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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
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
26
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
27
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
28
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
29
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
30
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
31
What are the benefits of using Google Cloud for data processing?
Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark's flexibility and declarative programming model.
32
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
33
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
34
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
35
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
36
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
37
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
38
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
39
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
40
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
41
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
42
What are the benefits of using Google Cloud for data processing?
Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark's flexibility and declarative programming model.
43
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
44
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
45
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
46
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
47
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
48
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
49
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
50
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
51
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
52
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
53
What are the benefits of using Google Cloud for data processing?
Built-in support for Hadoop and Spark; Managed hardware and configuration; Simplified version management; Flexible job configuration; Spark's flexibility and declarative programming model.
54
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
55
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
56
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
57
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
58
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
59
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
60
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
61
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
62
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
63
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
64
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
66
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
67
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
68
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
69
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
70
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
71
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
72
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
73
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
74
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
75
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
77
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
78
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
79
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
80
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
81
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
82
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
83
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
84
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
85
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
86
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
88
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
89
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
90
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
91
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
92
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
93
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
94
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
95
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
96
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
97
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
99
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
100
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
101
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
102
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
103
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
104
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
105
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
106
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
107
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
108
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
110
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
111
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
112
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
113
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
114
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
115
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
116
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
117
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
118
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
119
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
121
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
122
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
123
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
124
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
125
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
126
What are the advantages of using a declarative programming model in Spark?
Users specify the desired outcome, and the system determines how to achieve it efficiently.
127
What are the main components of the Hadoop ecosystem?
HDFS, MapReduce, Hive, Pig, Spark.
128
What is the purpose of Hive in the Hadoop ecosystem?
To provide a data warehousing infrastructure and SQL-like query language for data analysis.
129
What is the purpose of Pig in the Hadoop ecosystem?
To provide a high-level platform for creating MapReduce programs used for processing large data sets.
130
What are the benefits of using Google Cloud for data processing?
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
What are the benefits of using a managed Hadoop and Spark environment in Google Cloud?
Built-in support for existing jobs; No need to worry about physical hardware; Scalability and flexibility in resource allocation.
132
How does DataProc simplify version management in Hadoop clusters?
By managing versioning work and ensuring compatibility between components.
133
What is the purpose of HDFS in Hadoop?
To distribute data and workloads across nodes in a Hadoop cluster.
134
What are the benefits of using Spark in data processing compared to Hadoop?
In-memory processing capabilities; Faster processing speed; Support for batch and streaming data; Advanced features like RDDs and data frames.
135
What are some challenges with on-premises Hadoop clusters that Google Cloud can address?
Physical limitations, lack of separation between storage and compute resources, scaling limitations.
136
How does Google Cloud address the challenges of on-premises Hadoop clusters?
By providing managed hardware and configuration, flexible resource allocation, and simplified version management.
137
What are the advantages of using a declarative programming model in Spark?
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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