The very first step of any data-centric process is to figure out the right data and get it ready for performing analysis. Obviously, this data will be spread across different sources and to be able to process it there is a need to have a centralized singular source. That’s where the process of Data Ingestion comes into play. With data ingestion, the data stored in different file formats, over different sources, is gathered, sanitized, and transformed into a uniform format.
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What is Data Ingestion?
Data ingestion is a process of gathering data stored in different file formats, across different sources to one single source to carry out the data analysis. This is the first step of data analytics workflow and it is quite important because this is where you comprehend what kind of data your problem statement demands. Generally, companies gather data from various sources, such as websites, social media, Salesforce CRM systems, financial systems, Internet of Things (IoT) models, etc. Typically, data scientists take on this task because this task demands deep knowledge of machine learning alongside programming skills in Python or R programming language.
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Why is Data Ingestion Important?
Data ingestion is critical considering that it is the first step of any analytics workflow. Not only that, if you ponder upon the importance of getting the right data to solve an analytics problem, you will be able to comprehend the purpose of data ingestion. With this process, you figure out what kind of data is needed by the target environment, how the environment will use that information once it arrives, etc.
Below are some more factors that make the data ingestion process highly important:
1. Enhances Data Quality
Data ingestion is critical when it comes to enhancing the quality of data. While setting up the data environment lot of validation checks can be set to ensure the consistency and accuracy of the data. Tasks like data cleaning, standardization, or normalization of data are generally attained in this step, making sure that data is readily analyzable.
2. Provides High Flexibility
By gathering data from a multitude of sources businesses attain the possibility of comprehensively understanding their operations, market trends, and customer base. Once the data ingestion process is set up businesses don’t have to worry about data sources, volumes, and velocity.
3. Reduces the Complexity of Analysis Process
Data ingestion makes it easier for companies to analyze the data, as it is transformed into a unified format. By ensuring that the right data is gathered at the target data environment, the unnecessary data variables are mostly omitted, leading to ease in exploratory data analysis.
Types of Data Ingestion
Data ingestion can be classified based on how the data is extracted. The three types of data ingestion are mentioned below:
Batch Processing
In batch ingestion, the data from various sources is collected, grouped, and sent in batches to storage locations like a data warehouse or a cloud storage system. The transfer is done based on schedules or when certain conditions are satisfied. This type of ingestion is less expensive compared to other forms of data ingestion.
For example, a company that handles sales can use batch processing to set up a schedule that sends the sales and inventory reports to the company daily. The image below depicts a simple illustration of Batch Processing:
Batch Processing Architecture
Real-Time Ingestion
Real-time ingestion is also known as stream processing. In this type of ingestion, there is no grouping of data; rather, the data is transferred as individual events or messages in real time. Then, new data is received, and it is immediately sent to the storage location. This is usually implemented by using a solution known as change data capture (CDC). This type of ingestion is more expensive as the system needs to monitor the sources for change. The snapshot highlighted below represents the Real-time data ingestion framework:
Real-Time Data Ingestion Architecture
For example, let’s think of the stock markets. An analyst or a stock trader works on the real-time rates of the stocks. To implement this, real-time ingestion can be used to update the prices of the stocks whenever a change occurs in the prices.
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Lambda-Based Data Ingestion
Lambda-based data ingestion is a hybrid approach to data ingestion as it uses both batch processing and real-time ingestion, where batch processing is used to gather the data into groups and real-time ingestion is used for time-sensitive data.
There are three layers in lambda-based data ingestion:
- Batch Layer: This layer is responsible for batch processing.
- Speed Layer: This layer handles the real-time processing.
- Serving Layer: This layer is responsible for responding to queries.
The architecture diagram given below represents how the Lambda data ingestion occurs:
Lambda Data Ingestion Architecture
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Data Ingestion Framework
The three different data ingestion architectures we went through in the above section, resemble how data ingestion framework operates. In very simple terms, a data ingestion framework (DIF) is a set of services that allow you to ingest data into your target environment be it a database or warehouse. You can choose any one of the above data ingestion architectures such as Batch Processing, Real-Time Processing, or Lambda Processing based on the kind of sources you’re dealing with.
The framework would contain a cloud storage unit, data transformation tools, data source APIs, stream processing tools, etc. to carry out the ingestion process.
The data ingestion tools handle the transfer of data from the source to the destination. Both structured and unstructured data can be transferred using these tools. These tools include:
- Amazon Kinesis: It is a cloud-based service from AWS that handles data ingestion and processing.
- Airbyte: It is an open-source tool that can be used to extract and load data into storage.
- Apache Flume: Apache Flume is a data ingestion tool that can be used to handle large amounts of data.
- Apache Kafka: It is a data ingestion service that is best suited for ingesting and processing large streams of data in real-time.
