Top 11 Data Ingestion Tools for 2025

Data ingestion is an important component of any successful data initiative. It refers to the process of collecting data from multiple sources and loading it into another system.

Businesses most commonly use a subtype of data ingestion called ETL (extract, transform, load), which allows the data to be transformed before it's loaded.

This extra step provides many benefits. Most importantly, it allows organizations to automatically match and correlate data from a variety of different sources.

In this guide, we'll take a look at the top 11 data ingestion tools for 2025, covering their features, use cases, and what makes each one unique.

Here are five key takeaways from this guide about best data ingestion tools:

• Data ingestion tools import data from various sources to one target location. This location is often a data warehouse.
• ETL (extract, transform, load) is a subtype of data ingestion. Through this process, data is extracted and cleaned up before being loaded in a warehouse.
• Various big data ingestion tools can complete the ETL process automatically. These tools include features such as pre-built integrations and even reverse ETL capabilities.
• Some of the most popular ETL tools include Integrate.io, Airbyte, Matillion, Talend, and Wavefront.
• Integrate.io is a no-code data pipeline platform that simplifies the ETL process.

What is Data Ingestion and Data Ingestion Tools?

Data ingestion is a critical process in data management that involves collecting and importing raw data from various sources into a centralized storage system, such as a database, data warehouse, or data lake. This process enables organizations to analyze and utilize the data effectively for decision-making and insights generation.

Definition and Process:

Data ingestion refers to the act of obtaining and importing data for immediate use or storage. It can occur in two primary modes: batch ingestion, where data is collected at scheduled intervals, and real-time ingestion, where data flows continuously from the source to the destination.

The core components of data ingestion often include extraction, transformation, and loading (ETL). While ingestion primarily focuses on moving raw data without transformation, it sets the stage for later processes that may involve cleaning, validating, and structuring the data for analysis.

Types of Data Sources:

Data can originate from various sources, including:

• Structured Data: Traditional databases and spreadsheets.
• Semi-structured Data: JSON or XML files.
• Unstructured Data: Text documents, images, and social media content.
• Streaming Data: Real-time data from IoT devices or web applications.

Relevance of Data Ingestion Tools

Data ingestion tools play a pivotal role in modern organizations by automating the collection and processing of data. Their relevance includes:

• Efficiency: These tools streamline the ingestion process, allowing businesses to handle large volumes of data quickly and reduce manual effort involved in data collection.
• Data Quality Improvement: By incorporating validation and cleansing processes during ingestion, these tools ensure that the ingested data is accurate and reliable. This enhances the overall quality of insights derived from the data.
• Real-Time Insights: Many modern tools support real-time data ingestion, enabling organizations to respond swiftly to changing conditions and make timely decisions based on up-to-date information.
• Centralized Access: By consolidating data from disparate sources into a single repository, these tools provide a unified view of information across the organization. This facilitates better collaboration among teams and simplifies access to critical data.
• Scalability: As organizations grow, their data needs increase. Data ingestion tools are designed to scale efficiently, accommodating rising volumes of incoming data without compromising performance.
• Security: Robust ingestion processes incorporate security measures to protect sensitive information during transit and ensure that only authorized users can access the ingested data once stored.

Now, let's jump right into the best data ingestion tools in the market now.

What are the Top Data Ingestion Solutions for Ensuring Data Accuracy and Detecting Anomalies in Data Flows?

Integrate.io, Airbyte, and Talend are the top data ingestion solutions for maintaining data accuracy and detecting anomalies. Integrate.io offers real-time monitoring, data validation rules, and transformation functions to clean and standardize data during ingestion. It supports anomaly detection through configurable thresholds, error alerts, and pipeline-level quality checks, ensuring consistent, trustworthy datasets across systems. 

1. Integrate.io

Rating: 4.3/5.0 (G2)

Integrate.io is a cloud-based data pipeline platform that enables businesses to connect multiple data sources to extract, transform, and load data to a data warehouse or other destinations. This platform is among the top data ingestion solutions for ensuring data accuracy and detecting anomalies in data flows.

