Skip to main content

Understanding Data Ingestion: Types, Techniques, and Tools

Discover the basics of data ingestion, techniques, tools, and best practices for managing different data types effectively!
Jan 22, 2025  · 20 min read

Imagine you own an e-commerce company. To optimize your website and generate more sales, you can track customer behavior. This involves tracking every click, search query, and purchase, all of which provide valuable data.

However, the data you’re collecting is scattered across multiple systems (e.g., web analytics platforms, transaction databases, customer support logs, etc.), which is a big problem.

To mitigate this problem, all the collected data should be consolidated into one place, and this is where data ingestion comes in handy.

Through data ingestion, you can pull all of this data into a central data warehouse, which enables you to gather real-time insights faster and perform analytics when necessary.

In this article, we will explore the fundamentals of data ingestion and the techniques used to facilitate the process. You'll learn about tools that help streamline data ingestion, common challenges that arise, and best practices to optimize the process for scalability, efficiency, and data quality.

Let’s dive into it!

What is Data Ingestion?

Data ingestion is the process of collecting, importing, and loading data from various sources into a centralized system (e.g., a data warehouse, data lake, or cloud platform). This process enables you to consolidate data from different systems and make it available for further processing and analysis.

Efficient data ingestion is important because it forms the foundation for other data-driven operations, such as analytics, visualization, and sharing.

Types of Data Ingestion

Technically, data ingestion can be broadly categorized into two primary types

  • Batch ingestion
  • Real-time ingestion

Each approach serves different needs and is suitable for various use cases depending on the speed and volume of data being processed.

Batch data ingestion

Batch ingestion refers to the process of collecting and processing data at scheduled intervals. For example, you may collect and process data hourly, daily, or even weekly. This approach is typically used when instantaneous insights are not required, and it helps manage large volumes of data efficiently.

Examples of batch ingestion use cases:

  • Reporting: Collecting data periodically to generate business performance reports.
  • Periodic data syncs: Synchronizing data across systems at regular intervals to keep them up-to-date.
  • End-of-day financial data analysis: Aggregating transaction data to analyze financial performance after business hours.

Real-time data ingestion

Real-time (or streaming) data ingestion involves continuously collecting and processing data as it is generated. This method allows you to gain near-instantaneous insights, which is ideal for scenarios where time-sensitive decision-making is required.

Examples of real-time ingestion use cases:

  • Fraud detection: Monitoring transactions in real time to identify and respond to suspicious activity.
  • IoT sensor data processing: Gathering and processing data from connected devices, such as temperature or motion sensors, in real time.
  • Live analytics dashboards: Providing up-to-the-minute data for dashboards that track key performance indicators (KPIs) and other metrics.

If you want to learn more about the difference between batching and streaming, scaling streaming systems, and real-world applications, I recommend the Streaming Concepts course.

Become a Data Engineer

Become a data engineer through advanced Python learning
Start Learning for Free

Data Ingestion Techniques

Several techniques facilitate data ingestion. Each is designed to address different use cases and data processing needs. 

The choice of technique you choose depends on 3 factors:

  • The specific requirements of the business.
  • The complexity of the data.
  • The infrastructure in place.

In this section, I will touch on three data ingestion techniques and when they are most effective.

ETL (Extract, Transform, Load)

ETL is a traditional and widely used data ingestion technique. In this approach, data is extracted from various source systems, transformed into a consistent and clean format, and finally loaded into a data storage system, such as a data warehouse. 

This method is highly effective for use cases that require consistent, structured data for reporting, analytics, and decision-making.

When to use ETL:

  • When data needs to be pre-processed (cleansed or transformed) before it is loaded into the storage system.
  • Scenarios where structured data is a priority, such as generating business reports or adhering to strict data quality requirements.
  • When dealing with relational databases or smaller-scale data environments that prioritize clean, ready-to-analyze data.

ETL pipeline architecture.

ETL pipeline architecture.

ELT (Extract, Load, Transform)

ELT reverses the order of operations compared to ETL. In this approach, data is extracted from source systems and loaded directly into the target system (usually a data lake or cloud-based platform). Once in the target storage system, the data is transformed as needed, often within the destination environment. 

The ELT approach is particularly effective when dealing with large volumes of unstructured or semi-structured data, where transformation can be deferred until analysis is required.

When to use ELT:

  • When working with data lakes or cloud storage, which can handle raw data at scale.
  • When dealing with diverse, unstructured, or semi-structured data that may not need immediate transformation.
  • When flexibility is needed, as ELT allows data transformations to be performed on demand within the storage environment.

ELT pipeline architecture.

ELT pipeline architecture.

