Big data architecture is the framework that handles data storage, processing, and analysis across multiple sources. It is useful for processing real-time data and historical data. A big data architecture describes how we gather , store, process, and analyze analyze analyze said data with contemporary tools and practices. Technologies like Hadoop, Spark, and cloud technologies work on big data technology that helps build mighty systems. It leads to better decisions, real-time insights, and powerful analytics for the companies. This article explains big data architecture in simple words and how each part works and fits into today’s data world.
What is Big Data Architecture?
Big data in systems requires big data systems to process data. It has a lot to do with the architecture of Hadoop. It is a big data technology used worldwide and is one of the most important. Hadoop allows you to store and process large quantities of data on commodity hardware.
What is Hadoop?
Hadoop Architecture is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It shrinks big data into smaller chunks and distributes them across several computers. It has two major parts — Hadoop distributed file system (HDFS) and MapReduce. It stands for Hadoop Distributed File System. It stores large files broken into small blocks on many machines. MapReduce works on these blocks of data and provides results quickly.
The Hadoop-based architecture gives it a simple and elastic setup. It runs on clusters of machines. Each machine focuses on a microtask. Once finished, they return the result to the primary system. It allows data to be processed quickly and at a lower cost. It is capable of working with petabytes of data.
Big Data Technologies
This makes a wide variety of big data technologies compatible with Hadoop. To query big data analyze, and manage big data in Hadoop systems, using use tools like Hive, Pig, HBase, etc. Hive allows you to write SQL-like queries without much effort. Of course, Pig rhymes big. Pig used to write data flows. So, HBase provides real-time data access.
These tools enable Hadoop to serve near real-time requirements along with batch processing. In many big data framework setups, Hadoop serves as a base layer. This makes it extremely flexible and open-source, allowing companies to build reliable yet inexpensive systems.
Hadoop Architecture Resulting in Big Data Solutions
Hadoop architecture solutions are the foundation of all scalable big data architecture. Companies can spin additional machines as data scales to cope with the load. This is known as horizontal scaling. Hadoop is also compatible with cloud-based big data architecture. Cloud tools like AWS and Azure enable companies to run Hadoop clusters without purchasing hardware.
Not only does it add fault tolerance, it also has a Hadoop. If one of the machines fails, Hadoop will keep working. It replicates data across multiple machines to prevent loss. These reasons have made it an integral component of the current big data ecosystem.
| Feature | Benefit |
| Distributed Storage | Stores data across many machines |
| Horizontal Scaling | Add machines as data grows |
| Fault Tolerance | Keeps data safe even if machines fail |
| Open Source | Free to use and improve |
| Tool Integration | Works with Hive, Pig, HBase, etc. |
The foundation of robust big data solutions is the Hadoop architecture. It integrates well with several significant data components and enables batch and real-time data processing. Its power and flexibility still retain the title of the key technology in the big data world.
Data Lake Architecture vs Data Warehouse
Both data lakes and data warehouses are used for data storage. But they differ in how they function. This part discusses the differences and compares data lake architecture and data warehouse vs. data lake concepts.
What is Data Lake Architecture?
Data lake architecture stores different types of data together. It stores structured, semi-structured, and unstructured data. In other words, it can process files, logs, videos, images, etc. Data lakes do not need to have a defined structure. You load the raw data to a lake and process it later.
Machine learning, advanced analytics, and big data analytics architecture can benefit from this setup. Cheap: leverages on low-cost storage and cloud solutions. Tools such as AWS S3 and Azure Data Lake are common alternatives. Data lake architecture allows enterprises the flexibility to explore data in numerous ways.
What is a Data Warehouse?
Not so for a data warehouse. It only holds structured data. It works with tables, columns, and immutable rules. This allows easy support for reports and business analysis. It is necessary to cleanse and transform the data before storing it. This is known as ETL (Extract, Transform, Load).
Data warehouses work great for dashboards and static reports. They provide quick responses to business queries. They are not, however, suitable for building raw data or new data types.
| Feature | Data Lake Architecture | Data Warehouse |
| Data Type | All types (raw) | Only structured |
| Cost | Low (cheap storage) | High (expensive systems) |
| Processing | Schema-on-read | Schema-on-write |
| Flexibility | High | Low |
| Use Case | ML, Analytics, Big Data | BI, Reports |
Scalable big data architecture in modern systems is where data lakes fit well. Data pipeline architecture works great with them. It allows data to be gathered, stored, and later processed in a single location. Data warehouses require demand and organization organization organization of data, so they work well for stable requirements.
Why Businesses Use Both?