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Data Ingestion vs. Data Integration
As per our exploration to the point, data ingestion is nothing but gathering data from multiple sources into one unified data environment like a database or warehouse. In contrast, data integration comes after this step. It generally means that we extract the unified data from one source and load it into another.
To work seamlessly with other companies, you might need data integration to bring your information together. Below we have listed the key differences between both concepts:
| Aspect | Data Ingestion | Data Integration |
| Definition | Process of collecting and transferring data from multiple input sources to the unified target storage for further processing | Combines multiple datasets into a single dataset or data model. Involves extraction and loading of data from one source to another. |
| Data Quality | Ingestion does not improve data quality automatically, quality checks must be set up. | Since data transformation is an important part of the integration, the quality is majorly maintained |
| Complexity | Less Complex Pipeline | Complex due to including processes like data transformation, ETL, Governance, etc. |
| Coding and Domain Expertise Required? | Yes | Yes |
Data Ingestion vs. ETL
Data ingestion and ETL (Extract, Transform, Load) are two integral steps in data processing for analysis. While data ingestion primarily focuses on the initial collection and transportation of raw data, ETL steps in to refine, clean, and transform this raw data into a structured format suitable for analysis. The difference between both of them is given below:
| Parameters | Data Ingestion | ETL (Extract, Transform, and Load) |
Description | Data ingestion is the process of extracting data from various sources, transforming it into the required format, and sending it to a central location. | ETL (Extract, Transform, and Load) is the process of extracting data, transforming it into the desired format, and then loading it into storage. |
| What It Is | Data ingestion can have many processes and methods. | ETL is a method that is used in data ingestion. |
| Source of Data | The source of data might be unknown. | The source of data is usually known or pre-planned. |
| Tools | Apache Kafka, Apache Nifi, Amazon Kinesis, etc. | AWS Glue, Fivetran, Talend Open Studio, etc. |
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Challenges and Benefits of Data Ingestion
By now we have understood how the data ingestion process works, why it is important, and how it differs from other data management pipelines. Moving further, let us look into the challenges that you may face while working on establishing a data ingestion framework:
Data Ingestion Challenges
All of us agree to the fact that data is an important asset. Without data, we won’t be able to make winning business decisions and get the work done efficiently. Data does keep an organization on top of its game even though there is immense competition in the market. But considering the fact that the data recorded is tremendous, how do you determine what data to keep and what to remove?
Like this challenge, there are four types of problems a data ingestion process may encounter: data quality, data capture, coding and maintenance, and latency. Let’s try and understand these challenges one by one.
- Data Quality: What if the available information is not enough and does not address the purpose of the analysis? What if there are missing variables and broken records?
- Data Capture: How do we collect the right data without losing any information?
- Coding and Maintainance: How to ensure the end-to-end automation of the ingestion pipeline?
- Latency: When dealing with large volumes of data how do you battle processing delays? How to tackle network latency and bandwidth problems?
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Benefits of Data Ingestion
- Availability: Since the data collected from different sources is available in a single location, it is easier for analysts to analyze the data.
- Uniform Data: As the data collected is grouped, it is unified and can be understood easily.
- Scalability: Data ingestion tools can handle large volumes of data and can scale themselves according to the size of the data.
- Saves Time: Data ingestion saves time, as this job was done manually by people before.
- Improves Accuracy: Data ingestion improves accuracy, as live data can be received as soon as data is created.
- Better Decision Making: Companies can make better decisions as they receive live data in a formatted manner.
Conclusion
Data ingestion is an important process of bringing raw data into a system for analysis. As businesses and organizations continue to gather large amounts of data from various sources, the future of data ingestion looks promising. Advancements in technology, such as real-time data processing, automated ingestion pipelines, and improved scalability, will revolutionize how we handle and make use of data. This means faster insights, better decision-making, and more efficient operations across industries. The new ways of collecting data will lead to amazing progress in how we analyze data, use artificial intelligence, and make businesses more digital in the future.
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FAQs
Where is the data stored?
The data is usually stored in a database, a data warehouse, or a data lake.
What is a data warehouse?
A data warehouse is a data management system that can store large amounts of data from various sources. A data warehouse can store present and past data, which helps analysts make accurate reports.
Examples of data sources.
The data sources can include social media sites, streaming platforms, websites, and even databases where the data is stored.
What is CDC?
CDC stands for change data capture. It refers to the process of detecting changes in the data source and sending the changes to the target location.
What is the difference between ETL and CDC?
ETL extracts the bulk data from the source, whereas CDC detects the changes made in the source.
Differentiate between structured and unstructured data.
Structured data refers to the data stored in a structured manner, usually in a database. Unstructured data refers to data that doesn’t have any particular structure, usually the data from social media sites.