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Differentiate between a data engineer and data scientist.
Data scientists study and understand complicated data, whereas data engineers create, test, and manage the entire architecture for data generation. They concentrate on organizing and translating big data. Data engineers also build the infrastructure data scientists need to function.
Data scientists study and understand complicated data, whereas data engineers create, test, and manage the entire architecture for data generation. They concentrate on organizing and translating big data. Data engineers also build the infrastructure data scientists need to function.
See lessWhat are the differences between an operational database and a data warehouse?
Databases that use Delete SQL commands, Insert, and Update are operational standards with a focus on quickness and effectiveness. As a result, data analysis may be a little more challenging. On the other hand, a data warehouse places more emphasis on aggregations, calculations, and select statementsRead more
Databases that use Delete SQL commands, Insert, and Update are operational standards with a focus on quickness and effectiveness. As a result, data analysis may be a little more challenging.
On the other hand, a data warehouse places more emphasis on aggregations, calculations, and select statements. Because of these, data warehouses are a great option for data analysis.
See lessWhat does a skewed table mean in Hive?
Skewed refers to a table's tendency to contain column values more frequently. Skewed values are saved in separate files, and the remaining data is written to a different file when a table is formed in Hive with the SKEWED flag.
Skewed refers to a table’s tendency to contain column values more frequently. Skewed values are saved in separate files, and the remaining data is written to a different file when a table is formed in Hive with the SKEWED flag.
See lessCan you create more than one table in Hive for the same data file?
Yes, you can generate many table schemas for a single data file. Hive stores its schema in the Hive Metastore. We can retrieve several results from the same data using this model.
Yes, you can generate many table schemas for a single data file. Hive stores its schema in the Hive Metastore. We can retrieve several results from the same data using this model.
See lessDescribe the purpose of the .hiverc file in Hive.
The .hiverc file is Hive’s initialization file. When we launch Hive's Command Line Interface (CLI), this file is initially loaded. In the .hiverc file, we can set the parameter's starting values.
The .hiverc file is Hive’s initialization file. When we launch Hive’s Command Line Interface (CLI), this file is initially loaded. In the .hiverc file, we can set the parameter’s starting values.
See lessDescribe how Hive is used in the Hadoop ecosystem.
Hive offers a management interface for data stored within the Hadoop environment and allows you to work with and map HBase tables. The complexity involved in setting up and running MapReduce jobs is concealed by converting Hive searches into MapReduce jobs.
Hive offers a management interface for data stored within the Hadoop environment and allows you to work with and map HBase tables.
The complexity involved in setting up and running MapReduce jobs is concealed by converting Hive searches into MapReduce jobs.
See lessList the elements of the Hive data model.
The Hive data model consists of these elements: Tables Partitions Buckets
The Hive data model consists of these elements:
- Tables
- Partitions
- Buckets
See lessWhat does SerDe in the Hive mean?
Serializer or Deserializer is the full form of SerDe. Hive's SerDe feature lets you read data from a table and write data in any format you like for a particular field.
Serializer or Deserializer is the full form of SerDe. Hive’s SerDe feature lets you read data from a table and write data in any format you like for a particular field.
See lessWhat role does Apache Hadoop’s distributed cache play?
Distributed cache, a key utility feature of Hadoop, enhances job performance by caching the files used by applications. Using JobConf settings, an application can specify a file for the cache. The Hadoop framework copies these files to each node where a task must be run. This is carried out prior toRead more
Distributed cache, a key utility feature of Hadoop, enhances job performance by caching the files used by applications. Using JobConf settings, an application can specify a file for the cache.
The Hadoop framework copies these files to each node where a task must be run. This is carried out prior to the task’s execution. In addition to zip and jar files, Distributed Cache offers the dissemination of read-only files.
See lessDescribe the HDFS Safe mode.
In a cluster, NameNode operates in read-only mode, while NameNode starts out in Safe Mode. Safe Mode inhibits writing to the file system. At this point, it gathers information and statistics from each DataNode.
In a cluster, NameNode operates in read-only mode, while NameNode starts out in Safe Mode. Safe Mode inhibits writing to the file system. At this point, it gathers information and statistics from each DataNode.
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