Spark Sql Create Table

types import * Infer Schema. A managed table is a Spark SQL table for which Spark manages both the data and the metadata. We will continue to use the baby names CSV source file as used in the previous What is Spark tutorial. The LIKE form of CREATE TABLE allows you to copy an existing table definition exactly (without copying its data). Hive comes bundled with the Spark library as HiveContext, which inherits from SQLContext. 0) on our spark Dataframe. 0 or later, you can configure Spark SQL to use the AWS Glue Data Catalog as its metastore. This series targets such problems. Create a new view over events_partitioned using standard SQL D. You can modify the case of the SQL keywords and identifiers to upper case, lower case or keep them as-is. createorReplaceTempView is used when you want to store the table for a particular spark session. In the second part, you'll create a temporary table of fifa_df DataFrame and run SQL queries to extract the 'Age' column of players from Germany. saveAsTable operator) SparkSqlAstBuilder is requested to visitCreateTable (for CREATE TABLE SQL command) or visitCreateHiveTable (for CREATE EXTERNAL TABLE SQL command) CatalogImpl is requested to create a table (for Catalog. import connector classes: Scala > import com. It has the capability to load data from multiple structured sources like “text files”, JSON files, Parquet files, among others. We have already discussed in the above section that DataFrame has additional information about datatypes and names of columns associated with it. For example, most SQL environments provide an UPPER function returning an uppercase version of the string provided as input. I hope you get the common idea about how to join 2 tables with examples. types import * Infer Schema. In this example, I have some data into a CSV file. Manipulating Data with dplyr Overview. To develop an ability to write scripts using T-SQL. Two concepts that are basic: Schema: In one DataFrame Spark is nothing more than an RDD composed of Rows which have a schema where we indicate the name and type of each column of the Rows. For instance: Here the simple mistake to make with this approach is to avoid the CREATE EXTERNAL TABLE step in Hive and simply make the table using the Dataframe API's write methods. When I run a ctas on the single setup, it behaves as expected. This problem is due to a change in the default behavior of Spark in version 2. Python Spark SQL Tutorial Code. Create table syntax for Teradata: create table. sql_command = """ CREATE TABLE employee ( staff_number INTEGER PRIMARY KEY, fname VARCHAR(20), lname VARCHAR(30), gender CHAR(1), joining DATE, birth_date DATE);""" Concerning the SQL syntax: You may have noticed that the AUTOINCREMENT field is missing in the SQL code within our Python program. This SQL tutorial explains how to use the SQL ALTER TABLE statement to add a column, modify a column, drop a column, rename a column or rename a table (with lots of clear, concise examples). Spark SQL data types; Spark SQL Metadata; Spark SQL functions and user-defined functions. In the current version, the credentials from Spark are not yet passed to the SQL engine automatically. Users who do not have an existing Hive deployment can still create a HiveContext. Use custom SQL to connect to a specific query rather than the entire data source. Kudu tables have their own syntax for CREATE TABLE, CREATE EXTERNAL TABLE, and CREATE TABLE AS SELECT. Spark SQL executes upto 100x times faster than Hadoop. Ignite provides its own implementation of this catalog, called IgniteExternalCatalog. >>> from pyspark. with data; In a similar way, how can we create a table in Spark SQL?. The spark-bigquery-connector takes advantage of the BigQuery Storage API when reading data from BigQuery. In the temporary view of dataframe, we can run the SQL query on the data. Source code for pyspark. The first query in Script #2 selects data from the Department table and uses a CROSS APPLY to evaluate the Employee table for each record of the Department table. The SQL Server Express versions are free to download, use and can even be redistributed with products. SQLContext(sc) Basic Query. This is part 2 of our series on event-based analytical processing. This enables a DBA to execute multiple SQL statements in one call to the server. KodeKloud 589,960 views. In this blog post, we introduce Spark SQL's JSON support, a feature we have been working on at Databricks to make it dramatically easier to query and create JSON data in Spark. In this blog post, I'm going to do a quick walk through on how easy it is to create tables, read them and then delete them. The new table contains no. But there are numerous small yet subtle challenges you may come across which could be a road blocker. As Apache Hive, Spark SQL also originated to run on top of Spark and is now integrated with the Spark stack. Analytics with Apache Spark Tutorial Part 2 : Spark SQL Using Spark SQL from Python and Java. We will call the withColumn() method along with org. I placed my sample CSV file on the C: drive and now