8 Data Ingestion Tools for Building Strong Data Strategy

In the modern era, companies rely highly on data to predict trends, forecast the market, plan for future needs, understand consumers, and make business decisions. However, to accomplish such tasks, it is critical to have quick access to enterprise data in one centralized location.

Collecting and storing structured and unstructured data in a centralized location is called data ingestion.

This article lists the best paid and open-source data ingestion tools in 2025. Read along to choose the right tool for your business!

What is Data Ingestion?

Data ingestion involves assembling data from various sources in different formats and loading it to centralized storage such as a data lake or a data warehouse. The stored data is further accessed and analyzed to facilitate data-driven decisions.

Data processing systems can include data lakes, databases, and dedicated storage repositories. While implementing data ingestion, data can either be ingested in batches or streamed in real time.

When data is ingested in batches, it is imported in discrete chunks at regular intervals, whereas in real-time data ingestion, each data item is continuously imported as the source emits it. 

What Should You Consider Before Choosing a Tool?

Aspect to Consider	Key Questions to Ask
Data Sources and Connectivity	Does the tool support all my data sources (databases, cloud, SaaS, APIs, streaming)? Are there pre-built connectors available? How easy is it to integrate new data sources?
Data Integration Capabilities	Does the tool support batch, real-time, or both types of data ingestion? Are ETL/ELT data transformation features available? Can I perform data cleansing and preparation within the tool?
Scalability and Performance	Can the tool handle large volumes of data without performance degradation? Is the tool scalable as my data needs grow? How does it perform under high data velocity and variety?
Ease of Use and Automation	Is the interface user-friendly, with low-code or no-code options? Does the tool support scheduling and automation of data pipelines? How much manual intervention is required for maintenance?
Security and Compliance	Does the tool offer encryption for data in transit and at rest? Is it compliant with regulations like GDPR, HIPAA, or CCPA? What security features (e.g., role-based access, audit logs) are available?
Cost and Pricing Model	What is the pricing model (e.g., subscription, pay-as-you-go, enterprise)? Are there hidden costs, such as for connectors or add-ons? How does the total cost of ownership compare to similar tools?
Support and Community	What support channels are available (e.g., chat, email, phone)? Is there comprehensive documentation and onboarding support?

Top Data Ingestion Tools To Consider In 2025

1. Hevo Data

G2 Rating: 4.4(260)

Hevo Data, a no-code data pipeline helps to load data from any data source such as MySQL, SaaS applications, cloud storage, SDK’s, and Streaming Services and simplifies the ETL process.

Key Features

Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.

• Wide Range of Connectors: Instantly connect and read data from 150+ sources, including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
• In-built Transformations: Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface or our nifty Python interface. Generate analysis-ready data in your warehouse using Hevo’s post-load transformations. 
• Near Real-Time Replication: Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.   
• Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with the destination warehouse so you don’t face the pain of schema errors.

2. Amazon Kinesis

G2 Rating: 4.7(26)

Amazon Kinesis is a powerful and automated cloud-based service that empowers businesses to extract, & analyze real-time data streams. The platform can capture, process, and store both videos (via Kinesis Video Streams) and data streams (using Kinesis Data Streams). 

Using the Kinesis Data Firehose, Amazon Kinesis captures and processes terabytes of data per hour from hundreds of thousands of data sources.

Key Features

1. Real-time Data Streaming: Processes and analyzes real-time data streams from various sources like IoT devices, logs, and social media feeds.
2. Kinesis Data Streams: Enables high-throughput, low-latency ingestion of data streams with the ability to scale horizontally.
3. Kinesis Data Firehose: Provides fully managed data delivery to AWS services like S3, Redshift, and Elasticsearch, with real-time data transformation support.
4. Kinesis Data Analytics: Allows real-time processing and analysis of streaming data using SQL-based queries, enabling insights as data flows in.

Pricing

The pricing of  Amazon Kinesis varies depending on your AWS region. You can use the AWS Pricing Calculator to estimate the total price of Amazon Kinesis based on your requirements and use cases.

3. Matillion

G2 Rating: 4.4(80)

Matillion is a cloud-native ETL/ELT tool that makes data integration easier and faster. Its low-code, intuitive interface allows non-technical users to build sophisticated data workflows with minimal coding. The tool integrates nicely with famous cloud data warehouses like Snowflake, Amazon Redshift, and Google BigQuery, thereby becoming one of the favorite solutions for companies using these Data Warehouses in the cloud.

