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We want to connect the people who have knowledge to the people who need it, to bring together people with different perspectives so they can understand each other better, and to empower everyone to share their knowledge.
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 lessDefine Balancer in HDFS.
The balancer in HDFS is a tool the admin staff leverages to shift blocks from overused to underused nodes and redistribute data across DataNodes.
The balancer in HDFS is a tool the admin staff leverages to shift blocks from overused to underused nodes and redistribute data across DataNodes.
See lessWhat is Hadoop’s “Data Locality?”
Data movement over the network is unnecessary in a Big Data system due to the amount of data. Hadoop is now attempting to bring processing closer to the data. The information is kept local to the storage place in this manner.
Data movement over the network is unnecessary in a Big Data system due to the amount of data. Hadoop is now attempting to bring processing closer to the data. The information is kept local to the storage place in this manner.
See lessWhy does Hadoop employ the context object?
The Hadoop framework uses context objects with the Mapper class to communicate with the rest of the system. The system configuration information and job are passed to the context object in its function Object() { [native code] }. To pass information in the setup(), cleanup(), and map() functions, weRead more
The Hadoop framework uses context objects with the Mapper class to communicate with the rest of the system. The system configuration information and job are passed to the context object in its function Object() { [native code] }.
To pass information in the setup(), cleanup(), and map() functions, we employ context objects. With the help of this object, crucial data is made available for map operations.
See lessWhat occurs if a user submits a new job when NameNode is down?
Hadoop's NameNode is a single point of failure, making it impossible for users to submit or run new jobs. The user must wait for NameNode to restart before performing any jobs since if NameNode is down, the job may fail.
Hadoop’s NameNode is a single point of failure, making it impossible for users to submit or run new jobs. The user must wait for NameNode to restart before performing any jobs since if NameNode is down, the job may fail.
See lessWhat are the Secondary NameNode’s functions?
Secondary NameNode's functions are as follows: FsImage, which keeps a copy of both the FsImage and EditLog files. NameNode failure: The Secondary NameNode's FsImage can be used to reconstruct the NameNode if it crashes. Checkpoint: Secondary NameNode uses this checkpoint to ensure that HDFS data isRead more
Secondary NameNode’s functions are as follows:
- FsImage, which keeps a copy of both the FsImage and EditLog files.
- NameNode failure: The Secondary NameNode’s FsImage can be used to reconstruct the NameNode if it crashes.
- Checkpoint: Secondary NameNode uses this checkpoint to ensure that HDFS data is not damaged.
- Update: The EditLog and FsImage files are both automatically updated. Updating the FsImage file on the Secondary NameNode is beneficial.
See lessExplain “rack awareness.”
When reading or writing any file located closer to the neighboring rack to the Read or Write request in the Hadoop cluster, Namenode leverages the Datanode to reduce network traffic. Namenode keeps track of each DataNode's rack id, which is known as rack awareness.
When reading or writing any file located closer to the neighboring rack to the Read or Write request in the Hadoop cluster, Namenode leverages the Datanode to reduce network traffic.
Namenode keeps track of each DataNode’s rack id, which is known as rack awareness.
See lessHow do you turn off the HDFS Data Node’s Block Scanner?
Set dfs.datanode.scan.period.hours to 0 to disable Block Scanner on HDFS Data Node.
Set dfs.datanode.scan.period.hours to 0 to disable Block Scanner on HDFS Data Node.
See lessList the standard port numbers on which Hadoop’s task tracker, NameNode, and job tracker operate.
Hadoop’s task and job trackers all run on the following default port numbers: The task tracker runs on the 50060 port. NameNode runs on the 50070 port. Job Tracker runs on the 50030 port.
Hadoop’s task and job trackers all run on the following default port numbers:
- Job Tracker runs on the 50030 port.
See lessWhat does FIFO entail?
FIFO is a scheduling algorithm for Hadoop jobs.
FIFO is a scheduling algorithm for Hadoop jobs.
See lessWhat is big data?
Big data is data of immense volume, variety, and velocity. It entails larger data sets from various data sources.
Big data is data of immense volume, variety, and velocity. It entails larger data sets from various data sources.
See lessWhat does Hadoop’s Heartbeat mean?
NameNode and DataNode converse with one another in Hadoop. The heartbeat is the regular signal DataNode sends to NameNode to confirm its presence.
NameNode and DataNode converse with one another in Hadoop. The heartbeat is the regular signal DataNode sends to NameNode to confirm its presence.
See lessHow can you achieve security in Hadoop?