we will create a table which we will import data from the CSV file. Creating Remote Sources and Virtual Tables in HANA to Hive and Vora can be accomplished using HANA Studio to create remote sources and virtual tables, but what about using DDL? There are 3 types of connections that can be created from HANA to Vora or Hive using a Remote Source. With Spark Streaming, as the name implies, you can create streaming application in a micro-batch fashion by defining a window. SQL Server SUBSTRING() function overview. KodeKloud 589,960 views. Creating And Inserting Data Into A Temporary Table In SQL Server May 17, 2018 September 23, 2018 Jack SQL Development, SQL The script outlined below will create a table called employee. The CREATE TABLE statement for an index-organized table can be parallelized either with or without an AS SELECT clause. The following examples show how to use org. SQL Queries. The Spark SQL is fast enough compared to Apache Hive. 100,000 X 100,000 = 10 Billion • Alternative to a full blown cartesian join: • Create an RDD of UID by UID. It can also be used to read data from an existing Hive installation. They won't be performance overhead du. Other than making column names or table names more readable, alias also helps in making developer life better by writing smaller table names in join conditions. 24 April 2015. In the previous article, we covered the basics of event-based analytical data processing with Azure Databricks. Step 1 - Create Azure Databricks workspace Microsoft Azure Databricks offers an intelligent, end-to-end solution for all your data and analytics challenges. Instead of forcing users to pick between a relational or a procedural API, Spark SQL tries to enable users to seamlessly intermix the two and perform data querying, retrieval and analysis at scale on Big Data. Pics of : Create Hive Table From Spark Sql. Next, create a table with a CREATE TABLE statement:. (A) hive> CREATE TABLE myflightinfo2007 AS > SELECT Year, Month, DepTime, ArrTime, …. Then I need to repeat that same write to SQL for col 4 but use hard coded (002). This allows us to process data from HDFS and SQL databases like Oracle, MySQL in a single Spark SQL query Apache Spark SQL includes jdbc datasource that can read from (and write to) SQL databases. If u wanna check more details, refer to case class HiveGenericUdtf. The examples can make it clear:. They would be easy to update 3. Las tablas son la estructura básica donde se almacena la información en la base de datos. The ETL pipeline will start with a. sql("select * from global_temp. There are so many ways using which user can fetch the records for multiple tables. Spark SQL is Apache Spark's module for working with structured data. In particular, sparklyr allows you to access the machine learning routines provided by the spark. Understanding Spark SQL & DataFrames. From Spark 2. Create views creates the sql view form of a table but if the table name already exists then it will throw an error, but create or replace temp views replaces the already existing view , so be careful when you are using the replace. The data files are stored in a newly created directory under the location defined by spark. In some cases we create tables from spark. sql_command = """ CREATE TABLE employee ( staff_number INTEGER PRIMARY KEY, fname VARCHAR(20), lname VARCHAR(30), gender CHAR(1), joining DATE, birth_date DATE);""" Concerning the SQL syntax: You may have noticed that the AUTOINCREMENT field is missing in the SQL code within our Python program. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. createDataFrame takes two parameters: a list of tuples and a list of column names. Working with Spark. FROM additional_tables. Parallel create (partitioned) table as select and parallel create (partitioned) index run with a degree of parallelism equal to the number of partitions. Spark - Add new column to Dataset A new column could be added to an existing Dataset using Dataset. It converts a UDTF to a catalyst. Spark SQL lets you run SQL queries as is. There is no direct library to create Dataframe on HBase table like how we read Hive table with Spark sql. Spark SQL Create Table. Assuming having some knowledge on Dataframes and basics of Python and Scala. SQL with Spark. val create_table = hiveContext. A Spark DataFrame is a distributed collection of data organized into named columns that provides operations to filter, group, or compute aggregates, and can be used with Spark SQL. Spark also automatically uses the spark. In that model, a table is a set of tuples, while in SQL, tables and query results are lists of rows: the same row may occur multiple times, and the order of rows can be employed in queries (e. These examples are extracted from open source projects. Notice in the above example we set the mode of the DataFrameWriter to "append" using df. CREATE TABLE. Python is used to query and manage data in BigQuery. There are multiple ways through which we can create a dataset. There's no need to load the data, create and maintain schemas, or transform the data before it can be