Hive Context In Spark

In this case we want to use blank as null. The first is command line options such as --master and Zeppelin can pass these options to spark-submit by exporting SPARK_SUBMIT_OPTIONS in conf/zeppelin-env. For example, one the key difference is using HiveContext you can use the new window function feature. Hortonworks and the Spark community suggest using the HiveContext. The recommended entry point is the HiveContext to provide access to HiveQL and other Hive-dependent functionality. spark-shell --master yarn-client --driver-memory 512m. It provides a programming abstraction called DataFrame and can act as distributed SQL query engine. A few words about Apache Spark. We will continue on with our example from the previous Walk-Though and work with our term-extracted UFO Sightings dataset. Create Data in MySQL and using SQOOP move it to HDFS. For analysis/analytics, one issue has been a combination of complexity and speed. spark_dataframe() Retrieve a Spark DataFrame. xml, the context automatically creates metastore_db and warehouse in the current directory. spark » spark-hive Spark Project Hive. Talend Data Mapper Advanced – Spark. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large. xml , hdfs-site. Spark originally started out shipping with Shark and SharkServer (a portmanteau of Spark and Hive). Getting ready To enable Hive functionality, make sure that you have Hive enabled (-Phive) assembly JAR is available on all worker nodes; also, copy hive-site. The following code examples show how to use org. Using HiveContext, you can create and find tables in the HiveMetaStore and write queries on it using HiveQL. And the table does not exists in hive. spark_version() Get the Spark Version Associated with a Spark. 第一次尝试使用java写spark flink 有状态(stateful)的计算 maven项目打包说有依赖jar包到一个文件夹. Xiny, Cheng Liany, Yin Huaiy, Davies Liuy, Joseph K. Speed : 100x faster in memory; 10x faster on disk 2. 1, and Spark SQL can be connected to different versions of Hive Metastore (from 0. Saving DataFrames. sh, Zeppelin uses spark-submit as spark interpreter runner. If we are using earlier Spark versions, we have to use HiveContext which is. The new DataFrame API was created with this goal in mind. Hive built-in functions that get translated as they are and can be evaluated by Spark. spark-submit supports two ways to load configurations. Hive doesn't support transactions. Out of that context, Spark creates a structure called an RDD, or Resilient Distributed Dataset, which represents an immutable collection of elements that can be operated on in parallel. Spark SQL - It is used to load the JSON data, process and store into the hive. You can vote up the examples you like and your votes will be used in our system to product more good examples. _ scala> val hc = new HiveContext(sc) Though most of the code examples you see use SqlContext, you should always use HiveContext. In general, intake-spark will make use of any context/session that already exists. 4 and Hive 1. Supports different data formats (Avro, csv, elastic search, and Cassandra) and storage systems (HDFS, HIVE tables, mysql, etc). Expertise in using J2EE application servers such as IBM Web Sphere, JBoss and web servers like Apache Tomcat. However, since Hive has a large number of dependencies, it is not included in the default Spark assembly. So far we have seen running Spark SQL queries on RDDs. Quick query in the Big Data is important for mining the valuable information to improve the system performance. spark_jobj() Retrieve a Spark JVM Object Reference. What is Spring ? Spring is an open source framework created to address the complexity of enterprise application development. SQL Hive Context: the Hive query language supported by Spark. engine=spark; Hive on Spark was added in HIVE-7292. There are 72 nodes that can be used as predessesor for a node with an input port of type Spark Context. Apache Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. 3 and for metastore and catalog API’s in later versions. Hive queries, especially those involving multiple reducer stages, will run faster, thus improving user experience as Tez does. The following are code examples for showing how to use pyspark. On all of the worker nodes, the following must be installed on the classpath:. Root data from csv online and move to Receptacle using hive moment. Can be easily integrated with all Big Data tools and frameworks via Spark-Core. Invalidate and refresh all the cached the metadata of the given table. When not configured by the Hive-site. In SparkContextInitializer. The most important difference to other technologies such as Hive is that Spark has a rich ecosystem on top of it, such as Spark Streaming (for real-time data), Spark SQL (a SQL interface to write SQL queries and functions), MLLib (a library to run MapReduce versions ofmachine learning algorithms on a dataset in Spark), and GraphX (analyzing. To create a basic SQL Context, val sc = SparkCommon. Spark SQL is to execute SQL queries written using either a basic SQL syntax or HiveQL. You can vote up the examples you like and your votes will be used in our system to product more good examples. We would like to add a custom authorization plugin to our remote Hive Metastore to authorize the query requests that the spark application is submitting which would also add. The first type of table is an internal table and is fully managed by Hive. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. Analyzed large amounts of data sets to determine the optimal way to aggregate and report on it. With an SQLContext, you can create a DataFrame from an RDD, a Hive table, or a data source. Introduction. 1, and Spark SQL can be connected to different versions of Hive Metastore (from 0. 8 brings a new way to directly submit Spark jobs from a Web UI. sparkConf is required to create the spark. If we are using earlier Spark versions, we have to use HiveContext which is. Invalidate and refresh all the cached the metadata of the given table. Users can interact with Spark utilizing some well-known languages, mainly Java, Scala, and Python. For an example tutorial of setting up an EMR cluster with Spark and analyzing a sample data set, see New — Apache Spark on Amazon EMR on the AWS News blog. It processes the data in the size of Kilobytes to Petabytes on a single-node cluster to multi-node clusters. In fact, you can consider an application a Spark application only when it uses a SparkContext (directly or indirectly). Spark SQL: I have written a spark application using hive context to connect to the hive and fetch the data, and then used SQL on top of these datasets to calculate the result and store it in HDFS. HDInsight supports the latest open source projects from the Apache Hadoop and Spark ecosystems. Any files copied from other locations into this directory is detected. For information on configuring Hive on Spark for performance, see Tuning Hive on Spark on page 59. SharkServer was Hive, it parsed HiveQL, it did optimizations in Hive, it read Hadoop Input Formats, and at the end of the day it actually ran Hadoop style Map/Reduce jobs on top of the Spark. SparkContext is the entry gate of Apache Spark functionality. Simple Apache Spark PID masking with DataFrame, SQLContext, regexp_replace, Hive, and Oracle. SparkContext is the entry gate of Apache Spark functionality. val sqlContext = new org. This API is inspired by data frames in R and Python (Pandas), but designed from the ground up to support. What is Spring ? Spring is an open source framework created to address the complexity of enterprise application development. table("default. Over time, Apache Spark will continue to develop its own ecosystem, becoming even more versatile than before. N-grams are generally used to find the occurrence of certain words in a sequence, which helps in the calculation of sentiment analysis. With Spark using Hive context, Spark does both the optimization (using Catalyst) and query engine (Spark). Spark sql tutorial how to access hive tables using spark sql tutorial how to access hive tables using spark sql tutorial understanding with examples edureka spark sql tutorial how to access hive tables using. inputtable': 'TAB'} This LKM uses StreamingContext. HiveContext is a superset of SqlContext, so it can do what SQLContext can do and much more. If a table with the same name already exists in the database, an exception is thrown. xml, the context automatically creates metastore_db in the current directory and creates a directory configured by spark. This article explains what is the difference between Spark HiveContext and SQLContext. The host from which the Spark application is submitted or on which spark-shell or pyspark runs must have a Hive gateway role defined in Cloudera Manager and client configurations deployed. xml , hdfs-site. Hive adds extensions to provide better performance in the context of Hadoop and to integrate with custom extensions and even external programs. Writing a Spark DataFrame to ORC files Created Mon, Dec 12, 2016 Last modified Mon, Dec 12, 2016 Spark Hadoop Spark includes the ability to write multiple different file formats to HDFS. Unable to create hive context with spark2 jdbc channel Question by Naresh Meena Nov 30, 2017 at 08:03 AM Hive spark2 jdbc hive-jdbc spark2-thrift-server Comment. You can use a small built-in sample dataset to complete the walkthrough, and then step through tasks again using a. You must build Spark with Hive. 0 - via Spark. If you need to read or write data to Hive metastore, use tHiveInput or tHiveOutput instead and in this situation, you need to design your Job differently. test2") org. HiveContext(). Users who do not have an existing Hive deployment can still create a HiveContext. Spark SQL is to execute SQL queries written using either a basic SQL syntax or HiveQL. It is best to create contexts/sessions ahead of time, in the standard way of creating them for your system. HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase. As RDD was main API, it was created and manipulated using context API's. The most important step of any Spark driver application is to generate SparkContext. xml for Spark. Hi, I am using HDP2. HDInsight supports the latest open source projects from the Apache Hadoop and Spark ecosystems. Hi, I'm using Cloudera CDH 5. It provides new interoperability features between Hive and Spark. For integration with Hive, you can readily open a hive context, then execute Spark SQL against the data enabling full compatibility with existing Hive data, and external applications can connect to Spark via APIs and. When not configured. If you do sqlCtx. 