Big Data Architecture: Layers, Process, Benefits, Challenges

Big Data Architecture

Over the past several years, big data has quickly become one of the hottest buzzwords. Traditional database management systems were originally intended to store organized information. Still, with big data’s surge, traditional database methods are losing favor, and businesses must develop innovative methods of storing and processing their data. Hence, big data-related architectures are becoming essential tools.

Big Data Architecture provides the means to control an ever-increasing volume of information, with tools, technologies, instruments, processes, tools, etc., required for collecting, storing, analyzing, and processing Big Data. A typical Big Data architecture typically comprises four layers: collection, ingestion processing analysis, visualization reporting governance security, and visualization reporting governance of security. These layers may utilize other layers or even have separate tools, technologies, instruments, processes, and tools within them if applicable to their purpose.

The advantages of having structures in Big Data include making smarter choices, processing more significant amounts of information quickly, and increasing operational efficiencies. A big data stack architecture also poses several difficulties: it requires specialist skills, expensive equipment, and software licenses and must provide high levels of security protection.

What Is Big Data Architecture?

“Big Data architecture” refers to the system and software used to handle big data. Its architecture must be capable of handling the size complexity, variety, and complexity of Big Data. Additionally, it must accommodate the different needs of people who want to use and analyze the data differently.

The Big Data pipeline architecture must accommodate all these processes so users can effectively use Big Data. It consists of the structure and procedures employed to manage information. Examples of Big Data Architecture include Azure Big Data architecture, Hadoop’s prominent data structure, and Spark Architecture in Big Data.

There are many different types of work associated with extensive data systems. These generally can be classified in the following manner:

  • Simply batching data when the significant data-based sources have been still in motion is an example of data processing.
  • Real-time processing of massive information is possible through motion-based processing.
  • Explore the latest interactive technologies for big data and tools.
  • Machine training and analysis that is predictive.

Big Data Architecture Layers

Big Data Architecture helps design the data pipeline with the various demands of the batch and stream processing systems. The architecture consists of layers that ensure the security of data flow.

Data Ingestion

The layer is responsible for collecting and storing data gathered from different sources. The Big Data layer is Big Data, the data ingestion process of taking data from various sources before placing it in a data repository. Ingestion of data is an essential element of the Big Data Solution Architecture because it decides the method by which data is processed, ingested, and saved.

Big Data Processing Layer

The data was gathered from various sources and let it flow through the pipeline. Our job is to perform magical things with data. Since the data is ready, we must transfer it to various locations. This is the primary layer. We will focus on specializing in the data pipeline processing system, or, as we say, we’ve gathered data through the previous layer. That is now within the following layer needed to perform the data processing.

Big Data Storage Layer

In the next phase, the primary concern of the data intake process is keeping records in the proper spot based on their use. Relational databases have proved to be an excellent place to store our data over time. However, with the advent of data analytics being used in healthcare strategic enterprise applications, it is essential not to think that persistence is the same as that of a relational database in data ingestion. Different databases are required to manage the various types of data. However, the use of other databases can result in an overhead. This is why we briefly introduce the latest notion in databases, i.e., Polyglot Persistence.

Polyglot persistence refers to multiple databases being used for a single application. Polyglot persistence allows you to divide or share your data across different databases and harness the power of each. This makes use of the strengths of various databases. The various kinds of information can be arranged in multiple ways. This is choosing the best software for the best application. The same principle applies to Polyglot Programming, which states that programs should be written using various languages for data ingestion. So that you can take advantage of the reality that multiple languages work well for solving diverse problems with the appropriate data ingestion framework.

Big Data Query Layer

It’s the part of data architecture where the active analysis occurs. This is where interactive queries are essential to perform the task, and it is a domain traditionally filled with SQL experts. We needed more storage capacity before Hadoop was developed, so it took a lengthy analysis procedure.

In the beginning, it is put through an extended process, i.e., ETL, to prepare a fresh data source for storage in the future, and then it stores the data in a data warehouse or database. Data Ingestion and Data Analytics were two crucial methods to solve problems when calculating large amounts of data when creating the Data Ingestion Framework.

Data Visualization Layer

This is the thermometer that determines the effectiveness of the program. It is how the user sees the value of data. Although it helps manage the storage of large volumes of information, Hadoop and other software do not need built-in features for data visualization and distribution. There is no method to render that data consumable by business end users within the Data Ingestion Pipeline.

Benefits Of Big Data Architecture

Now, let’s look at the advantages of big data architecture.

