--- title: "Data analytics process and distributed computing" subject: "Big Data Technologies" chapter: 1 key: "semester_8/big_data_technologies:data-analytics-distributed-role" source: https://www.bctengineer.com/q/semester_8/big_data_technologies/data-analytics-distributed-role site: BCT Engineering --- # Data analytics process and distributed computing Big Data Technologies · Chapter 1: Introduction to Big Data - Asked in: 2073 Magh, 5+5 marks - Video: [01 What is Distributed Computing, Big Data and Hadoop? | History of Distributed File System](https://www.youtube.com/watch?v=vf0L0Ui69tA) — Ease With Data - Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14 - Page: https://www.bctengineer.com/q/semester_8/big_data_technologies/data-analytics-distributed-role - Whole chapter: https://www.bctengineer.com/md/semester_8/big_data_technologies/chapter-1 ## Question Why do we need data analytics process? Explain the role of Distributed computing in Big data. ## Short answer Data analytics is needed to turn raw big data into better decisions, predictions, and fraud detection. Distribute computing provides storage (HDFS splitting files across nodes), parallel processing (MapReduce/Spark), fault tolerance through replication, and cost-effectiveness by using commodity hardware with data locality. ## Answer #### Why do we need the data analytics process? **Data analytics** is the process of collecting, cleaning, transforming and modelling data to discover useful information and support decisions. A defined process is needed because raw data by itself has no value. 1. **Better decisions:** replaces guesswork with evidence (e.g. which product to stock). 2. **Finding patterns and trends:** sales seasonality, fraud patterns, customer behaviour. 3. **Prediction:** forecast demand, churn or failures using historical data. 4. **Efficiency and cost reduction:** find waste in operations and supply chains. 5. **Customer understanding:** personalised offers and recommendations. 6. **Risk and fraud detection:** spot unusual transactions in time. 7. **Competitive advantage:** firms that learn faster from data win. 8. **Handling big data:** an organised process (collect, store, clean, analyse, visualise, act) keeps results reliable and repeatable at large scale. Typical steps: define the objective, collect data, clean and prepare, explore, model, validate, visualise/report, and deploy. #### Role of distributed computing in big data Big data cannot be stored or processed on one machine, so distributed computing is its foundation. - **Distributed storage:** files are split into blocks and spread across nodes (HDFS, GFS), giving petabyte capacity. - **Parallel processing:** frameworks like MapReduce and Spark run the same task on many data blocks at once, reducing time from hours to minutes. - **Scalability:** add commodity nodes when data grows (horizontal scaling). - **Fault tolerance:** replication of data and re-execution of failed tasks keep jobs running when nodes fail. - **Data locality:** computation moves to the node holding the data, which saves network bandwidth. - **Cost reduction:** cheap commodity servers replace expensive supercomputers. - **Support for variety and velocity:** different nodes and tools (Hive, HBase, Kafka, Storm) handle structured, unstructured and streaming data. - **High availability:** no single machine failure stops the service. Example: a telecom firm analyses billions of call records stored on HDFS using MapReduce to find churn patterns in a few hours. ## Check yourself The answer's key points as multiple-choice questions, with the correct option and why. 1. Why is a defined data analytics process needed? A. Raw data by itself has no value; a process turns it into reliable insight for decisions B. It reduces the amount of data stored to zero C. It replaces the need for any data D. It is only needed for small data Answer: A. An organised process (collect, clean, analyse, visualise, act) makes results reliable and repeatable, turning raw data into decisions. (Key point: Why a data analytics process is needed) 2. How does distributed computing help when the data grows? A. It requires replacing the server with a bigger supercomputer B. Commodity nodes can be added (horizontal scaling) and tasks run in parallel on many blocks C. It deletes old data D. It stops replication Answer: B. Distributed storage and parallel frameworks like MapReduce and Spark scale by adding commodity nodes. (Key point: Parallel processing and scalability) 3. What keeps a big data job running when a node fails? A. The job is cancelled and restarted manually B. Data is moved to a single machine C. Data replication and re-execution of failed tasks D. Clock synchronisation only Answer: C. Replicated data and re-run tasks mean a node failure does not stop the job. (Key point: Fault tolerance in distributed computing) ## Related in Big Data Technologies - [Why distributed computing for big data](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adistributed-computing-necessary) — Big Data Technologies, chapter 1 - [Distributed system in big data example](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adistributed-system-example) — Big Data Technologies, chapter 1 - [Role of data scientist in big data](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Arole-of-data-scientist) — Big Data Technologies, chapter 1, asked 2× - [Big data trends, challenges, characteristics](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Abig-data-trends-challenges) — Big Data Technologies, chapter 1 - [Hybrid data in big data warehouse](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Ahybrid-data-warehouse) — Big Data Technologies, chapter 1 - [Attributes of Big Data](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Abig-data-attributes) — Big Data Technologies, chapter 1 ## Related in other subjects - [IS areas using AI / data analytics](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Aai-areas-data-analytics) — Information System, chapter 4, asked 2× - [Why do we need DFT](https://www.bctengineer.com/question.md?key=semester_7%2Fdigital_signal_analysis_and_processing%3Awhy-need-dft) — Digital Signal Analysis and Processing, chapter 7, asked 6× - [Why do we need FFT](https://www.bctengineer.com/question.md?key=semester_7%2Fdigital_signal_analysis_and_processing%3Awhy-need-fft) — Digital Signal Analysis and Processing, chapter 7, asked 2× - [Big data and its processing](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Abig-data-processing) — Information System, chapter 8