Change data capture (CDC)

CDC is a technique for identifying and capturing changes (e.g., inserts, updates, deletes) made to data in source systems and then replicating those changes in real time to the destination systems. 

This technique ensures that target systems are always up-to-date without reloading the entire dataset. CDC is highly effective for maintaining real-time synchronization between databases and ensuring consistency across systems.

When to use CDC:

  • When real-time data synchronization is critical, such as in high-frequency trading, e-commerce transactions, or operational reporting.
  • When you need to keep destination systems updated with minimal lag or data refresh times.
  • When it is necessary to avoid the overhead of re-ingesting entire datasets.

Data Sources for Ingestion

We’ve established that data ingestion involves pulling information from various sources. But we haven’t discussed what those structures are — and that’s what we’ll do in this section. 

The data sources for ingestion generally fit into 3 main types: 

  • Structured 
  • Semi-structured
  • Unstructured  data

As you will learn when we examine the different sources, each requires different handling and processing techniques to be utilized effectively. 

Structured data sources

Structured data is highly organized and follows a predefined schema, making it easier to process and analyze. It is typically stored in relational databases, which store and provide access to related data points. The data is arranged into tables with clearly defined rows and columns. 

Due to this organized nature, structured data is the most straightforward to ingest. 

Traditional tools like MySQL and PostgreSQL are typically used to handle structured data. These SQL-based tools simplify the validation process during ingestion by enforcing strict schema rules. When data is ingested into these systems, they automatically ensure it adheres to the structure defined by the database tables. 

For example, if a column is set to accept only integer values, MySQL or PostgreSQL will reject any data that doesn’t meet this requirement.

Other things to note about structured data are: 

  • It is typically collected in large volumes (and in a predictable format).
  • It is often ingested through batch processes, which enable periodic updates to refresh the data as needed.

Semi-structured data sources

Semi-structured data, while still organized to some degree, does not conform to a rigid, predefined schema like structured data. Instead, it uses tags or markers to provide some organization, but the structure remains flexible. 

This data type is typically found in formats like JSON files, CSV files, and NoSQL databases, and it is commonly used in web applications, APIs, and log systems. 

Since the structure can vary, semi-structured data offers flexibility in storage and processing, making it suitable for systems that handle evolving or inconsistent data formats.

Handling semi-structured data requires tools to parse and extract meaningful information from the embedded tags or markers. For instance, a JSON file may contain a collection of key-value pairs, and the ingestion process would involve parsing these pairs and transforming them into a suitable format. 

Semi-structured data is often ingested into flexible environments like data lakes or cloud platforms, where it can be stored in its raw form and processed later as needed.

Other things to note about semi-structured data include:

  • Depending on the use case, it can be ingested in real-time or through scheduled batches.
  • It requires transformation during ingestion to ensure it can be processed or analyzed effectively.

Unstructured data sources

Unstructured data is the most complex type to manage because it lacks any specific format or organizational structure. This category includes data like:

  • Images
  • Videos
  • Audio
  • Documents

Unlike structured or semi-structured data, unstructured data does not have clear rows, columns, or tags to define its content, making it more challenging to process and analyze.

Despite its complexity, unstructured data can hold valuable insights, particularly when analyzed using advanced technologies like machine learning or natural language processing.

Because unstructured data is not organized in a predefined way, specialized tools are required to handle the ingestion process. These tools can extract relevant information from raw, unstructured sources and transform it into formats that can be analyzed. 

Lastly, unstructured data is typically stored in data lakes, where it can be processed as needed.

Comparison Table: Structured vs. Semi-Structured vs. Unstructured Data

Feature

Structured data

Semi-structured data

Unstructured data

Definition

Highly organized data with a predefined schema (e.g., rows and columns).

Partially organized data with tags or markers for flexibility (e.g., JSON, CSV).

Data without any predefined structure or organization (e.g., images, videos).

Examples

Relational databases like MySQL, PostgreSQL.

JSON files, NoSQL databases, CSV logs.

Images, videos, audio files, text documents.

Ease of processing

Easiest to process due to its strict schema.

Moderate difficulty; requires parsing and transformation.

Most challenging due to lack of structure.

Storage

Relational databases or data warehouses.

Data lakes, NoSQL databases, cloud storage.

Data lakes or specialized storage for raw files.

Ingestion approach

Typically ingested in batches.

Flexible; can be ingested in real-time or batches.

Primarily ingested in raw format for later processing.

Tools used

SQL-based tools like MySQL, PostgreSQL.

Tools that handle flexible schemas, such as MongoDB, Google BigQuery.

Advanced tools like Hadoop, Apache Spark, or AI/ML-based processing tools.

Use case examples

Generating reports, transactional systems.

API responses, web application logs.

