Update Hive Table Using Spark


The major Hadoop distributions have configuration settings that specify which mechanism to use in running Hive queries. Understanding Authorization of Hive Objects in Spark¶ Spark on Qubole supports SQL Standard authorization of Hive objects in Spark 2. Using HBase and Impala to Add Update and Delete Capability to Hive DW Tables, and Improve Query Response Times 19 May 2015 on Big Data, Technical, obiee, Oracle BI Suite EE, hadoop, Hive, Impala, hbase, DW Offloading. Table of Contents Who is using DataSketches? Overview · Download · GitHub · Comments · Licensing. Researchers and developer predicted that tomorrow is an era of Big Data. Tested on both external and internal tables and reached the same result. This article steps will demonstrate how to implement a very basic and rudimentary solution to CDC in Hadoop using MySQL, Sqoop, Spark, and Hive. the whole thing in Hive on spark including updates works fine. We recommend this configuration when you require a persistent metastore or a metastore shared by different clusters, services, applications, or AWS accounts. Generally, Spark sql can not insert or update directly using simple sql statement, unless you use Hive Context. Define the location of Solr (or ZooKeeper if using SolrCloud), the collection in Solr to index the data to, and the query to use when reading the table. Building a unified platform for big data analytics has long been the vision of Apache Spark, allowing a single program to perform ETL, MapReduce, and complex analytics. update base_table set name2=”sinha” where rt=3 and name1=”preetika”;. HDFS datasets in DSS are always true “HDFS datasets”. Rename an existing table or view. Here we explain how to use Apache Spark with Hive. 2 for examples mentioned below. Dropping external tables will not remove the data. 0, Hive offers another API for mutating (insert/update/delete) records into transactional tables using Hive’s ACID feature. We don't seem to make Hive work on Spark engine with a newer version of Spark. Now, you have a file in Hdfs, you just need to create an external table on top of it. Hi Everyone, I have a basic question. streaming and part of the hive-hcatalog-streaming Maven module in Hive. 14 version or not. Continue reading How to: Run Queries on Spark SQL using JDBC via Thrift Server Querying HDFS data using Hive External Table by Tapas Saha on August 19, 2016 in Apache Hadoop , Hive , BI Cloud. This class is appropriate for Business Analysts, IT Architects, Technical Managers and Developers. Java is a very wordy language so using Pig and Hive is simpler. You need to understand how to use HWC to access Spark tables from Hive in HDP 3. But, using Hive, we just need to submit merely SQL queries. 1 using a commit marker in the destination directory (that the reader waits for). Spark SQL: In Spark, we use Spark SQL for structured data processing. But let's abuse Hive with M/R2 a bit more first. So the data now is stored in data/weather folder inside hive. Hive transactions are not currently supported in Apache Spark. The source for this guide can be found in the _src/main/asciidoc directory of the HBase source. You can think of Hive as providing a data workbench where you can examine, modify and manipulate the data in Apache Hadoop. We perform a Spark example using Hive tables. Here a list of things you can do with Spark-SQL on top of your Hive tables: "almost everything" 🙂 That is, you can run any type of query that you would run on top of Azure HDInsight with Hive, with some four import exceptions: ACID tables update are not supported by Spark-SQL. Spark and Hive now use independent catalogs for accessing SparkSQL or Hive tables on the same or different platforms. local: Run Spark locally with one worker thread. what i learnt - Data and Analytics now i manually updated the maven using update-alternatives. Purpose tHiveOutput writes data of different formats into a given Hive table or a directory in HDFS. When not configured. Key Customer Benefits. Change Data Capture Using Apache NiFi change introduced was an insert or an update; from the source and insert them into the Hive table. Spark SQL is a Spark module for structured data processing. Here, the dataframe from use case 2. You can also choose which database in Hive to create your table in. A SerDe is a powerful (and customizable) mechanism that Hive uses to "parse" data stored in HDFS to be used by Hive. Is it feasible to update data in a table hosted on HDInsight Spark Cluster? I understand that Spark cluster's primary purpose is fast retrieval of data in ranges of over million records. Writes an incoming Spark DataFrame/RDD into a Hive table. Thus, there is successful establishement of connection between Spark SQL and Hive. Thanks Anbeswaran K. You can use BI tools to connect to your cluster via JDBC and export results from the BI tools, or save your tables in DBFS or blob storage and copy the data via REST API. Most importantly, impala works so fast that you will love it better than hive. The above steps will be executed for all the prod_id's in delivery table. 