Many companies use both. They drill down into lakes and transport functional segments into warehouses. This is known as the Lambda Architecture. This combination provides reliable reporting and real-time data processing. This setup allows for agile, cost-effective operations in cloud-based significant data architecture.
From a bigger-picture point of view, data lake architecture is booming. It integrates well with big data elements like Spark, Kafka & Flink. Anything that can help businesses scale requires adaptable storage. The data lakes simplify this problem and serve as the power behind modern big data solutions.
Big Data Processing Module in the Cloud
A big data processing pipeline is a system for processing large datasets that moves from data source to data storage to generate insights. It consists of procedures such as data collection, cleansing, storage, processing, and utilization. Pipeline: An essential aspect of big data analytics architecture.
What is Big Data Processing Pipeline?
The first step in any big data processing pipeline is collecting data. Mobile apps, websites, sensors, and other sources generate data. This data is cleansed of errors by the pipeline. It then places the data in the appropriate storage location: a data lake or a warehouse.
Data is transferred to the processing layer after it has been stored. Data is processed using tools like Apache Spark, Flink, or Hadoop MapReduce. From there, the pipeline pushes results to dashboards, alerts, or AI models. This entire flow is known as a data pipeline architecture.
Essential Components of Big Data Pipeline
A scalable architecture big data architecture should contain the components below.
- Ingestion — Real-time or batch data collection
- Storage — Stores data in data lakes or warehouses
- Processing – Leveraging big data technologies such as Spark or Hadoop
- Finds insights using BI or ML tools – Analysis
- Output – Delivers results to users or systems.
All these components have to function in unison. These should accommodate failure and evolve as the data accrues.
- These pipelines are designed to handle real-time data processing.
- A lot of modern systems require processing data in real time. This means both getting data and acting on it in seconds. Apache Kafka, Flink, and Spark Streaming are used for real-time pipelines. They are living with events quickly and working with cloud-based big data architecture.
- Banks use real-time systems to catch cards, apps use them to show instant updates, and factories use them to monitor machines. These are systems that need ruggedized design and fast tools. They should process millions of events a second.
Building Scalable Pipelines
- You will probably need to read this article until the end to start processing big data.
- Leverage open-source big data technologies
- Store raw data in data lakes
- Cut costs by adopting a cloud-based big data architecture
- Decouple by modules (ingest, store, process, analyze analyze analyze)
Auto-scaling during the peak time for handling the increased amount of data. These steps allow companies to develop pipelines that scale when they scale. They quickly convert raw data into decisions.
Pipelines can also integrate with big data infrastructure. This encompasses networks, storage systems, and compute clusters. All of these are collectively creating the foundations of modern big data systems.
Relevance to ACCA Syllabus
As big data architecture has a significant role in data-driven decisions, risk management, and strategic planning, it is crucial for ACCA. ACCA’s emphasis on Strategic Business Leader (SBL), Audit and Assurance (AA) and Financial Management (FM) highlights knowledge of technologies such as big data in delivering better quality reporting, business intelligence and governance. Big data architecture prepares future accountants to work with the large sets of data used by organizations for forecasting, analytics and compliance.
Big Data Architecture ACCA Questions
Q1 What is the primary use of a data lake in big data architecture?
A) To employ a financial ratio analysis
B) When structured and unstructured data at scale are normalized
C) Accountancy de old-fashioned systems to replace the
D) To manage journal entries
Ans: D) To manage journal entries
Q2: In a big data architecture, which component serves the purpose of processing financial data streams in real-time?
A) Data Warehouse
B) Batch Processing Engine
C) Stream Processing Engine
D) ERP System
Answer: C) Streaming Processing Engine
Q3: What can new auditors learn from big data architecture?
In order to do the ledgers manually A) Accounting software
B) To confirm network connectivity
C) carry out audits with the confidence of data accuracy and consistency
D) To learn new formulas in the spreadsheet
Answer: C) For data consistency and accuracy for audits
Q4: Why does big data architecture benefit financial reporting from a business perspective?
A) No need for tax reports
B) Improved the timeliness and accuracy of the report
C) Automates company payrolls
D) Prohibits double-entry accounting
Solution: B) Improved timeliness and accuracy of reports
Q5: Which ACCA paper is most relevant to using big data to guide strategic decision making?
A) Performance Management(PM)
B) Strategic Business Leader (SBL)
C) Taxation (TX)
D) Audit and Assurance (AA)
Answer: . B) Strategic Business Leader (SBL)
Relevance to US CMA Syllabus
Big data architecture falls under Part 1 (Financial Planning, Performance and Analytics) of the US CMA syllabus. It significantly improves decision analysis, performance management, and technology integration. Data infrastructure knowledge allows CMAs to collect data from multiple unrelated sources and comprehend large datasets that help formulate budgets, variances analysis, and cost control and play a major role in performance evaluation analytics.
