Tag Archives: apache hive

Cloudera at ACM SIGMOD/PODS 2019

Categories: Events Hive

Sigmod conf 2019

The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. This year ACM SIGMOD/PODS will be held in Amsterdam, The Netherlands on June 30th – July 5th, 2019, and Cloudera will be present in the conference, contributing to and learning from the broader research community.

Last year,

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Partition Management in Hadoop

Categories: Hadoop Hive

Guest blog post written by Adir Mashiach

In this post I’ll talk about the problem of Hive tables with a lot of small partitions and files and describe my solution in details.

partition management in hadoop

A little background

In my organization,  we keep a lot of our data in HDFS. Most of it is the raw data but a significant amount is the final product of many data enrichment processes.

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What’s new in the Hue Data Warehouse Editor in Cloudera 6.2

Categories: Analytic Database Hue

Self-service exploratory analytics is one of the most common use cases we see by our customers running on Cloudera’s Data Warehouse solution.

With the recent release of Cloudera 6.2, we continue to improve the end user query experience with Hue, focusing on easier SQL query troubleshooting and increased compatibility with Hive. Read on to learn more and try it out in one-click at demo.gethue.com.

Easier SelfService Query Troubleshooting

Hue has great assistance for finding tables in the Data Catalog and getting recommendations on how to write (better) queries with the smart autocomplete,

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Faster Swarms of Data : Accelerating Hive Queries with Parquet Vectorization

Categories: CDH Hive Parquet Performance

Background

Apache Hive is a widely adopted data warehouse engine that runs on Apache Hadoop. Features that improve Hive performance can significantly improve the overall utilization of resources on the cluster. Hive processes data using a chain of operators within the Hive execution engine. These operators are scheduled in the various tasks (for example, MapTask, ReduceTask, or SparkTask) of the query execution plan. Traditionally, these operators are designed to process one row at a time.

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Faster Performance for Selective Queries

Categories: CDH Impala

One of the principal features used in analytic databases is table partitioning. This feature is so frequently used because of its ability to significantly reduce query latency by allowing the execution engine to skip reading data that is not necessary for the query. For example, consider a table of events partitioned on the event time using calendar day granularity. If the table contained 2 years of events and a user wanted to find the events for a given 7-day window,

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