12) via -Phive. Users who are comfortable with SQL, Hive is mainly targeted towards them. The source for this guide can be found in the _src/main/asciidoc directory of the HBase source. Also, gives information on computations performed. Getting Started With Apache Hive Software¶. Any Acid table partition that had Update/Delete/Merge statement executed since the last Major compaction must execute Major compaction before upgrading to 3. It integrates with HIVE metastore to share the table information between both the components. All the hive tables have been used in spark console using sqlContext object. Hive magic. Function tHiveOutput connects to a given Hive database and writes data it receives into a Hive table or a directory you specify. I don't think SparkSQL supports DML on text file datasource just yet. Next we create a dedicated database and put all table on this. Hive ACID tables support UPDATE, DELETE, INSERT, MERGE query constructs with some limitations and we will talk about that too. This is Part 1 of a 2-part series on how to update Hive tables the easy way. This can result in data loss if these applications write to a Managed table in HDP 2. Any data which has been exposed to Spark through the DataFrame abstraction can be written. xml that you had configured in the above steps into the $SPARK_HOME/conf folder where $SPARK_HOME is the root folder for Spark (e. The syntax and example are as follows: Syntax. 10/08/2019; 7 minutes to read; In this article. Best Hadoop Training Institute: NareshIT is the best Hadoop Training Institute in Hyderabad,Vijayawada and Chennai providing Hadoop Training classes by realtime faculty with course material and 24x7 Lab Facility. Getting Started With Apache Hive Software¶. We now have an HBase table, a Hive external table and data. The result is that using Hive on HBase should be used conservatively. While doing hive queries we have used group by operation very often to perform all kinds of aggregation operations like sum, count, max, etc. Function tHiveOutput connects to a given Hive database and writes data it receives into a Hive table or a directory you specify. Spark Implementation. We can sqoop the data from RDBMS tables into Hadoop Hive table without using SQOOP. A library to read/write DataFrames and Streaming DataFrames to/from Apache Hive™ using LLAP. Apache Hive does support simple update statements that involve only one table that you are updating. Therefore, you do not need to write any WHERE clause to restrict the selected partitions. Rename an existing table or view. Further Reading. Granted it helps to use Hive 2 on Spark 1. fallBackToHdfs's doc: This flag is effective only if it is Hive table. Hive is, however, a tool that operates on top of MapReduce2 by default and with that, it comes with a notable performance overhead. Follow the below steps: Step 1: Sample table in Hive. Home » Inserting hive data into Oracle tables using Spark and Scala. It is not known whether this support may be added to Apache Spark in future releases. To create a Hive table using Spark SQL, we can use the following code:. The simplest way is to convert the RDD in to Spark SQL (dataframe) using method rdd. 1, will perform broadcast joins only if the table size is available in the table statistics stored in the Hive Metastore (see spark. It requires that the schema of the class:DataFrame is the same as the schema of the table. Once data are imported and present as a Hive table, it is available for processing using a variety of tools including Hive’s SQL query processing, Pig, or Spark. Hadoop • Hive How to Update and Delete Hive Tables. Using HWC, we can write out any DataFrame into a Hive table. This is Part 1 of a 2-part series on how to update Hive tables the easy way. Affects: Hive with Hive-on-Spark enabled. local: Run Spark locally with one worker thread. The use case is to join the table movie with customer activity event data that has been previously loaded into an HDFS file using Flume and is now exposed as a Hive table movieapp_log_odistage. Below is the details-I am using Putty to connect to hive table and access records in the tables. To perform loading and storing Hive data into Pig we need to use HCatalog. Starting with MEP 6. One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Data Management. 10/08/2019; 7 minutes to read; In this article. Now, you have a file in Hdfs, you just need to create an external table on top of it. which are running fine for me. Currently this works only with tables in orc. Bucketed tables will create almost equally distributed data file parts. Just to be clear I used Spark 1. In this section of Apache Hive tutorial, we will compare Hive vs Spark SQL in detail. It's straight forward to delete data from a traditional Relational table using SQL. There are two caveats the guidelines above. Row-level Transactions Available in Hive 0. caseSensitiveInferenceMode (see SPARK-20888): INFER_AND_SAVE. Each Hive recipe runs in a separate Hive environment (called a metastore). Apache Hive. You can also query tables using the Spark API's and Spark SQL. Impala makes use of existing Apache Hive (Initiated by Facebook and open sourced to Apache) that many Hadoop users already have in place to perform batch oriented , long-running jobs in form of SQL queries. This chapter explains how to create a table and how to insert data into it. In this post "Connecting Python 3 to SQL Server 2017 using pyodbc", we are going to learn that how we can connect Python 3 to SQL Server 2017. Often our Spark jobs need to access tables or data in various formats from different sources. Table of Contents Uses for an external metastoreMetastore password