Apache NiFi Data Ingestion: A Comprehensive Guide 101

Last Modified: December 29th, 2022

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Data ingestion is the process of transferring data from different sources to a centralized location. The data can come from IoT devices, on-premises databases, and SaaS apps, and can go to various target environments like data marts or cloud data warehouses. Businesses design data ingestion pipelines to collect and store their data from various sources. Apache NiFi, short for Niagara Files, is an enterprise-grade data flow management tool that helps collect, enrich, transform, and route data in a scalable and reliable manner. It’s a top-level Apache product that is based on the concepts of Flow-Based Programming. Using Apache NiFi Data Ingestion Pipelines; businesses can set up Data Integration workflows for a smoother data flow.

In this blog, you’ll learn about the features of Apache NiFi, data ingestion, and how to set up and use Apache NiFi Data Ingestion Pipelines.

Table of Contents


  • Fundamental understanding of Data Integration.

What is Apache NiFi?

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Apache NiFi Data Ingestion tool is open-source software that helps process and distribute data flow between systems. It allows users to pull data from sources into Apache NiFi and manipulate flows in real-time. With Apache NiFi, businesses can take data from sources, process and transform it, and push it to a different data storage. Essentially, NiFi is a highly scalable, fully secure, and user-friendly platform that can accommodate diverse and highly complex data flow. Businesses can use Apache NiFi Data Ingestion, Acquisition, Transformation, and Data-based event processing. 

Key Core Concepts

  • FlowFile: Represents the moving objects through the system. NiFi keeps track of key/value pair attributes and the associated content of bytes. With this, you can process CSV records, pictures, audio, videos, and any other binary data.
  • FlowFile Processor: FlowFile Processors perform the work of data routing, mediation, or transformation between systems. They have access to attributes of a given FlowFile, and they can operate on zero or more FlowFiles in a given unit of work. 
  • Flow Controller: It acts as the broker that facilitates the exchange of FlowFiles between processors. It also maintains the information about how processes connect. 
  • Process Group: It’s a set of specific processes along with their connections. The process group can send data out via output ports and receive data via input ports. 

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What are the Key Features of Apache NiFi?

Following are the features of the Apache NiFi Data Ingestion platform:

1) Prioritized Queuing

You can set prioritization schemes of how you want to retrieve data from the queue. By default, the oldest data is retrieved first. However, you can set prioritization if you would like to pull the newest data or smallest data, or any custom schema. 

2) Guaranteed Delivery

Passing ensures distribution even at a large scale by making optimum use of a content depository. The purpose-built persistent write-ahead log is designed so that NiFi can offer effective load-spreading, very high transaction rates, and copy-on-write. 

3) Flow Management

Apache NiFi allows you to move data to multiple destinations at the same time. It supports buffering for all queued data and back-pressure ability. The Apache NiFi Data Ingestion tool offers flow-specific configuration for such concerns at points where data loss is intolerant. 

4) Ease of Use

Dataflows can be complex, and the Apache NiFi Data Ingestion tool allows teams to visualize those flows. It helps to reduce complexity and allows users to see changes in real-time. Apache Nifi can automatically index, record, and make available provenance data through the system across transformations, fan-in, fan-out, and more. Data Provenance becomes extremely critical in troubleshooting, optimization, supporting compliance, etc. 

5) Site-to-Site Communication Protocol

Apache NiFi Data Ingestion offers Site-to-Site (S2S) protocol for quick and easy transfer of data between instances. S2S protocol makes it easy for client libraries to be bundled into applications or communicate with NiFi. S2S supports HTTP(S) and socket-based protocols, making it easy to embed a proxy server into S2S Communication.

6) Security

Apache NiFi Data Ingestion platform also offers a secure exchange at every point in the data flow through protocols and two-way SSL encryption. It also enables content encryption and decryption for both senders and recipients. NiFi also offers Pluggable authorization so that it can control users’ access, such as data flow manager, admin, and read-only access. In addition, admins have fine-grained access to the entire data flow to easily handle requirements and management.