processed. Oracle provides a dummy table called dual. In this latest article in our series, we have learned to create an external table for SQL Server data source with the Azure Data Studio Create external table wizard along with T-SQL as well. Here’s an appendix of commonly used commands. There are several options to upload SQL Server backups files, scripts or other files to Azure. The only challenge I see was in converting Teradata recursive queries into spark since Spark does not support Recursive queries. While developing SQL applications using datasets, it is the first object we have to create. Spark SQL executes upto 100x times faster than Hadoop. With Spark Streaming, as the name implies, you can create streaming application in a micro-batch fashion by defining a window. You may have to give alias name to DERIVED table as well in SQL. CREATE TABLE weather (wban INT, date STRING, precip INT) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’ LOCATION ‘ /hive/data/weather’;. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Caching Tables In-Memory; Why Spark SQL Came Into Picture? Spark SQL originated as Apache Hive to run on top of Spark and is now integrated with the Spark stack. 0, spark session has merged SQL context and Hivecontext in one object. spark sql - create new_table as select * from table. Spark Packages is a community site hosting modules that are not part of Apache Spark. To create a basic instance of this call, all we need is a SparkContext reference. We will configure a storage account to generate events in a […]. We do not allow users to create a MANAGED table with the users supplied LOCATION. If you’re already a SQL user then working with Hadoop may be a little easier than you think, thanks to (FORMATTED|EXTENDED) table; Creating a database CREATE. He has authored 12 SQL Server database books, 30 Pluralsight courses and has written over 5000 articles on the database technology on his blog at a https://blog. Help us to solve this problem. CREATE OR REPLACE TABLE: Creates a table and replaces an existing table with the same name in the specified dataset. How to Use Snappy SQL shell (snappy-sql) How to Create Row Tables and Run Queries; How to Create Column Tables and Run Queries; How to Load Data into SnappyData Tables; How to Load Data from External Data Stores (e. But in order to apply SQL queries on DataFrame first, you need to create a temporary view of DataFrame as a table and then apply SQL queries on the created table (Running SQL Queries Programmatically). Beginner architects, developers, and data engineers will be able to: Create a Kudu table with SQL. This lesson will teach you how to take data that is formatted for analysis and pivot it for presentation or charting. This is part 2 of our series on event-based analytical processing. Prior to Impala 2. If you are already familiar with Apache Spark and Jupyter notebooks you may want to go directly to the example notebook and code. XML Word Printable JSON. Data scientists love Jupyter Notebook, Python, and Pandas. r/datascience: A place for data science practitioners and professionals to discuss and debate data science career questions. Users who do not have an existing Hive deployment can still create a HiveContext. from pyspark. We recommend this configuration when you require a persistent metastore or a metastore shared by different clusters, services, applications, or AWS accounts. Different parallelism is used for different operations. If you are working on migrating Oracle PL/SQL code base to Hadoop, essentially Spark SQL comes handy. Spark SQL is Apache Spark's module for working with structured data. It is an aggregation where one of the grouping columns values transposed into individual columns with distinct data. Spark SQL data types. 250+ Spark Sql Programming Interview Questions and Answers, Question1: What is Shark? Question2: Most of the data users know only SQL and are not good at programming. Exposing Hive tables in RAM. Conceptually, it is equivalent to relational tables with good optimizati. Spark SQl is a Spark module for structured data processing. Sequence object feature is not available on versions before SQL Server 2012. Note that in Spark, when a DataFrame is partitioned by some expression, all the rows for which this expression is equal are on the same partition (but not necessarily vice-versa)!. The final test can be found at: MultiFormatTableSuite. If you have spark >= 2. Pics of : Create Hive Table From Spark Sql. Every Spark SQL table has metadata information that stores the schema and the data itself. Spark SQL lets you run SQL queries as is. However, the SQL is executed against Hive, so make sure test data exists in some capacity. jdbc(url=jdbcUrl, table = tableName, connectionProperties). All Published Ticket Prices are in US Dollars; This course will be taught in English language; 4 Weekends SQL Server Training Schedule. Next, create a table with a CREATE TABLE statement:. Download the SQL cheat sheet, print it out, and stick to your desk. After each write operation we will also show how to read the data both