Extracting data from multiple sources in real time is still a challenging task for several firms. Open-source Data Ingestion Tools simplifies this process by automatically ingesting data from several sources and loading it directly to your desired destination.
Being open-source, these solutions are an economical choice for businesses. They not only eliminate human errors but also allow your Engineering team to focus their efforts on core objectives rather than creating and fixing pipelines.
In this article, you will learn about the Top Open-source Data Ingestion Tools available and how they can assist you in simplifying your Data Integration process.
List of 10 Open-source Data Ingestion Tools
Open-source data ingestion tools eliminate the need to build pipelines individually and simplify the process by automating data extraction. These tools are an economical solution as they save time and resources. The tools are:
- Apache Kafka
- Apache Storm
- Apache Nifi
- Airbyte
- Apache Flume
- Elastic Logstash
- Amazon Kinesis
- Dropbase
- Integrate.io
- Matillion
The above tools also help you process, modify, and format your data to fit your target system schema properly.
Hevo is an automated data pipeline that assists you in ingesting data in real-time from 150+ data sources, enriching the data, and transforming it into an analysis-ready form without having to write a single line of code.
Here are more reasons to try Hevo:
- Smooth Schema Management: Hevo eliminates the tedious task of schema management. It automatically detects the schema of incoming data and maps it to your schema in the desired destination.
- Exceptional Data Transformations: Best-in-class and native support for complex data transformation is at your fingertips. Code and no-code flexibility is designed for everyone.
- Quick Setup: Hevo, with its automated features, can be set up in minimal time. Moreover, its simple and interactive UI makes it extremely easy for new customers to work on and perform operations.
Get Started with Hevo for Free
1. Apache Kafka
Apache Kafka is a popular open-source tool for high-performance data ingestion and processing. It was launched as an open-source messaging queue system and has evolved into a full-fledged event streaming platform. It is an excellent choice for building real-time streaming data pipelines and applications that adapt to data streams.
Key features of Kafka:
- Real-time Processing: The read and write of event streams are efficiently processed with constant import/export of your data in real-time.
- Scalability: You can add additional servers without the system going offline.
- Durability: Multiple message replicas ensure your message is never lost.
- Fault-tolerant: The design is such that if one of your servers fails, the process restarts on another server.
You can easily Install Kafka on Windows, Mac, or Linux OS. Adding to its Flexibility, Kafka works for both Online & Offline Message Consumption.
2. Apache Storm
Apache Storm is a distributed open-source data ingestion framework based on Clojure and Java programming languages. It offers best-in-class performance, as it can effectively handle 1 million tuples per second on each node.
Key features of Storm:
- Real-time Processing: It processes large volumes of data and makes it analysis-ready in real-time.
- Scalability: Large volumes of data can be handled easily by adding more nodes to the existing cluster easily.
- Fault-tolerant: Storm ensures no data loss. If one node fails, the task is assigned to another node, and the process continues.
Apache Storm is applicable in several scenarios, such as real-time analytics, online machine learning, continuous computation, distributed RPC, ETL, and more.
3. Apache Nifi
Apache Nifi is a popular open-source data ingestion tool for distributing and processing data. It supports data routing and transformation and is also an integration and automation tool for performing data ingestion faster.
Key features of Nifi:
- Scalability: It allows parallel processing across several nodes. You can work with NiFi in both standalone mode and cluster mode.
- Data Streaming: You can have your data flow in batch or real-time as per your needs.
- Robust System: Compared to other tools, Nifi uses a robust system that processes and distributes data from several resources.
It lets you get incoming messages, filters, and formats using several processors. Most of all, Apache Nifi is fault-tolerant, leverages automation, manages the flow of information between systems and promotes data lineage, security, and scalability.
4. Airbyte
Airbyte is an open-source data ingestion tool built to assist organizations in quickly starting a data ingestion pipeline. It facilitates access to raw data (for engineers) and normalized data (for analysts) to meet all data needs.
