Apache Hive VS beam

Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.

Hive, A data warehouse infrastructure that provides data summarization and ad hoc querying.

Apache Drill, Druid, Flink, Hive, Kafka, Spark

In this article, I'm showing you how to create a Spring Boot app that loads data from Apache Hive via Apache Spark to the Aerospike Database. More than that, I'm giving you a recipe for writing integration tests for such scenarios that can be run either locally or during the CI pipeline execution. The code examples are taken from this repository.

ListItem(name='Apache Hive', website='https://hive.apache.org/', category='Interactive Query', short_description='Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.'),

Apache Hive takes in a specific SQL dialect and converts it to map-reduce.

Apache Hive

Apache Calcite is a popular parser/optimizer that is used in popular databases and query engines like Apache Hive, BlazingSQL and many others.

The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.).

As for the framework called MapReduce, it isn't used much, but its descendant https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.

Apache Beam is one of many tools that you can use

Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.

To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow

Apache Beam: Batch/streaming data processing 🔗Link

What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all.

That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.