Introduction to Data Ingestion
Data Ingestion is a part of the Big Data Architectural Layer in which components are decoupled so that analytics capabilities may begin. It is all about storage and furthering its analysis, which is possible with various Tools, Design Patterns, and a few Challenges.
- Data-to-Decisions
- Data-to-Discovery
- Data-to-Dollars
Ingestion mystery can be understood using the Layered Architecture of Big Data. Let us understand the Layered architecture of the pipeline. It is divided into different layers, where each layer performs a particular function.
What is Data Ingestion?
Ingestion is the process of bringing data into the Processing system. An ingestion framework is about moving data – especially unstructured one – from where it originated into a system that 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 begins with the Pipeline, where it obtains or imports data for immediate use. Information can be streamed in real-time or ingested in batches. When it is ingested in real-time, then it is ingested immediately as soon as it arrives. When it is ingested in batches using the Ingestion pipeline, it is ingested in some chunks at periodic time intervals.
What is Big Data Ingestion Architecture?
It is the first step in building a 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 speeds in different formats in the Ingestion framework. The Effective Ingestion process begins by prioritizing sources, validating individual files, and routing information to the correct destination. That’s why we should properly ingest the data using the right ingestion tools for successful business decisions making in Ingestion architecture.
What is the architecture of Big Data?
The Architecture of it 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.
- Ingestion Layer
- Collector Layer
- Processing Layer
- Storage Layer
- Query Layer
- Visualization Layer
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.
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.
Processing Layer
In this primary layer, the focus is to specialize in the 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 analytics may occur.
Read usecase of Data Processing in IOT for Smart Cities
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.”
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.
Visualization Layer
The visualization, or presentation tier, is 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 in, and make your findings well-understood.
Learn how to analyze Big Data effectively with these Big Data Visualization Tools and Techniques
Parameters of Data Ingestion
This process is the most complex and time-consuming part of the entire Big Data processing architecture. Consider the following parameter while creating an Ingestion pipeline -
- Velocity
- Size
- Frequency
- Format
Velocity
It deals with how data flows in different sources like machines, networks, human interaction, media sites, and social media. The movement of data can be massive or continuous in ingestion.Size
It implies an enormous volume of workload. Collect information from different sources that may increase the timely ingestion pipeline.Frequency (Batch, Real-Time)
Information can be processed in real-time or batch, in real-time processing, as data is 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 the ingestion process flow.Format (Structured, Semi-Structured, Unstructured)
Ingestion can be done in different formats. Mostly it can be a structured format, i.e., tabular one or unstructured format, i.e., images, audio, videos, or semi-structured, i.e., JSON files, CSS files, etc.
What are the 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 workloads. It has a straightforward and flexible architecture based on streaming data flows. Apache Flume is robust and fault 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 ingestion process flow. Functions of Apache Flume are:- Stream Data: Ingest streaming information 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 Ingestion streams and additional volume as needed.
Download the guide to know how to Building Data Ingestion Platform Using Apache Nifi
Apache Nifi
It is another of the best 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 routing, transformation, and system mediation logic. The functions of Apache Nifi are Track information flow from beginning to end.
The seamless experience between design, control, feedback, and monitoring
Secure because of SSL, SSH, HTTPS, and encrypted content.
Elastic Logstash
Elastic Logstash is an open-source ingestion tool, a server-side processing pipeline that ingests information 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, and stores.
Multiple AWS services and done in a continuous, streaming fashion
Ingest Data of all Shapes, Sizes, and Sources
How to build a data ingestion platform?
Steps to Build a Data Ingestion Platform
1. Define Data Sources
Identify and catalog the various data sources you will be ingesting data from. These can include:
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Structured Data: Databases (e.g., SQL databases)
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Semi-structured Data: JSON, XML files
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Unstructured Data: Text files, images, videos, IoT devices
2. Choose the Right Ingestion Method
Decide between batch processing and streaming data ingestion based on your needs:
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Batch Processing: Suitable for large datasets collected at scheduled intervals.
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Streaming: Ideal for real-time data collection and immediate analytics.
3. Select Ingestion Tools
Choose appropriate tools that fit your architecture and requirements. Some popular tools include:
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Apache Kafka: For real-time streaming data.
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Apache NiFi: For automating data flow between systems.
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AWS Glue: For ETL processes in cloud environments.
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Airbyte: For open-source data integration.
4. Design the Ingestion Pipeline
Create a robust ingestion pipeline that includes:
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Data Extraction: Utilize APIs or SQL queries to pull data from sources.
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Data Transformation: Clean and format the data to ensure consistency and usability.
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Data Loading: Store the ingested data into target systems like data lakes or warehouses.
