Data ingestion is a crucial part of a data-centric process, ensuring organizations get the right information at the right time to understand business performance and improve it.
Modern organizations generate massive amounts of data every day that are of high value to their businesses.
By performing business analytics, organizations can get deeper insights, which helps them make informed, data-driven decisions.
This data also plays a key role in understanding customers, predicting the market, planning, predicting trends, and gaining other benefits.
However, to execute certain tasks, it is crucial to extract and analyze data and access it easily from a centralized location.
This is where data ingestion comes in.
This technique extracts data from several sources, allowing you to uncover insights hidden within it and further use it to grow your business.
In this article, I’ll talk about data ingestion and its types, step-by-step process, architecture, use cases, benefits, best practices, and challenges.
Here we go!
What is Data Ingestion?
Data ingestion is the process of collecting data from one or more sources and importing it into a data warehouse for immediate use. It is one of the most essential steps in the data analytics workflow.
Data can be ingested in batches or streamed in real-time. When the data moves to the targeted site, it is stored properly and then used for analysis.
The data sources might be data lakes, databases, IoT devices, SaaS applications, on-premise databases, and other platforms that may have relevant and essential data.
Data ingestion is a simple process that takes data from an origin, cleans it, and forwards it to a destination where an enterprise can use, access, and analyze the data.
Data ingestion enables organizations to make data-driven decisions from the increasing complexity and volume of data that they produce every day.
When an organization collects data, it remains in its original and raw state, the same as it is in the source. You will need to perform a transformation operation when there is a need to transform or parse the data into a readable format that is compatible with different applications.
The primary goal of data ingestion is to move a large set of data from one place to another efficiently with the help of software automation. It only ingests data, not transforms it. For many organizations, it works as a critical tool that allows them to manage their front end of the data.
There are multiple ways of ingesting data in your data mart. According to your particular needs and design requirements, you can choose any ingestion method that works best for you.
How Does Data Ingestion Work?
Data ingestion collects data from multiple sources where the data was originally stored or generated. It loads or transfers data to the destination or staging area. The data ingestion pipeline applies light transformations wherever needed to filter out or optimize the data before sending it to a message queue, data store, or destination.
Data ingestion also performs complex transformations, including sorts, joins, and aggregates for specific applications, reporting, and analytics systems with supplementary pipelines.
To understand the step-by-step process of data ingestion, you need to dive into its architecture.
Architecture of Data Ingestion
The architecture of data ingestion tells you about the flow of data in the following layers:
Data collection layer: It collects data from different sources and stores it in your data warehouse. This layer defines how data is transferred or parsed to other layers of the ingestion architecture. Also, it helps break down the data for analytical processing.
Data processing layer: This layer collects data from the previous layer to process the transfer of data that is in storage. It defines the destination where you want to send the data and groups them accordingly.
Data storage layer: The data once grouped, are stored in an efficient location for further transfer.
Data query layer: This is the analytical layer of the data ingestion architecture. Here, data is queried so that the layer can extract valuable insights.
Data visualization layer:Data visualization is the final layer that deals with data presentation. It displays the data in an understandable and visual format for your organization to get real-time insights.
Benefits of Data Ingestion
Let’s discuss some of the benefits of data ingestion:
Availability: When an organization implements a data ingestion process, data can be accessible and available easily for the organization. Since data is collected from several sources and transferred to a storage location, anyone with valid authorization can gain access easily to the data for analysis.
Uniformity: A good data ingestion practice enhances the data quality by turning multiple data types into a unified data type. To this, it is easier to manipulate and understand data for future analytics.
Enhanced productivity: Data ingestion lets you use data to become more productive. This helps data engineers become more flexible and lets them develop the power to scale.
Improved decision-making: The data ingestion process allows organizations to make better and more informed decisions using real-time data. In addition, you can derive analytics that are helpful in making tactical decisions and tracking KPIs and potential targets.
Enhanced user experience: Organizations use recent data to serve their valuable customers. Data-driven analytics allow them to build efficient tools and applications for customers.
Types of Data Ingestion
There are three types of data ingestion – batch processing, real-time data ingestion, and Lambda-based data ingestion. The choice of choosing one of them largely depends on the type of business, your IT infrastructure, budget, timeline, and goals to be achieved. Also, businesses choose their model and tools based on the data sources they use.
Let’s dive deeper into each in more detail.
#1. Batch Processing
It is the most common ingestion method. Here, the ingestion layer gathers and groups data coming from several sources incrementally. It then transfers the data in bunches to an application, system, or location where it is required.
The transfer of data is based on the activation of political conditions via trigger events, analogical ordering, or existing schedules to ensure that data is transferred. Batch processing is useful for organizations that need to gather specific data every day with activities that require attendance sheets, report generation, etc.
This approach is less expensive and considered a legacy approach in many cases.
#2. Real-Time Data Ingestion
Real-time data ingestion is also known as stream processing. It involves the collection and transfer of data from a given source in real time to the destination. Here, there is no grouping; instead, you will find data is sourced, loaded, and processed as soon as the ingestion layer finds new data.
In order to implement real-time data ingestion, there is a common solution named Change Data Structure (CDC). However, this type of data ingestion is more expensive than batch ingestion. This is because it needs you to monitor sources constantly in order to recognize new data and ensure it reflects correctly in the targeted platform.
