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Questions and Answers
What are Dstreams used for in Spark?
Why is micro-batch processing advantageous in Spark?
What is a key benefit of using MLlib in Spark?
How much faster does Spark process computations in-memory compared to MapReduce?
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What plays a crucial role in the development of distributed systems?
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Which statement about distributed computing (DC) is accurate?
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Which of the following is not an example of a distributed system?
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What mainly drives the evolution from single computers to distributed systems?
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What is the primary advantage of Apache Spark's in-memory computing?
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Which module of Apache Spark is specifically designed for handling SQL queries?
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The micro-batching technique used by Spark allows it to operate in which of the following ways?
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Which of the following best describes the role of GraphX within the Spark ecosystem?
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How does the Spark framework interact with HDFS?
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In terms of resource management within Hadoop, which component fulfills this function?
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Which of the following statements is true regarding MapReduce?
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What is the primary role of the Streaming module in Apache Spark?
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Which of the following best describes real-time processing?
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Which tool is specifically associated with real-time processing?
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An example of non-real-time processing would be:
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What is a key feature of real-time data processing?
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Which of the following systems typically supports real-time processing?
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What distinguishes batch processing from real-time processing?
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Why is real-time processing crucial in certain applications?
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In real-time processing, the output of data is characterized by:
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Study Notes
Spark Ecosystem
- Spark is an in-memory, distributed computing system that sits on top of HDFS
- Spark processes data in micro-batches (3 second cycles)
- Spark has modules for streaming, SQL, machine learning, and graph processing
Spark Components
- Spark SQL: Built-in SQL package to work with structured data
- GraphX: Used to store and process network data
- Streaming: The module where big data processing takes place
- MLlib: Analyzes data, generates statistics, and deploys machine learning algorithms
- Supports Java, Scala, Python, and R
- Can pull data directly from HDFS, reducing reliance on data engineers
- Computations are 100 times faster than traditional MapReduce frameworks
Distributed Computing Systems (DCS)
- DCS is a field of computing science that studies the use of distributed systems to solve computational problems
- DCS technology emerged 50 years ago to solve complex problems without expensive, massive computing systems
- Examples include:
- Distributing programs on the same physical server and using messaging services to communicate
- Utilizing different servers each with their own memory to work together
Hadoop System
- Hadoop (v2 or later) platform is composed of three frameworks:
- MapReduce: For bulk/batch data processing (Implemented in Java)
- YARN: For resource management (Implemented in Java)
- HDFS: For data storage, used by SQL to query data
MapReduce
- The process includes 2 phases:
- Map: Tags data by associating keys with values
- Reduce: Aggregates pairs into smaller sets of data using aggregation operations
- YARN and HDFS can work together for efficient processing
Content Management Systems (CMS)
- A computer system that can manage the complete life-cycle of content
- Deals with unstructured data like web content, documents, and others
- Used to run websites like blogs, news sites, and online stores
- Important in big data management because they offer:
- Low cost
- Workflow management
- Easy customization
- User-friendliness
- Improved search engine optimization
Real-Time and Non-Real-Time Processing
-
Real-Time Processing:
- Continual input, constant processing, and steady output
- Examples: Data streaming, radar systems, ATMs
- Spark is a good tool for real-time processing
-
Non-Real-Time (Batch) Processing:
- Consists of three steps (Data collection, processing, and output)
- Examples: Payroll, monthly billing
- MapReduce is a good tool for batch processing
Organizing Data Services and Tools
- Techniques include:
- Aggregation & Statistics (Data warehousing, OLAP)
- Indexing, Searching, and Querying (Keyword search, Pattern matching)
- Knowledge Discovery (Data mining, Statistical Modeling, Prediction, Classification)
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Description
This quiz explores the fundamentals of the Spark ecosystem, covering its components such as Spark SQL, GraphX, and MLlib. Additionally, it delves into the principles of distributed computing systems and their impact on data processing efficiency. Test your knowledge on in-memory computing and the capabilities of Spark in handling large datasets.