XenonStack Recommends

Enterprise Data Management

Top 9 Challenges of Big Data Architecture and its solutions | Overview

Chandan Gaur | 15 December 2022

Introduction to Big Data Architecture

Big Data has been a buzzword in recent years. The increasing amount of data raises both the opportunities and the challenges of managing it.

Big Data Architecture is a conceptual or physical system for ingesting, processing, storing, managing, accessing, and analyzing vast quantities, velocity, and various data, which is difficult for conventional databases to handle. And use them to gain business value since today's organizations depend on data and insights to make most of their decisions.

A well-designed Architecture makes it simple for a company to process data and forecast future trends to make informed decisions. The architecture of Big data is designed in such a way that it can handle the following:

  • Real-time processing 

  • Batch processing 

  • For Machine learning applications and Predictive analytics

  • To get insights and make decisions.

What is the Big Data Architecture Challenges?

Big data has tremendous changes in industries, but it is not without challenges. Opting for a Big-data enabled Data Analytics solution is not straightforward. It requires vast technology land space for components to ingest data from numerous sources. It is also essential to have proper synchronization between these components.

The building, testing, and troubleshooting of Big data WorkFlow is quite complex. Keeping up with varying use cases in it is a significant challenge for many organizations. Below is the list of challenges; we look at them individually. 

  • Data Storage
  • Data Quality 
  • Scaling
  • Security 
  • Complexity
  • Skillset 
  • Lack of Awareness/Understanding 
  • Technology Maturity 
  • Big Data Tool Selection 

Challenge #1 - Data Storage in Big Data Architecture

While new technology for processing and storing data is on the way, data volume remains a significant challenge because data volumes are doubling in size about every two years. 

Besides data size, the number of file formats used to store data is also growing. As a result, effectively storing and managing information is often a challenge for the organization.


Companies use current approaches like compression, tiering, and deduplication to handle these massive data collections. Compression reduces the number of bits in data, resulting in a smaller overall size. The process of deleting duplicate and unnecessary data from a data set is known as deduplication.

Companies can store data in different storage tiers via data tiering. It guarantees that the data is stored in the best possible location. Data tiers might include public cloud, private cloud, and flash storage, depending on the size and significance of the data.

Companies are also turning to Big Data technologies like Hadoop, NoSQL, and others.

Challenge #2 - Data Quality in Big Data Architecture

Data quality aspects include accuracy, consistency, relevance, completeness, and use fitness.

For Big Data Analytics solutions, diverse data is required. Data Quality is a challenge anytime working with diverse data sources, for example, matching data format, joining them, checking for missing data, duplicates, outliers, etc. 

It is required to clean and prepare data before bringing it for analysis.

Consequently, obtaining useful data requires a significant effort to clean the data to obtain a meaningful result. It is estimated that data scientists have to spend 50% - 80% of their time preparing data.


You have to check and fix any data quality issues constantly. Also, duplicate entries and typos are typical, especially when data originates from multiple sources.

The team designed an intelligent data identifier that recognizes duplicates with minor data deviations and reports any probable mistakes to ensure the quality of the data they collect.

As a result, the accuracy of the business insights derived from data analysis has improved.

Challenge #3 - Scaling in Big Data Architecture

Big data solutions are used to handle large volumes of data. It can cause issues if the planned architecture is unable to scale. The output may suffer if the design cannot scale them. 

With the exponential increase in data volume being processed, the architecture may overwhelm the deluge of data they ingest. Thus it may degrade the application performance and efficiency.

To handle an overflow of data, Auto-scaling allows the system always to be capable with the right amount of capacity to handle the current traffic demand. There are two types of scaling.

Scaling up is a feasible scaling solution until it is impossible to scale up individual components any larger. Therefore dynamic scaling is required.

Dynamic scaling provides a combined power of scaling up with capacity growth and economic benefits of scale-out. It ensures that the system's capacity expands with the exact granularity needed to meet business demands.


Compression, tiering, and deduplication are some of the latest approaches businesses use to deal with enormous data volumes. Compression is a technique for lowering the number of bits in data and, as a result, the total size of the data.

Deleting duplicate and unnecessary material from a knowledge set is known as deduplication. Companies can store data in many storage layers via data tiering. It guarantees that the information is stored in the most appropriate location.

Depending on the size and relevance of the data, data tiers may include public cloud, private cloud, and flash storage. Companies also opt for Big Data technologies such as Hadoop, NoSQL, and others technologies.

Challenge #4 -Security in Big Data Architecture

Although Big data can provide great insight for decision-making, protecting data from theft is challenging.

Data collected may contain personal and PII(Personally Identifiable Information) data of a person. GDPR (General Data Protection Regulation) is the data protection law to ensure the security of PII and personal information across and outside the European Union (EU) and European Economic Area (EEA).

According to the GDPR, the organization must protect its customers' PII data from internal and external threats. Organizations that store and process the PII of European citizens within EU states must comply with GDPR.

But, If architecture has a minor vulnerability, it is more likely to be hacked.