If you cut the cost part, this method is very useful for companies that want to run analytics with fresh data every time to make operational decisions.
For example, if you want to make stock market trading decisions, real-time data ingestion is your best option. This method is also useful in monitoring your infrastructure.
#3. Lambda-Based Data Ingestion
This method is the combination of two types of data ingestion, i.e., batch processing and real-time ingestion.
Batch processing is used to gather data in batches, while real-time data ingestion is employed to provide a different angle to time-sensitive data. Lambda-based data ingestion divides the data it collects into groups and ingests them in smaller increments, making it effective for different applications that need streaming data.
Use Cases of Data Ingestion
Organizations across the world use data ingestion processes as an essential part of data pipelines in their operations.
Internet of Things (IoT): Data ingestion is used in several IoT systems to gather and transform data from a wide range of connected devices.
Big Data Analytics: Big data analytics is a common requirement for every organization. Ingesting large data volumes from numerous sources is therefore needed in big data analytics, where data is being processed with distributed systems like Spark or Hadoop.
Fraud detection: Organizations use the data ingestion process to detect fraud by importing and transforming data from different sources. This includes customer behavior, third-party data feeds, and transactions.
Ecommerce: Ecommerce businesses use data ingestion process to receive data from several sources, such as customer transactions, product catalogs, website analytics, and more. This helps them grow bigger with the right data in real-time.
Personalization: The data ingestion process can be used to provide personalized experiences or recommendations to users by extracting data from different sources, such as customer interactions, social media data, website analytics, etc.
Supply chain management: To manage the supply chain, an organization needs data from sources like inventory, logistics, and supplier data. Data ingestion ingests this data from multiple sources and processes it for your effective supply chain management.
Sentiment and social media analysis: Real-time data ingestion helps businesses monitor social media feeds, identify emerging trends, and analyze brand sentiment effectively by collecting data from various sources. This leads to improved customer relationships, the development of market capture strategies, and effective marketing strategies.
You can experience some challenges with the data ingestion process:
Scalability: You may find difficulty in scaling a large set of data while ingesting data from different sources. The amount of processed data requires vertical or horizontal scaling of the infrastructure to handle the increased load, hence, complications occur.
Data quality: Data quality is a major challenge in the data ingestion process. While extracting data, you can’t always ensure the data you receive is of high quality.
Diverse ecosystem: There are many data sources and types, making it difficult for your teams to develop a sound-proof ingestion model. Some tools and features only support basic technologies, letting organizations use several tools that require several skill sets.
Cost: Ingestion cost is directly proportional to data volumes. As your business in data values grows, the overall ingestion costs also increase. In order to ingest all the data, you will require more servers and storage systems, leading to a rise in the ingestion cost.
Security: As the data is stored at numerous points in the pipeline during its ingestion, it’s prone to data exposure and security risks. This makes the data ingestion process vulnerable which will lead to security breaches. Thus, organizations find it challenging to maintain compliance standards and regulations during the process.
Data integration: You will find a little difficulty in integrating data from third-party sources with the ingestion pipeline. This is why you need a comprehensive tool that allows you to integrate data.
Unreliability: If somehow, you ingest data incorrectly, it might be subject to unreliable connectivity. This results in disrupting communication and losing data.
Let’s discuss some data integration practices that you can follow to enhance your business performance.
Automated Data Ingestion
Automated data ingestion can solve many challenges that come with manual ingestion. It acknowledges the difficulty and inevitability of transforming raw data into useful insights, especially when the data derives from several disparate sources.
Organizations can use data ingestion tools to automate recurring processes of collecting data for better analytics and reports, reducing human error.
Create Data SLAs
Data SLAs require:
What a business need
What expectations a business must have for the data
When data can meet expectations
Who gets affected
How one should know when the SLA is met and what will be the response when it is violated?
Thus, the data ingestion approach helps you get all the required data to create data SLAs effectively.
The data ingestion pipeline can be built in a way that it can handle network bandwidth effectively.
The traffic is not always constant, sometimes it increases or decreases based on the social and physical parameters. The network bandwidth also depends on the amount of data to be ingested at a specific time.
Heterogeneous Systems and Technologies
An organization needs to check whether the data ingestion pipeline model is compatible with third-party tools and applications as well as various operating systems.
Support for Unreliable Data
The data ingestion pipeline receives data from several sources and various structures like audio files, log files, images, and many more.
Different structures need different speeds, allowing an unreliable network to make the whole pipeline unreliable. Organizations must design a data ingestion pipeline that supports all the formats without being unreliable.
The data ingestion process is directly proportional to auditable data. It requires a well-designed process so that it can alter the intermediary functions based on requirements.
Enterprises require real-time and batch processing data ingestion processes to enhance their services and gain maximum efficiency.
Some organizations, especially large ones, directly integrate their analytics or business intelligence database with the operational database. Decoupling the analytical and operational databases helps organizations cascade the issues into one another.
Data ingestion provides immediate insights so you can understand current market trends, maintain low latency, and measure customer experiences. The data ingestion pipeline consists of various layers which start from extracting and collecting data to visualizing and analyzing it.
With data ingestion, organizations can easily improve operational efficiency, perform faster fraud detection, get real-time analytics, and initiate proactive maintenance. Businesses can also use real-time data ingestion to get up-to-date information and utilize it for competitive advantage and informed decision-making.
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