managementWalkthroughSetting up the metastoreDeploying Azure Databricks in a VNETSetting up the Key Vault Uses for an external metastore Every Azure Databricks deployment has a central Hive metastore accessible by all clusters to persist table metadata, including table and column names as well as storage location. Data is inserted or appended to a file which has table on top of it. **Update: August 4th 2016** Since this original post, MongoDB has released a new certified connector for Spark. Hive ACID tables support UPDATE, DELETE, INSERT, MERGE query constructs with some limitations and we will talk about that too. Spark SQL: Relational Data Processing in Spark Michael Armbrusty, Reynold S. ACID support. After updating the files underlying a table, refresh the table using the following command:. Hive has updated adding new parameters to optimize the usage of S3, now you can avoid the usage of S3 as the stagingdir using the parameters hive. Hortonworks Apache Spark Component Guide; Apache Spark. e user/hive/warehouse. DSS cannot properly read the underlying files of these tables. but let's keep the transactional table for any other posts. Hive failed to create /user/hive/warehouse I just get started on Apache Hive, and I am using my local Ubuntu box 12. I then found out that Spark 2. CREATE DATABASE yztest; USE yztest; Now we create dedicated tables for each csv (Most of my attributes are string, just to make it simple, doesn’t need to be the best solution). Apache Hadoop. For further information on Spark SQL, see the Spark SQL, DataFrames, and Datasets Guide. Some more configurations need to be done after the successful. One use of Spark SQL is to execute SQL queries. Setting the location of 'warehouseLocation' to Spark warehouse. Now lets try to update some records which has been pushed into base_table. Points to consider:. The following examples use Hive commands to perform operations such as exporting data to Amazon S3 or HDFS, importing data to DynamoDB, joining tables, querying tables, and more. First, we connect via the Hive shell, beeline or any other compatible SQL tool. Spark executors can connect directly to Hive LLAP daemons to retrieve and update data in a transactional manner, allowing Hive to keep control of the data. Its pretty simple writing a update statement will work out UPDATE tbl_name SET upd_column = new_value WHERE upd_column = current_value; But to do updates in Hive you must take care of the following: Minimum requisite to perform Hive CRUD using ACI. We can call this Schema RDD as Data Frame. Further Reading. An important aspect of unification that our users have consistently requested is the ability to more easily import data stored in external sources, such as Apache Hive. Hive Transactional Table Update join. For non-partitioned data source table, it will be automatically recalculated if table statistics are not available For partitioned data source table, It is 'spark. On tables NOT receiving streaming updates, INSERT OVERWRITE will delete any existing data in the table and write the new rows. Learn how to use the UPDATE (table) syntax of the Delta Lake SQL language in Databricks. Update and Delete on Hive table (Hive supports CRUD) In this blog we have a quick overview of how to use spark SQL and dataframes for common use cases in SQL. Streaming Mutation API. You can then directly load tables with Pig or MapReduce without having to worry. You may have a huge. if you have a managed table, you will have to drop just the table on hive and the data will be automatically deleted: [code]Drop table $table [/code]For external. This section provides a reference for Apache Spark SQL and Delta Lake, a set of example use cases, and information about compatibility with Apache Hive. In Qubole we have solved this issue in Hive 3. CREATE DATABASE yztest; USE yztest; Now we create dedicated tables for each csv (Most of my attributes are string, just to make it simple, doesn’t need to be the best solution). Hortonworks Apache Spark Component Guide; Apache Spark. Running HiveQL queries using Spark SQL. Normally currently users do not use manual locking on Hive tables, because Hive queries themselves will take care of that automatically. This article steps will demonstrate how to implement a very basic and rudimentary solution to CDC in Hadoop using MySQL, Sqoop, Spark, and Hive. Once data are imported and present as a Hive table, it is available for processing using a variety of tools including Hive’s SQL query processing, Pig, or Spark. Following the official “Getting Started” guide on Apache website, I am now stuck at the …. By default, when this table is queried through the Spark SQL using spark-shell, the values are interpreted and displayed differently. 0, Spark tables and Hive tables are kept in separate meta stores to avoid confusion of table types. 1 From LFS to Hive Table Assume we have data like below in LFS file called Now We can use load statement like below. I then found out that Spark 2. Once done, you are good to perform the update and delete operations on Hive tables. To load the data from local to Hive use the following command in NEW terminal:. Finally, allowing Hive to run on Spark also has performance benefits. In cloudera quickstart vm, object of HiveContext is created as sqlContext after launching spark. For further information on Delta Lake, see Delta Lake. 6 with Hive 2. 