Are you confused about which Apache ETL tools to use? There are a wide variety of Apache ETL tools available in the market today. Apache has been one of the most trustworthy and reliable providers of these tools that you can trust your data with.
This article will walk you through the 4 best Apache ETL tools in the market. These 4 Apache ETL tools include Apache NiFi, Apache StreamSets, Apache Airflow, and Apache Kafka. Let’s dive deep into these Apache ETL tools.
Table of Contents
- Introduction to Apache ETL Tools
- 4 Best Apache ETL Tools
Introduction to Apache ETL Tools
It is critical to choose the proper ETL tool for your company. ELT extracts data from a source, transforms it to meet requirements, and then puts the modified data into a database, data warehouse, or business intelligence platform. There are a large number of ETL tools in the market.
Apache is one of the popular Web Server Software. Apache is free open-source software developed and maintained by the Apache Software Foundation. It is installed on 67 percent of all webservers on the planet. Apache Software Foundation has developed numerous ETL tools as well that can benefit companies. This article will walk you through some of the popular Apache ETL tools that have gained significant market share and can definitely benefit any company to achieve its goals.
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4 Best Apache ETL Tools
Apache ETL tools have gained wide acceptance in the market worldwide. Few of the popular Apache ETL tools include:
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- Data Transformations: Using Transformations in Hevo, you can perform multiple operations like data cleansing, data enrichment, data normalization before loading the data from Apache Airflow, Kafka, Nifi, and many more to the desired destination. You can perform these transformations using 2 methods – Writing a Python-based transformation script or Using Hevo’s drag and drop transformation blocks. Learn more about Hevo’s Transformations.
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1) Apache Nifi
Apache NiFi is an open-source ETL tool and is free for use. It allows you to visually assemble programs from boxes and run them without writing code. So, it is ideal for anyone without a background in coding. It can work with numerous different sources, including RabbitMQ, JDBC query, Hadoop, MQTT, UDP socket, etc. You can use it to filter, adjust, join, split, enhance, and verify data.
Apache NiFi lets you create long-running jobs and is suitable to process both streaming data and periodic batches. Manually managed jobs are also a possibility. However, you may face a few difficulties while setting them up.
It is not limited to data in CSV format. You can easily process photos, videos, audio, and binary data. Another great feature it provides is being able to use different queue policies (FIFO, LIFO, and others). Data Provenance is a connected service that records almost everything in your dataflows. It’s very convenient because you can see how the data was saved or performed. The only drawback is that the function requires lots of disk space.
However, one of the biggest drawbacks is the absence of live monitoring and per-record statistics.
2) Apache StreamSets
Apache StreamSets is a very strong competitor for Apache NiFi, being a free tool as well. It’s difficult to identify the better Apache ETL tools between the two. The data that is put into StreamSets is automatically converted into exchangeable records. Unlike Apache Nifi, StreamSets does not show queues between processors. In order to be able to utilize different formats, Apache Nifi requires turning from one version of the processor to another whereas StreamSets avoids these manipulations. This lets you stop only one processor instead of the whole data flow to change the settings. Debugging in StreamSets is easier than in NiFi due to the real-time debugging tool. It also has a more user-friendly interface.
StreamSets checks each processor before you are able to run the data flow. StreamSets does not allow you to leave disconnected processors for fixing bugs in the future.
In StreamSets, each processor has individual per-record statistics with nice visualization for effective debugging.
3) Apache Airflow
Airflow is a modern platform used to design, create and track workflows is an open-source ETL software. It can be integrated with cloud services, including GCP, Azure, and AWS. It has a user-friendly interface for clear visualization. It can be scaled up easily due to its modular design. The code is written in Python, but you won’t have to worry about XML or drag-and-drop GUIs.
Airflow was developed to act as a perfectly flexible task scheduler. However, its functionality doesn’t end here. It is also used in training ML models, sending notifications, tracking systems, and powering functions within various APIs. Even though Apache Airflow is adequate for most of the day-to-day operations (running ETL jobs and ML pipelines, delivering data, and completing DB backups), it is not the best choice to perform stream jobs.
It enables you to perform tasks on DAGs due to its modern UI, full of visualization elements. You will be able to see the running pipelines, track progress, and also fix bugs. The workflows are constant and stable, making them easily manageable.
One of the major drawbacks of Airflow is that it can be challenging to run alone. It is beneficial to use different operators.
4) Apache Kafka
Apache Kafka is an open-source distributed event streaming platform used by many companies to develop high-performance data pipelines, perform streaming analytics and data integration.
It is a distributed streaming platform that lets you publish and subscribe to streams of records (similar to a message queue). It also provides support for fault-tolerant storing of streams of records and enables the processing of these streams as they occur.
Typically, Kafka is used to building real-time streaming data pipelines that can either move data between systems or applications or even transform or react to the streams of data. The underlying concept of this project includes running a cluster on one or more servers, strong streams of records in categories, and working with records, where each record includes a key, a value, and a timestamp.
Benefits of using Kafka include reliability due to its fault-tolerant architecture. Moreover, it can be scaled easily without any downtime. Kafka uses a distributed commit log which implies that messages persist on disk as fast as possible, hence it is durable.
Kafka is most suitable for stream processing, log aggregation, and monitoring operational data.
All the ETL tools provided by Apache are open source, thus, your choice would depend mainly on your use case. It is important to understand the type of data you will be handling, whether you will require stream or batch processing, etc. It is important to find answers to these questions before finding the right Apache ETL tools.
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