Explanation:
In MaxCompute, to view all tables in a project from the command line, you can execute the command "desc tables;". This command describes or displays information about all the tables available in the specified project, providing users with details such as table names, columns, and data types.
Explanation:
When DataWorks is activated in pay-as-you-go mode, the billing is based on resources consumed by data development and debugging, data integration tasks executed, and storage costs for project data. Task nodes created by developers do not incur additional billing costs; they are included in the pay-as-you-go pricing model and are not billed separately.
Explanation:
A snapshot is a point-in-time copy of data on a disk. It captures the state of the disk at the moment the snapshot is taken, allowing you to create a backup or restore the data to that specific state if needed. Snapshots are commonly used for data backup, disaster recovery, and creating consistent images of data for testing or development purposes.
Explanation:
OSS is the best solution because it provides scalable and durable storage for rapidly growing video data, ensuring quick access to historical footage when needed.
Explanation:
MaxCompute is primarily used for ETL (Extract, Transform, Load), data analysis and mining, and real-time stream processing tasks. While it can support data warehousing activities to some extent, it is not typically the primary use case for MaxCompute. Data warehousing solutions may involve other technologies or specialized platforms tailored specifically for data warehousing purposes.
Explanation:
Ingesting user profile records from an OLTP database into the Hadoop file system can be efficiently achieved using Hive. Hive provides a convenient SQL-like interface to query and process data stored in Hadoop, allowing you to create external tables that reference data in the OLTP database and then ingest it into the Hadoop file system. This approach facilitates data integration and enables you to join the user profile records with web server logs for analysis and insights.
Explanation:
The PyODPS Node in DataWorks allows users to edit Python code to operate data in MaxCompute. This node type provides the capability to write custom Python scripts for data processing, analysis, and manipulation tasks within the DataWorks environment, enabling advanced data processing operations on MaxCompute tables.
Explanation:
In an E-MapReduce cluster, the three types of node instances are master, core, and task. The task nodes are responsible for executing computation tasks in the cluster, processing data, and running MapReduce jobs. They work in conjunction with the master and core nodes to efficiently process and analyze data on the Alibaba Cloud platform.
Explanation:
"Through OSS cmd tool," is not supported because OSS cmd tools typically focus on managing objects within buckets rather than creating buckets directly.
Explanation:
In Hive, metadata such as table schemas, column names, and data types are stored in an RDBMS like MySQL, which serves as the Hive metastore database. The metastore database acts as a central repository for storing metadata related to Hive tables and partitions. This architecture allows Hive to separate metadata management from data storage, providing scalability and flexibility in managing large datasets.
Explanation:
In MaxCompute, when using odpscmd to connect to a project, the command "size table_a;" can be executed to view the size of the space occupied by table table_a. This command provides information about the storage consumption of the specified table, helping users to monitor and manage their data resources effectively.