Key features 

• Built-In Connectors: Matillion offers 150+ pre-built connectors, making connections between source and destination accessible and faster for the user. 
• Custom Connectors: Matillion also allows businesses to create custom connectors to REST API sources and request connectors, which are delivered in a few days.
• Visual Interface: Its simple user interface and drag-and-drop interface allow users to build workflows quickly without technical know-how. This intuitive interface is helpful for users who prefer a low-code approach to building data pipelines.
• Scalability: Matillion’s architecture is designed to handle large data sets, making it suitable for organizations with big data analytics.

Pricing

Matillion offers a flexible and predictable pricing model where users only pay for what they require and use. It provides a credit-based pricing model and has three tiers of pricing, which are: 

• Basic – $2.00 / credit, which starts at 500 credits a month.
• Advanced – $2.50 / credit, which starts at 750 credits a month.
• Enterprise—$2.70 / credit, which starts at 1000 credits a month. Additional add-ons offered for enterprise include AI capabilities and mission-critical support(dedicated support and rapid response time).

4. Airbyte

G2 Rating: 4.5(49)

Airbyte is an open-source data integration tool designed to simplify syncing data from various sources to your data warehouse, lake, or other destinations. It’s mainly known for its extensive library of pre-built connectors and its ease of use, even for non-technical users.

Key Features

• Data connectors: Airbyte supports 350+ data connectors, with 271 connectors in their marketplace. 
• Open Source: Being open-source, Airbyte allows you to customize connectors and pipelines to fit your specific needs.
• Incremental Data Syncs: Airbyte supports incremental data syncs, meaning only new or updated data is transferred, reducing load and improving efficiency.
• Customizable: You can build or modify connectors if your specific data source isn’t supported.
• Real-Time Monitoring: Airbyte provides a user-friendly interface for monitoring syncs with real-time logs and alerts.

Pricing

• Open-source: Free, community support, ideal for small tech-savvy teams.
• Cloud: $2.50/credit, pay-as-you-go for startups and small teams.
• Team: Custom pricing, enterprise security, and professional support.
• Enterprise: Tailored pricing, advanced features, and priority support.

5. Talend

G2 Rating: 4.0(46)

Founded in 2005 and acquired by Qlik in 2023, Talend offers a comprehensive suite of data integration and management solutions. It’s designed to handle complex data workflows, making it suitable for enterprises requiring extensive customization and control over their data processes.

Key Features

• Data Integration: Provides tools for real-time data integration, allowing seamless connectivity across various databases, cloud storage, and SaaS applications.
• Data Quality and Governance: Includes robust data preparation tools, data lineage tracking, and data quality management to ensure accuracy and compliance.
• Extensive Connectivity: Offers over 1,000 out-of-the-box processors and connectors, facilitating integration with a wide array of data sources.

Pricing: Talend operates on a subscription-based model, with pricing typically based on the number of users and specific features required. 

6. Fivetran

G2 Rating: 4.2(402)

Fivetran is a cloud-native data extraction tool that emphasizes simplicity and automation. It provides a zero-maintenance pipeline, ensuring efficient and transparent data delivery from source to destination, making it ideal for businesses seeking streamlined data integration solutions.

Key Features

• Data Connectors: Fivetran supports 500+ data connectors, making integrating data from multiple sources easy.
• Automated Data Sync: It automatically synchronizes data, ensuring your data is always fresh and ready for analysis.
• Security: Fivetran adheres to SOC 1 & 2, PCI DSS, ISO 27001, HIPAA, CCPA, GDPR, and HITRUST regulations.
• Support and Community: Fivetran offers robust customer support and a vibrant community, which provides ample resources for troubleshooting and learning.

Pricing: Fivetran employs a consumption-based pricing model, charging based on Monthly Active Rows (MAR), which is calculated connector-wise. 

7. DropBase

G2 Rating: 4.5(2)

Dropbase is a data ingestion platform designed to simplify the process of importing, cleaning, and integrating data from various sources. It caters to teams and businesses looking for an easy-to-use solution to manage their data workflows without extensive technical expertise.

Key Features

• Data Importation: Allows users to import data from multiple sources, including CSV files, databases, and APIs.
• Data Cleaning and Transformation: Provides intuitive tools to clean, validate, and transform data, ensuring it is analysis-ready.
• Collaboration Tools: Enable teams to collaborate on data projects with features that support sharing and real-time collaboration.

Pricing: Dropbase offers tiered pricing plans to accommodate different user needs, ranging from basic plans suitable for small teams to more advanced plans for larger organizations. 