Key features of Airbyte:
- Pre-built Connectors: It offers an extensive library of pre-built connectors, enabling better integrations. It also comes with a CDK (Cloud Development Kit) that allows you to create your custom connectors.
- Scalability: It fully manages and efficiently handles large volumes of data and scales on the cloud as you demand.
- Batch Streaming: It only offers batch data streaming with a minimum interval of 5 minutes.
In addition, Airbyte offers log-based incremental replication capabilities that allow users to keep their data up-to-date. You can also execute custom data transformations using the selected dbt transformation models.
5. Apache Flume
Similar to Apache Kafka, Apache Flume is one of Apache’s open-source Big Data Ingestion tools. It primarily intends to bring data into the Hadoop distributed file system(HDFS).
Key features of Flume:
- Scalability: It can scale horizontally and is an extensible, reliable, highly available tool.
- Guaranteed message delivery: Your message transactions are channel-based. There are 2 transactions for each message, so the delivery is guaranteed.
- Streaming data processing: Flume can process and transfer your real-time data.
- Reliability: It ensures no data loss during transmission as it stores events on disk during data delivery.
By employing this tool, you can easily extract, combine, and load large amounts of streaming data from a vast sea of data sources into HDFS. Apache Flume is primarily used to load log data into Hadoop, but it also supports other frameworks such as Hbase and Solr.
6. Elastic Logstash
Elastic Logstash is an open-source data processing pipeline that allows you to extract data from multiple sources and transfer it to your desired target system. These data sources include logs, metrics, web applications, data stores, and various AWS services.
Key features of Elastic Logstash:
- Flexibility: Logstash can transform or parse your data on the fly regardless of the data format or complexity.
- Input plugins: It supports various input plugins that can transform and pull events together from multiple sources.
- Real-time Transformations: It provides real-time data processing and collection.
- Durability and Security: It guarantees to deliver in-flight events if the node fails. Also, there is an option to review and retry data loads for unprocessed events securely.
Compared to other Open-source Data Ingestion Tools, Elastic Logstash can derive structure from unstructured data with Grok, decipher geo coordinates from IP addresses, anonymize or exclude sensitive fields, and ease overall processing.
7. Amazon Kinesis
It is cloud-based data ingestion software that enables the capturing, storing, and processing of data in real-time through its Kinesis data stream. It is used in various applications, such as IoT devices, sensors, web applications, etc.
Key features of Amazon Kinesis:
- Streamline Data Flow: It simplifies data flow in the AWS ecosystem by integrating various AWS services, including Amazon S3, Amazon Redshift, and more.
- Real-time Streaming: Your data is collected, processed, transferred, and analyzed in real-time.
- Scalability: You don’t need any server to manage the scalability. Its on-demand mode manages everything.
Amazon Kinesis can capture and run data from various sources on a scale of terabytes per hour. The information is then loaded to Amazon Warehouse Services, which stores data using Kinesis Data Firehose.
8. Dropbase
Dropbase is a platform that can ingest data from diverse sources, including CSV and Excel files. It has the ability to convert offline data into live databases. You can edit, delete, rearrange, or add data to your databases.
Key features of Dropbase:
- AI Developer Features: You can add your OpenAI or Anthropic API key to enable AI features using LLM models, such as ChatGPT and Claude Sonnet.
- Cloud Console: Your cloud infrastructure or resources like servers or databases can be monitored, restarted, and managed with cloud consoles.
Using its open-source data ingestion framework, you can efficiently perform data ingestion and transformation.
9. Integrate.io
With a drag-and-drop interface, Integrate.io provides connectivity with over 100 connectors to enable data ingestion. It is a cloud-based, no-code, and fast data change capture tool. It automates data transformation, reducing the need for manual intervention.
Key features of Integarte.io:
- Security: Your data is always secure as the tool complies with the highest industry-grade security features.
- Integrations: It is integrated with over 120 data sources, including databases, SaaS platforms, data warehouses, BI tools, and cloud storage services.
