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Bulding Batch Data Pipelines on Google Cloud > Hadoop Ecosystem and Google Cloud for Data Processing - Sheet1 > Flashcards

Hadoop Ecosystem and Google Cloud for Data Processing - Sheet1 Flashcards

1
Q

What is Hadoop?

A

An open-source framework for distributed processing of large data sets across computer clusters.

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2
Q

What is the Hadoop Distributed File System (HDFS)?

A

A file system used by Hadoop to distribute work to nodes on the cluster.

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3
Q

What is Apache Spark?

A

An open-source analytics engine for processing batch and streaming data, known for its in-memory processing capabilities.

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4
Q

What are some limitations of OSS Hadoop?

A

Tuning and utilization issues, physical limitations in on-premises clusters.

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5
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

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6
Q

What does Google Cloud offer for Hadoop data processing?

A

Managed Hadoop and Spark environment with built-in support.

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7
Q

What advantages does Google Cloud offer in terms of hardware and configuration?

A

No need to worry about physical hardware; Flexible cluster configuration and resource allocation.

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8
Q

How does Google Cloud simplify version management for Hadoop clusters?

A

DataProc manages much of the versioning work, ensuring compatibility between components.

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9
Q

What is the advantage of creating multiple clusters in Google Cloud for Hadoop tasks?

A

Focus on individual tasks without complexity of a single cluster with growing dependencies.

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10
Q

What are the benefits of using Spark in data processing?

A

Flexibility in mixing different kinds of applications; Efficient resource utilization; Declarative programming model.

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11
Q

What is the main purpose of HDFS in Hadoop?

A

To distribute work to nodes on the cluster.

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12
Q

What is the main advantage of Spark over Hadoop for data processing?

A

In-memory processing capabilities, making it up to 100 times faster for equivalent jobs.

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13
Q

What are some challenges with on-premises Hadoop clusters?

A

Physical limitations, lack of separation between storage and compute resources.

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14
Q

What does DataProc offer for running Hadoop on Google Cloud?

A

Managed hardware, simplified version management, flexible job configuration.

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15
Q

What is the benefit of declarative programming in Spark?

A

Users specify what they want to achieve, and the system figures out how to implement it.

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16
Q

What is the purpose of Hadoop in distributed processing?

A

To process large data sets across computer clusters.

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17
Q

What are some components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

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18
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

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19
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

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20
Q

What are the advantages of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

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21
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

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22
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

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23
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

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24
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

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25
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

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26
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

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27
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

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28
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

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29
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

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30
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

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31
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

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32
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

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33
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

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34
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

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35
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

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36
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

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37
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

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38
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

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39
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

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40
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

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41
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

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42
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

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43
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

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44
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

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45
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

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46
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

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47
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

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48
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

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49
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

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50
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

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51
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

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52
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

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53
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

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54
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

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55
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

56
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

57
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

58
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

59
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

60
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

61
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

62
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

63
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

64
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

65
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

66
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

67
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

68
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

69
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

70
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

71
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

72
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

73
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

74
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

75
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

76
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

77
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

78
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

79
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

80
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

81
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

82
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

83
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

84
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

85
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

86
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

87
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

88
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

89
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

90
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

91
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

92
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

93
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

94
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

95
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

96
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

97
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

98
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

99
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

100
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

101
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

102
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

103
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

104
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

105
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

106
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

107
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

108
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

109
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

110
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

111
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

112
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

113
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

114
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

115
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

116
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

117
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

118
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

119
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

120
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

121
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

122
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

123
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

124
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

125
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

126
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

127
Q

What are the main components of the Hadoop ecosystem?

A

HDFS, MapReduce, Hive, Pig, Spark.

128
Q

What is the purpose of Hive in the Hadoop ecosystem?

A

To provide a data warehousing infrastructure and SQL-like query language for data analysis.

129
Q

What is the purpose of Pig in the Hadoop ecosystem?

A

To provide a high-level platform for creating MapReduce programs used for processing large data sets.

130
Q

What are the benefits of using Google Cloud for data processing?

A

Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark’s flexibility and declarative programming model.

131
Q

What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?

A

Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.

132
Q

How does DataProc simplify version management in Hadoop clusters?

A

By managing versioning work and ensuring compatibility between components.

133
Q

What is the purpose of HDFS in Hadoop?

A

To distribute data and workloads across nodes in a Hadoop cluster.

134
Q

What are the benefits of using Spark in data processing compared to Hadoop?

A

In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.

135
Q

What are some challenges with on-premises Hadoop clusters that Google Cloud can address?

A

Physical limitations, lack of separation between storage and compute resources, scaling limitations.

136
Q

How does Google Cloud address the challenges of on-premises Hadoop clusters?

A

By providing managed hardware and configuration, flexible resource allocation, and simplified version management.

137
Q

What are the advantages of using a declarative programming model in Spark?

A

Users specify the desired outcome, and the system determines how to achieve it efficiently.

Bulding Batch Data Pipelines on Google Cloud (3 decks)

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