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Introduction to Data Ingestion

Data Ingestion is a part of the Big Data Architectural Layer in which components are decoupled so that analytic capabilities may begin. It is all about data storage and further its analysis, which is possible by using various Tools, Design Patterns, and few Challenges. In the era of the Internet of Things and Mobility, a huge amount of data is becoming available quickly. There is also the need for an efficient Analytics System. The right management of Big Data using Data Ingestion, pipelines, tools, design patterns, use cases, best practices, and Modern Batch Processing makes everything Quantified and Tracked. Today, Big Data is meeting D2D Communication. Put,
  • Data-to-Decisions
  • Data-to-Discovery
  • Data-to-Dollars
Data ingestion mystery can be well understood using the Layered Architecture of Big Data . The Layered Architecture of the Big Data ingestion pipeline is divided into different layers, where each layer performs a particular function.

What is the architecture of big data?

The Architecture of Big Data helps design the Data Pipeline with the various requirements of either the Batch Processing System or Stream Processing System. This architecture consists of 6 layers, which ensure a secure flow of data. Big Data Framework

Data Ingestion Layer

This layer is the first step for the data coming from variable sources to start its journey. This means the data here is prioritized and categorized, making data flow smoothly in further layers in this process flow.

Data Collector Layer

In this Layer, more focus is on the transportation of data from the ingestion layer to the rest of the data pipeline. It is the Layer where components are broken so that analytic capabilities may begin.

Data Processing Layer

In this primary layer, the focus is to specialize in the data pipeline processing system. We can say that the information we have collected in the previous layer is processed in this layer. Here we do some magic with the data to route them to a different destination and classify the data flow, and it’s the first point where the analytic may occur.

Data Storage Layer

Storage becomes a challenge when the size of the data you are dealing with becomes large. Several possible solutions, like Data Ingestion Patterns can rescue from such problems. Finding a storage solution is very much important when the size of your data becomes large. This layer focuses on “where to store such large data efficiently.”

Data Query Layer

This is the layer where active analytic processing takes place. Here, the primary focus is to collect the data value to make it more helpful for the next layer.

Data Visualization Layer

The visualization, or presentation tier, probably the most prestigious tier, where the data pipeline users may feel the VALUE of DATA. We need something that will grab people’s attention, pull them into, make your findings well-understood.
Data lake architecture has capability to quickly and easily ingest multiple types of data, such as real-time streaming data and bulk data assets. Source: Data Ingestion Methods

What is Data Ingestion?

Data ingestion framework is about moving data – and especially the unstructured data – from where it originated into a system where it can be stored and analyzed. We can also say that this is all about collecting information from multiple sources and putting it somewhere it can be accessed. This process flow is the beginning of the Data Pipeline, where it obtains or imports data for immediate use. Data can be streamed in real-time or ingested in batches. When Big Data is ingested in real-time, then it is ingested immediately as soon as data arrives. When data is ingested in batches using the Data ingestion pipeline, data items are ingested in some chunks at a periodic time interval. Data ingestion is the process of bringing data into the Data Processing system.

Big Data Ingestion Architecture

It is the first step in building a Data Pipeline and the Big Data platform's System's toughest task. We plan to ingest data flows from hundreds or thousands of sources into the Data Center in this layer. The Data is coming from Multiple sources at variable speed, in different formats in the Data ingestion framework. The Effective Data Ingestion process begins by prioritizing data sources, validating individual files, and routing data items to the correct destination. That’s why we should properly ingest the data using the right Data ingestion tools for successful business decisions making in Data ingestion architecture.

What are the Parameters of Data Ingestion?

Data Velocity

It deals with how data flows in different sources like machines, networks, human interaction, media sites, social media. The movement of data can be massive or continuous in Data ingestion.

Data Size

It implies an enormous volume of data. Collect information from different sources that may increase timely in the Data ingestion pipeline.

Data Frequency (Batch, Real-Time)

Information can be processed in real-time or batch, in real-time processing as data received at the same time, it further proceeds, but in batch, data is stored in batches, fixed at some time interval, and then further moved to Data ingestion process flow.

Data Format (Structured, Semi-Structured, Unstructured)

Data ingestion can be done in different formats. Mostly it can be the structured format, i.e., tabular one or unstructured format, i.e., images, audios, videos or semi-structured, i.e., JSON files, CSS files, etc. Goals of Data Ingestion

What are the Data Ingestion tools?

In the subsequent section, we will disclose some of the most common ingestion tools, and here we go:

Apache Flume

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a straightforward and flexible architecture based on streaming data flows. Apache Flume is robust and faults tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It uses a simple, extensible Big Data Security model that allows for an online analytic application and Data ingestion process flow. Functions of Apache Flume are:
  • Stream Data: Ingest streaming data from multiple sources into Hadoop for storage and analysis.
  • Insulate System: Buffer storage platform from transient spikes, when the rate of incoming data exceeds the rate at which data can be written to the destination
  • Scale Horizontally: For new Data ingestion streams and additional volume as needed.

Apache Nifi

It is another of the best Data ingestion tools that provide an easy-to-use, powerful, and reliable system to process and distribute information. Apache NiFi supports robust and scalable directed graphs of data routing, transformation, and system mediation logic. Functions of Apache Nifi are:
  • Track data flow from beginning to end.
  • The seamless experience between design, control, feedback, and monitoring
  • Secure because of SSL, SSH, HTTPS, encrypted content.

