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Engineering Module 1 > Big Data Management > Flashcards

Big Data Management Flashcards

1
Q

Characterization (3Vs)

A

Variety: Different forms of data
Volume: Petabytes of data
Velocity: Real-time data

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2
Q

Big Data Analysis Pipeline

A
  1. Data acquisition: Select important data to be stored
  2. Information extraction & cleaning: Pull out required information from underlying sources
  3. Data integration & aggregation & representation: Full integration not always possible. Problem, that origin can not be tracked on derived data, selection of storage complex
  4. Modeling & analysis: Big data discloses hidden patterns and knowledge. Big pictures shows simple models
  5. Interpretation: Annotate base data and discuss interpretation of metadata
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3
Q

Data Lake requirements

A
  1. Secure
  2. Scalable
  3. Reliable
  4. Throughput
  5. Low Latency
  6. Store details
  7. Store native Forman
  8. All sources
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4
Q

Advantages Cloud

A
  • Cost
  • Extensibility
  • Reliability
  • Workload
  • Sharing
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5
Q

Disadvantages Cloud

A
  • Custom software
  • Networking
  • Maintenance
  • Security
  • Parallelization not always possible
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6
Q

Three-Tier Server

A

Presentation ➔ Logic ➔ Data

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7
Q

Design Cloud

A
  • Transparent
  • Flexible
  • Reliable
  • Performant
  • Scalable
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8
Q

Fallancies of cloud

A
  1. Network is reliable
  2. Latency is zero
  3. Bandwidth is infinite
  4. Network is secure
  5. Topology doesn’t change
  6. There is one administrator
  7. Transport cost is zero
  8. The network is homogeneous
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9
Q

Cloud characteristics

A
  1. Dynamic
  2. Massively scalable
  3. Multi-tenant
  4. Self-service
  5. Per-usage based pricing model
  6. IP-based architecture
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10
Q

Google File System

A

Store chunks across chunk servers, replicate chunks, access control by master node

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11
Q

Map Reduce

A
  • Extract data as key value
  • Group by key
  • Reduce groups
  • Split data and perform mapping parallel
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12
Q

ACID

A
  • Atomicity
  • Consistency
  • Isolation
  • Durability
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13
Q

CAP

A
  • Consistency
  • Availability
  • Partition-tolerance
    ➔ Not all three possible at the same time
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14
Q

BASE

A
  • Basically Available
  • Soft state
  • Eventual Consistency
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15
Q

Types of NoSQL storage

A
  • Key/Value
  • Wide-column
  • Document database
  • Graph database
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16
Q

Steps of machine learning

A

Data ➔ Preprocessing ➔ Featuring ➔ Learning ➔ Testing ➔ Analysis

17
Q

Decision tree

A
  • Created using greedy top down

- Choose attribute with most information value for each node step

18
Q

K-means clustering

A
  • Variance within clusters minimal

- Random start points, assign data based on least distance to start points, recalculate start points, iterate

Engineering Module 1 (4 decks)

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