Massive amounts of data are generated on daily basis and this data has lots of value to a business. Business Analytics is performed on the data to gain insights, but this analytics is performed on data already processed and stored. But the question is where does this data come from?. The data is ingested from various sources into the data warehouses using the Data Ingestion Pipeline.
Data Ingestion is the process of moving data from a variety of sources to a system, a platform for analytics and storage. It is the first step of a Data Pipeline, where the raw data is streamed from sources into Dataware houses for processing, transforming, and analyzing the data. Before the data can be analyzed it needs to be ingested into a location and this is done through a Data Ingestion Pipeline.
This article gives a comprehensive overview of Data Ingestion, Data Ingestion Pipeline, its architecture, and use cases.
Table of Contents:
What is Data ingestion?
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Data Ingestion is the process of obtaining huge amounts of data from various sources and moving it to a location where it can be stored, processed, and analyzed. The destination can be a database, data warehouse, data lakes, and many more.
Data ingestion is one of the most important parts of data analytics architecture because this is the process that brings in the data. Data ingestion deals with the consistent supply of the data to the destination for seamless analytics.
Data Ingestion helps in speeding up the process of data pipelining. It helps in determining the scale and complexity of the data that is needed for business. It helps in determining the sources that are most beneficial to a business and organizations can decide the data sources of their own for the ingestion of data.
Data Ingestion pipeline extracts data from sources and loads it into the destination. The data ingestion layers apply one or more light transformations to enrich and filter the data to move it to the next steps or store it to destinations.
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Types of Data Ingestion
There are many different types of Data Ingestion that can be categorized as real-time, batches, or a combination of both types.
Batch-based Data Ingestion:
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When the ingestion of data in batches, the data is transferred repetitively at scheduled intervals. This process is useful when the data is collected at fixed intervals, like daily reports generation, attendance sheets, and many more.
Real-time Data Ingestion:
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The data ingestion that is performed in real-time also called streaming data by the developers, is the process of ingesting data that is time-sensitive. Real-time data plays an important role wherein there is a requirement of processing, extracting, and loading the data to provide insights that impact the product and strategy in real-time. For example, the data from a power grid needs to be processed, extracted, loaded, and analyzed in real-time to avoid power outages and errors in transmissions.
Lambda-based Data Ingestion Architecture:
Lambda architecture for data ingestion is a combination of both worlds. It employs batch processing to gather the data and provides a broader perspective on the results of batch data. Lambda-based data ingestion also employs the use of real-time processing to offer a perspective on time-sensitive data.
What is the Architectural Pattern of Data Ingestion Pipeline?
To understand how the data ingestion process work first you need to understand the architectural layers of the data pipeline. It is a 6 layer architecture that ensures a stable flow of data.
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Data Ingestion Layer
This is the first layer of architecture. The data comes from various sources. This layer prioritizes and categorizes the data and determines the data flow for further processing.
Data Collector Layer
This layer focuses on the transfer of data from the ingestion layer to the other layers of the data ingestion pipeline. This layer breaks the data for further analytical processing.
Data Processing Layer
This is the prime layer of the data ingestion pipeline processing system. The information collected in previous layers is processed for the next layers. This layer also determines the different destinations and classifies the data flow. This is the first point of the analytics process.
Data Storage Layer
This layer determines the storage of the processed data. This process becomes complex when the amount of data is large. This layer determines the most efficient data storage location for large amounts of data.
Data Query Layer
This layer is the main analytical point in the data ingestion pipeline. This layer queries different operations on the data and prepares it for the next layers. The focus of this layer is to provide value to data from the previous layer and send it next layers.
Data Visualization Layer
This is the final layer of the data ingestion pipeline, which mainly deals with the presentation of data. This layer is responsible to show users the values of data in an understandable format. This layer provides a proper representation of data that defines the strategy and insights to users.
What are the Parameters of Data Ingestion?
Data Velocity
This parameter deals with the data flows from different sources like machine sensors, human-webpages interactions, mouse clicks, social media searches, and many more. The movement of data is continuous and in very large quantities for data ingestion.
Data Size
This parameter deals with the volumes of data that are ingested into the pipelines. The data from sources may be in enormous volumes and even scale and increase the time for the ingestion into the data pipeline.
Data Frequency
This parameter deals with determining the processing of the data in real-time or batch processing. In real-time processing, the data is received, processed, and analyzed at the same time. In batch processing, the data received is stored and made into batches. These batches are processed individually at a fixed interval. Then the processed data from both types are sent for data ingestion process flow.