The data ingestion platform features a user-friendly, drag-and-drop workflow builder, a powerful data transformation engine, and over 130 built-in connectors to various databases, applications, and API ingestion.

What Makes Integrate.io Stand Out?

Integrate.io provides a unified platform for data ingestion and manipulation. It’s packed with powerful features that can help you quickly generate a single source of truth covering all your data.

• Easy-to-use, no-code platform: Use the drag-and-drop editor to quickly and easily create a data pipeline.
• Cost reduction: Automate the data ingestion process, reducing the need for manual intervention and extra data engineers.
• ELT capabilities: Extract data from source systems, load it into a data warehouse, and then transform it into a format ready for analysis.
• Reverse ETL functionality: Reverse the process of ETL by transforming data from a data warehouse into a format that can be used by source systems.
• Data warehouse insights: Access insights into the data stored in a data warehouse.
• Data observability: Monitor and analyze data in real-time, with up to three free alerts.
• Fast Change Data Capture (CDC): Capture data changes quickly and accurately.
• Enhanced data quality: Ensure your data is accurate and up-to-date.
• More accurate data transformation: Automate your data transformation processes to ensure data is transformed accurately.

Pros

• Intuitive low/no-code interface

• Broad connector library and scripting support

• Fast deployment and strong customer support

Cons

• Documentation is limited for complex pipelines

• Performance may degrade with very large datasets

Pricing

• Fixed fee, unlimited usage based pricing model.

2. Airbyte

Rating: 4.3/5.0 (G2)

Airbyte is an open-source data integration platform that enables businesses to create ELT data pipelines. One of the main advantages of Airbyte is that it allows data engineers to set up log-based incremental replication, ensuring that data is always up-to-date.

What Makes Airbyte Stand Out?

Businesses of all sizes can use Airbyte free of charge, as the platform is one of the open source data ingestion tools alternative to cloud-based data pipelines. But this can also be detrimental for some organizations that do not have the necessary resources to hire data engineers and programmers.

Even so, Airbyte provides a wide range of features that help businesses unify their data:

• 300+ out-of-the-box connectors: Experience faster setup time with built-in, editable connectors.
• Quick support: Gain an average response time of fewer than 10 minutes.
• Build custom connectors: Use Airbyte’s CDK to quickly create new connectors in any programming language.
• CDC replications: Easily schedule incremental and log-based replications.
• Cloud hosting: Airbyte also provides cloud hosting and management services.

If you don’t want to deploy your own instance of Airbyte, you can opt for a paid plan with pricing starting at $2.50 per credit. Writing in database and warehouse destinations is free, but all other operations have a credit cost:

• Read API source: 6 credits per million rows
• Read database, warehouse, and file sources: 4 credits per GB
• Read custom source: 6 credits per million rows

Pros

• Open-source, flexible

• Wide range of connectors with self-hosting options

Cons

• Pricing can be unpredictable due to credit-based model and hidden costs

• Less polished UI and requires maintenance

Pricing

• Credit-based billing model; hidden costs can add up quickly depending on usage and deployment

3. Amazon Kinesis

Rating: 4.2/5.0 (G2)

Amazon Kinesis is a fully managed, cloud-based service from Amazon Web Services that enables real-time processing of streaming data on a massive scale. It's designed to capture, store, and process streaming data from various sources, including websites, applications, sensors, and IoT devices. 

What Makes Amazon Kinesis Stand Out?

Kinesis can process terabytes of data per hour from hundreds of thousands of data sources, but its biggest advantage is that it can seamlessly integrate with other AWS services.

Its capabilities can be broken down into four components:

• Kinesis Video Streams: Stream video from connected devices to AWS for various processing purposes.
• Kinesis Data Streams: Access a scalable real-time data streaming service that can capture gigabytes of data per second from hundreds of thousands of sources.
• Kinesis Data Firehose: Capture, transform, and load data streams into AWS data stores.
• Kinesis Data Analytics: Process data streams in real-time with SQL or Apache Flink.

