Big Data Architecture: Key Layers, Processes, & Benefits
Updated on Mar 07, 2025 | 10 min read | 23.7k views
Share:
For working professionals
For fresh graduates
More
Updated on Mar 07, 2025 | 10 min read | 23.7k views
Share:
Table of Contents
Big Data architecture is a framework that defines the components, processes, and technologies needed to capture, store, process, and analyze Big Data. It typically includes four layers: data collection and ingestion, data processing and analysis, data visualization and reporting, and data governance and security. Each layer has its own set of technologies, tools, and processes.
The benefits of a Hive architecture in Big Data include the ability to make better and faster decisions, the ability to process and analyze more data, and the ability to improve operational efficiency. The challenges of Big Data stack architecture include the need for specialized skills and knowledge, expensive hardware and software, and a high level of security.
Let's explain traditional and big data analytics architecture reference models.
It refers to the systems and software used to manage Big Data. A Big Data architecture must be able to handle the scale, complexity, and variety of Big Data. It must also be able to support the needs of different users, who may want to access and analyze the data differently.
The Big Data pipeline architecture must support all these activities so users can effectively work with Big Data. It includes the organizational structures and processes used to manage data.
Some of its examples include - Azure Big Data architecture, Hadoop Big Data architecture, and Spark architecture in Big Data.
Here's a Big Data architecture diagram for your reference:
Big Data Architecture is a sophisticated architecture for efficiently managing and processing massive amounts of data. The data lifecycle is managed by a number of interdependent parts that operate cohesively from data intake to analysis. Data sources, data storage, batch processing, real-time message intake, stream processing, analytical data store, analysis and reporting, and orchestration are the essential elements of big data architecture.
Data sourcing involves obtaining data from various sources like transactional databases, social media feeds, sensors, IoT devices, and log files. This data can be structured (e.g., SQL databases), semi-structured (e.g., JSON, XML files), or unstructured (e.g., text, images, videos), and is essential for further processing.
Scalable solutions are vital to handle large amounts of data efficiently. Options include Hadoop Distributed File System (HDFS) for large-scale storage, NoSQL databases like Cassandra and MongoDB for flexible, horizontal scaling, and cloud storage services such as Amazon S3 and Google Cloud Storage for cost-effective solutions.
Batch processing involves handling data in scheduled batches using frameworks such as Hadoop MapReduce for distributed processing and Apache Spark for fast, in-memory data transformation.
Real-time message ingestion captures data immediately, crucial for applications needing real-time processing. Technologies like Apache Kafka, Amazon Kinesis, and Google Pub/Sub aid in distributed event streaming and real-time data ingestion.
Stream processing enables real-time analytics, providing immediate insights. Frameworks like Apache Flink for low-latency processing, Apache Storm for real-time computations, and Spark Streaming for fault-tolerant streaming are utilized.
Analytical data stores are optimized for query performance and advanced analytics, with solutions such as Amazon Redshift, Google BigQuery, and Apache Druid offering high-performance analytics databases.
For analysis and reporting, tools like Tableau, Power BI, and Looker are employed to generate insights, create interactive dashboards, and integrate data into daily workflows.
Orchestration ensures smooth data flow through processing stages. Tools like Apache Airflow, AWS Step Functions, and Apache Oozie coordinate workflows, managing tasks efficiently.
Lambda Architecture is designed to handle massive quantities of data by utilizing both batch and real-time processing methods to provide comprehensive and immediate insights. It comprises three main layers:
Lambda Architecture is particularly useful for applications requiring real-time analytics on large datasets, such as fraud detection, recommendation engines, and real-time monitoring.
Kappa Architecture is a simplified approach focusing solely on stream processing for real-time data ingestion and analysis. Key components include:
Kappa Architecture is ideal for scenarios where real-time data processing is crucial, such as IoT data processing, real-time analytics, and event-driven
Big data quantities that are inefficient for standard data management systems to handle are managed, processed, and analyzed with the help of big data architecture. It makes it possible to take in, store, process, and analyze a wide range of data kinds from different sources, giving insightful information and assisting in data-driven decision-making. Important use cases consist of:
When we explain traditional and big data analytics architecture reference models, we must remember that the architecture process plays an important role in Big Data.
Connectors and adapters can quickly connect to any storage system, protocol, or network and connect to any data format.
From the time data is ingested through processing, analysis, storage, and deletion, there are protections for privacy and security.
Contemporary Lambda architecture Big Data is often developed on large-scale distributed clusters, which are highly scalable and require constant monitoring via centralized management interfaces.
The Quality-of-Service framework supports the definition of data quality, ingestion frequency, compliance guidelines, and sizes.
A few processes are essential to the architecture of Big Data. First, data must be collected from various sources. This data must then be processed to ensure its quality and accuracy. After this, the data must be stored securely and reliably. Finally, the data must be made accessible to those who need it.
