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