5. Implement Data Quality Checks
Incorporate mechanisms to validate the quality and accuracy of the ingested data. This can involve automated checks and monitoring tools to ensure data integrity throughout the ingestion process.
6. Monitor and Optimize
Establish monitoring systems to track the performance of your ingestion pipelines. Use analytics to identify bottlenecks or failures in the process, allowing for continuous optimization.
7. Scale as Needed
Ensure that your platform can scale to handle increased data loads without compromising performance. This may involve leveraging cloud services for dynamic resource allocation.
What is the Data Ingestion framework?
It is a unified framework for extracting, transforming, and loading a large volume of data from various sources. It can ingest data from different sources in the same execution framework and manages the metadata of different sources in one place. Gobblin combined with other features such as auto scalability, fault tolerance, quality assurance, extensibility, and the ability to handle model evolution. It is an easy-to-use, self-serving, and efficient ingestion framework. Explore Apache Gobblin.
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, Challenges and Solutions
Challenges in Big Data Ingestion
As the number of IoT devices increases, the volume and variance of information Sources are expanding rapidly. Therefore, get an insight into our IoT Analytics Platform, which is used for extracting information from the Real-Time Ingestion pipeline, and Streaming Analytics so that the destination system can be a significant challenge regarding time and resources. Some of the other problems this process faces are –When there are numerous sources in different formats. It is the biggest challenge for the business to ingest data at a reasonable speed and further process it efficiently so that it can be prioritized and improves business decisions.
- Modern Sources tools and consuming applications evolve rapidly during Data ingestion.
- Data produced changes without notice independent of consuming application.
- Semantic Change over time as the same Data Powers new cases.
- Detection and capture of changed data This task is difficult 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 ingestion can result in unreliable connectivity This can disrupt communication and cause information 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 for Data Ingestion?
To complete the process of Ingestion, we should use the right tools and principles:
- Network Bandwidth
- Support for Unreliable Network
- Heterogeneous Technologies and Systems
- Choose Right Format
- Streaming Data
- Business Decisions
- Connections
- High Accuracy
- Latency
- Maintain Scalability
- Quality
- Capacity and reliability
- Data volume
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 Pipeline challenge. Ingestion tools are necessary for bandwidth throttling and compression capabilities.Support for unreliable Network
Ingestion Pipeline takes data with multiple structures, i.e., images, audio, videos, text files, tabular files, XML files, log files, etc. Due to the variable speed of data coming, it might travel through an unreliable network. The pipeline should be capable of supporting this also. It is one of the most important ingestion best practices.Heterogeneous Technologies and Systems
Tools for Ingestion Pipeline must use different source technologies and different operating systems.Choose Right Format
Ingestion tools must provide a serialization format. Information comes in a 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 Ingestion pipeline. So, tools must be capable of supporting both.Business Decisions
Critical Analysis is only possible when combining information from multiple sources. For making business decisions, we should have a single image of all the data coming.Connections
Data keeps increasing in the Ingestion framework, new information comes, and old data is 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 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 Ingestion process flow.Latency
The fresher your information, the agiler your company’s decision-making can be. Extracting data from APIs and databases in real time can be difficult. Many target information sources, including large object stores like Amazon S3 and analytics databases like Amazon Athena and Redshift, are optimized for receiving 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.Other Recommendations
- Data Quality - Assure that the consuming application works with correct, consistent, and trustworthy information to apply this data approach's best practices.
- 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.
Importance of data ingestion in analytics
3. Data Quality: Ingestion ensures data is clean, consistent, and accurate through validation, cleansing, and transformation.
4. Real-time & Batch Support: It supports both real-time and batch data processing, catering to different business needs.
5. Scalability & Flexibility: Modern ingestion solutions can handle large volumes of data and adapt to growing data sources.
6. Time Efficiency: Automating data ingestion saves time and reduces manual work, allowing analysts to focus on higher-value tasks.
7. Data Governance & Compliance: It helps enforce data governance policies and ensures compliance with regulations (e.g., GDPR, HIPAA).
8. Better Decision-Making: Provides the right data at the right time, enhancing the accuracy of decision-making.
9. Cost-Effectiveness: Optimizes costs by reducing redundant processes, preventing data silos, and improving storage and compute efficiency.
What are the use cases of it?
A Use Case is a written description that indicates the interactions between the users and a system. This helps the user by representing 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 an 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 Ingestion and CDC can help build, execute, and manage information flows for Batch and Streaming data. Don't miss to check out our use case on the Stream Sets Real-Time Ingestion platform to know it better.
Future trends in data ingestion
In the Internet of Things and Mobility era, a tremendous amount of information is becoming available quickly. There is a need for an efficient analytics system and excellent management of it using Data Ingestion, pipelines, tools, design patterns, use cases, best practices, and Modern Batch Processing to quantify and track everything.
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