Home>Data Engineering>Data Pipeline>Five essential data pipeline tools for data engineers
Data pipeline tools for engineers
Data Pipeline Learning & Development Popular

Five essential data pipeline tools for data engineers

If dealing with data was not difficult, many of us would be out of the job, literally. But, on a serious note, even as real-time data analytics becomes one of the key drivers of revenues for many businesses, implementing a robust data IT architecture is quite challenging. To extract the real insights, one needs to extract the data from multiple sources, clean and enrich it, store it, and then use it for analytics. All these functions are performed manually as well. However, manual is certainly not the best option to work with when we talk about real-time insights. This is why there are many data pipeline tools & softwares are available to automate the process. A data pipeline is mainly used to automate the data processing and ensure that the data is processed in a highly reliable and secured manner.

There are various types of data processing, such as Batch-based, real-time, open-source, on-premise. There is a wide variety of data pipeline tools available for specific purposes.

In this article, we are listing five essential tools for the data pipelines.

Apache Spark

Apache Spark is one of the most important big data distributed processing platforms. Spark can be deployed in various ways. It is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
It supports Java, Scala, Python, and R programming languages. In addition, it provides an interface for programming the entire cluster, supporting SQL, streaming data, and graph processing.

Apache Kafka

Apache Kafka is an open-source software platform for real-time processing data. In recent years, Kafka has become a key data processing platform. Companies across industries use Kafka for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. It is written in Scala and Java programming languages. It connects to external systems for data through Kafka Connect.


Keboola is a Cloud-based platform that covers the entire operational cycle of data management, including ETL, monitoring, security, and analytics. Instead of separately selecting, acquiring, configuring, and integrating different technologies to create a data stack, Keboola provides one platform for that. It allows for customization.


ETleap is analyst-friendly as it enables creating, maintaining, and scaling ETL (extract, transfer, and load) pipelines without code. It provides greater customization just with a click, without codes. It automatically monitors ETL pipelines and resolves issues like schema to ensure reliable and available data.


Talend provides a unified platform for data integration, integrity, governance, and real-time delivery. It is deployable in the cloud, on-premise, and hybrid configuration and can connect any data source to any destination. It lowers the data integration cost and reduces the data governance compliance time.

Leave a Reply

Your email address will not be published. Required fields are marked *