Image recognition, speech analysis, sentiment analysis.

Challenges in Data Ingestion

Data ingestion comes with several challenges that you must address to ensure efficient and effective data management. In this section, we will discuss what these challenges are and solutions that can be employed to overcome them. 

Handling large volumes of data

One of the primary challenges in data ingestion is the sheer volume of data that needs to be processed. As businesses grow, the amount of data they generate and collect can increase exponentially, particularly with the rise of real-time data streams from sources like IoT devices and social media platforms. 

Scaling data ingestion processes to handle these growing volumes can be difficult, especially with streaming data that requires continuous ingestion. 

Teams must ensure their infrastructure can support batch and real-time data ingestion at scale without degradation. The Streaming Concepts course is a great way to get started. 

Data quality and consistency

Data quality and consistency are another significant challenge, especially when data is ingested from multiple disparate sources. Each source may have different formats, levels of completeness, or even conflicting information.

Ingesting data without addressing these inconsistencies can lead to inaccurate or incomplete datasets, eventually impacting downstream analytics. 

Data validation checks must be implemented during ingestion to ensure that only high-quality data enters the system. Additionally, teams need to maintain consistency across various sources to avoid data discrepancies.

I recommend deep diving into this topic with the excellent Introduction to Data Quality course. 

Security and compliance

When sensitive data is ingested, security and compliance become top priorities. Teams that handle personal information (e.g., customer names, addresses, payment details, etc.) must protect this data throughout the ingestion process. 

For example, a financial services company must adhere to stringent regulations like GDPR or HIPAA when ingesting client data to avoid legal repercussions. Failure to secure data during ingestion could result in data breaches, compromising customer trust and leading to substantial financial penalties. 

To address this, teams should implement encryption and secure access controls during the ingestion process. This ensures that sensitive data is protected from unauthorized access. Additionally, data must be ingested in a manner that complies with all relevant regulatory standards, with regular audits to verify ongoing compliance.

Discover how to keep data safe and secure with the beginner-friendly Data Security interactive course.

Latency requirements

In scenarios where immediate insights are needed, delays in data ingestion can result in outdated or irrelevant information. Low-latency data ingestion requires the ability to process and make data available for analysis almost instantaneously, which can be challenging when dealing with large volumes of data. 

Teams must carefully optimize their systems to reduce latency and ensure data is available when needed.

Best Practices for Data Ingestion

There are a few best practices to ensure efficient and effective data ingestion.   

Prioritize data quality

Ingested data will be used for analytics, reporting, and decision-making, so it must meet predefined standards of accuracy and completeness. 

Implementing data validation checks during ingestion can help verify that data conforms to the expected formats and quality standards. I’ve written about this before in a Great Expectations tutorial.

For example, if data is expected to be in a certain range or meet specific criteria, use validation rules to automatically reject or flag any data that doesn’t meet these standards. This minimizes errors and ensures that only reliable data is processed downstream.

Choose the right ingestion approach

Another best practice is to ensure you select the appropriate ingestion approach for your business needs. 

Batch ingestion is typically used for less time-sensitive data, such as historical or periodic reports. In contrast, real-time ingestion is used for applications that require instant access to the latest data, such as fraud detection or IoT monitoring.

Assess your needs based on the frequency and urgency of data updates.

Ensure scalability

Your data volume will grow over time. Thus, it’s important to select scalable tools and infrastructure to handle increasing amounts of data without compromising performance. This lets you expand your data pipelines and processing capabilities to accommodate future growth.

Use data compression

Less storage space required means lower infrastructure costs. Compression also speeds up data transfer, which means ingestion will be faster – mainly when dealing with large datasets or high-volume streams. This practice is especially beneficial when dealing with semi-structured or unstructured data, as they often come in large sizes.

Data Ingestion Tools

As you can see, data ingestion is a simple concept but it can be quite difficult to implement on your own. What are the best tools to help you with data ingestion? I’ve curated a list of the most commonly used.

Apache Kafka

Apache Kafka is a distributed streaming platform that excels at real-time data ingestion. It is designed to handle high-throughput, low-latency data streams, which makes it ideal for use cases that require instant access to large volumes of continuously generated data. 

A significant benefit of Kafka is that it enables businesses to process, store, and forward streaming data to various systems in real time. It is widely used in scenarios like fraud detection, IoT data processing, and real-time analytics.

The Introduction to Kafka course can teach you how to create, manage, and troubleshoot Kafka for real-world data streaming challenges.

Apache Nifi

Apache Nifi is a data ingestion tool known for its user-friendly visual interface. Its accessible interface enables businesses to design, monitor, and manage data flows using a drag-and-drop mechanism. 

