8 Data Ingestion Tools for Building Strong Data Strategy

In the modern era, companies highly rely 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.

The task of collecting and storing both structured and unstructured data in a centralized location is called Data Ingestion.

In this article, you will learn about data ingestion and top 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 it is emitted by the source. 

Top 5 Open Source Data Ingestion Tools for Cost-Effective Data Strategies

Choosing a Data Ingestion tool that can support your Data Team’s needs can be a challenging task, especially when the market is full of similar tools. To simplify your task, here is a list of the 5 open Source Data Ingestion Tools in the market for building cost-effective data strategies:

1. Hevo Data

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 Postload Transformation 
• 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 that you don’t face the pain of schema errors.

2. Apache Nifi

Apache NiFi is specifically designed to automate large data flow between software systems. It takes advantage of 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. Data Ingestion in NiFi can be 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.

3. 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.).

4. 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.

5. 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 data ingestion ETL tasks such as task partitioning, error correction, data quality management, and so on.

Key Features

1. Gobblin-is-a-service feature capitalizes on the containerization trend by allowing Gobblin jobs to be containerized and run independently from 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 data 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.

Other Tools You Might Consider

1. Amazon Kinesis

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 the 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.

2. Matillion

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).

3. Airbyte

Airbyte is an open-source data integration tool designed to simplify the process of syncing data from various sources to your data warehouse, lake, or other destinations. It’s particularly 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: If your specific data source isn’t supported out of the box, you can easily build or modify connectors.
• Real-Time Monitoring: Airbyte provides a user-friendly interface for monitoring syncs with real-time logs and alerts.

Why do You Need Data Ingestion Tools?

• Automates Data Flow: Simplifies the process of 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 that 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 an integral part of data governance, and ingestion tools can help establish proper controls and tracking.

Pricing

• OpenSource Edition: This edition is free and has community support on Slack. It is ideal for small teams or projects with technical expertise.
• Cloud Edition: Designed for startups and small teams, the Cloud Starter Edition offers a pay-as-you-go model with $2.50 per credit.
• Team Edition: This tier offers custom pricing and is designed for larger organizations. It provides additional features and enhanced support, including enterprise-grade security, dedicated customer success, and professional support.
• Enterprise Edition: This tier offers customized pricing for large-scale enterprises requiring advanced features, priority support, and custom solutions. It offers extensive customization, advanced security options, and dedicated account management.

Conclusion

In this article, you learned about data ingestion and top data ingestion tools in 2025. This article only focused on seven of the most popular data ingestion tools.

However, there are other data ingestion tools available in the market with other unique features and functionalities. You can further explore the features and capabilities of other data ingestion tools and use them on your data pipelines based on the use cases and requirements.

Frequently Asked Questions