Podcast
Questions and Answers
What does Big Data architecture primarily define?
What does Big Data architecture primarily define?
Which layer considers the data types that will be ingested?
Which layer considers the data types that will be ingested?
What is the primary function of Layer L2 in data processing architecture?
What is the primary function of Layer L2 in data processing architecture?
What type of data processing does batch processing refer to?
What type of data processing does batch processing refer to?
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Which of the following is NOT a consideration in Big Data architecture?
Which of the following is NOT a consideration in Big Data architecture?
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What is a source data-type in Layer L1?
What is a source data-type in Layer L1?
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Which statement accurately describes the interaction between layers in data processing architecture?
Which statement accurately describes the interaction between layers in data processing architecture?
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In what scenarios would real-time ingestion be preferred over batch processing?
In what scenarios would real-time ingestion be preferred over batch processing?
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What is the primary function of the Data Storage Layer (L3) in Big Data Analytics?
What is the primary function of the Data Storage Layer (L3) in Big Data Analytics?
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Which software is NOT typically associated with the Data Processing Layer (L4)?
Which software is NOT typically associated with the Data Processing Layer (L4)?
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What types of processing can be performed in the Data Processing Layer (L4)?
What types of processing can be performed in the Data Processing Layer (L4)?
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Which of the following is involved in the Data Consumption Layer (L5)?
Which of the following is involved in the Data Consumption Layer (L5)?
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Which layer focuses on the identification of data sources for ingestion?
Which layer focuses on the identification of data sources for ingestion?
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What is a key characteristic of the Data Processing Layer (L4)?
What is a key characteristic of the Data Processing Layer (L4)?
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In Big Data Analytics, what does the term 'knowledge discovery' refer to in the context of the Data Consumption Layer (L5)?
In Big Data Analytics, what does the term 'knowledge discovery' refer to in the context of the Data Consumption Layer (L5)?
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What type of data storage systems are mentioned for use in the Data Storage Layer (L3)?
What type of data storage systems are mentioned for use in the Data Storage Layer (L3)?
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Study Notes
Big Data Architecture
- Big Data architecture is the logical and/or physical layout for how Big Data is stored, accessed, and managed.
- It defines how Big Data solutions work.
- It outlines the core components (hardware, database, software, storage), data flow, security, and more.
Lowest Layer (L1)
- This layer considers the amount of data needed at the ingestion layer (L2).
- It determines whether data will be pushed from L1 to L2 or pulled by L2.
- The source data types are databases, files, web or services.
- Source formats can be structured, semi-structured, or unstructured.
Data Ingestion and Acquisition Layer (L2)
- This layer considers data ingestion and ETL (Extract, Transform, Load) processes.
- Processes can take place in real-time or in batches.
- Batch processing uses discrete datasets at scheduled or periodic intervals.
Data Storage Layer (L3)
- This layer specifies storage types (historical or incremental).
- It defines data formats, compression, frequency of incoming data, querying patterns, and consumption requirements.
- This layer uses Hadoop distributed file systems or NoSQL data stores (HBase, Cassandra, MongoDB).
Data Processing Layer (L4)
- This layer utilizes data processing software such as MapReduce, Hive, Pig, Spark, Spark Mahout, and Spark Streaming.
- Processing can occur in scheduled batches, real-time, or in hybrid modes.
- Processing follows synchronous or asynchronous requirements for L5.
Data Consumption Layer (L5)
- This layer focuses on data integration.
- It defines dataset usages for reporting and visualization, along with real-time and near real-time processing and batching.
- Analytics, business processes (BPs), business intelligence (BIs), and knowledge discovery are also part of this layer.
- Datasets can be exported to cloud, web, or other systems.
Summary of Layers
- There are five design layers in Big Data architecture.
- L1 identifies internal and external data sources for ingestion and acquisition.
- L2 handles ingestion and acquisition, potentially using ETL processes in real-time or batch modes.
- L3 stores data, considering aspects like format, compression, and query patterns.
- L4 performs data processing using various software tools, with batch or real-time options.
- L5 focuses on data consumption, reporting, visualization, and exporting to other systems.
- L3 formats data for L4, and L5 uses that processed data for business needs.
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Description
Explore the essential components of Big Data architecture, including layers for data ingestion, processing, and storage. Understand how data flows through different stages from source to storage, and the various formats involved. This quiz will test your knowledge on the structure and management of Big Data solutions.
