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What is Data Ingestion? Types, Frameworks, Tools, Use Cases

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20th Sep, 2023
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    What is Data Ingestion? Types, Frameworks, Tools, Use Cases

    An end-to-end Data Science pipeline starts from business discussion to delivering the product to the customers. One of the key components of this pipeline is Data ingestion. It helps in integrating data from multiple sources such as IoT, SaaS, on-premises, etc., into a single master source which is then processed and analyzed further down the pipeline. Organizations build data ingestion architecture to make sense of the complexity in the data and derive more value from it. To learn more about it, you can check out this bootcamp for Data Engineers.

    What is Data Ingestion?

    Data Ingestion is the process of gathering data from multiple diverse sources into a single target site which enables data engineers, analysts, scientists, and stakeholders to analyze it further down the stream and draw insights from it.  Many companies have a separate data ingestion framework which results in seamless data movement across the applications. You can find a comprehensive guide on how data ingestion impacts a data science project with any Data Science course.

    Why Data Ingestion is Important?

    Data ingestion provides certain benefits to the business:

    • The raw data coming from various sources is highly complex. However, a data ingestion framework reduces this complexity and makes it more interpretable. This data then could be utilized by various teams and stakeholders to make informed business decisions.
    • Like the previous point, it makes the data more readable and user-friendly.
    • The manual tasks which a Data engineer often must do could be automated by the data ingestion pipeline saving both time and money. Hence, we can focus on more important tasks like defining the problem statement, building models, generating insights and so on.
    • The less complex and interpretable data allows stakeholders to make informed business decisions.
    • Better tools could be built which leverage this seamless movement of data and flawless user experience. This allows applications to run as expected with better latency and throughput.

    Why Data Ingestion is Important?

    Data ingestion provides certain benefits to the business:

    • The raw data coming from various sources is highly complex. However, a data ingestion framework reduces this complexity and makes it more interpretable. This data then could be utilized by various teams and stakeholders to make informed business decisions.
    • Like the previous point, it makes the data more readable and user-friendly.
    • The manual tasks which a Data engineer often must do could be automated by the data ingestion pipeline saving both time and money. Hence, we can focus on more important tasks like defining the problem statement, building models, generating insights and so on.
    • The less complex and interpretable data allows stakeholders to make informed business decisions.
    • Better tools could be built which leverage this seamless movement of data and flawless user experience. This allows applications to run as expected with better latency and throughput.

    Types of Data Ingestion?

    A Data ingestion pipeline could be grouped under several types:

    1. Batch architecture: In this system, the raw data from various sources is collected in batches and moved to a target location. The batch processing system could be triggered by a user query or scheduled automatically at specific intervals. Batch processing is less expensive and easier to implement than real-time systems.
    2. Real Time architecture: Unlike batch processing, real time data ingestion process is more complex which needs data to be transferred to the target system continuously. The benefit of this system is that data could be analysed in real-time instead of waiting for the extract, load, and transform process to be completed in a batch system.
    3. Lambda architecture: A combination of both batch and real-time processing, the lambda architecture has three layers. The first two layers are about loading and indexing data in batches while the third layer indexes any data which has not been indexed in the first two layers. The lambda architecture ensures completeness of data with minimal latency.

    KnowledgeHut Bootcamp for Data Engineers offers you a brilliant opportunity to learn about various data ingestion types in detail.

    Data Ingestion Framework

    A Data Ingestion framework consists of the following steps:

    • An API which allows to extract data from various sources and load it to the database and eventually save it to cloud stores for later processing. This data then could be used by a Data Analyst, Data Scientist, Business Analyst and other functions for their respective workflows.
    • This API acts as a proxy between the application and the cloud services ensuring seamless transfer of data.
    • All the code required to interact with external sources of data are present in the data service.

    Top Data Ingestion Tools

    Some of the most popular Data ingestion tools used in the industry these days are mentioned below along with their prominent features:

    • Apache Kafka: Written in Scala and Java, it delivers data with low latency and high throughput. It is useful for Big Data ingestion. Since processing big data is a challenge in on-premises, leveraging Apache Kafka allows for seamless transfer between data storage and the application.
    • Wavefront: A cloud hosted streaming analytics service for storing, analyzing, and monitoring data. It is highly scalable to very high query loads. Wavefront is useful in internet and e-commerce applications where the data needs to be fetched, read and processed in real time.
    • Talend: It allows retrieving data from 1000+ sources and connecting them to any target destination such as databases or cloud services.