snapshot and incrementally. Using the interface provided by Spark SQL we get more information about the structure of the data and the computation performed. This blog is about my performance tests comparing Hive and Spark SQL. Enter the following command:. How to create SQL DataSets in Spark. We recommend this configuration when you require a persistent metastore or a metastore shared by different clusters, services, applications, or AWS accounts. To get started, log into SQL Management Studio and connect to the SQL Server containing the desired database. As you can see I give URL, table name and my credentials (as properties). apache spark sql and dataframe guide. They would be easy to update 3. Spark 2 cannot create table when CLUSTERED. The SQL GROUP BY Statement The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country". Spark SQL is gaining popularity because of is fast distributed framework. Here's the code to push my dataframe df to Azure SQL Server. , declarative queries and optimized storage), and lets SQL users call complex. For those familiar with Shark, Spark SQL gives the similar features as Shark, and more. Creating a table inside a database. Scenario #5: Spark with SQL Data Warehouse. 0, HIVE is supported to create a Hive SerDe table. Franklinyz, Ali Ghodsiy, Matei Zahariay yDatabricks Inc. This article shows three ways of how to move your data from SQL Server table or query to Excel or CSV file. While using create table t1 as select from. sql("create table if not exists src (key INT, value STRING") SQLContext. You can create tables in the Spark warehouse as explained in the Spark SQL introduction or connect to Hive metastore and work on the Hive tables. Using the interface provided by Spark SQL we get more information about the structure of the data and the computation performed. XML Word Printable JSON. In Part One, we discuss Spark SQL and why it is the preferred method for Real Time Analytics. Following is the syntax used to create a Row/Column table: CREATE TABLE [IF NOT EXISTS] table_name ( column-definition [ , column-definition ] * ) USING [row | column] // If not specified, a row table is created. Unlike RDD, this additional information allows Spark to run SQL queries on DataFrame. 3 Steps for reproduce: - grant privileges in sentry: GRANT ALL ON DATABASE some_database. To Spark SQL, spark session is the entry point. Apache Spark SQL is a module for structured data processing in Spark. Spark SQL - DataFrames - A DataFrame is a distributed collection of data, which is organized into named columns. Use custom SQL to connect to a specific query rather than the entire data source. It’s relatively straightforward to translate R code to SQL (or indeed to any programming language) when doing simple mathematical operations of the form you normally use when filtering, mutating and summarizing. However, we do not want to create many tables for each experiment. SQLContext(sc) Basic Query. With SQL Server you have the ability to create derived tables on the fly and then use these derived tables within your query. Temporary tables or temp tables in Spark are available within the current spark session. escapedStringLiterals' that can be used to fallback to the Spark 1. It has the capability to load data from multiple structured sources like "text files", JSON files, Parquet files, among others. Starting here? This lesson is part of a full-length tutorial in using SQL for Data Analysis. It allows you to use input and output parameters allowing your dynamic SQL code to be secure and efficient. Oracle provides a dummy table called dual. Tableau has a connection for Spark SQL, a feature of Spark that allows users and programs to query tables using SQL. They were introduced in SQL Server version 2005. Spark SQL is Spark's interface for working with structured and semi-structured data. Spark SQL UDFs. SparkSession. How to create permanent tables in spark-sql. It ensures fast execution of existing Hive queries. KodeKloud 589,960 views. Is there a similar data-structure as a "vector" in R that can be refered to? PROC SQL; CREATE TABLE Tab2 AS. With the prevalence of web and mobile applications, JSON has become the de-facto interchange format for web service API's as well as long-term. Please refer to the Hive manual for details on how to create tables and load/insert data into the tables. Spark SQL is a new module in Apache Spark that integrates rela-tional processing with Spark's functional programming API. Syntax and Examples for Common Table Expressions The CTE query starts with a "With" and is followed by the Expression Name. Spark SQL Create Temporary Tables. Spark SQL UDFs. In the temporary view of dataframe, we can run the SQL query on the data. In Part One, we discuss Spark SQL and why it is the preferred method for Real Time Analytics. A managed table is a Spark SQL table for which Spark manages both the data and the metadata. 0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. Here is an example of Creating and querying a SQL table in Spark:. As it is not a relational database so there is no point of creating relations betwee. 