包括Spark Sql ,hive on tez ,hive on spark. Book Your Seat Today! Kindly advise me your company detail and our consultant will contact you soonest!. You create a SQLContext from a SparkContext. It can also be used to read data from an existing Hive installation. For performance reasons, Spark SQL or the external data source library it uses might cache certain metadata about a table, such as the location of blocks. xml and hive-site. You can vote up the examples you like and your votes will be used in our system to product more good examples. xml, the context automatically creates metastore_db and warehouse in the current directory. 0 is the next major release of Apache Spark. With Spark using Hive context, Spark does both the optimization (using Catalyst) and query engine (Spark). To correct this, we need to tell spark to use hive for metadata. HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase. Example 9-2 Scala SQL import //Spark SQL import import org. Spark streaming app will parse the data as flume events separating the headers from the tweets in json format. Version Compatibility. However, since Hive has a large number of dependencies, it is not included in the default Spark assembly. Hive-on-Spark parallel ORDER BY This is the one of the greatest feature, as if we need to sort the records then we have to manually set / force the reducer count to 1 to have it in single file. HiveContext(sc) sqlContext. Speed : 100x faster in memory; 10x faster on disk 2. This article provides a step-by-step introduction to using the RevoScaleR functions in Apache Spark running on a Hadoop cluster. Hue now have a new Spark Notebook application. spark sql hive spark-sql spark dataframes sqlcontext spark streaming json thrift-server sparksql spark java orc hive udf ide sql snappy window functions dynamic window dataframe hive metastore nested table create lead rdd. Hello, I am using Apache Spark as a service in Bluemix and I've been using it in version 1. xml for Spark. Simply click on the Click here to open link and the Spark WebUI is opened in the internal web browser. x, we needed to use HiveContext for accessing HiveQL and the hive metastore. xml and hive-site. Finally, allowing Hive to run on Spark also has performance benefits. mode ("overwrite"). The following are code examples for showing how to use pyspark. 0 to make it easy for the developers so we don’t have worry about different contexts and to streamline the access to different contexts. create spark streaming context,. We will also look at Hive Context and see how its different from SQL Context. Starting from Spark 1. Spark SQL is to execute SQL queries written using either a basic SQL syntax or HiveQL. spark_dataframe() Retrieve a Spark DataFrame. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large. For further information on Spark SQL, see the Spark SQL, DataFrames, and Datasets Guide. Spark + Hive + StreamSets: a hands-on example Configure Spark and Hive. Out of that context, Spark creates a structure called an RDD, or Resilient Distributed Dataset, which represents an immutable collection of elements that can be operated on in parallel. Talend Big Data Advanced – Spark. The spark driver program uses spark context to connect to the cluster through a resource manager (YARN orMesos. For further information on Delta Lake, see Delta Lake. Third one is a weird way, where you can still run spark locally and test the storage part on remote, for instance you have spark installed on your machine but do not have Hive. Topics include: Hadoop, YARN, HDFS, MapReduce, data ingestion, workflow definition, using Pig and Hive to perform data analytics on Big Data and an introduction to Spark Core and Spark SQL. Introduction. HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase. Can be easily integrated with all Big Data tools and frameworks via Spark-Core. Hadoop Spark Hive Big Data Admin Class Bootcamp Course NYC 3. You can use org. Please read my blog post about joining data from CSV And MySQL table to understand JDBC connectivity with Spark SQL Module. Needing to read and write JSON data is a common big data task. 0 to make it easy for the developers so we don’t have worry about different contexts and to streamline the access to different contexts. test2") org. 12/19/2017; 29 minutes to read; In this article. This