Big Data Ingestion: Parameters, Challenges, and Best Practices

Businesses are going through a major change where business operations are becoming predominantly data-intensive. As per studies, more than 2.5 quintillions of bytes of data are being created each day. This pace suggests that 90% of the data in the world is generated over the past two years alone. A large part of this enormous growth of data is fuelled by digital economies that rely on a multitude of processes, technologies, systems, etc. to perform B2B operations.

Data has grown not only in terms of size but also variety. Large streams of data generated via myriad sources can be of various types. Here are some of them:

Marketing data: This type of data includes data generated from market segmentation, prospect targeting, prospect contact lists, web traffic data, website log data, etc.

Consumer data: Data transmitted by customers including, banking records, banking data, stock market transactions, employee benefits, insurance claims, etc.

Operations data: Data generated from a set of operations such as orders, online transactions, competitor analytics, sales data, point of sales data, pricing data, etc.


Big Data

The gigantic evolution of structured, unstructured, and semi-structured data is referred to as Big data. Processing Big data optimally helps businesses to produce deeper insights and make smarter decisions through careful interpretation. It throws light on customers, their needs and requirements which, in turn, allow organizations to improving their branding and reducing churn. However, due to the presence of 4 components, deriving actionable insights from Big data can be daunting. Here are the four parameters of Big data:

  • Volume: Volume is the size of data, measured in GB, TB and Exabytes. Big data is increasing in terms of volume and heaps of data is generating at astronomical rates. Conventional methods fail to tackle such large volume data.
  • Velocity: Velocity indicates the frequency of incoming data that requires processing. Fast-moving data hobbles the processing speed of enterprise systems, resulting in downtimes and breakdowns.
  • Variety: Variety signifies the different types of data such as semi-structured, unstructured or heterogeneous data that can be too disparate for enterprise B2B networks. Videos, pictures etc. fall under this category.
  • Veracity: Veracity refers to the data accuracy, how trustworthy data is. Analyzing loads of data that are not accurate and contain anomalies is of no use as it corrupts business operations.

The 4Vs of Big data inhibits the speed and quality of processing. This leads to application failures and breakdown of enterprise data flows that further result in incomprehensible information losses and painful delays in mission-critical business operations. Moreover, an enormous amount of time, money, and effort goes into waste while discovering, extracting, preparing, and managing rogue data sets. Additionally, business is not able to recognize new market realities and capitalize on market opportunities.

Big data: Architecture and Patterns

The Big data problem can be comprehended properly using a layered architecture. Big data architecture consists of different layers and each layer performs a specific function. The architecture of Big data has 6 layers.

  1. Data Ingestion Layer: In this layer, data is prioritized as well as categorized. This layer ensures that data flows smoothly in the following layers.
  2. Data Collector Layer: This layer transports data from data ingestion layer to rest of the data pipeline.
  3. Data processing Layer: Data is processed in this layer to route the information to the destination.
  4. Data Storage Layer: In this layer, the processed data is stored.
  5. Data query Layer: In this layer, active analytic processing occurs. In actuality, this layer helps to gather the value from data.
  6. Data Visualization Layer: In this layer, users find the true value of data.

Big Data Ingestion

Data ingestion, the first layer or step for creating a data pipeline, is also one of the most difficult tasks in the system of Big data. In this layer, data gathered from a large number of sources and formats are moved from the point of origination into a system where the data can be used for further analyzation.

Need for Big Data Ingestion

Ingestion of Big data involves the extraction and detection of data from disparate sources. Data ingestion moves data, structured and unstructured, from the point of origination into a system where it is stored and analyzed for further operations. It is the rim of the data pipeline where the data is obtained or imported for immediate use.

Data can be either ingested in real-time or in batches. Real-time data ingestion occurs immediately, however, data is ingested in batches at a periodic interval of time.

Effective data ingestion process starts with prioritizing data sources, validating information, and routing data to the correct destination.

