Does not need Hive metastore to query data on HDFS. 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. 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. Speed: Presto is faster due to its optimized query engine and is best suited for interactive analysis. Other major Presto users include Netflix (using Presto for analyzing more than 10 PB data stored in AWS S3), AirBnb and Dropbox. They needed 4 ClickHouse servers (than scaled to 9), and estimated that similar Druid deployment would need “hundreds of nodes”. Apache Arrow with Apache Spark. 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 an in-memory data structure specification for use by engineers building data systems. Apache Spark is a storage agnostic cluster computing framework. 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. It was mainly targeted for Data Science workloads to use a … CloudFlare: ClickHouse vs. Druid. Disaggregated Coordinator (a.k.a. Throttling functionality may limit the concurrent queries. In this post, I will share the difference in design goals. One example that illustrates the problem described above is Marek VavruÅ¡a’s post about Cloudflare’s choice between ClickHouse and Druid. This post is focused on the performance of Presto, more specifically on the performance comparison between Amazon’s S3 object storage service and MinIO’s object storage software. Presto-on-Spark Runs Presto code as a library within Spark executor. RaptorX – Disaggregates the storage from compute for low latency to provide a unified, cheap, fast, and scalable solution to OLAP and interactive use cases. Apache Arrow is a proposed in-memory data layer designed to back different analytical loads. It uses Apache Arrow for In-memory computations. Hive, in comparison is slower. Presto allows for data queries that traverse data stores and locations - a big plus in the multi-everything world of big data analytics. The actual implementation of Presto versus Drill for your use case is really an exercise left to you. 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