A great thing about Kinesis is that it can be scaled up or down depending on your needs. This also means its pricing varies greatly but can't go lower than $20 per month.

Pros

• Highly scalable, low-latency streaming

• Built-in replay, integrates well with AWS Lambda and other AWS services

Cons

• Setup complexity; cost estimation ahead of time is tough

• Not beginner-friendly

Pricing

• Pay-as-you-go pricing; no upfront fees; costs based on shards, throughput, data transfer, and retention settings

4. Matillion

Rating: 4.4/5.0 (G2)

Matillion is a cloud-based data integration and transformation platform that helps organizations move, transform, and analyze data in the cloud.

You can use the platform to create a complete data pipeline by leveraging its range of features, including data ingestion, data transformation, data orchestration, and data visualization. 

What Makes Matillion Stand Out?

Matillion can be quickly deployed and scaled up or down as needed, making it a flexible solution for businesses both in terms of pricing and implementation time. 

Organizations rely on Matillion because of its advanced capabilities:

• Automate repetitive tasks: Eliminate the need for manual coding and reduce the time and effort required to move data from source to destination.
• Advanced security: The built-in encryption and authentication features ensure your data is safe and secure throughout the ingestion process.
• Hundreds of pre-built connectors: Ingest data from a variety of sources, including databases, files, and APIs, into a single platform.

Matillion offers a free plan which gives you access to up to a million rows a month. However, if you need to move more data, you'll want to upgrade. The Basic plan costs $2.00 per credit and includes unlimited users, volume, and sources. The other plans move up from there.

Pros

• Friendly to non-technical users

• Strong integration with cloud warehouses, scalable performance, supports scripting (Python)

• Good documentation and learning resources

Cons

• Requires substantial set-up responsibilities

• More akin to iPaaS than pure SaaS, setup overhead for enterprise-level deployments

Pricing

• Flexible pricing; available via marketplace billing (AWS, Azure) or direct contracts; transparent and scalable

5. Apache NiFi

Rating: 4.2/5.0 (G2)

Apache NiFi is a powerful and scalable data routing, transformation, and system mediation logic platform. It was designed to automate the flow of data between software systems.

What Makes Apache NiFi Stand Out?

Apache NiFi’s data ingestion engine works without a schema, meaning each NiFi processor is responsible for interpreting the content of the data it receives. You can use Apache NiFi as an individual tool or in a cluster configuration with its built-in clustering system.

Here are some more features that make NiFi a great data ingestion tool:

• Visual command and control: Gain real-time visual establishment of data flows.
• Flow templates: Pre-built components help you get started with NiFi in no time.
• Advanced security: The encrypted protocols ensure data is safely exchanged between systems at every point of the data flow.
• Flexible scaling model: Scale up and down depending on your hardware resources.

Apache NiFi is an open-source platform, which means its free to use.

Pros

• Powerful and scalable real-time dataflow engine

• Web-based UI with rich data provenance and flow control

• Enterprise-grade flexibility across sources and protocols

Cons

• Monitoring and cluster management can be complex

• Stability issues with memory-heavy workflows or large tables

• Not cloud-native; limited documentation in advanced areas

Pricing

• Open-source and free; enterprise deployment costs depend on infrastructure and management overhead

6. Apache Kafka

Rating: 4.5/5.0 (G2)

Apache Kafka is an open-source stream-processing software widely used for its advanced ETL capabilities. With it, businesses can build data pipelines by integrating data from multiple sources in real-time.

What Makes Apache Kafka Stand Out?

Apache Kafka is renowned for its high-performance throughput, which allows it to handle thousands of messages per second. But this platform has more to offer than that:

• Scalability: Elastically expand or contract storage and processing as needed.
• Permanent storage: Store streams of data in a distributed, durable, fault-tolerant cluster.
• Out-of-the-box connect interface: Integrates with hundreds of event sources and event sinks.
• Large ecosystem of open-source tools: Enhance your data ingestion process with a vast array of community-driven open-source tools.

Just like Apache NiFi, Apache Kafka is also an open-source tool that's free to use.

