infrastructure, paying only for resources used.

Cons

• Potential for High Costs if Not Optimized
• Learning Curve for Advanced Spark Customization
• Potential for High Costs if Not Optimized

Customer Testimonial:

“Glue- Best Integration Tool : Absolutly love Glue for its functionality. Provides everything needed.”

H. Google Cloud Dataflow

Google Cloud Dataflow is a managed service that unifies batch and streaming data processing, built on Apache Beam. It is straightforward to work with complex data using it while avoiding infrastructure issues.

Its autoscaling feature adjusts resources based on demand, helping manage costs. It integrates deeply with Google Cloud tools like BigQuery for real-time analytics.

Dataflow’s strength lies in flexibility and smooth cloud integration, allowing teams to focus on insights rather than infrastructure.

Key Features

• Processing Guarantees: Dataflow ensures exactly-once processing for every record, or at-least-once mode for lower cost and latency with tolerable duplicates.
• Portable (Apache Beam Based): Built on open-source Apache Beam, Dataflow executes pipelines written in SDKs like Java or Python, allowing platform changes without rewrites.
• Flexible Pipeline Creation: Suitable for simple data movement or advanced streaming analytics, Dataflow supports pipeline creation via Apache Beam SDKs, templates, or JupyterLab.
• Observable (Monitoring & Profiling): Monitor job status with a graphical interface in the Google Cloud console for progress, execution details, and problem spotting, plus job profiling.

Pros

• Scalable with Autoscaling: Dataflow supports large-scale batch and streaming, processing data in parallel and autoscaling worker VMs while optimizing work distribution.
• Fully Managed Service: As a fully managed service, Google handles all resources, allocating and deleting worker VMs automatically so you don’t manage them.

Cons

• Dependency on the Google Cloud Ecosystem.
• Template Limitations for Highly Custom Needs.
• Debugging Complexity in Distributed Systems

Customer Testimonial: 

“Dataflow is pretty well integrated in the Google cloud ecosystem: Fault tolerance is the best thing about dataflow with the easy to launch the job and monitor.”

Factors That Drive Data Pipeline Tool Decisions

With so many data pipeline tools available in the market, one should consider a couple of factors while selecting the best-suited one as per the need. 

Data Reliability 

It has to ensure that data can be moved easily and without dependency. When data is lost or damaged, it might create inaccurate information and pricey business moves, so ensuring data reliability is very important. If it fails, the data strategy might not work properly.

Scalability

As the amount of data you want to collect grows, your pipeline should scale without major disruptions or skyrocketing costs. An open platform preserves your investment and supports growth. If you can’t scale efficiently, your analytics will become a bottleneck and your business will suffer.

Data Sources Supported

A broad range of data connectors allows you to integrate new and evolving data sources seamlessly. Without this flexibility, you risk being locked into outdated systems that limit innovation and slow down your data ecosystem development.

Real-time Data Availability

Knowing your business’s timing needs,  whether you’re looking for real-time streaming for immediate insights or batch processing for occasional updates, will impact what tool you choose. Misestimating this requirement will result in lost opportunities or too much complexity.

Customer Support and Maintenance

Select tools with low maintenance requirements and good customer support. Fast, expert support reduces downtime and ensures piping runs at peak performance. And there is little in the way of support – if things go wrong, your data flow is busted, and there is no way to fix things on your own.

Paying attention to these main points has made it possible for me to suggest data pipeline tools that support present operations and upcoming growth. Setting it up accurately provides a solid base for data infrastructure growth.

How Does a Data Pipeline Work?

A data pipeline automates the task of ingesting data from one or more sources and forwarding it to a destination storage or processing location. In my experience, understanding the key stages helps appreciate how data pipeline tools simplify complex data workflows and make data reliable and actionable.

Below are the main steps a typical data pipeline must follow:

1. Data Sources

Data pipelines connect to diverse sources such as databases, APIs, IoT devices, and logs. They support both batch ingestion, collecting data at scheduled intervals, and real-time streaming, capturing data instantly via event-driven mechanisms or message queues.

2. Data Transformation

The data is then transformed for consumption. Arguably, this could be classified into exactly the processes defined in a data preparation step: clean (removing duplicate or error causes), format, filter, enhance with additional meta-knowledge, and summarize. These conversions map the data to business rules and target system schemas.

3. Dependencies and Orchestration

The development and operations of pipelines are affected by technology as well as business matters. If there are technical dependencies, the team must wait for the previous job to complete before moving on. Meanwhile, checking the business won’t involve automation but will be done manually. These workflows are handled by orchestration, so tasks are carried out in the right sequence, and errors are dealt with automatically.

4. Data Destinations

Destinations such as data lakes, data warehouses, or analytics platforms like Azure Synapse receive processed data. Batch or incremental loading, with a view to being regular and low in latency, so that it will facilitate timely decisions.

In summary, a data pipeline tool streamlines the complex work of moving and preparing data, delivering clean, ready-to-use data that powers smarter business actions.

Business Challenges That Data Pipelines Mitigate

When I look at how organizations handle data, a few challenges stand out that data pipelines help solve effectively.

1. Operational Efficiency

Managing and coordinating complex data workflows manually is often time-consuming and prone to errors. Data pipeline tools automate these workflows, reducing human intervention and minimizing mistakes. For example, automating repetitive tasks like data extraction and transformation frees teams to focus on higher-value activities, improving overall operational productivity.

2. Real-Time Decision-Making

Many businesses rely on batch processing, which updates data in intervals, causing delays in insights. This lag can slow down crucial decisions. Data pipelines enable real-time data processing, allowing companies to access fresh data instantly. This capability supports quicker responses to market changes, customer needs, or operational issues, giving businesses a competitive edge.

3. Scalability

I’ve noticed that many traditional data systems often fail to handle rapidly growing volumes of data, leading to performance bottlenecks and system crashes. Cloud-based data pipeline software provides the flexibility to scale infrastructure dynamically. This means organizations can efficiently manage increasing data loads without compromising speed or reliability, ensuring consistent performance as they grow.

4. Data Integration

Data scattered across different systems, formats, and locations can create silos that hinder decision-making. Data pipeline tools use ETL processes to extract, clean, and consolidate this scattered data into a centralized repository. This unified data view improves accuracy and enables comprehensive analysis, empowering businesses with trustworthy information for strategic planning.

Learn More:

• AI Data pipeline
• Serverless data pipeline

Why Choose Hevo as Your Data Pipeline Tool

Choosing the right data pipeline tool can be tough. At Hevo, we know how important it is to have a solution that’s both powerful and easy to use. That’s why we built a no-code platform that automates data integration with features like automated schema mapping and real-time data sync. As is also clear from the blog content, these capabilities make managing data pipelines much simpler.

Our data pipeline software helps you save time by handling complex ETL processes, so your team can focus on analyzing and using data to drive decisions. Whether you’re a small business or a growing enterprise, Hevo scales with you and connects seamlessly with cloud platforms like Azure.

Supporting both batch and streaming data pipelines, Hevo offers the flexibility and reliability modern businesses need. If you want to simplify your data workflows and improve efficiency, Hevo is one of the best data pipeline tools you can trust.

Try Hevo’s free trial for 14 days or book a demo and see the difference for yourself.

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