Big Data Architecture CMA Questions
Q 1: Which part of the big data architecture allows the CMAs to bring together financial data from multiple sources?
A) Data Pipeline
B) Budget Forecasting Tool
C) General Ledger
D) Balanced Scorecard
Answer: A) Data Pipeline
Q2: Which kind of processing helps a real time tracking of your expenses specifically in CMA analysis?
A) Batch Processing
B) Stream Processing
C) Periodic Reporting
D) Journal Adjustments
Answer: B) Stream Processing
Q3: What must CMAs know about big data architecture?
A) To conduct audits
B) For real-time analytics to forecast budgets
C) To manage employee payroll
D) To record entries
The answer is B) Predicting budgets with live analytic
Q4: Which layer of the big data architecture assists to analyse KPIs and metrics as part of the dashboards?
A) Ingestion Layer
B) Storage Layer
C) Presentation Layer
D) Encryption Layer
Answer: C) Presentation Layer
Q5: Which CMA standardized topic is directly affected by analyzing-analyzing analyzing structured and unstructured financial data?
A) Investment Appraisal
B) Cost Allocation
C) Performance Management
D) Ethics and Governance
The answer is C) Performance Management
Relevance to CFA Syllabus
The CFA curriculum focuses on big data architecture, particularly in Portfolio Management, Quantitative Methods, and Fintech in Investments. CFA candidates study massive datasets, identify risks, develop models, and formulate investment strategies. Doing so guarantees that data-driven models make valid conclusions about data.
Big Data Architecture CFA Questions
Q1: What does a strong big data architecture bring to your investment analysis?
A) Manually increases firm value
B) Enables predictive modeling to be done with heavy data-sets
C) Reduces interest rates
(D) Only supports qualitative data
Answer: B) Facilitates predictive modeling with large data sets
Q2: In big data architecture, what component holds financial market feeds for subsequent access?
A) Presentation Layer
B) Batch Engine
C) Data Lake
D) Pricing Sheet
Answer: C) Data Lake
Q3: What layer of big data architecture is used for algo trading signals?
A) Data Storage
B) Real-Time Analytics Layer
C) Spreadsheet Interface
D) CRM System
Ans: B) Real-Time Analytics Layer
Q4: How do CFA charterholders utilize big data in Fintech?
A) For compliance-only workloads
B) In support of investment automation and risk modeling
C) To prepare tax reports
D) For employee relations management
Ans: B) In support of investment automation and risk modeling
Q5: Which CFA topic area covers analysis of both structured and unstructured data by using big data tools?
A) Financial Reporting & Analysis
B) Quantitative Methods
C) Economics
D) Fixed Income
Answer: B) Quantitative Methods
Relevance to US CPA Syllabus
Although for the US CPA candidates big data architecture applies to Business Environment & Concepts (BEC) and Audit and Attestation (AUD) sections. CPAs need to evaluate risks, verify data accuracy and test internal controls. Being familiar with the architecture of big data systems allows CPAs to test systems efficiently when conducting audits.
Big Data Architecture CPA Questions
Q1: Why is it important for CPAs to understand the architecture of big data in auditing?
A) To create journal entries
B) For evaluating the dependability of automated monetary systems
C) To write marketing plans
D) To compute payroll
Answer: To evaluate the dependability of automated financial systems
Q2: Explain its purpose for accountants in big data architecture?
A) Deletes duplicate data
B) Collects financial data through multiple channels
C) Calculates tax liabilities
D) Issues invoices
Answer: B) Collects unrefined financial data from multiple sources
Q3: What makes the presentation layer important for CPAs running financial dashboards?
A) It encrypts all data
B) It displays visual outputs as part of reporting and analysis
C) It stores passwords
D) To record bank reconciliations
Answer options: B) Which displays visual outputs that is used in reporting and analysis
Q4: How does big data architecture aid audit risk assessments?
A) Reduces audit hours
B) You train it on data until October 2023.
C) Prepares salary slips
D) Posts accounting entries
Answer: B) Detects anomalous patterns in live data
Q5: In which CPA exam section the impact of technologies such as the big data architecture is included?
A) Regulation (REG)
B) Financial Accounting and Reporting (“FAR”)
Business Environment & Concepts (BEC) (C)
D) Ethics and Professional Responsibility
Answer: C) (Business Environment & Concepts)