Pros

• High-throughput, fault-tolerant streaming

• Large ecosystem for streaming analytics and integrations

• Open-source flexibility

Cons

• Requires ZooKeeper (though options exist for newer versions)

• Complex setup and maintenance, high operational overhead

• Hidden costs when self-managing; better suited to teams with strong DevOps

Pricing

• Software is free (Apache License 2.0), but production costs, hardware, ops, managed services, can be significant

7. Talend

Rating: 4.0/5.0 (G2)

Talend is an end-to-end data ingestion and management tool that combines data integration, integrity, and governance in a single, low-code platform. It’s extremely flexible and can be deployed on-premises or in the cloud.

What Makes Talend Stand Out?

Talend provides a comprehensive solution for managing data by combining rapid data ingestion, transformation, and mapping with automated quality checks.

One of its biggest strengths is its ability to connect to virtually any data source while ensuring the highest levels of data accuracy.

Here are more features that make Talend stand out from other data ingestion tools:

• 1,000+ connectors and components: Quickly ingest data from virtually any source.
• Drag-and-drop interface: Develop and deploy reusable data pipelines without code.
• Data observability capabilities: Discover, highlight, and fix issues as data moves through your systems.
• Data flexibility: Access data behind secure firewalls, locked in data centers, or in secure cloud environments.

As for pricing, Talend offers a wide range of plans to fit your specific needs, including Stitch, Data Management Platform, Big Data Platform, and Data Fabric. For accurate pricing, you must contact Talend's sales team.

Pros

• Visual lineage and data mapping

• Supports broad data integration and governance use cases

Cons

• High total cost of ownership, estimates range from $50,000 to $500,000+ per year for enterprise packages

• Limited technical support in some regions; setup can be nontrivial

Pricing

• Quote-based; enterprise-level pricing in the $50K–$500K+ annual range

8. Dropbase

Rating: 0 ratings (G2)

Dropbase is a cloud-based platform that can extract, transform, and load data from CSV files and spreadsheets into live databases.

What Makes Dropbase Stand Out?

Dropbase helps you import and manage all your spreadsheet data in a fully-featured SQL database. This process is done in three simple steps:

1. Select the production table you want to stage, specify the primary key, and add validation checks to the columns you need.
2. Edit the data using a simple, spreadsheet-like UI. You can invite other team members to make edits, insert rows, or import more data as needed.
3. Resolve any conflicts between staging and production, then sync the changes.

The best part is that you don’t need to set up or host your own database, as Dropbase manages this out-of-the-box. 

Dropbase features pay-as-you-grow or usage-based pricing. Other plans exist for larger organizations, such as the Pro plan and the Enterprise plan. For a custom quote, you'll need to contact sales.

Pros

• AI-powered platform for building internal apps quickly using Python/SQL

• Free self-hosted version for developers

Cons

• Enterprise tier pricing undisclosed (requires sales contact)

• Still niche compared to broader ETL platforms

Pricing

• Free self-hosted developer tier; enterprise version is custom-priced

9. Tanzu Observability by Wavefront

Rating: 4.1/5.0 (G2)

Tanzu Observability by Wavefront is a high-performance streaming analytics service that enables users to ingest, store, visualize, and monitor all forms of metric data. 

What Makes Wavefront Stand Out?

Wavefront is capable of scaling to very high query loads and data ingestion rates, reaching millions of data points per second. Some other important features of this platform include:

• Advanced charts and dashboards: Use filters and functions to see exactly what you’re interested in.
• Create custom alerts: Detect problems early with advanced custom alerts and notifications.
• Simple query language: The Wavefront query language (WQL) allows you to extract exactly the information you need.

Tanzu Observability pricing depends on your VMware plan, if applicable. For more detailed pricing information, you must contact VMware's sales team.

Pros

• Powerful time-series metric analytics and alerting

• Flexible queries and visualization, stream-processing background

Cons

• Pricing depends heavily on ingestion volume and contract details

• Details are often opaque and require vendor engagement

Pricing

• Cloud-based pricing based on data ingestion and retention; contract-dependent

10. Apache Flume

Rating: 3.9/5.0 (G2)

Flume is a powerful system that facilitates the process of gathering, combining, and transferring large amounts of log data.