This parameter deals with the formatting that can be followed during the data ingestion process. Different formats are available for data ingestion. Structured format deals with data in tabular formats, unstructured format deals with the data present as images, videos, audio and semi-structured deals with data present as JSON files, CSS files, and many more. The data ingestion pipeline is capable of handling all data formats.
Benefits of Data Ingestion Pipeline
A few key advantages of Data Ingestion Pipelines are:
- Data Ingestion helps a business better understand the target audience and the enterprise data through the use of a data ingestion pipeline to analyze and process the data. Data ingestion allows businesses to stay competitive by performing advanced analytics without requiring much effort.
- The insights generated from the data ingestion pipeline allow it to make a better marketing strategy, model the product to cater to a larger audience, and also improve the customer interaction by a huge margin.
- Data Ingestion pipeline automates tasks like repetitive processing, daily activities, and many more. This allows for free time spent on those tasks and this allows for dedicating those efforts to other tasks.
- The Data Ingestion pipeline enables the developers to use data ingestion to ensure the software applications move the data and have a keen observation of the total application.
Limitations of Data Ingestion Pipeline
Even though the Data ingestion pipeline provides many benefits it has its fair share of limitations. A few are mentioned below:
- Scalability: When that data ingestion is performed on humongous amounts of data, it becomes complex for the data ingestion pipeline to determine the structure and formats of the data for the destination apps. It also becomes difficult to assess the consistency of the data from various sources. Sometimes the Data ingestion pipelines also suffer from performance issues when presented with enormous data scales.
- Data Quality: Maintaining the integrity and quality of data during the process of data ingestion is a complex process. Data Ingestion pipeline usually requires complete data that matches the desired quality to be able to show useful and accurate informational insights.
- Risk to Data Security: Data security is one of the most important aspects of maintaining a data ingestion pipeline. This is a concern because the data is moved from one stage to other and there are multiple stages present in the pipeline. Data may get corrupted and since the volume of data is very large it becomes difficult to find the root cause of the issue. Also, due to the volume, it becomes a mammoth task to maintain compliance during the ingestion process.
- Unreliability: Incorrect ingestion of data leads to irregular data integrity and unreliable insights. With large amounts of data sometimes irregular data is also ingested and finding it becomes a very difficult task.
- Data Integration: creating a self-designed pipeline that ingests data, it becomes difficult to connect it to the other platforms and third-party sources.
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What are the Best Practices for Data Ingestion?
Some practices that can be followed to improve the performance of Data ingestion
Network Bandwidth
The Data ingestion Pipeline should be designed in such a way that it can handle the business traffic. The traffic never remains constant, it may increase or decrease based on the physical and social parameters. The network bandwidth is the parameter that defines the maximum amount of data that can be ingested at a particular instance. This parameter must be made flexible and must be able to accommodate the necessary throttling.
Support for Unreliable Network
Data Ingestion Pipeline accepts the ingestion from different sources and different structures like images, audio files, log files, and many more. Since the different formats have different speeds of data ingestion, it may cause an unreliable network when ingested from the same paint making the whole pipeline unreliable. Data Ingestion Pipeline should be designed in such a way that all the formats are supported without becoming unreliable.
Heterogeneous Technologies and Systems
The Data Ingestion Pipeline design must incorporate the fact that it must be compatible with third-party applications as well as different operating systems.
Choose Right Data Format
Data Ingestion pipeline must provide the options of serializing different data formats. Data is usually ingested in different formats and converting it to a generalized format will help in understanding and gaining insights from the data.
Streaming Data
The enterprises usually require batch as well as real-time streaming and data ingestion. The data ingestion pipeline should support both the steam services for maximum efficiency.
Business Decisions
The critical enterprise analysis and insights can only be performed when the data is combined from multiple sources. The data ingestion pipeline must provide a singular image of the data from various sources for better insight generation.
Connections
The data Ingestion framework must be able to support the newer connection without removing the older connection. Also, newer must accommodate the required changes. connection takes time from days to a few months to complete. The data ingestion pipeline
High Accuracy
The data ingestion is directly proportional to making auditable data. The data ingestion pipeline requires to be designed in such a way that it can alter the intermediate process based on requirements.
Latency
The relatively newer data provide enterprises to gain better insights. Extracting data from API and databases is difficult in real-time. The data pipeline must be able to accommodate the latency associated with extracting data in real-time.
Conclusion
Data Ingestion is an important process of a Data Ingestion Pipeline. It is responsible for the data entering the pipeline as well as its integrity and stability. This layer gathers data from various sources and then moves it to the next layers. The Data Ingestion pipeline is a combination of various layers that start from gathering data to analyzing and visualizing it.
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