SQL on Big Data: Technology, Architecture, and Innovation by Sumit Pal

By Sumit Pal

Learn numerous advertisement and open resource items that practice SQL on mammoth facts structures. you are going to comprehend the architectures of some of the SQL engines getting used and the way the instruments paintings internally by way of execution, facts stream, latency, scalability, functionality, and procedure requirements.

This ebook consolidates in a single position recommendations to the demanding situations linked to the necessities of velocity, scalability, and the range of operations wanted for facts integration and SQL operations. After discussing the background of the how and why of SQL on massive information, the e-book offers in-depth perception into the goods, architectures, and thoughts occurring during this quickly evolving space.

SQL on tremendous Data discusses intimately the techniques occurring, the features at the horizon, and the way they remedy the problems of functionality and scalability and the power to address diverse facts varieties. The booklet covers how SQL on tremendous facts engines are permeating the OLTP, OLAP, and Operational analytics house and the swiftly evolving HTAP systems.

You will research the main points of:

    Batch Architectures—an realizing of the internals and the way the present Hive engine is equipped and the way it truly is evolving consistently to help new positive aspects and supply reduce latency on queries

  • Interactive Architectures—an figuring out of the way SQL engines are architected to help low latency on huge info sets
  • Streaming Architectures—an realizing of the way SQL engines are architected to help queries on info in movement utilizing in-memory and lock-free information structures

  • Operational Architectures—an figuring out of ways SQL engines are architected for transactional and operational structures to help transactions on immense info platforms
  • Innovative Architectures—an exploration of the quickly evolving more recent SQL engines on great facts with cutting edge rules and concepts

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It supports enhanced aggregation and analytic functions, such as Cube, Grouping Sets, and Rollup. • Hive offers user-defined functions (UDFs), which can be written in Python or Java. • It supports out-of-the-box UDFs to work with XML and JSON data, such as xpath, explode, LATERAL VIEW, json_tuple, get_ json_object, etc. • Hive has out-of-the-box support for Text processing with UDFs. Hive Architecture Deep Dive Hive uses a relational store to store the metadata for all the database objects it manages.

2. Leverage existing relational engines, which incorporate all the 40-plus years of research and development in making them robust, with all the storage engine and query optimizations. An example would be to embed MySQL/Postgres inside each of the data nodes in the Hadoop cluster and build a layer within them to access data from the underlying distributed file system. This RDBMS engine is collocated with the data node, communicates with the data node to read data from the HDFS, and translates it to their own proprietary data format.

Avro format stores metadata with the data and also allows for specifying an independent schema for reading the file. Avro is the epitome of schema evolution support, because one can rename, add, delete, and change the data types of fields by defining new independent schema. Avro files are also splittable and support block compression. Sequence Files Sequence files store data in a binary format with a structure similar to CSV. Sequence files do not store metadata with the data, so the only schema evolution option is to append new fields.

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