Apache Hive

Overall Satisfaction with Apache Hive

1. Used Apache Hive to create external and internal tables in Hadoop / BigData projects on Cloudera and Azure platforms. 2. Apache Hive supports different file formats to create tables. Supported file formats are CSV, Parquet, Avro, JSON. 3. Apache Hive can store billions of records in distributed storage and retrieve them efficiently. 4. Apache hive used spark/ Tez / MapReduce engines in the backend for computation.
  • Apache Hive is fault-tolerant.
  • Apache Hive's latest version supports ACID transactions.
  • Apache Hive supports UPDATE, DELETE and MERGE.
  • Apache Hive should support ROLLBACK, COMMIT operations.
  • Apache Hive should support XML SerDe.
  • Apache Hive.
  • Hive supports partitioning and bucketing for faster SQL queries results.
  • Hive support UPDATE, DELETE orations.
  • Apache hive external tables data can be accessed by other applications.
  • Apache hive helped to manage data on HDFS.
  • Apache hive helped to do data cleansing and data transformation.
  • Apache hive queries were slow, so we had to use Impala (MPP) for exposing the data to end-users.

Do you think Apache Hive delivers good value for the price?

Yes

Are you happy with Apache Hive's feature set?

Yes

Did Apache Hive live up to sales and marketing promises?

Yes

Did implementation of Apache Hive go as expected?

Yes

Would you buy Apache Hive again?

Yes

Azure Synapse Analytics (Azure SQL Data Warehouse), Azure Data Factory
Well suited for: For accessing the structured data and tables using SQL-like syntax. A hive is a good option for creating tables in different layers of Data Lake. Not well suited for Transactional databases.