A hacker can fabricate data and introduce it in data architecture. They can penetrate the system by adding some noise, making it challenging to protect data. 

Big data solutions typically store data in centralized locations, and various applications and platforms consume data. As a result, securing data access becomes a problem. To protect data from theft and attacks, a robust framework is needed.


Businesses are recruiting more cybersecurity workers to protect their data. Other steps to safeguard Big Data include Data encryption, Data segregation, Identity, and access management, Implementation of endpoint security, Real-time security monitoring, Make use of security software for Big Data, such as IBM Guardian.

Challenge #5 -Complexity in Big Data Architecture

Big data systems can be challenging to implement since they must deal with various data types from various sources. 

Different engines might choose to run this, such as Splunk to analyze log files, Hadoop for batch processing, or Spark for data stream processing. Since each of these engines required its data universe, the system had to integrate all of them. Integrating such amounts of data makes it complex.

Moreover, organizations are mixing on cloud-based big data processing and storage. Again data integration is required here. Otherwise, each computer cluster that needs its engine will be isolated from the rest of the architecture, resulting in data replication and fragmentation.

As a result, developing, testing, and troubleshooting these processes becomes more complicated. 

Furthermore, it requires many configuration settings across different systems to improve performance.


Some firms use a data lake as a catch-all store for vast amounts of big data obtained from various sources without thinking about how the data would be merged.

Various business domains, for example, create data that is helpful for joint analysis, but the underlying semantics of this data is frequently confusing and must be reconciled. According to Silipo, ad hoc project integration might lead to much rework.

For the highest ROI on big data initiatives, it's frequently best to have a systematic approach to data integration.

Challenge #6 - Skillset in Big Data Architecture

Big data technologies are highly specialized, and they use frameworks and languages that aren't common in more general application architectures. On the other hand, This technologies are developing new APIs based on more developed languages. 

For example, The U-SQL language in Azure Data Lake Analytics is a hybrid of Transact-SQL and C#. For Hive, HBase, and Spark, SQL-based APIs are available.

To operate these modern technologies and data tools, skilled data professionals are required. These will include data scientists, analysts, and engineers to operate tools and get data patterns.

A shortage of data experts is one of the Big Data Challenges that companies face. It's usually because data-handling techniques evolved rapidly, but most practitioners haven't. It is a must to take solid action to close this gap.


Some firms use a data lake as a catch-all store for vast amounts of big data obtained from various sources without thinking about how the data would be merged.

Various business domains, for example, create data that is helpful for joint analysis, but the underlying semantics of this data is frequently confusing and must be reconciled. According to Silipo, ad hoc project integration might lead to much rework.

For the highest ROI on big data initiatives, it's frequently best to have a systematic approach to data integration.

Challenge #7 -Lack of Proper Understanding in Big Data Architecture

Insufficient awareness causes companies to fail with their Big Data projects. Employees can not understand what data is, how it is stored, processed, and where it comes from. Data professionals may undoubtedly know about it, but others might not clearly understand. If organizations don't understand the importance of knowledge storage, it is challenging to keep sensitive data. 

It may be possible that they could not use databases properly for storage. As a result, when vital data is required, it will become difficult to retrieve data.


Everyone should be able to attend big data workshops and seminars. Military training sessions must be developed for all personnel who deal with data on a regular basis or who work near large data projects. At all levels of the organization, a basic understanding of knowledge concepts must be instilled.

Challenge #8 - Technology Maturity 

Consider this scenario: your cutting-edge big data analytics examines what item combinations clients purchase (for example, needle and thread) simply based on prior consumer behavior data.

Meanwhile, a soccer player posts his latest outfit on Instagram, with the white Nike sneakers and beige cap being the two standout pieces. It looks fantastic on them, and those who see it tend to dress similarly.

They rush out to get a matching set of shoes and headgear that matches. However, your store just sells shoes. As a result, you're losing money and possibly some regular customers.


Technology does not analyze data from social networks or compete for online retailers, which explains why you didn't have the necessary goods in stock.

And, among other things, your competitor's big data shows social media changes in near-real-time. And their store carries both pieces and offers a 15% discount if you buy them both. Technology Maturity - Tricky Method of turning into Useful Insights

You should create a good system of variables and data sources whose analysis will provide essential insights to ensure that nothing is out of range.

External databases should be used in such a method, even though gathering and interpreting external data can be difficult.


Organizations often get confused while selecting the most straightforward tool for giant Data analysis and storage. Organizations bother, and sometimes they're unable to seek out the result. They find themselves making poor decisions and selecting irrelevant technologies due to waste of everything like money, time, effort, and work hours.

  • You can either engage seasoned specialists who are significantly more knowledgeable about these instruments. On the other hand, traveling for big data consultancy is a unique experience.

  • Consultants will recommend the most basic equipment that can benefit your business. Based on their advice, you'll compute a technique and choose the easiest instrument.

Some of the big data architecture challenges are discussed above. It is necessary to address them, so it becomes possible to have correct, and real data use. As a result, the above points must be considered when developing a big data architecture.