High-Performance Parallel Computation

Immense data structures employ parallel computing. In this, multiprocessor computers perform a variety of computations at the same time, speeding the procedure. Big data sets can be efficiently processed by parallelizing them with multiprocessor computers. A portion of the work can be completed simultaneously.

Scalability Elastic

Big Data architectures can be extended horizontally to adjust the setting to the work requirements. The big data platform is generally managed through the cloud. You pay for the storage and the processing power you use.

The Freedom To Choose

Big Data architectures can utilize various technologies and options available on the market, such as Azure-managed service, MongoDB Atlas, and Apache technologies. Choose the appropriate solution specifically for your workload, your existing system, and the level of IT knowledge to get the most effective results.

Integrity Between Systems

Big Data architecture components provide IoT processing and BI and analytics workflows. As a result, it creates interoperable platforms capable of handling various applications.

Big Data Architecture Processes

Suppose we are discussing traditional and extensive data analytics references. In that case, we must know that the architectural process is a critical element of Big Data.

Connecting To Data Sources

Connectors and adapters will easily connect to any storage system, protocol, or network. They can also connect to almost any format of data.

Data Governance

As soon as data enters the system through processing or analysis, storage and even deletion are safeguards that protect privacy and security.

Managing Systems

The present Lambda structure Big Data is often developed using large-scale distributed clusters that are highly scalable and require continuous monitoring through central management interfaces.

Protecting Quality Of Service

The Quality-of-Service framework is a basis for the definition of quality data, ingestion frequency, size guidelines for compliance, and the sizes.

Some processes are vital for the structure of Big Data. In the beginning, data needs to be obtained from many sources. These data are then treated to guarantee its integrity as well as accuracy. Following this, data is stored securely. In addition, data should be accessible to all who require the information.

What Are The Big Data Architecture Challenges & Its Solutions?

Big data is bringing about massive changes across industries. However, it has its challenges. Deciding to go with a big data-enabled data Analytics option can be challenging. It will require a vast space that ingests data from various sources. Additionally, it is essential for proper synchronization among these parts.

The development, testing, and troubleshooting process of Big Data WorkFlow is quite tricky. The constant evolution of its applications is an enormous issue for many companies.

Data Storage in Big Data Architecture

As new methods of the processing and storage of data are being developed, however, the volume of data remains an obstacle as data volumes double in size approximately every two years. In addition to the size of data in addition to data size, the number of formats used for storing data is increasing. In the end, efficiently keeping and managing data is often a problem for any organization.


Companies employ current methods such as the tiering process, compression, and deduplication to handle this massive data collection. The compression process reduces the number of bits in the data, which results in a decrease in overall size. Deduplication is the process of eliminating redundant and unneeded data in a database.

Companies can store data at various levels with data tiering. This guarantees that the information is kept in the ideal place. The data storage tiers can comprise private cloud, public cloud, or flash storage, depending on the importance and size of the information.

Data Quality in Big Data Architecture

Data quality includes precision, consistency, relevancy to the data, completeness, and fitness. To be able to use Big Data Analytics solutions, different data sources are required. Data quality is an issue when working with diverse data sources, such as the matching data format connecting them and examining for missing data, duplicates, and outliers. The task is to cleanse and prep data before making it available for examination.

Thus, gathering valuable information takes much effort to purify the data and get an accurate result. Data scientists must devote 50- 80 percent of their time preparing data.


It is essential to monitor and correct any quality problems regularly. In addition, duplicate entries and mistakes are not uncommon, especially when data comes from different sources. The team developed an advanced identification system for data that can detect duplicates with minor deviations in data and also reported possible mistakes to verify the authenticity of the information they acquired. This has led to an increase in the accuracy of the analysis of business information derived from data analysis.

Scaling in Big Data Architecture

Big data technology is used for handling large quantities of data. This can lead to issues if your planned architecture can’t expand. It could affect the output if the plan is not able to scale. As data processing volumes proliferate, they could easily overwhelm any design solutions used for processing it. It could also affect the app’s performance and effectiveness.

To handle the data overflow, auto-scaling lets the system remain equipped with the appropriate capacity to meet the demands of current data traffic. There are two kinds of scaling. The possibility of scaling up can be a viable option until it becomes impossible to increase the size of each component any further. Thus, dynamic scaling is needed. Dynamic scaling offers the capability of scaling up capacity increase and the benefits from scale-out. This ensures that the system’s capacity grows to the size needed for business needs.