For Hadoop security, take the following actions: 1) Secure the client's authentication channel with the server, and give the client time-stamped documents. 2) The client asks TGS for a service ticket using the time-stamped information. 3) The client uses a service ticket to self-authenticate to a paRead more
For Hadoop security, take the following actions:
1) Secure the client’s authentication channel with the server, and give the client time-stamped documents.
2) The client asks TGS for a service ticket using the time-stamped information.
3) The client uses a service ticket to self-authenticate to a particular server in the last phase.
See lessDescribe the distributed Hadoop file system.
Scalable distributed file systems like S3, HFTP FS, FS, and HDFS are compatible with Hadoop. The Google File System is the foundation for the Hadoop Distributed File System. This file system is made to be easily operable on a sizable cluster of the computer system.
Scalable distributed file systems like S3, HFTP FS, FS, and HDFS are compatible with Hadoop. The Google File System is the foundation for the Hadoop Distributed File System. This file system is made to be easily operable on a sizable cluster of the computer system.
See lessDescribe the Snowflake Schema.
A Snowflake Schema is an extended model of the Star Schema, which adds new dimensions and resembles a snowflake. It divides data into extra tables by the normalization of the dimension tables.
A Snowflake Schema is an extended model of the Star Schema, which adds new dimensions and resembles a snowflake. It divides data into extra tables by the normalization of the dimension tables.
See lessWhat do you know about FSCK?
File System Check or FSCK is a command that HDFS leverages. This command checks inconsistencies and problems in files.
File System Check or FSCK is a command that HDFS leverages. This command checks inconsistencies and problems in files.
See lessHow is a big data solution deployed?
This is one of a few big data engineer interview questions you might encounter. Here’s how you can deploy a big-data solution: Combine data from many sources, including RDBMS, SAP, MySQL, and Salesforce. Save the extracted data in a NoSQL database or an HDFS file system. Utilize processing frameworkRead more
This is one of a few big data engineer interview questions you might encounter.
Here’s how you can deploy a big-data solution:
- Combine data from many sources, including RDBMS, SAP, MySQL, and Salesforce.
- Save the extracted data in a NoSQL database or an HDFS file system.
- Utilize processing frameworks like Pig, Spark, and MapReduce to deploy a big data solution.
See lessDescribe the Star Schema.
A star schema, often known as a star join schema, is the most fundamental type of data warehouse model. It is called a star schema due to its structure. The Star Schema allows for numerous related dimension tables and one fact table in the star's center. This model is ideal for querying large data cRead more
A star schema, often known as a star join schema, is the most fundamental type of data warehouse model. It is called a star schema due to its structure. The Star Schema allows for numerous related dimension tables and one fact table in the star’s center. This model is ideal for querying large data collections.
See lessWhat does COSHH stand for?
COSHH stands for Classification and Optimization based Schedule for Heterogeneous Hadoop systems. It lets you schedule tasks at both application and cluster levels to save on task completion time.
COSHH stands for Classification and Optimization based Schedule for Heterogeneous Hadoop systems. It lets you schedule tasks at both application and cluster levels to save on task completion time.
See lessDescribe the attributes of Hadoop
The following are key attributes of Hadoop: Open-source, freeware framework Compatible with a wide range of hardware to simplify access to new hardware inside a given node Enables faster-distributed data processing Stores data in the cluster, separate from the other operations. Allows the creation oRead more
The following are key attributes of Hadoop:
- Open-source, freeware framework
- Compatible with a wide range of hardware to simplify access to new hardware inside a given node
- Enables faster-distributed data processing
- Stores data in the cluster, separate from the other operations.
- Allows the creation of three replicas for each block using various nodes.
See lessWhat happens when Block Scanner finds a faulty data block?
First, DataNode alerts NameNode. Then, NameNode creates a new replica using the corrupted block as a starting point. The goal is to align the replication factor with the replication count of the proper replicas. If a match is discovered, the corrupted data block won't be removed.
First, DataNode alerts NameNode. Then, NameNode creates a new replica using the corrupted block as a starting point.
The goal is to align the replication factor with the replication count of the proper replicas. If a match is discovered, the corrupted data block won’t be removed.
See lessExplain HDFS’s Block and Block Scanner.
A block is the smallest data file component. Hadoop automatically divides large files into small workable segments. On the flip side, the Block Scanner verifies a DataNode's list of blocks.
A block is the smallest data file component. Hadoop automatically divides large files into small workable segments. On the flip side, the Block Scanner verifies a DataNode’s list of blocks.
See less