Elastic Logstash

Elastic Logstash is an open-source Data ingestion tool, server-side data processing pipeline that ingests data from many sources, simultaneously transforms it, and then sends it to your “stash, ” i.e., Elasticsearch. Functions of Elastic Logstash:
  • Easily ingests from your logs, metrics, web applications, data stores.
  • Multiple AWS services and done in a continuous, streaming fashion
  • Ingest Data of all Shapes, Sizes, and Sources

There are various major challenges that come into the way while dealing with Big Data which need to be taken care of with Agility.Click to explore about our, Big Data Challenges and Solutions

Data Ingestion Framework

The best Data Ingestion Framework is described below:

Apache Gobblin

It is a unified framework for extracting, transforming, and loading a large volume of data from various data sources. It can ingest data from different data sources in the same execution framework and manages metadata of different sources in one place. Gobblin combined with other features such as auto scalability, fault tolerance, data quality assurance, extensibility, and the ability to handle data model evolution. It an easy-to-use, self-serving, and efficient data ingestion framework. Explore Apache Gobblin to have an insight on its:
  • What is Apache Gobblin?
  • Why Apache Gobblin?
  • How Apache Gobblin Works?
  • How to Use Apache Gobblin?
  • Apache Gobblin Best Practices
  • Apache Gobblin Benefits
  • Key Apache Gobblin Tools

What are the Challenges of Data Ingestion?

As the number of IoT devices increases, both the volume and variance of Data Sources are expanding rapidly. Therefore, get an insight into our IoT Analytics Platform, which is used for extracting information from the Real-Time Data Ingestion pipeline, and Streaming Analytics so that the destination system can use as a significant challenge regarding time and resources. Some of the other problems faced by this process are –
  • When numerous Big Data testing sources exist in different formats, it’s the biggest challenge for the business to ingest data at the reasonable speed and further process it efficiently so that data can be prioritized and improves business decisions.
  • Modern Data Sources, tools, and consuming applications evolve rapidly during Data ingestion.
  • Data produced changes without notice independent of consuming application.
  • Data Semantic Change over time as same Data Powers new cases.
  • Detection and capture of changed data – This task is difficult, not only because of the semi-structured or unstructured nature of data.
  • Due to the low latency needed by individual business scenarios that require this determination.

Incorrect data ingestion can result in unreliable connectivity. This can disrupt communication and cause data loss.
Big data is fuel for businesses and today’s analytical applications.Click to explore about our, Veracity in Big Data

What are the Best Practices of Data Ingestion?

To complete the process of Data Ingestion, we should use the right tools and principles:

Network Bandwidth

Data Pipeline must be able to compete with business traffic. Sometimes traffic increases or sometimes decreases, so Network bandwidth scalability is the biggest Data Pipeline challenge. Data ingestion tools are necessary for bandwidth throttling and compression capabilities.

Support for Unreliable Network

Data Ingestion Pipeline takes data with multiple structures, i.e., images, audios, videos, text files, tabular files data, XML files, log files, etc. Due to the variable speed of data coming, it might travel through the unreliable network. Data Pipeline should be capable of supporting this also. It is one of the most important Data ingestion best practices.

Heterogeneous Technologies and Systems

Tools for Data Ingestion Pipeline must use different data source technologies and different operating systems.

Choose Right Data Format

Data ingestion tools must provide a data serialization format. Data comes in the variable format, so converting them into a single format will provide an easier view to understand or relate the data.

Streaming Data

Best practices in this process are dependent upon business necessity whether to process the data in batch or streams or real-time. Sometimes we may require both processing in the data ingestion pipeline. So, tools must be capable of supporting both.

Business Decisions

Critical Analysis is only possible when combining data from multiple sources. For making business decisions, we should have a single image of all the data coming.


Data keeps increasing in the Data ingestion framework, new data comes, and old data modified. Each new integration can take anywhere from a few days to a few months to complete.

High Accuracy

The only way to build trust with data consumers is to ensure that your data is auditable. One best practice that’s easy to implement is never to discard inputs or intermediate forms when altering data in the Data ingestion process flow.


The fresher your data, the agiler your company’s decision-making can be. Extracting data from APIs and databases in real-time can be difficult. Many target data sources, including large object stores like Amazon S3 and analytics databases like Amazon Athena, Redshift, are optimized for receiving data in chunks rather than a stream.

Maintain Scalability

In this process, best practices vary with time. We can’t say that data will come less on Monday, and the rest of the days comes a lot for processing. So, the usage of data is not uniform. We can make our pipeline so scalable that it can handle any data coming at variable speed.

Data Ingestion Best Practices

  • Be capable enough to handle and upgrade the Data ingestion pipeline, new data sources, technology, and applications.
  • Assure that the consuming application works with correct, consistent, and trustworthy information to apply this data approach's best practices.
  • This process flow allows the rapid consumption of data.
  • Capacity and reliability – The system needs to scale according to input coming, and also, it should be fault-tolerant.
  • Data volume – Though storing all incoming data is preferable. Some cases are in which aggregate information is backup.

Java vs Kotlin
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Use Cases of Big Data Ingestion

A Use Case is a written description that indicates the interactions between the users and a system. This helps the user by representing them a series of tasks it contains with its features to fulfill any particular user’s goal. Some of the uses cases of Big Data Ingestion are below:
  • Building Data Ingestion Platform Using Apache Nifi could be tedious. Let’s explore this use case that reveals the challenges and techniques to build such a platform.
  • StreamSets -Real-Time Data Ingestion and CDC can help build, execute, and manage data flows for Batch and Streaming data. Don't miss to check out our use case on the Stream Sets Real-Time Data Ingestion platform to know it better.

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