Best Open-Source Data Ingestion Tools For Cost-Effective ETL 

1. Apache NiFi

Apache NiFi is specifically designed to automate large data flow between software systems. It uses the ETL concept to provide low latency, high throughput, guaranteed delivery, and loss tolerance.

Key Features:

1. Data Provenance Tracking: It provides a complete lineage of information from beginning to end. 
2. Data Ingestion: NiFi can collect data from various sources, including log files, sensors, and applications. Ingesting data using NiFi can be done in real-time or in batches.
3. Data Enrichment: NiFi enriches data by adding additional information, such as timestamps, geolocation data, or user IDs. This improves data quality and makes it analysis-ready.
4. Data Transformation: You can transform data by changing its format, structure, or content. This may help make the data more interoperable between different and dissimilar systems or further enhance performance in data analysis.
5. Data Routing: NiFi allows routing to various destinations, including Hadoop, Hive, and Spark. These may be helpful when distributing data through multiple systems or other data analysis uses.

2. Apache Flume

Apache Flume is a distributed and resilient service for efficiently collecting, aggregating, and moving large amounts of log data. It is fault-tolerant and robust, with tunable reliability mechanisms and numerous failover and recovery mechanisms.

Key Features

1. Reliable Data Flow: Ensures fault-tolerant, reliable data transfer between sources and destinations.
2. Scalability: Easily scales to handle large volumes of streaming data.
3. Distributed Architecture: Supports multiple agents working in a distributed manner for data collection.
4. Multiple Data Sources and Destinations: Supports various sources (log files, network traffic, etc.) and destinations (HDFS, HBase, etc.).

3. Apache Kafka

Apache Kafka is an open-source data ingestion software that is Apache-licensed and used for high-performance data pipelines, streaming analytics, data integration, and other purposes. It can deliver data at network-limited throughput with latencies as low as 2ms using a group of machines. 

Key Features

1. High Throughput: Handles large volumes of real-time data with low latency, making it ideal for high-speed data pipelines.
2. Distributed System: Kafka is designed to be distributed across multiple servers, ensuring high availability and fault tolerance.
3. Scalability: Easily scales horizontally by adding more brokers to handle increased data loads.
4. Publish-Subscribe Model: Supports multiple consumers reading from a single topic, enabling real-time streaming and event-driven architectures.

4. Apache Gobblin

Apache Gobblin is a universal data ingestion framework for extracting, transforming, and loading large data volumes from multiple sources into HDFS. Gobblin handles routine ingestion ETL tasks such as task partitioning, error correction, data quality management, and so on.

Key Features

1. Gobblin-as-a-service feature capitalizes on the containerization trend by allowing Gobblin jobs to be containerized and run independently of other jobs.
2. Data Integration Platform: Gobblin is a distributed framework designed for large-scale data ingestion, replication, and management across various data sources and destinations.
3. Multiple Source and Sink Support: Supports a wide range of data sources (HDFS, Kafka, MySQL, etc.) and sinks (HDFS, Amazon S3, databases), making it versatile for different data pipeline needs.
4. Scalability: Designed to handle large-scale ingestion pipelines in a highly scalable manner, with support for both batch and streaming data.
5. Pluggable Architecture: Allows easy integration with new data sources and sinks via a modular, pluggable framework.

Why Do You Need Data Ingestion Tools?

• Automates Data Flow: Simplifies importing data from various sources to your storage or data warehouses.
• Real-Time Data Processing: Enables near real-time data ingestion for faster analysis and decision-making.
• Scalability: Handles growing volumes of data without compromising performance, ensuring your data infrastructure can grow as needed.
• Data Transformation: Many tools offer built-in capabilities to transform and cleanse data during ingestion, reducing the need for separate ETL processes.
• Data Control: Ensuring data quality and security during the ingestion process is integral to data governance, and ingestion tools can help establish proper controls and tracking.

Conclusion

Selecting the right tool is pivotal to building a robust data strategy that ensures seamless data integration, high data quality, and efficient analytics. Whether your business requires real-time data streaming, batch processing, or a hybrid approach, the market offers a wide range of solutions—from no-code platforms like Hevo Data to open-source options like Airbyte.

When choosing a tool, carefully assess your organization’s specific needs, including data volume, integration complexity, scalability, security, and budget. By leveraging the right tool, your organization can unlock the full potential of its data, drive informed decision-making, and maintain a competitive edge in today’s data-driven world.

If you’re looking for a powerful, no-code data ingestion solution, Try Hevo for Free and transform your data strategy today!

Frequently Asked Questions