section will focus on Apache Spark to see how we can achieve the same results using the fast in-memory processing while also looking at the tradeoffs. This tutorial also demonstrates an use case on. 3 and Knime 4. We will work through an example showing how to use Hive datasource in this blog (we will cover Parquet in a future blog). xml then the context automatically creates metastore_db in the current directory and creates warehouse directory indicated by HiveConf (which defaults user/hive/warehouse). Create a table using a data source. Performing context Ngram in Hive Ngrams are sequences that are collected from specific sets of words and are based on their occurrence in a given text. 0 is the next major release of Apache Spark. Within these languages users create an object called a Spark Context, which lets YARN know to allocate resources on the Hadoop cluster for Spark. Sometimes you need to create denormalized data from normalized data, for instance if you have data that looks like; CREATE TABLE flat ( propertyId string, propertyName String, roomname1 string, roomsize1 string, roomname2 string, roomsize2 int,. Extensive experience in installing, configuring and using ecosystem components like Hadoop MapReduce, HDFS, Sqoop, Pig, Hive, Impala & Spark. Spark streaming will read the polling stream from the custom sink created by flume. enable-hive-context is set to "true"), livy will further check if hive classes are present or not. 0: Tags: spark apache: Used By: 240 artifacts: Central (72) Typesafe (6). We will work through an example showing how to use Hive datasource in this blog (we will cover Parquet in a future blog). Demonstrate how to use Hive context and invoke built-in ESRI UDFs for Hive from Spark SQL. xml on the classpath. py", line 267, in. 280 having Spark 1. Spark streaming will read the polling stream from the custom sink created by flume. Streaming data to Hive using Spark Published on December 3, 2017 December 3, 2017 by oerm85 Real time processing of the data into the Data Store is probably one of the most spread category of scenarios which big data engineers can meet while building their solutions. To allow the spark-thrift server to discover Hive tables, you need to configure Spark to use Hive's hive-site. Role of Driver in Spark Architecture. But you can also run Hive queries using Spark SQL. It can also be used to read data from an existing Hive installation. Visualizations are not limited to SparkSQL query, any output from any language backend can be recognized and visualized. xml then the context automatically creates metastore_db in the current directory and creates warehouse directory indicated by HiveConf (which defaults user/hive. A common scenario is to use ETL to populate hive tables with the incoming data. Clone via HTTPS Clone with Git or checkout with SVN using the repository's web address. For every other API,we needed to use different contexts. collect() Enjoy!. Creates a fully functional local big data environment including Apache Hive, Apache Spark and HDFS. xml file into spark/conf directory. Some basic charts are already included in Apache Zeppelin. 0 release, Spark compute context now supports Hive and Parquet data sources so you can directly work with them. This blog post illustrates an industry scenario there a collaborative involvement of Spark SQL with HDFS, Hive, and other components of the Hadoop ecosystem. We are going to install Spark 1. The driver program runs the main function of the application and is the place where the Spark Context is created. For streaming, we needed StreamingContext, for SQL sqlContext and for hive HiveContext. On spark shell use data available on meta store as source and perform step 3,4,5 and 6. This Running Queries Using Apache Spark SQL tutorial provides in-depth knowledge about spark sql, spark query, dataframe, json data, parquet files, hive queries Running SQL Queries Using Spark SQL lesson provides you with in-depth tutorial online as a part of Apache Spark & Scala course. In addition, Spark can run over a variety of cluster managers, including Hadoop YARN, Apache Mesos, and a simple cluster manager included in Spark. 280 having Spark 1. For further information on Spark SQL, see the Spark SQL, DataFrames, and Datasets Guide. One of the branches of Spark SQL is Spark on Hive, which uses logic such as HQL's HQL parsing, logical execution plan translation, and execution plan optimization, and approximates that the physical execution plan only replaces the MR. CREATE EXTERNAL TABLE newsummary(key String, sum_billamount_perday double,count_billamount_perday int, sum_txnamount_perday double, count_txnamount_perday int,) STORED BY 'org. A data engineer gives a quick tutorial on how to use Apache Spark and Apache Hive to ingest data and represent it in in Hive tables using ETL processes. parallelize() method. It is best to create contexts/sessions ahead of time, in the standard way of creating them for your system. 