What Makes Apache Flume Stand Out?

Apache Flume has a flexible architecture based on streaming data flows. Its tunable reliability mechanisms make it a robust and fault-tolerant solution even when working with incredibly large amounts of data.

Flume has multiple failover and recovery mechanisms and an extensible data model that allows for online analytics applications.

Just like the other Apache tools in this list, Flume is open-source and free to use.

Pros

• Designed for reliable, distributed log collection and transfer

• Integrates well with Hadoop ecosystems and supports high-throughput use cases

Cons

• Configuration complexity; simpler tools may suffice for lightweight needs

• Less active community compared to newer alternatives; aging technology

Pricing

• Open-source and free; operational costs depend on deployment and maintenance

11. Precisely Connect

Rating: 0 ratings (G2)

Connect is a platform that enables users to migrate their data from the mainframe to the cloud. It provides batch and real-time data ingestion for analytics, machine learning, and data migration. 

What Makes Precisely Connect Stand Out?

Connect can save you hundreds of hours of development time and speed up the implementation of ETL processes by up to ten times through its self-tuning engine.

You can also use Connect to replicate changes to application data as they occur across various architectures and topologies, ensuring your databases are always in-sync.

Currently, Precisely doesn't disclose its pricing for the Connect tool. For more information, you'll need to reach out to Precisely's sales team.

Pros

• Strong enterprise connectivity for mainframes and legacy systems

• Robust transformations and secure data transfer capabilities

Cons

• Enterprise licensing costs; less common in cloud-native environments

• Specific tool, niche use cases; complexity in setup

Pricing

• Quote-based; typically part of broader enterprise agreements

Comparison of Top Data Ingestion Tools

Tool	Primary Function / Deployment	Real-Time / Streaming Support	ETL / Ingestion Capabilities	Connector / Integration Scope	Notable Strengths
Integrate.io	Cloud-native ETL/ELT platform	Partial stream; batch-focused ingestion	ETL/ELT, reverse ETL, legacy & REST connectors	200+ prebuilt sources/destinations	No-code UI, flexible pipelines, legacy system support
Airbyte	Open-source ETL/ELT platform	Batch + CDC replication	ETL/ELT with schema sync, custom connectors	600+ connectors for broad source/destination support	Highly extensible, strong connector coverage, low-code
Amazon Kinesis	AWS-managed streaming service	Yes, millisecond latency	Real-time data streaming, analytics pipelines	Native AWS ecosystem (Lambda, S3, Redshift, etc.)	Real-time telemetry/data streaming at scale within AWS
Matillion	Cloud-native ETL/ELT for modern warehouses	Supports CDC and scheduled batch loads	ETL/ELT with UI-driven orchestration	100+ connectors (cloud sources/warehouses)	Visual interface, scalable cloud ETL, new AI assistant (Maia)
Apache NiFi	Open-source dataflow / ETL system	Yes, real-time flow-based architecture	Complex routing, transformation, provenance	Processors support HTTP, Kafka, DBs, files, APIs	Visual flow builder, strong security, extensibility, provenance
Apache Kafka	Distributed streaming platform	Yes, high-throughput, durable logs	Stream data transport; relies on upstream tools	Kafka Connect for extensible integration	Scalable, fault-tolerant streaming backbone with ecosystem support
Talend	Enterprise ETL / data integration suite	Some real-time via Talend pipelines	Rich ETL, data quality, governance, batch & streaming	Broad connectors (legacy, cloud, SaaS)	Full suite of integration with governance and transformation tools
Dropbase	"At-the-edge" ETL for flat files	Not real-time; edge-triggered	ETL pipelines from flat files to warehouse-ready DB	Flat file sources; repeatable edge pipelines	Automates offline-to-analytics pipelines; simple transformation setup
Wavefront	Observability & time-series analytics (VMware)	Yes,  streaming metrics/traces	Ingests and visualizes metrics/traces in real time	Collectd, StatsD, JMX, AWS, custom proxies	High-throughput metrics ingestion, querying, visualization
Apache Flume	Distributed log ingest tool	Yes,  stream ingestion of logs/events	Designed for log/event data ingestion	Sources like logs, HDFS, streaming systems	Lightweight, scalable log pipeline, ideal for Hadoop environments
Precisely Connect	Enterprise data connectivity/ingestion tool	Mostly batch, some real-time support	Ingests data from enterprise sources to central systems	Wide array of enterprise data system connectors	Strong for legacy/enterprise data environments

