Yahoo Canada Web Search

Search results

  1. Spark SQL supports operating on a variety of data sources through the DataFrame interface. A DataFrame can be operated on using relational transformations and can also be used to create a temporary view. Registering a DataFrame as a temporary view allows you to run SQL queries over its data.

  2. Seamlessly mix SQL queries with Spark programs. Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, Python and R.

  3. May 11, 2017 · Yes! SparkSql(Spark) is the best fit for your usecase. As per my knowledge, SparkSql supports RDBMS, Hive, and any NoSQL data store. SparkSQL may not have APIs to directly access few stores but with a little help from Spark's API, you should be able to connect any data store.

  4. Apr 3, 2024 · Alongside standard SQL support, Spark SQL provides a standard interface for reading from and writing to other datastores including JSON, HDFS, Apache Hive, JDBC, Apache ORC, and...

    • Ian Pointer
  5. Datasets and DataFrames. A Dataset is a distributed collection of data. Dataset is a new interface added in Spark 1.6 that provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine.

  6. Spark SQL brings native support for SQL to Spark and streamlines the process of querying data stored both in RDDs (Spark’s distributed datasets) and in external sources. Spark SQL conveniently blurs the lines between RDDs and relational tables.

  7. People also ask

  8. Jan 9, 2015 · Early users loved Spark SQL’s support for reading data from existing Apache Hive tables as well as from the popular Parquet columnar format. We’ve since added support for other formats, such as JSON. In Apache Spark 1.2, we've taken the next step to allow Spark to integrate natively with a far larger number of input sources.

  1. People also search for