Apache Hive vs. VictoriaMetrics Community

Software work execution is on a large scale, it is good to use for new projects or organizational changes, data lineage mapping has always been dubious but this one has had good results. You can store and synchronize data from different departments, the storage process can be manual but it is best automated.

Incentivized

Best suited, where your data is highly cardinal since it does a better job at maintaining it than other competitors. It is also well suited if you are using Prometheus and are looking for something that is less hungry for resources in comparison since the migration would be easier. But in case the company is small and wants a solution which is cheap and relies on built-in visualizations, it is not something that is suited. Although it takes fewer resources than Prometheus, it is still resource-intensive and attracts a high cost for maintenance.

Incentivized

• Apache Hive allows use to write expressive solutions to complex problems thanks to its SQL-like syntax.
• Relatively easy to set up and start using.
• Very little ramp-up to start using the actual product, documentation is very thorough, there is an active community, and the code base is constantly being improved.

Incentivized

• Easy to configure and manage
• Ready to be configured at cloud and microservices
• Easy to scale vertically and horizontally

Incentivized

• Some queries, particularly complex joins, are still quite slow and can take hours
• Previous jobs and queries are not stored sometimes
• Switching to Impala can sometimes be time-consuming (i.e. the system hangs, or is slow to respond).
• Sometimes, directories and tables don't load properly which causes confusion

Incentivized

• Community and Eco system -> Although it supports the ecosystem of Prometheus, it is still troublesome for someone who's not using Prometheus
• Configuration is not so easy, especially if you are a beginner, you will struggle a bit. Even the documentation is not so welcoming for beginners
• Built-in Visualisation is bare minimal, leading almost everyone to use some other extension/tool

Incentivized

Since I do not know the second data warehouse solution that integrate with HDFS as well as Hive.

Hive is a very good big data analysis and ad-hoc query platform, which supports scaling also. The BI processes can be easily integrated with Hadoop via the Hive. It can deal with a much larger data set that traditional RDBMS can not. It is a "must-have" component of the big data domain.

Incentivized

Apache Hive is a FOSS project and its open source. We need not definitely comment on anything about the support of open source and its developer community. But, it has got tremendous developer support, awesome documentation. I would justify the fact that much support can be gathered from the community backup.

Incentivized

Besides Hive, I have used Google BigQuery, which is costly but have very high computation speed. Amazon Redshift is the another product, I used in my recent organisation. Both Redshift and BigQuery are managed solution whereas Hive needs to be managed

Incentivized

Prometheus only support PromQL and it is very complex with different exporter required for different requirement like Windowsexporter,linixexporter,sqlexporter etc but VictoriaMetrics is very simple comapred to it. VictoriaMetrics support both PromQL and MetricQL and can be integrated with Graphana easily. It is very easy to setup and learn compared to mutiple Prometheus exporters

Incentivized

• Apache hive is secured and scalable solution that helps in increasing the overall organization productivity.
• Apache hive can handle and process large amount of data in a sufficient time manner.
• It simplifies writing SQL queries, hence helping the organization as most companies use SQL for all query jobs.

Incentivized

• Lowered our infra costs in comparison with our older tools
• Need minimal effort for migration since it already supports Prometheus ecosystem
• Saved on Storage costs

Incentivized