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What is Data Ingestion? Challenges and Types

  • Vrinda Mathur
  • Jun 21, 2022
What is Data Ingestion? Challenges and Types title banner

Intelligence is used to make business decisions. And intelligence is obtained through data. The appropriate quantity of data, and enough of it, to churn into your system so you can get relevant insights and make lucrative business decisions.


However, in order to make data-driven choices, firms must collect massive amounts of data from a number of sources. This is when the data intake procedure comes into play.


What is Data Ingestion?

Data ingestion is the process of moving data from one or more sources to a destination where it may be stored and processed further. The data might be in numerous forms and from multiple sources, such as RDBMS, other types of databases, S3 buckets, CSVs, or streams. 


Because the data originates from many sources, it must be cleaned and changed so that it may be analyzed alongside data from other sources. Otherwise, your data is like a jumble of mismatched jigsaw pieces.


Data can be ingested in real time, batches, or a combination of the two (this is called lambda architecture). When you import data in batches, it is imported at regular intervals. This is very beneficial when you have processes that run on a regular basis, such as reports that run every day at a specified time. 


When the information gained is particularly time-sensitive, such as data from a power grid that must be monitored in real time, real-time ingestion is beneficial. 


Of course, a lambda architecture may also be used to absorb data. This technique tries to combine the advantages of batch and real-time modes by employing batch processing to offer comprehensive views of batch data and real-time processing to provide views of time-sensitive data


The conveyance of data from many sources to a storage medium where it may be accessed, used, and analyzed by an organization is known as data ingestion. Typically, the destination is a data warehouse, data mart, database, or document storage. 


Almost anything may be used as a source, including SaaS data, in-house apps, databases, spreadsheets, and even material scraped from the internet.


The data intake layer serves as the foundation of any analytics architecture. Data consistency and accessibility are critical for downstream reporting and analytics systems. There are several methods for ingesting data, and the design of a specific data intake layer might be based on a variety of models or architectures.


Where Does this Data Come From?


A typical business obtains data from a variety of sources. For starters, it obtains leads from third-party lead producers, websites, and mobile apps. This information is stored in the CRM and is often retained by the marketing department. The firm then has a list of converted clients, which is normally available in the sales department. 


Similarly, the customer service staff has access to the queries and chat logs of customers and visitors. The quality assurance section maintains a record of customers who have reported a defect or requested a customized product. The business development team has its own list of potential clients who have seen the product demo and are in the conversion funnel.


All of this data adds up to over a million data points that must be transformed into understandable insights that senior management can utilize to make future choices.


Furthermore, the example we provided is just of internal data from a single firm. What if the company purchases a startup? If the data comes from more than one capture, it will double or even triple. A merger will typically add over a million data points to the system. Many businesses have many subsidiaries that operate under their umbrella.


Unless it is absorbed into a data warehouse in a refined style, all of this data becomes overwhelming to handle, much alone extract important insights from. The first step in cloud modernisation is data intake. It transports and copies source data with minimum alteration into a landing or raw zone (e.g., cloud data lake). 


Data ingestion works well with real-time streaming and CDC data because it can be utilized right away – with minimum transformation for data replication and streaming analytics use cases. Companies may use data intake to expedite the availability of various sorts of data for driving innovation and growth.


Also Read | Advantages of Big Data


Challenges of Data Ingestion


Now that you know how data may be ingested into a medium, here is a list of challenges of data ingestion that businesses frequently experience when ingesting data and how a data ingestion tool can assist in resolving those issues.

Challenges of Data Ingestion :-1. Slow procedures2. Maintaining Data Quality3. Enhanced Complexity4. The price aspect5. Data security threats6. Data synchronization from several sources7. Creating a consistent structure

Challenges of Data Ingestion 


  1. Slow Procedures


Writing code to ingest data and manually building mappings for extracting, cleaning, and loading data can be time-consuming as data volumes and diversity have risen. 


As a result, there is a shift toward data intake automation. The traditional data ingestion techniques are not quick enough to keep up with the amount and variety of data sources. As a result, an enhanced data intake technology is necessary to facilitate the process.


  1. Maintaining Data Quality


The most difficult aspect of consuming data from any source is maintaining data quality and completeness. It is crucial for any business intelligence transactions you execute on your data. 


