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.
Table of Contents
A fundamental understanding of the data handling process.
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.
Refer to What is Data Ingestion? 10 Critical Aspects guide, to learn more about Data Ingestion and its architecture.
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:
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.
1) Apache Flume
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!
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Share your experience with Data Ingestion Types, Best Practices, Frameworks & Parameters in the comments section below!