0 version, you can use CreateOrReplaceTemoView or CreateGlobalTempView to create the temp table from the given Data frame. This page shows how to operate with Hive in Spark including: Create DataFrame from existing Hive table Save DataFrame to a new Hive table Append data. This problem is due to a change in the default behavior of Spark in version 2. KodeKloud 589,960 views. Related articles. In this blog post, I'm going to do a quick walk through on how easy it is to create tables, read them and then delete them. In this new article, we will show how to use a new tool, Microsoft Azure Storage Explorer (MASE). It is an aggregation where one of the grouping columns values transposed into individual columns with distinct data. Conceptually, it is equivalent to relational tables with good optimizati. How Spark Streaming Works. Spark SQL is built on two main components: DataFrame and SQLContext. jar' Note that for Phoenix versions 4. Global views lifetime ends with the spark application , but the local view lifetime ends with the spark session. In addition, you will learn an efficient way to delete all rows from a table by using the TRUNCATE statement. For instance: Here the simple mistake to make with this approach is to avoid the CREATE EXTERNAL TABLE step in Hive and simply make the table using the Dataframe API's write methods. create external, csv table, external table, Hive, hive, hive csv, hive table Merging Two Dataframes in Spark Requirement Let's say we are getting data from two different sources (i. Spark SQL executes upto 100x times faster than Hadoop. spark sql简介. In Part One, we discuss Spark SQL and why it is the preferred method for Real Time Analytics. When we want to join the tables, we can use the value of the partition column. This enables you to read, write, and process big data from T-SQL or Spark, allowing you to easily combine and analyze high-value relational data with high-volume big data. Spark; SPARK-24064 [Spark SQL] Create table using csv does not support binary column Type. The following example registers a characters table and then queries it to find all characters that are 100 or older:. The SQLContext encapsulate all relational functionality in Spark. Learn how to connect an Apache Spark cluster in Azure HDInsight with an Azure SQL database and then read, write, and stream data into the SQL database. You can do it by the grade temp view function. He has authored 12 SQL Server database books, 30 Pluralsight courses and has written over 5000 articles on the database technology on his blog at a https://blog. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. With this extra information, one can achieve extra optimization in Apache Spark. can any one please tell me how to create permanent tables in spark-sql which will be available for all session. How to Execute a Hive Sql File in Spark Engine? What is Spark SQL (Ref: Apache Spark Documentation): Spark SQL is a Spark module for structured data processing. types import * Infer Schema. Distribute By. This enables you to read, write, and process big data from T-SQL or Spark, allowing you to easily combine and analyze high-value relational data with high-volume big data. Table Operations such as Creation, Altering, and Dropping tables in Hive can be observed in this tutorial. also provide the predefined variable z. How to Change Schema of a Spark SQL DataFrame? For the reason that I want to insert rows selected from a table I try to create a new DataFrame based on the. Hive, Impala and Spark. In this article, we will check how to create Spark SQL temporary tables, its syntax and some examples. Check out the beginning. Spark SQL conveniently blurs the lines between RDDs and relational tables. sql(sqltext) org. Spark Packages is a community site hosting modules that are not part of Apache Spark. jdbc(url=jdbcUrl, table = tableName, connectionProperties). webpage Output Directory (HDFS): /smartbuy/webpage_files In this exercise you will use Spark SQL to load data from an Impala/Hive table, process it, and store it to a new table. create table [DB]. The Parameters not only serve a purpose for flexibility, but they also inhibit SQL Injection attacks since they appear as operands and not part of the actual code. If u wanna check more details, refer to case class HiveGenericUdtf. Spark SQL is Spark's interface for working with structured and semi-structured data. To develop an ability to write scripts using T-SQL. The spark_connection object implements a DBI interface for Spark, so you can use dbGetQuery to execute SQL and return the result as an R data. sql(" CREATE TABLE employees USING org. Spark SQL can operate on the variety of data sources using DataFrame interface. They are SQL-compliant and part of the ANSI SQL 99 specification. How to Execute a Hive Sql File in Spark Engine? What is Spark SQL (Ref: Apache Spark Documentation): Spark SQL is a Spark module for structured data processing. NET to SQL Server, and there is a detailed description exactly of the case of passing a comma-separated list to a TVP. CLUSTER BY is a part of spark-sql query while CLUSTERED BY is a part of the table DDL. For experimenting with the various Spark SQL Date Functions, using the Spark SQL CLI is definitely the recommended approach. Objective - Spark SQL Tutorial. Built on our experience with Shark, Spark SQL lets Spark program-mers leverage the benefits of relational processing (e. A Spark DataFrame is an interesting data structure representing a distributed collecion of data. jar' Note that for Phoenix versions 4. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Using that, we will create a table, load the employee record data into it using HiveQL language, and apply some queries on it. As on date, if you Google for the Spark SQL data types, you won't be able to find a suitable document with the list of SQL data types and appropriate information about them. In Spark SQL, temporary views are session-scoped and will be automatically dropped if the session terminates. Today's blog is brought to you by our latest committer and the developer behind the Spark integration in Apache Phoenix, Josh Mahonin, a Software Architect at Interset. Using Hive and ORC with Apache Spark. 0) on our spark Dataframe. We will show examples of JSON as input source to Spark SQL's SQLContext. In order to check the connection between Spark SQL and Hive metastore, the verification of the list of Hive databases and tables using Hive prompt could be done. Thus, there is successful establishement of connection between Spark SQL and Hive. SparkSession is the entry point to Spark SQL. You need to add hbase-client dependency to achieve this. We will once more reuse the Context trait which we created in Bootstrap a SparkSession so that we can have access to a SparkSession. Note that to create a function, the user also must have ALL permissions on the JAR where the function is located, i. Using Hive and ORC with Apache Spark. Spark SQL can operate on the variety of data sources using DataFrame interface. It has the capability to load data from multiple structured sources like "text files", JSON files, Parquet files, among others. Here is an example of jdbc implementation: val df = spark. table ("src") df. tsv file which is loaded into Databricks as a table. Bradleyy, Xiangrui Mengy, Tomer Kaftanz, Michael J. Following is the way you can create a table in Hive through Spark Shell. Developing Spark SQL Applications; Fundamentals of Spark SQL Application Development SparkSession — The Entry Point to Spark SQL Builder — Building SparkSession using Fluent API assertion failed: create table without data insertion can only use ErrorIfExists or Ignore as SaveMode. To create sequence object use script create sequence [schema]. It is one of the very first objects you create while developing a Spark SQL application. The only challenge I see was in converting Teradata recursive queries into spark since Spark does not support Recursive queries. escapedStringLiterals' that can be used to fallback to the Spark 1. Importing Data into Hive Tables Using Spark. We are best Oracle SQL Training Institute in Bangalore with 100% Job Assist. We have been thinking about Apache Spark for some time now at Snowplow. TechBrothersIT is the blog spot and a video (Youtube) Channel to learn and share Information, scenarios, real time examples about SQL Server, Transact-SQL (TSQL), SQL Server Database Administration (SQL DBA), Business Intelligence (BI), SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS), Data Warehouse (DWH) Concepts, Microsoft Dynamics AX, Microsoft Dynamics. Please can you also elaborate Big data clusters too in sql 2019. Starting with SQL Server 2019, SQL server big data clusters allow you to deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes. Figure: Runtime of Spark SQL vs Hadoop. They can be constructed from a wide array of sources such as an existing RDD in our case. It is a dummy table that always has a single row. But when I run it on the cluster, table is created but empty. Use HDInsight Spark cluster to read and write data to Azure SQL database. Following is a step-by-step process to load data from JSON file and execute SQL query on the loaded data from JSON file: Create a Spark Session. SPARK SQL HANDSON LAB HIVE SETUP, & USING SPARKSQL CREATE A HIVE TABLE & LOAD DATA By www. Your votes will be used in our system to get more good examples. You need to add hbase-client dependency to achieve this. In order to check the connection between Spark SQL and Hive metastore, the verification of the list of Hive databases and tables using Hive prompt could be done. I'm creating tables in spark using following commands but these tables will be available only for that session. VIEWS and a loop to directly give our users access or create a role that has only access to a view and grant our users that role. Spark SQL CLI: This Spark SQL Command Line interface is a lifesaver for writing and testing out SQL. CREATE TABLE my_table (name STRING, age INT) CREATE. You can grant the CREATE privilege on a server or database with the following commands, respectively:. Use Spark SQL for ETL.