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. These topics can help you with Datasets, DataFrames, and other ways to structure data using Spark and Databricks. 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. The default location of Hive table is overwritten by using LOCATION. Now if we try to re-insert the same data again, it will be appended to the previous data as shown below: Updating the Data in Hive Table UPDATE college set clg_id = 8. This demo creates a python script which uses pySpark to read data from a Hive table into a DataFrame, perform operations on the DataFrame, and write the results out to a JDBC DataSource (PostgreSQL database). When using the Hive Query executor with Impala, you can use the default driver included with Data Collector, or you can install an. This API is used by Apache NiFi, Storm and Flume to stream data directly into Hive tables and make it visible to readers in near real time. Related reading: How to update Hive Table without Setting Table Properties? Here is an example that inserts some records, deletes one record and updates one record. • Help Business in querying the hive tables using Apache Impala. Managing Slowly Changing Dimensions. or Hive tables. 2 - Use and abuse of Spark-SQL on top of "Hive" tables. Performance-wise, we find that Spark SQL is competi-tive with SQL-only systems on Hadoop for relational queries. Weitere Informationen zu Spark SQL finden Sie im Leitfaden zu Spark SQL, DataFrames und Datasets. Xiny, Cheng Liany, Yin Huaiy, Davies Liuy, Joseph K. We all know HDFS does not support random deletes, updates. And flexibility is required since new tools are introduced over time. Some links, resources, or references may no longer be accurate. It provide much better performance btter to running a query in Hive console. Tag - update hive table using spark. This blog illustrates, how to work on data in MySQL using Spark. Looking at the logs, I found out that this select was actually performing an update on the database "Saving case-sensitive schema for table". Valid only when using the execution version of Hive, i. When I read table I want data from last hour (last added partition). service running Spark, use Spark SQL within other programming languages. my Spark is 1. 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. When a table is small, this integration can work well, but Hive on HBase will not perform well on large tables. Important Note that. local: Run Spark locally with one worker thread. Also, gives information on computations performed. 0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below. HBase column names are fully qualified by column family, and you use the special token :key to represent the rowkey. Apache Hadoop. March 19, 2016. Using HBase and Impala to Add Update and Delete Capability to Hive DW Tables, and Improve Query Response Times 19 May 2015 on Big Data, Technical, obiee, Oracle BI Suite EE, hadoop, Hive, Impala, hbase, DW Offloading. Moreover, it is possible to portion and bucket, tables in Apache Hive. This tutorial provides example code that uses the spark-bigquery-connector within a Spark application. I hope with the help of this tutorial, you can easily import RDBMS table in Hive using Sqoop. Hi Everyone, I have a basic question. UPDATE kudu_table SET c3 = upper(c3), c4 = FALSE, c5 = 0 WHERE c6 = TRUE; The following examples show how to perform an update using the FROM keyword with a join clause:. The data flow can be seen as follows: Docker. For example, you can use the Hive Query executor to perform the Invalidate Metadata query for Impala as part of the Drift Synchronization Solution for Hive or to configure table properties for newly-created tables. 0 or later, you can configure Spark SQL to use the AWS Glue Data Catalog as its metastore. An important aspect of unification that our users have consistently requested is the ability to more easily import data stored in external sources, such as Apache Hive. Note that this is just a temporary table. 1 (beta) does not have the restriction on the file names in the source table to strictly comply with the patterns that Hive uses to write the data. Under the hood, Redshift Data Source for Spark will first create the table in Redshift using JDBC. Building Big Data Applications using Spark, Hive, HBase and Kafka 1. This 4 day training course is designed for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Pig and Hive. Welcome to Apache HBase™ Apache HBase™ is the Hadoop database, a distributed, scalable, big data store. In the new world of HDInsight 4. Apache Hive does support simple update statements that involve only one table that you are updating. Writing into hive table using spark is taking too long time. The spark-bigquery-connector is used with Apache Spark to read and write data from and to BigQuery. The result is that using Hive on HBase should be used conservatively. To load the data from local to Hive use the following command in NEW terminal:. Join HdfsTutorial. You need to enter the Hive query statement you want to use to select the data to be used. @ashishth 2. Any query you make, table that you create, data that you copy persists from query to query. Key Customer Benefits. The syntax and example are as follows: Syntax. caseSensitiveInferenceMode (see SPARK-20888): INFER_AND_SAVE. If you just use Spark without going in fact through Hive, you