8 brings a new way to directly submit Spark jobs from a Web UI. Additional features include the ability to write queries using the more complete HiveQL parser, access to Hive UDFs, and the. Visualizations are not limited to SparkSQL query, any output from any language backend can be recognized and visualized. Create Example DataFrame. When not configured by the hive-site. spark_context_config() Runtime configuration interface for the Spark Context. Sometimes you need to create denormalized data from normalized data, for instance if you have data that looks like; CREATE TABLE flat ( propertyId string, propertyName String, roomname1 string, roomsize1 string, roomname2 string, roomsize2 int,. Using HiveContext to read Hive Tables I just tried to use Spark HiveContext to use the tables in HiveMetastore. It can also be used to read data from an existing Hive installation. If we are using earlier Spark versions, we have to use HiveContext which is. test2") org. A Spark “driver” is an application that creates a SparkContext for executing one or more jobs in the Spark cluster. 0: Tags: spark apache: Used By: 240 artifacts: Central (72) Typesafe (6). Users who do not have an existing Hive deployment can still create a HiveContext. In this tutorial, I am using stand alone Spark. To allow the spark-thrift server to discover Hive tables, you need to configure Spark to use Hive’s hive-site. PolyBase vs. Complete stack trace as below. SparkSession context will automatically create metastore_db in the current directory of a Spark application and a directory configured by spark. Accessing Hive from Spark Script Spark Script allows you to extend your processes with custom scripts. HDInsight supports the latest open source projects from the Apache Hadoop and Spark ecosystems. In many programming-language interpreters (e. _ You can see the same in the following screen shot. A Spark “driver” is an application that creates a SparkContext for executing one or more jobs in the Spark cluster. Whats people lookup in this blog: Create Hive Table Using Spark Sql Context; Complete The Code To Create A Hive Table Using Spark Sqlcontext. Spark SQL main purpose is to enable users to use SQL on Spark, the data source can either RDD, or external data sources (such as Parquet, Hive, Json, etc. We need write a custom java class to define user defined function which extends org. ImportantNotice ©2010-2019Cloudera,Inc. Version Compatibility. format ” which can be used to treat a character of your choice as null in Hive SQL. Spark SQL CSV examples in Scala tutorial. SparkContext is the entry gate of Apache Spark functionality. Since upgrading, we can no longer query our large webrequest dataset using HiveContext. Introduction This post is to help people to install and run Apache Spark in a computer with window 10 (it may also help for prior versions of Windows or even Linux and Mac OS systems), and want to try out and learn how to interact with the engine without spend too many resources. using HiveQL. This gives you more flexibility in configuring the thrift server and using different properties than defined in the spark-defaults. • Installed Oozie workflow engine to run multiple HBase, Hive, Pig jobs and Spark jobs. xml into the conf directory of the Spark installation. Learn self placed job oriented professional courses. Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. Spark failed to delete temp directory created by HiveContext , ,. There are 72 nodes that can be used as predessesor for a node with an input port of type Spark Context. I am not using spark2, but the v1. 2 and Hive 1. We will also look at Hive Context and see how its different from SQL Context. This blog post illustrates an industry scenario there a collaborative involvement of Spark SQL with HDFS, Hive, and other components of the Hadoop ecosystem. The Spark WebUI of the created local Spark context is available via the Spark context outport view. They are extracted from open source Python projects. textFileStream() method to transfer file context as data stream. spark-submit supports two ways to load configurations. How to load some Avro data into Spark First, why use Avro? The most basic format would be CSV, which is non-expressive, and doesn't have a schema associated with the data. dir, which defaults to the directory spark-warehouse in the current directory that the Spark application is started. The first is command line options such as --master and Zeppelin can pass these options to spark-submit by exporting SPARK_SUBMIT_OPTIONS in conf/zeppelin-env. You can even join data from different data sources. For analysis/analytics, one issue has been a combination of complexity and speed. The sparklyr package is an R interface to Apache Spark. By having access to SparkSession, we automatically have access to the SparkContext. Run the hive query in pure Hive via Hive CLI on the. Users who do not have an existing Hive deployment can still create a HiveContext. It processes the data in the size of Kilobytes to Petabytes on a single-node cluster to multi-node clusters. Hi ,I have created a external table on top of my hbase table in hive. 0 and later. For further information on Delta Lake, see Delta Lake. Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. spark_version() Get the Spark Version Associated with a Spark. If you're using a version of Spark that has Hive support, you can also create aHiveContext, which provides additional features, including: •the ability to write queries using the more complete HiveQL parser •access to Hive user-defined functions. Last year we released Spark Igniter to enable developers to submit Spark jobs through a Web Interface. 1 LKM File to Spark. Hive offers no support for row-level inserts, updates, and deletes. Hive supports two types of tables. Spark: Big Data processing framework • Spark is fast, general-purpose data engine that supports cyclic data flow and in-memory computing. Using Spark SQLContext, HiveContext & Spark Dataframes API with ElasticSearch, MongoDB & Cassandra. spark_version() Get the Spark Version Associated with a Spark. Thankfully this is very easy to do in Spark using Spark SQL DataFrames. In this final part of the Walk-Though we explore Spark DataFrames and the SQL Context. Data Frames and Spark SQL - Leverage SQL skills on top of Data Frames created from Hive tables or RDD. We cannot pass the Hive table name directly to Hive context sql method since it doesn't understand the Hive table name. sparkConf is required to create the spark. HiveContext(sc) sqlContext. It has now been replaced by Spark SQL to provide better integration with the Spark engine and language APIs. Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. Below is the sample code 1. The most important step of any Spark driver application is to generate SparkContext. Spark has native scheduler integration with Kubernetes. You can also define "spark_options" in pytest. Users can interact with Spark utilizing some well-known languages, mainly Java, Scala, and Python. Available upon request. Spark SQL is to execute SQL queries written using either a basic SQL syntax or HiveQL. Hive supports two types of tables. 2 with Spark 1. hive_context. 0 and Hive 1. 0 and Hive 0. Hive can store tables in a variety and different range of formats, from plain text to column-oriented formats, inside HDFS or also contains other storage systems. Example 9-2 Scala SQL import //Spark SQL import import org. Once SPARK_HOME is set in conf/zeppelin-env. SparkSQL is a Spark component that supports querying data either via SQL or via the Hive Query Language. retainedStages 500 Hang up or suspend Sometimes we will see the web node in the web ui disappear or in the dead state, the task of running the node will report a variety of lost worker errors, causing the same reasons and the above, worker memory to save a lot of ui The information leads to. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. spark » spark-hive Spark Project Hive. The sparklyr package is an R interface to Apache Spark. So, you don't have to run statements like the following to set the contexts:. Please note that there are four important requirements additionally to the hands-on work:. Simply click on the Click here to open link and the Spark WebUI is opened in the internal web browser. Hello, I am using Apache Spark as a service in Bluemix and I've been using it in version 1. Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. 5,142 Hadoop Hive jobs available on Optimize performance of the built Spark applications in Hadoop using Spark Context, Sqoop, Oozie , Hive, Spark and other. They are extracted from open source Python projects. Hive, Impala and Spark SQL all fit into the SQL-on-Hadoop category. Speed : 100x faster in memory; 10x faster on disk 2. Please note that there are four important requirements additionally to the hands-on work:. It has attracted developers from across the ecosystem, including from organizations such as Intel, MapR, IBM, and Cloudera, and gained critical help from the Spark community. val sqlContext = new org. You can even join data from different data sources. Complete stack trace as below. Hive supports two types of tables. Book Your Seat Today! Kindly advise me your company detail and our consultant will contact you soonest!. It allows your Spark Application to access Spark Cluster with the help of Resource Manager. spark » spark-hive Spark Project Hive. Some basic charts are already included in Apache Zeppelin. With the introduction of Spark SQL and the new Hive on Apache Spark effort (HIVE-7292), we get asked a lot about our position in these two projects and how they relate to Shark. Accessing Hive from Spark Script Spark Script allows you to extend your processes with custom scripts. HDInsight supports the latest open source projects from the Apache Hadoop and Spark ecosystems. Spark: Big Data processing framework • Spark is fast, general-purpose data engine that supports cyclic data flow and in-memory computing.