However, because ingested data is not used for Business Intelligence on an ad hoc basis, data quality concerns are frequently overlooked. You may reduce this by employing a data input technology with enhanced quality characteristics.


  1. Enhanced Complexity


Businesses are finding it difficult to extract value from their data due to the continual expansion of new data sources and internet gadgets. This is mostly due to the ability to connect to that data source and clean the data obtained from it, such as finding and removing data defects and schema inconsistencies.


  1. The Price Aspect


Data intake can be costly due to a variety of variables. For example, the infrastructure required to support the different data sources and proprietary tools can be extremely expensive to maintain over time.


Similarly, it is costly to employ a staff of data scientists and other professionals to support the intake process. Furthermore, when you can't make business intelligence judgments swiftly, you risk losing money.


  1. Data Security Threats


When migrating data from one location to another, security is the most difficult task. This is because data is frequently staged in many stages throughout the ingestion process. This makes meeting compliance standards during consumption difficult.


  1. Data synchronization from several sources


An organization's data is available in a variety of formats. As the business expands, more data will accumulate, making it difficult to manage. The solution is to sync all of this data or to ingest it into a single warehouse. 


However, because this data is available from various sources, retrieving it might be difficult. Data ingestion technologies with numerous interfaces for extracting, transforming, and loading data can help with this.


  1. Creating a Consistent Structure


To ensure that business intelligence services run effectively, you must develop a consistent framework by utilizing data mapping features that can arrange data points. A data ingestion tool may cleanse, process, and map data to its proper location.


Most, if not all, of the issues stated above may be avoided by using a data intake tool. Dedicated ingestion technologies solve the issues raised by automating the human operations required in the creation and maintenance of data pipelines. 

Today's market offers a diverse choice of ELT solutions and ETL tools, whether they are cloud-native offerings like Azure Data Factory, ETL tools like Informatica, or dedicated SaaS products like Fivetran, Airbyte, or Stitch for ELT.


Tools like Apache Kafka, Amazon Kinesis, and Snowplow tend to dominate the market for real-time data intake since they are especially intended to handle real-time streaming workloads.


Also Read | Top 10 Tools for Data Analytics 


Types of Data Ingestion


There are only two types of data intake methods in general: real-time and batch-based.


  1. Real-time Processing- It is concerned with gathering data as soon as it is created and producing a continuous output stream. For time-sensitive use cases where fresh information is critical for decision-making, real-time ingestion is critical. 


Exxon Mobil and Chevron, for example, need to monitor their equipment to guarantee that their machines are not drilling into rocks, therefore they create a great quantity of IoT (Internet of Things) data. 


Similarly, huge financial organizations such as CapitalOne, Discover, Coinbase, BankofAmerica, and others must be able to detect fraudulent activity.  These are only two examples of use cases, but both rely significantly on real-time data intake.


  1. Batch Processing- Focuses on mass intake (i.e. loading large quantities of data at a scheduled interval or after a specific triggered event.) When data is not required in real-time, this kind of data intake is quite advantageous.


When it comes to processing massive volumes of data collected over a given period of time, it's also considerably cheaper and more efficient.


In many cases, businesses may use a combination of batch and real-time data input to guarantee that data is always available with low latency. In general, real-time processing should be employed as seldom as possible since it is far more difficult and costly than batch-based processing. 


The data intake procedure is critical because it transports data from point A to point B. Without a data ingestion pipeline, data is stuck in the source from which it originated, rendering it unusable. The simplest approach to comprehend data intake is to imagine it as a pipeline. 


Data is delivered from the source to the analytics platform in the same manner that oil is transported from the well to the refinery. Data intake is critical because it enables business teams to get value from data that would otherwise be inaccessible.


Every firm has a somewhat different definition of what "real-time data" means. Some people get it every 10 seconds, while others have it every five or ten minutes. However, real-time data intake is only required for sub-second use cases. Batch-based data intake should function OK for anything equal to or greater than five minutes.


In conclusion, data intake is critical for intelligent data management and gaining business insights. It enables medium and big companies to maintain a federated data warehouse by consuming real-time data and making educated decisions via ad hoc data delivery.

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