- API Generation: You can access APIs for data sources for consumption as it allows instant API Generation.
It is a data pipeline that allows users to maintain APIs directly on its platform, which can then be connected to systems and applications present already. It is one of the prominent open-source data ingestion tools used to carry out the ETL process seamlessly.
10. Matillion
Matillion is an open-source data ingestion tool that is useful for SMBs migrating data from current to cloud-based databases. An ETL tool with more than 70 connectors, Matillion helps you move, transform, and analyze data in real-time.
Key features of Matillion:
- Scalability: Matillion supports incremental load and parallel processing with limited CDC.
- Serverless Architecture: Its serverless architecture minimizes operational overhead and allows for optimal utilization of resources.
- Security: It ensures your data is secure with audit logging, multi-factor authentication (MFA), role-based access control, SSO support, and more.
It offers other features, such as data orchestration and visualization, along with data ingestion and transformation. In addition, it offers advanced security and automates repetitive tasks, thereby reducing the effort required for the data ingestion process.
Integrate Data in Minutes!
No credit card required
Advantages of using Data Ingestion Tools
Building a scalable custom Data Ingestion platform requires you to assign a portion of engineering bandwidth to continuously monitor the pipeline. You must also ensure your solution is scalable and invest heavily in buying and maintaining infrastructure.
- Open-source data ingestion tools establish a framework for businesses to collect, transfer, integrate, & process data from multiple sources.
- Without worrying about building & managing the ever-evolving data connector, these tools provide a seamless data extraction process with complete support for several data transport protocols.
- Besides data collection, integration, and processing, open-source data ingestion tools also have data modification and formatting capabilities to facilitate analytics. You can either start the data ingestion process in batches(small chunks of data) or stream it in real-time.
- Using these tools, you can ingest data rapidly and deliver data to your targets at the lowest level of latency. It also allows you to scale the framework to handle large datasets and achieve fast in-memory transaction processing.
Choosing the Right Open-Source Data Ingestion Tool
The following steps will ensure you choose the most appropriate tool for your specific business needs:
- First, consider your requirements and understand the features a tool should have to cater to your business needs.
- Next, compare and assess the tools on the market by their features, ease of use, ingestion and integration capabilities, and security compliance.
- Moving forward, you should take free trials and demos of the tools you shortlisted and understand which tool works the best for you and your data team.
- Lastly, consider the cost of the tool and the additional overhead charges you would need to pay to maintain it.
In-Depth Analyses of Popular Open-source Tools
Final Take
This article provides a list of open-source data ingestion tools and a better understanding of their features. These tools can automatically extract data from your sources and seamlessly transfer it to your target system. These tools will help reduce any human errors caused during the manual process and provide accurate data for creating your business reports.
However, these also require some technical knowledge to customize pipelines and perform the simplest data transformations. Hence, they create potential bottlenecks as the business teams have to wait for the Engineering teams to provide the data.
With Hevo, you can easily ingest your data in-cloud with open-source like capabilities without the need of any technical knowledge. Explore Hevo’s full-features with a 14-Day Free Trial!
FAQs on Open-source Data Ingestion Tools
1. Is data ingestion an ETL?
Data ingestion is part of the ETL process. Ingestion refers to data extraction, and the ETL process further includes transforming and loading the data.
2. What is API data ingestion?
It is the process of collecting and importing data from multiple Application Programming Interfaces into one central storage space. This includes having to make API calls to retrieve data from external services or applications, transforming it as needed, and storing it in a target destination for analysis and further processing.
3. What are the two types of data ingestion?
The two types of data ingestion are:
Batch Ingestion: This type of ingestion occurs periodically.
Real-time Ingestion: This is a continuous type of ingestion.
Sanchit Agarwal is an Engineer turned Data Analyst with a passion for data, software architecture and AI. He leverages his diverse technical background and 2+ years of experience to write content. He has penned over 200 articles on data integration and infrastructures, driven by a desire to empower data practitioners with practical solutions for their everyday challenges.