Architectural Pattern of Big Data

Following are the layers of Big Data Architecture:

  • Data Ingestion Layer: First step for data coming from variable sources. Data is cleaned and categorized to ensure smooth data flow in further layers.
  • Data Collector Layer: The focus is on data transportation from the ingestion layer to the rest of the data pipeline. It’s where data is broken so that analytic capabilities may begin.
  • Data Processing Layer: The focus is on processing the data collected in previous layers. It specializes in the Data Pipeline Processing System. The data is classified and routed to a different destination.
  • Data Storage Layer: The layer focuses on finding the right medium for storing large data efficiently.
  • Data Query Layer: It’s where active analytic processing begins. The focus is to collect the data value to make it more helpful for the next layer.
  • Data Visualization Layer: It’s the visualization or presentation tier where Data Pipeline users get to see insights into the data that has been collected.

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What is Data Ingestion?

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Data ingestion is the process of transporting data from one or more sources to a storage medium where it can be further analyzed. Data can originate from various sources like RDBMS, CSVs, S3 buckets, or other streams. It can also be in various formats. Organizations can create/use Data Ingestion Pipelines such as Hevo Data to collect the data from various sources and transfer them to centralized storage for analytics. 

You can ingest data in batches, in real-time, or in a combination of both. 

1) Batch Processing

Batch processing is a group-wise collection of data that runs periodically and is sent to the destination. The priority of a batch or group depends on the condition or logical order applied to a batch. The Batch Data Ingestion method is useful when your processes run on a schedule since data is imported at regularly scheduled intervals. 

2) Streaming Processing

It is also called real-time processing. The data is sourced, manipulated, and then loaded by the data ingestion layer in this process. Real-time data ingestion is useful when the data is very time-sensitive and has to be monitored moment-to-moment. 

Check out our detailed guide on Batch Processing vs Stream Processing to learn more!

3) Lambda Architecture

Lambda architecture of data ingestion consists of both real-time and batch methods. It consists of the batch, serving, and speed layers. The ongoing hand-off between the three layers ensures that data is available for querying with low latency. With lambda architecture, the combination of Real-time and Batch methods balances the benefits of both methods by using real-time processing to provide views of time-sensitive data and comprehensive views of batch data. 

Benefits of Data ingestion

Data ingestion enables teams to manage data more efficiently and gain a competitive advantage. Some of the benefits of data ingestion include:

  • Readily Available: Helps companies gather data from multiple sources and move it to a unified environment for quick access and analysis.
  • Design Better Tools: Engineers can use this technology to ensure that the data movies quickly while designing their apps and software tools.
  • Less Complex: Combining advanced data ingestion pipelines with ETL solutions such as Hevo Data can help companies transform various data formats into a predefined structure and deliver it to a target source.
  • Better Decisions: Real-time Data Ingestion aids businesses to quickly detecting problems and opportunities. They can also make informed decisions with real-time access to data.

How to Set up the Apache NiFi Data Ingestion Platform?

Step 1: Configuring Tasks

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We will use the NiFi processor ‘PublishKafka_0_10’.  Go to the scheduling tab and configure the number of concurrent tasks you want to execute and schedule processors. In the properties tab, set up Kafka broker URLs, request size, topic name, etc. 

Step 2: Data Ingestion using Apache NiFi to Amazon Redshift

The next step is to connect Apache NiFi to Amazon Redshift. For this, you will need Amazon Kinesis Firehose Delivery Stream for storing data to Amazon Redshift. You can use the delivery stream to move data to Amazon S3, Amazon Redshift, Amazon, and ElasticSearch Service.


In this blog, you learned about Apache NiFi data ingestion that enterprises need for a big data project implementation. Apache NiFi acts like a data flow manager. Apache Nifi Data Ingestion helps companies to extract and transfer data automatically. With the Apache NiFi Data Ingestion Pipeline, businesses can focus on extracting value from their data and finding insights into their customers and business. 

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Share your experience of learning about the Apache NiFi Data Ingestion platform! Let us in the comments below!

Osheen Jain
Freelance Technical Content Writer, Hevo Data

Osheen has extensive experience in freelance writing within the data industry and is passionate about simplifying the complexities of data integration and data analysis through informative content for those delving deeper into these subjects.

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