Compression, tiering, and deduplication are new methods businesses employ to manage vast volumes of data. Volume session is a strategy for cutting down on the amount of bits that data contains, and, in turn, it reduces the overall amount of data. Eliminating redundant information from a set of knowledge is referred to as deduplication. Businesses can save data on multiple layers of storage via data tiering. It ensures that the data is kept in the best area.

The data tiers could be classified as private, public cloud, or flash storage by the size and significance of the information. Some companies also use big data technologies such as Hadoop and NoSQL.

Security in Big Data Architecture

Though big data can offer valuable information for making decisions, ensuring data is protected from loss is challenging. The data collected could include personal or PII (Personally identifiable information) information about a particular person. GDPR (General Data Protection Regulation) safeguards data and personal information that travels into or out of Europe through EU- and EEA-member nations.

As per the GDPR, it is the responsibility of an organization to protect customers’ PII information from internal and external dangers. Companies that process and store information about the PII of European citizens living in EU states must comply with GDPR.

In contrast, if the architecture contains an insignificant vulnerability or a minor flaw, it’s much more likely to be compromised. Hackers can create information and then introduce it into the data structure. They could penetrate systems by introducing noise, making securing information difficult.

Big data applications typically keep data in central locations, and various apps and platforms use data. In the end, protecting access to data is a challenge. An adequate system is required to protect your information from cyber-attacks and theft.


 Implement security for endpoints, real-time security monitoring, and use of security tools to protect Big Data.

Complexity in Big Data Architecture

Extensive data systems are complicated to build because they must deal with many data types from multiple sources. Various engines can use this feature, like Splunk for analyzing log files, Hadoop for batch processing, and Spark for processing data streams. Each of these engines had their specific data set, and the system needed to connect each. The sheer volume of data to be integrated creates a problem.

Furthermore, companies are mixing the cloud with big data processing and storage. Data integration is also required for this. In the absence of this, every computer cluster that needs an engine remains isolated from the other components of the structure, which results in data fragmentation and replication. This means that developing the process of testing, evaluating, and troubleshooting this process becomes more complex. In addition, it needs many configuration options for different platforms to increase efficiency.


A few companies employ the data lake to store vast amounts of data gathered from multiple sources without considering what the data will look like when combined. Different business areas are, for example, the source of information that can be useful in analyzing joint data. But the meaning behind the data can be confusing and require reconciling. To get the most ROI from massive data-related initiatives, you should follow a methodical approach to data integration.

Skill Set In Big Data Architecture

The Big Data technologies are particular, as they employ frameworks and languages that aren’t commonly used in other architectural frameworks for applications. In contrast, these technologies are creating new APIs based on mature language.

For example, Azure Data Lake Analytics’s U-SQL language is an amalgamation of Transact-SQL and C#. SQL-based APIs are readily available in the case of Hive, HBase, and Spark. These new technology and tools for data professional data specialists are needed to operate. This includes researchers, data scientists, and engineers who operate the devices and identify Big Data Architecture Patterns in data.

The need for more skilled data analysts is among companies’ most significant data challenges. This is usually because data handling techniques have changed rapidly, yet most users still need to. You must make sure that you take action to bridge the space.


Companies use data lakes to store vast amounts of information from various sources, but little thought is given to its combined form. Divers business fields are, for instance, able to create documents that are useful in analyzing joint data. Still, the fundamental meaning of the data can be confusing and needs to be evaluated. According to Silipo, the ad-hoc approach to project integration could result in many revisions. It is generally better to develop a planned strategy for data integration to get the best ROI from large-scale data projects.


Big Data Architecture is a broad-ranging technology that can manage vast amounts of data, which can be analyzed to guide big data analytics. It also provides the right environment in which Big Data Architecture Tools can collect and confirm essential business data.

The framework for significant data architecture can serve as a model for large data infrastructures and solutions since big data infrastructures and products serve to process, ingest, and analyze large quantities of data. Big data solutions and infrastructures manage large amounts of business-related data, guide data analytics, and create an environment where the tools for extensive data analysis can extract crucial business data. Large data-related infrastructures and solutions provide a model for large data infrastructures and solutions.

The phrase “Big Data” has become increasingly popular over the past few years because companies of all sizes have begun to gather and store massive quantities of data. The term “Big Data” is commonly employed to refer to an extensive data set in terms of velocity, speed, and variety, but the truth is that there’s no standard description of Big Data.


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