Data Ingestion Parameters

Data ingestion has 4 parameters.

  • Data velocity: It concerns the speed at which data flows from various sources such as machines, networks, human interaction, media sites, social media. This movement can either be massive or continuous.
  • Data frequency: Data frequency defines the rate in which data is being processed. Data can be processed in real-time or batch. In the real-time, data is moved immediately. Whereas, in batch processing, data is stored in batches first and then moved.
  • Data Size: It implies the volume of data which is generated from various sources.
  • Data Format: Data can have many formats, structured, semi-structured, & unstructured.

Challenges of Data Ingestion

With the rapid increase in the number of IoT devices, volume and variance of data sources have magnified. Hence, extracting data especially using traditional data ingestion approaches becomes a challenge. It can be time-consuming and expensive too. Other challenges posed by data ingestion are –

  • Data ingestion can compromise compliance and data security regulations, making it extremely complex and costly. In addition, verification of data access and usage can be problematic and time-consuming.
  • Detecting and capturing data is a mammoth task owing to the semi-structured or unstructured nature of data and low latency.
  • Improper data ingestion can give rise to unreliable connectivity that disturbs communication outages and result in data loss.
  • Enterprises ingest large streams of data by investing in large servers and storage systems or increasing capacity in hardware along with bandwidth that increases the overhead costs.

Data Ingestion Practices


In the days when the data was comparatively compact, data ingestion could be performed manually. A human being defined a global schema, and then a programmer was assigned to each local data source. Programmers designed mapping as well as cleansing routines and ran them accordingly. However, with data increasing both in size and complexity, manual techniques can no longer curate such enormous data. In fact, data ingestion process needs to be automated. Automation can make data ingestion process much faster and simpler. For example, defining information such as schema or rules about the minimum and maximum valid values in a spreadsheet which is analyzed by a tool play a significant role in minimizing the unnecessary burden laid on data ingestion. Many integration platforms have this feature that allows them to process, ingest, and transform multi-GB files and deliver this data in designated common formats. With an easy-to-manage setup, clients can ingest files in an efficient and organized manner. As opposed to the manual approach, automated data ingestion with integration ensures architectural coherence, centralized management, security, automated error handling and, top-down control interface that helps in reducing the data processing time. Integration automates data ingestion to:

  • process large files easily without manually coding or relying on specialized IT staff.
  • alleviate manual effort and cost overheads that ultimately accelerate delivery time.
  • get rid of expensive hardware, IT databases, and servers.
  • handle large data volumes and velocity by easily processing up to 100GB or larger files
  • deal with data variety by supporting structured data in various formats, ranging from Text/CSV flat files to complex, hierarchical XML and fixed-length formats
  • tackle data veracity by streamlining processes such as data validation, cleansing along with maintaining data integrity.

Artificial Intelligence

Apart from automation, manual intervention in data ingestion can be eliminated by employing machine learning and statistical algorithms. In other words, artificial intelligence can be used to automatically infer information about data being ingested without the need for relying on manual labor. Eliminating the need of humans entirely greatly reduces the frequency of errors, which in some cases is reduced to zero. Data ingestion becomes faster and much accurate.


In a host of mid-level enterprises, a number of fresh data sources are ingested every week. In such cases, an organization that functions on a centralized level can have difficulty in implementing every request. Hence, there is a need to make data integration self-service. In doing so, users are provided with ease of use data discovery tools that can help them ingest new data sources easily. In addition, the self-service approach helps organizations detect and cleanse outlier as well as missing values, and duplicate records prior to ingesting the data into the global database.


In the last few years, Big data has witnessed an erratic explosion in terms of volume, velocity, variety, and veracity. Such magnified data calls for a streamlined data ingestion process that can deliver actionable insights from data in a simple and efficient manner. Techniques like automation, self-service approach, and artificial intelligence can improve the data ingestion process by making it simple, efficient, and error-free.

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