    How Data Ingestion Helps Businesses?

    Data ingestion makes the data readily available to the business by retrieving it from various sources and transforming it into a cleaner and more interpretable form to a target database. It removes the hassle of manually fetching data from multiple sources which takes away the necessary time that could be spend on another important tasks. This allows stakeholders to analyse the data and make more informed business decisions.

    Moreover, any manual tasks which the Data engineers had to do could be automated saving both time and money.

    Challenges in Data Ingestion

    Data Ingestion processes entail a variety of challenges as below:

    • Coding and maintaining the pipeline is a big challenge which is faced by many data engineers. It is easier to discard the old data than modifying and organizing it for future purpose. When you are trying to modify an existing data, there are certain rules that need to be defined which should be in line with the requirements. Oftentimes, a small error in defining the rules, could lead to massive financial loss to business.
    • Applications which run in real-time often suffer from latency as wait time might be high of data transfer between an application and the ingestion pipeline. Any latency issue could have potential impact in user retention, loss of money, and so on. E.g. When customers search for products on amazon, they expect response in real time. In those cases, if the application takes longer time to return the query response, it could lead to users leaving the platform.
    • Data quality could be another issue where the transformation steps need to be monitored and tracked. Data is the oil of any analytics project. In Data Science, the first and foremost step is to validate the quality of data before building a model out of it. A poor-quality data could lead to wrong predictions. Hence, building a robust data ingestion pipeline is important as it could make or break the data quality.
    • Lastly, putting all these information into a single target source results in a major storage challenge. Most industries face a problem in storage space. The high volume of data that companies have these days needs large storage space as discarding any of it could lead to loss of signal.

    Data Ingestion Best Practices

    To implement flawless data ingestion pipelines, these practices need to be followed:

    • Collecting only relevant data at various stages would save time and money. Data collection process requires time and resource utilization. Hence, irrelevant data could lead to wastage of resource bandwidth which is not an optimal situation of the business.
    • The collected data should have a timestamp attached to it for tracking and matching it with other information later in the process. Oftentimes, we need to compare current dataset with its old version. The changes made in the new version could result in performance degradation and hence it is important to track various versions of data at various stages of processing or ingestion.
    • A clean and readable format to ensure everyone could use the data for their purposes. A properly formatted data allows stakeholders to analyse it and draw informed insights from it.

    Use Cases of Data Ingestion

    Data ingestion is used extensively in various organizations. Some of the common data ingestion examples are:

    • Moving data from various sources to cloud services such as Azure. Like Azure, there are other cloud and on-premises servers that leverage data ingestion pipeline.
    • Streaming data to Elasticsearch server from different databases. This could be referred to as streaming ingestion.
    • Processing the log files. Logs have different information that carries enormous signals specially in the context of internet businesses.

    Data Ingestion and ETL

    Data ingestion retrieves data from various sources and loads into the target site where in ETL involves transforming the data alongside extract and transfer. In ETL, data is delivered at a specific format after performing operations such as aggregation, cleaning, joining, etc.

    Conclusion

    Organizations that do not leverage the capabilities of having a robust data ingestion service should start setting up the ingestion framework and ensure better user experience. Data Ingestion allows having a unified source which lets the business prioritize other aspects of the pipeline. Major organizations like Google, Microsoft, Walmart, etc, have a separate team who maintains the Data ingestion pipeline. In this blog, we discussed how it benefits business in the long run.

    Frequently Asked Questions (FAQs)

    1What is data ingestion vs data integration?

    Data ingestion typically involves retrieving data from various sources and storing it in one place whereas integration involves converting the data into compatible format alongside retrieving it from different sources.

    2What are the steps of data ingestion pipeline process?

    Data ingestion involves retrieving data from various sources and save it into various target destinations like IoT, databases, etc.

    3What is a data ingestion pipeline?

    Data ingestion pipeline ensure seamless fetching and storing of data

    Profile

    Suman Dey

    Author

    Suman is a Data Scientist working for a Fortune Top 5 company. His expertise lies in the field of Machine Learning, Time Series & NLP. He has built scalable solutions for retail & manufacturing organisations.

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