Big Data Architecture Overview
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Questions and Answers

What does Big Data architecture primarily define?

  • The logical framework of access and management of data (correct)
  • The physical location of data storage
  • Only the software components used
  • The cost of data processing solutions

Which layer considers the data types that will be ingested?

  • Layer L1 (correct)
  • Layer L3
  • Layer L4
  • Layer L2

What is the primary function of Layer L2 in data processing architecture?

  • Storing data for long-term access
  • Performing data analytics
  • Data ingestion and ETL processes (correct)
  • Securely managing data access

What type of data processing does batch processing refer to?

<p>Using discrete datasets at scheduled intervals (C)</p> Signup and view all the answers

Which of the following is NOT a consideration in Big Data architecture?

<p>Version control of software (C)</p> Signup and view all the answers

What is a source data-type in Layer L1?

<p>Database, files, web, or services (A)</p> Signup and view all the answers

Which statement accurately describes the interaction between layers in data processing architecture?

<p>Layer L2 cannot function without Layer L1 (C)</p> Signup and view all the answers

In what scenarios would real-time ingestion be preferred over batch processing?

<p>When immediate data usage is required (C)</p> Signup and view all the answers

What is the primary function of the Data Storage Layer (L3) in Big Data Analytics?

<p>Storage of data in the required formats for L4 processing (A)</p> Signup and view all the answers

Which software is NOT typically associated with the Data Processing Layer (L4)?

<p>Redshift (A)</p> Signup and view all the answers

What types of processing can be performed in the Data Processing Layer (L4)?

<p>Scheduled batches or hybrid processing (A)</p> Signup and view all the answers

Which of the following is involved in the Data Consumption Layer (L5)?

<p>Export of datasets to cloud or other systems (D)</p> Signup and view all the answers

Which layer focuses on the identification of data sources for ingestion?

<p>Ingestion and Acquisition Layer (L2) (B)</p> Signup and view all the answers

What is a key characteristic of the Data Processing Layer (L4)?

<p>Processing can occur in both synchronous and asynchronous modes (C)</p> Signup and view all the answers

In Big Data Analytics, what does the term 'knowledge discovery' refer to in the context of the Data Consumption Layer (L5)?

<p>The extraction of insights and patterns from data (C)</p> Signup and view all the answers

What type of data storage systems are mentioned for use in the Data Storage Layer (L3)?

<p>Hadoop distributed file system or NoSQL data stores (A)</p> Signup and view all the answers

Flashcards

Big Data Architecture

The logical and/or physical structure of how big data is stored, accessed, and managed within an IT environment

Big Data Architecture (Logic)

Defines how a Big Data solution will function, including components (hardware, databases, software, storage), data flow, and security

Lowest Layer (L1)

Handles the amount of data needed at the ingestion layer (L2), either pushing data or pulling it, depending on the mechanism.

Data Ingestion and Acquisition Layer (L2)

Manages ETL (Extract, Transform, Load) and data ingestion, possibly in real-time or batches.

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Real-time Ingestion

Stores and uses data as it's generated.

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Batch Processing

Uses discrete datasets at regular intervals.

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Data Source types

Types of data sources like databases, files, web services.

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Data Formats

Structured, semi-structured or unstructured data formats from various sources.

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Data Storage Layer (L3)

Stores data in various formats (historical, incremental) for processing, considering query patterns and consumption requirements for subsequent layers.

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Hadoop Distributed File System (HDFS)

A distributed file system designed for storing large datasets across multiple nodes in a cluster, ideal for big data storage and processing.

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NoSQL Databases

Databases that don't adhere to traditional relational database structures, offering flexibility and scalability for large and diverse data sets.

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Data Processing Layer (L4)

Processes raw data using software like MapReduce, Hive, Pig, and Spark, handling batch or real-time processing based on the requirements of the consumption layer.

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MapReduce

A programming model for processing large datasets in parallel across multiple nodes in a cluster, splitting data into chunks for efficient processing.

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Data Consumption Layer (L5)

The layer where processed data is used for various purposes, including reporting, visualization, analytics, business intelligence, knowledge discovery, and data export to other systems.

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Data Integration in L5

The process of combining and harmonizing data from multiple sources to create a unified view, enabling comprehensive analysis and reporting.

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Types of Data Usage in L5

Data is utilized for various purposes, including real-time analytics, scheduled batch analysis, reporting, visualization, and knowledge discovery.

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L1: Data Sources

The initial layer identifying both internal and external sources that provide data for ingestion.

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L2: Ingestion & Acquisition

This layer handles the process of gathering data from different sources, transforming it into a usable format, and loading it into the system.

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L3: Data Storage

The layer responsible for storing large volumes of data in different formats, considering factors like processing needs and future usage.

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L4: Data Processing

This layer focuses on processing and transforming data using various tools like MapReduce, Hive, and Spark, enabling both batch and real-time analysis.

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L5: Data Consumption

The final layer where processed data is used for various purposes, such as reporting, visualization, analytics, and knowledge discovery.

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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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Related Documents

Lesson 3: Design Layers in Data Processing Architecture (Big Data Analytics) PDF

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.

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