What are Data Ingestion Types?[+ Definition, types, and Challenges]

In the modern era, businesses are undergoing a significant transformation in which business operations are becoming increasingly data-intensive. Companies gather data from various sources, including applications, SaaS solutions, social channels, mobile devices, IoT devices, and others.

In order to make the best use of gathered data for making productive decisions, businesses must pull such data from all available sources and consolidate it in one destination for optimal analytics and data management.

Data Ingestion is a major data handling approach that transfers data from one or more external data sources into an application data store or specialized storage repository.

In this article, you will learn about Data Ingestion. You will also explore the various Data Ingestion types.

What is Data Ingestion?

Data Ingestion is the process of ingesting massive amounts of data into the organization’s system or database from various external sources in order to run analytics and other business operations.

To put it another way, Data Ingestion is the transfer of data from one or more sources to a destination for further processing and analysis. Such data comes from a variety of sources, such as IoT devices, on-premises databases, and SaaS apps, and it can end up in centralized storage repositories like Data Lakes.

Elements of Data Ingestion

There are three main elements of Data Ingestion: 

1. Source: The software/application/platform that generates the data is the source. Examples of sources are customer apps, marketing software, sales or CRM, internal databases, and document stores.
2. Ingestion Methods: The data can be ingested using different methods, including batch ingestion, real-time ingestion, and micro-batching.
3. Destination: A destination can be a centralized storage system, such as a cloud data warehouse or a data lake, or an application, such as a business intelligence tool or messaging system.
4. Cloud migration: Data ingestion can also involve moving data from traditional storage into cloud-based storage and processing tools.

Refer to What is Data Ingestion? 10 Critical Aspects guide, to learn more about Data Ingestion and its architecture.

Why is Data Ingestion Important?

Data ingestion is crucial because of various reasons:

• Operational Efficiency: It minimizes the manual work that is otherwise consumed in long data ingestion processes. Therefore, teams focus more on analysis rather than data collection.
• Timeliness: It enables organizations to pull in the freshest data available, which is the backbone of real-time decision-making and analytics.
• Data Centralization: Consolidating data from various sources helps an organization gain a more comprehensive view of its data and enhances the analysis and reporting process.
• Advanced Analytics: With the right data ingestion, you can focus on advanced analytics, machine learning, and artificial intelligence initiatives with quality data.

Data Ingestion Types

Depending on the business requirements and IT infrastructure, various Data Ingestion Types were developed such as real-time, batches, or a combination of both. Some of the Data Ingestion methods are:

• Real-Time Data Ingestion
• Batch-Based Data Ingestion
• Lambda-Architecture-Based Data Ingestion

1) Real-Time Data Ingestion

The process of gathering and transmitting data from source systems in real-time solutions such as Change Data Capture (CDC) is known as Real-Time Data Ingestion. This is one of the widely used Data Ingestion Types used especially in streaming services.

CDC continuously monitors transactions as well as redo logs and moves changed data without trying to interfere with database workload. Real-time ingestion is critical for time-sensitive use cases such as stock market trading or power grid tracking, where organizations must react quickly to new data.

Real-time Data Pipelines are also necessary for quickly making operational choices and defining and acting on new insights. In real-time data ingestion, as soon as data is generated, it is extracted, processed, and stored for real-time decision-making. For example, data obtained from a power grid must be continuously monitored to ensure power availability.

2) Batch-Based Data Ingestion

The process of collecting and transferring in batches at regular intervals is known as Batch-based Data Ingestion. When data is ingested in batches, it is moved at regularly scheduled intervals, which is highly advantageous for repeatable processes.

With Batch-based Data Ingestion types, data can be collected by the ingestion layer based on simple schedules, trigger events, and any other logical ordering. When a company needs to collect specific data points on a daily basis or simply does not require data for real-time decision-making, batch-based ingestion is beneficial.

3) Lambda-Architecture-Based Data Ingestion

The Lambda architecture is one of the Data Ingestion techniques. Its configuration includes both Real-Time and Batch ingestion methodologies. The Lambda architecture balances the benefits of the two methods mentioned above by utilizing batch processing to provide broad views of batch data.

Furthermore, it employs real-time processing to provide viewpoints of time-sensitive data. The configuration includes batch, serving, and speed layers. The first two layers index data in batches, while the speed layer indexes data that has yet to be picked up by the slower batch and serving layers in real-time. This continuous hand-off between layers ensures that data is available for querying with minimal latency.

Data Ingestion vs Data Integration vs ETL

Aspect	Data Ingestion	Data Integration	ETL
Definition	The process of collecting and importing data from various sources into a storage system.	The process of combining data from different sources to provide a unified view.	A specific data integration method involving extraction, transformation, and loading of data.
Focus	Emphasizes the initial collection and loading of data.	Focuses on ensuring that disparate data sources work together seamlessly.	Concentrates on transforming data into a format suitable for analysis.
Methods Used	Can include batch ingestion, real-time ingestion, and micro-batching.	Uses various methods, including data federation and virtualization.	Involves defined steps: extracting data, transforming it, and then loading it into the destination system.
Use Cases	Useful for scenarios requiring immediate access to data or large data loads at specific intervals.	Essential for creating a comprehensive view of data across multiple sources for analytics.	Typically used for preparing data for data warehousing or analytics.
Tools	Hevo, Apache Kafka, AWS Kinesis, Fivetran, Apache Nifi.	Hevo, Informatica, Talend, Microsoft Azure Data Factory.	Hevo, Fivetran, Talend, Apache NiFi, Informatica PowerCenter.

Best Data Ingestion Tools in 2024

Here’s a list of popular open-source data ingestion tools to consider:

1. Hevo
2. Apache Nifi
3. Apache Flume
4. Apache Kafka
5. Apache Goblin

To learn more about these tools, check out our blog on data ingestion tools. 

Common Data Ingestion Challenges

Some of the common challenges that organizations face in data ingestion are as follows:

1. Data Quality: It’s about making the ingested data as accurate, complete, and consistent as possible because poor data quality leads to incorrect insights, among other problems.
2. Scalability: As volumes grow, the ingestion needs to scale. Scalable architectures and tools that can allow a large ingestion load are critical.
3. Latency: Low latency in data ingestion is the most critical factor where real-time applications will prevail. Organizations would choose the right ingestion method and the right set of tools to acquire low-latency processing.
4. Data Security: All sensitive information ingested must be protected. Organizations must comply with all security measures that guard against data breaches or unauthorized access.

Conclusion

In this article, you learned about Data Ingestion. You understood more about Data Ingestion types, best practices, frameworks, and parameters. You explored the Real-Time, Batch-based, and Lambda-based Data Ingestion Types.

This article only focused on a few attributes of best practices and frameworks. However, you can later explore other Data Ingestion best practices like network bandwidth, scalability maintenance, and data compression.

To stay competitive, most businesses now employ a range of automatic Data Ingestion solutions. This is where a simple solution like Hevo might come in handy!

Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 100+ Data Sources including 40+ Free Sources, into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.

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