Nifi supports many data sources—including batch and real-time data—and can integrate with systems such as Hadoop, relational databases, and NoSQL stores. Its ability to automate data ingestion workflows and handle complex data routing and transformation tasks makes it a flexible and powerful tool for small-scale and enterprise-level data ingestion needs.

Amazon Kinesis

Amazon Kinesis is a cloud-native service from AWS that specializes in ingesting, processing, and analyzing streaming data. It is designed for applications that require near real-time processing of high-throughput data streams, such as: 

  • Video feeds
  • Clickstream data
  • IoT sensor data 

One of the benefits of Kinesis is its ability to integrate with other AWS services for storage, analytics, and machine learning.

This tool is particularly well-suited for organizations that leverage cloud infrastructure and seek a fully managed solution to handle their data ingestion requirements without concerning themselves with hardware or scalability issues.

Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service provided by Google Cloud that supports both batch and real-time data processing. It is built on Apache Beam, an open-source unified programming model, and allows users to design complex data pipelines that can handle both large-scale batch processing and real-time streaming data. 

Dataflow automates resource management and scaling, making it ideal for businesses that want to process and analyze data without manual infrastructure management.

Airbyte

Airbyte is an open-source data ingestion platform that simplifies data integration from various sources into storage solutions. It offers pre-built connectors for hundreds of data sources and destinations and supports batch and real-time ingestion.

The cool thing about Airbyte is that it is highly customizable—this allows users to extend connectors to fit their needs. Airbyte is a flexible solution for businesses that want to manage and streamline data ingestion workflows. Additionally, it provides a managed cloud service for those seeking a hands-off solution.

Conclusion

In this article, we’ve explored the fundamentals of data ingestion, its types, and the techniques that enable it. You’ve learned about batch and real-time ingestion and popular approaches like ETL, ELT, and Change Data Capture (CDC). We’ve also covered best practices,  the various data sources, and the challenges organizations face while performing.

For more insights into data management and ingestion tools, I recommend exploring the following resources:

Become a Data Engineer

Prove your skills as a job-ready data engineer.

FAQs

How does data ingestion differ from data integration?

Data ingestion focuses on collecting and importing data from various sources into a centralized system, whereas data integration combines and harmonizes data from different sources to provide a unified view. Integration often involves transforming data to ensure compatibility across systems, which goes beyond the scope of ingestion.

What’s the difference between a data pipeline and data ingestion?

Data ingestion is a component of a data pipeline. While ingestion is about collecting and transferring data to a target system, a data pipeline encompasses the entire process, including ingestion, transformation, validation, and loading data for further use or analysis.

What role does data governance play in data ingestion?

Data governance ensures that the ingested data complies with organizational policies, quality standards, and regulatory requirements. It defines rules for data validation, access control, and documentation, helping maintain trust and security in the ingestion process.


Kurtis Pykes 's photo
Author
Kurtis Pykes
LinkedIn
Topics

Learn more about data engineering with these courses!

course

Introduction to Data Engineering

4 hr
116K
Learn about the world of data engineering in this short course, covering tools and topics like ETL and cloud computing.
See DetailsRight Arrow
Start Course
See MoreRight Arrow
Related

blog

Data Integration Explained: Techniques, Platforms & Tools

Explore the essentials of data integration and learn how to overcome challenges and implement effective strategies.
Kurtis Pykes 's photo

Kurtis Pykes

20 min

blog

Data Observability Explained: Concepts, Tools & Best Practices

Understand what data observability is, why it matters, and how to use it to monitor and optimize your data pipelines effectively!
Kurtis Pykes 's photo

Kurtis Pykes

15 min

blog

Top 10 Data Science Tools To Use in 2025

The essential data science tools for beginners and data practitioners to efficiently ingest, process, analyze, visualize, and model the data.
Abid Ali Awan's photo

Abid Ali Awan

9 min

blog

14 Essential Data Engineering Tools to Use in 2025

Learn about the top tools for containerization, infrastructure as code (IaC), workflow management, data warehousing, analytical engineering, batch processing, and data streaming.
Abid Ali Awan's photo

Abid Ali Awan

10 min

blog

What is Data Visualization? A Complete Guide to Tools, Techniques, and Best Practices

Learn what data visualization is and why it is an essential skill for data scientists. Discover the numerous ways you can visualize your data and boost your storytelling skills.
Kurtis Pykes 's photo

Kurtis Pykes

17 min

tutorial

An Introduction to Data Pipelines for Aspiring Data Professionals

This tutorial covers the basics of data pipelines and terminology for aspiring data professionals, including pipeline uses, common technology, and tips for pipeline building.
Amberle McKee's photo

Amberle McKee

22 min

See MoreSee More