It is used across various industries by organizations that require efficient handling of massive data volumes to gain insights and maintain a competitive edge. Key users include:
Designing a Big Data Hadoop architecture reference architecture, while complex, follows the same general procedure:
What do you hope to achieve with your Big Data architecture? Do you want to improve decision-making, better understand your customers, or find new revenue opportunities? Once you know what you want to accomplish, you can start planning your architecture.
What data do you have, and where does it come from? You'll need to think about both structured and unstructured data and internal and external sources.
Many different Big Data technologies are available, so it's important to select the ones that best meet your needs.
As your data grows, your Big Data solution architecture will need to be able to scale to accommodate it. This means considering things like data replication and partitioning.
Make sure you have the plan to protect your data, both at rest and in motion. This includes encrypting sensitive information and using secure authentication methods.
Once your architecture in Big Data is in place, it is important to test it to ensure it is working as expected. You should also monitor your system on an ongoing basis to identify any potential issues.
When we explain the architecture of Big Data in detail, we see there are many potential benefits of big data analytics architectures. Perhaps the most obvious is the ability to scale up data processing and analysis to handle extremely substantial data sets. Big data training enables you to use data more efficiently, leading to improved decision-making, more efficient operations, and new insights and opportunities.
Another potential benefit is the ability to integrate diverse data sources, including both structured and unstructured data. This can provide a more comprehensive view of the organization's data and help to identify new patterns and relationships.
Big Data platform architectures can also support real-time or near-real-time analysis, which can be critical for time-sensitive decision-making. By providing easier access to data for more users, Big Data processing architectures/systems can help to democratize data and analytics within organizations. Of course, realize that these are just potential benefits; Big Data warehouse architectures will only deliver value if they are designed and implemented properly, taking into account the specific needs and goals of the organization.
There are many challenges to Big Data analytics architecture, including:
As data grows, it becomes more difficult to manage and process. This can lead to delays in decision-making and reduced efficiency.
With so much data, it can be difficult to ensure that it is all accurate and high-quality. This can lead to bad decisions being made based on incorrect data.
With AWS Big Data architecture comes big expectations. Users expect systems to be able to handle large amounts of data quickly and efficiently. This can be a challenge for architects who must design systems that can meet these expectations.
With so much data being stored, there is a greater risk of it being hacked or leaked. This can jeopardize the security and privacy of those who are using the system.
Big Data solution architectures can be expensive to set up and maintain. This can be a challenge for organizations that want to use Big Data storage architecture but do not have the budget for it.
The ideal Big Data architecture patterns for a given organization will depend on factors such as the specific industry, company size, and data requirements. However, some general guidelines can be followed to ensure that Big Data reference architecture is effective and efficient.
One best practice is to use a Big Data Cloud architecture, which involves storing all data in a central repository in its raw, unprocessed form. This allows for greater flexibility and easier access to the data, as it can be processed and analyzed as needed without having to go through the time-consuming and expensive process of cleansing and transformation.
Another best practice is to use a distributed file system such as HDFS architecture in Big Data (Hadoop Distributed File System) to store and process the data. Hadoop architecture in Big Data is designed to work with large amounts of data and is highly scalable.
It is also important to have a good understanding of the specific data requirements of the organization to design an architecture that can effectively meet those needs. For example, suppose there is a need to process large amounts of stream data models and architecture in Big Data in real-time. In that case, an architecture of Hive in Big Data that includes a streaming data platform such as Apache Kafka will be required.
In general, however, some key considerations should be considered when designing a pattern, including
1 |
Scalability |
The Spark architecture in Big Data should be designed to be scalable in terms of the amount of data that can be processed and the number of users that can be supported. |
2 |
Flexibility |
The architecture of Big Data analytics should be flexible enough to support a variety of data types and workloads. |
3 |
Efficiency |
The architecture should be designed for both performance and cost efficiency. |
4 |
Security |
The HBase architecture has 3 main components: HMaster, Region Server, and Zookeeper. So, the Hbase architecture in Big Data should be designed with security in mind, ensuring that data is protected for rest and in motion. |
5 |
Governance |
The Big Data architecture design should include mechanisms for managing and governing data, ensuring that it is accurate, consistent, and compliant with applicable regulations. |
Looking to dive into the world of data science? Discover the secrets of this fascinating field with our comprehensive data scientist course syllabus. Unleash your analytical prowess and unlock endless career opportunities. Join us today!
The term "Big Data" has become increasingly popular in recent years as businesses of all sizes have started to collect and store large amounts of data. While the term is often used to describe data sets with large volume, velocity, and variety, the reality is that there is no single definition of Big Data.
There are many different types of big data architectures, and the best architecture for a particular organization will depend on its specific needs and goals.
Enhance your expertise with our comprehensive Software Engineering courses, designed to equip you with practical skills and knowledge to excel in the ever-evolving tech landscape.
Unlock your potential with our free Software Development courses, designed to equip you with the skills and knowledge needed to excel in the tech world. Start learning today and advance your career
Master the most in-demand software development skills that employers are looking for. From coding languages to development frameworks, these skills will help you stand out in the tech industry.
Get Free Consultation
By submitting, I accept the T&C and
Privacy Policy
Start Your Career in Data Science Today
Top Resources