One example that illustrates the problem described above is Marek VavruÅ¡aâs post about Cloudflareâs choice between ClickHouse and Druid. CloudFlare: ClickHouse vs. Druid. Comparison with Hive. Hive, in comparison is slower. Apache Arrow is an open source technology Dremio helped create that also uses columnar data compression and many other optimizations that take advantage of in-memory computing and GPUs. It was mainly targeted for Data Science workloads to use a ⦠Apache Spark is a storage agnostic cluster computing framework. Apache Arrow is integrated with Spark since version 2.3, exists good presentations about optimizing times avoiding serialization & deserialization process and integrating with other libraries like a presentation about accelerating Tensorflow Apache Arrow on Spark from Holden Karau. It doesnât require schema definition which could lead to ⦠RaptorX â Disaggregates the storage from compute for low latency to provide a unified, cheap, fast, and scalable solution to OLAP and interactive use cases. is it possible to query in memory arrow table using presto or is there some way to use a pandas data frame as a data source for presto query engine Ask Question Asked 2 years, 9 months ago Throttling functionality may limit the concurrent queries. The actual implementation of Presto versus Drill for your use case is really an exercise left to you. Other major Presto users include Netflix (using Presto for analyzing more than 10 PB data stored in AWS S3), AirBnb and Dropbox. Does not need Hive metastore to query data on HDFS. It shares same features with Presto which makes it a good competitor. The original reader conducts analysis in three steps: (1) reads all Parquet data row by row using the open source Parquet library; (2) transforms row-based Parquet records into columnar Presto blocks in-memory for all nested columns; and (3) evaluates the predicate (base.city_id=12) on these blocks, executing the queries in our Presto engine. In this post, I will share the difference in design goals. These two don't belong to the same category and don't compete with each other same as Arrow doesn't compete with Hadoop. Apache Arrow is a proposed in-memory data layer designed to back different analytical loads. It uses Apache Arrow for In-memory computations. This post is focused on the performance of Presto, more specifically on the performance comparison between Amazonâs S3 object storage service and MinIOâs object storage software. Presto-on-Spark Runs Presto code as a library within Spark executor. 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