What You Should Know Before Choosing a Data Ingestion Tool

Choosing the right data ingestion tool can be tedious, especially since so many options are available today. In the following section, we’ll answer some of the most frequently asked questions about data ingestion tools to help you make an informed decision.

What Is an Example of Data Ingestion?

An example of data ingestion is the process of extracting data from various IoT devices and placing it into a database or analytics platform for further analysis.

What Is a Data Ingestion Framework?

A data ingestion framework is a set of tools and processes used to collect, transform, and load data from multiple sources into a data warehouse or other data storage system. 

What Other Tools Are Important for Data-Driven Decision-Making?

Data ingestion is simply the first step when developing a data pipeline. That’s why companies rely on various tools to achieve a single source of truth. Some of these other tools include:

• ETL Tools
• Data Cleansing Tools
• Data Pipeline Tools
• Data Warehousing Tools
• Data Security Tools

FAQs

What tools are used for data ingestion?

Common tools used for data ingestion include Apache Kafka, Amazon Kinesis, Integrate.io, Apache NiFi, Airbyte, Talend, and Matillion. These tools facilitate the process of collecting and transferring data from various sources to a centralized storage system, supporting both batch and real-time ingestion methods.

Is Kafka a data ingestion tool?

Yes, Apache Kafka is a widely used data ingestion tool known for its high throughput and low latency capabilities. It serves as a distributed streaming platform that efficiently handles real-time data streams, allowing producers to publish and consumers to subscribe to data topics.

What is data ingestion?

Data ingestion is the process of importing and transferring data from various sources into a destination system for storage and analysis. This can involve collecting data from databases, applications, or IoT devices, and can occur in real-time or batch modes.

What is data ingestion vs ETL?

Data ingestion focuses on the initial step of collecting and loading data into a storage system, often with minimal transformation. In contrast, ETL (Extract, Transform, Load) involves extracting data from sources, transforming it into a suitable format, and then loading it into a target database or data warehouse. ETL typically includes more complex processing to ensure data quality and consistency.

What is data ingestion API?

A data ingestion API is an interface that allows applications to programmatically send or receive data from different sources into a centralized system. These APIs facilitate seamless integration between various systems, enabling automated data transfer and ensuring that incoming data is processed efficiently for analysis or storage.

What are the top data ingestion tools that offer real-time data synchronization for retail analytics?

• Integrate.io – Supports low-latency ingestion pipelines from POS, e-commerce, and marketing platforms into analytics systems.

• Apache Kafka – High-performance streaming platform ideal for capturing and processing retail events in real time.

• Estuary Flow – Delivers near-instant event streaming with schema evolution for agile retail analytics.

Which data ingestion solutions are best for automated pipeline management in healthcare?

• Integrate.io – HIPAA-compliant automation with encryption, audit logs, and real-time monitoring for healthcare data flows.

• Hevo Data – No-code ingestion platform with compliance controls and automated error handling for healthcare pipelines.

• Osmos – Automates secure onboarding of healthcare data from multiple sources while ensuring regulatory compliance.

Q3: What data ingestion platforms are suitable for education institutions needing GDPR compliance?

• Integrate.io – Provides secure ingestion with encryption, role-based access control, and GDPR-compliant processing.

• Microsoft Fabric Eventstream – No-code event capture and transformation with strong governance and privacy controls.

• Skyvia – Low-code integration with built-in encryption, auditing, and compliance-friendly pipeline management.