have to activate the option “spark. In Sqoop Commands every row is treated as records and the tasks are subdivided into subtasks by Map Task Internally. This scenario based certification exam demands basic programming using Python or Scala along with Spark and other Big Data technologies. Update and Delete on Hive table (Hive supports CRUD) In this blog we have a quick overview of how to use spark SQL and dataframes for common use cases in SQL. Creating Hive Transactional table. Moreover, We get more information of the structure of data by using SQL. sbt by adding Library dependency. Working as Big Data Architect for the last 4 years and having strong background of big data stack like Spark, Scala, Hadoop, Storm, Batch, HDFS, MapReduce, Kafka, Hive, Cassandra, Python, SQOOP, and PIG. For further information on Delta Lake, see Delta Lake. - Create a Hive table (ontime) - Map the ontime table to the CSV data - Create a Hive table ontime_parquet and specify the format as Parquet - Move the table from the ontime table to the ontime_parquet table In the previous blog, we have seen how to convert CSV into Parquet using Hive. • Create Spark and pig scripts to analyze and sort the data in hadoop tables. threads = 1 ; update. Unfortunately for real-time responsiveness HIVE SQL currently isn’t the most optimal tool in HADOOP [instead it’s better used for batched SQL commands]. Currently this works only with tables in orc. Using the data source APIs, we can load data from a database and consequently work on Spark. Will it be feasible to update a table with millions records for just 1/2 record with just 1/2 attributes out of may be 20 Appreciate for quick response. Also, we can use JDBC/ODBC drivers, since they are available in Hive. Hive has this wonderful feature of partitioning — a way of dividing a table into related parts based on the values of certain columns. Here is the basic workflow. I have explained using pyspark shell and a python program. You can think of Hive as providing a data workbench where you can examine, modify and manipulate the data in Apache Hadoop. So Hive jobs will run much faster there. Spark requires a direct access to the Hive metastore, to run jobs using a HiveContext (as opposed to a SQLContext) and to access table definitions in the global metastore from Spark SQL. As noted under the “Hive Tables”section of the Spark SQL Programming Guide, you will need to run “sbt/sbt -Dhadoop. xml that you had configured in the above steps into the $SPARK_HOME/conf folder where $SPARK_HOME is the root folder for Spark (e. maven - download the Hive jars from Maven repositories. Creates a new Hive table using the name provided. 2 for examples mentioned below. Sample Code. Writing into hive table using spark is taking too long time. Using Oracle SQL Developer, you can copy data and create a new Hive table, or append data to an existing Hive external table that was created by Copy to Hadoop. caseSensitiveInferenceMode (see SPARK-20888): INFER_AND_SAVE. Books I Follow: Apache Spark Books: Learning Spark: Update Data in Hive Table - Duration: 5:34. insert into table base_table select * from old_table. x can be downloaded. From the above image, we can see that the data has been inserted successfully into the table. This blog post is about accessing the Hive Metastore from Hue, the open source Hadoop UI and clearing up some confusion about HCatalog usage. *Note: In this tutorial, we have configured the Hive Metastore as MySQL. Some links, resources, or references may no longer be accurate. Purpose tHiveOutput writes data of different formats into a given Hive table or a directory in HDFS. Background. The first type of table is an internal table and is fully managed by Hive. 0 and later. Starting from Spark 1. Create Table is a statement used to create a table in Hive. Hadoop - Big Data Overview. If your company or tool uses ORC, please let us know so that we can update this page. Spring JDBC UPDATE;. Spark SQL supports a subset of the SQL-92 language. I am using HDP 2. Below is the details-I am using Putty to connect to hive table and access records in the tables. Creates a new Hive table using the name provided. format (stored as orc) Alternatively, use Hbase with Phoenix as the SQL layer on top. Moreover, We get more information of the structure of data by using SQL. Once done, you are good to perform the update and delete operations on Hive tables. Search this site Loading Data from a. Denodo uses the Hive JDBC driver provided by the Hadoop vendor or the generic Apache Hive driver to run queries on Hive. caseSensitiveInferenceMode (see SPARK-20888): INFER_AND_SAVE. Users who are comfortable with SQL, Hive is mainly targeted towards them. The time values differ from the Impala result set by either 4 or 5 hours, depending on whether the dates are during the Daylight Savings period or not. Building Big Data Applications using Spark, Hive, HBase and Kafka 1. This will download all the dependency. Better to use this method if you want your application to be back-word compatible. When using the Hive Query executor with Impala, you can use the default driver included with Data Collector, or you can install an. I will use crime data from the City of Chicago in this tutorial. or Hive tables. You can create ACID tables in Hive (in the ORC format). As described, Spark doesn’t natively support writing to Hive’s managed ACID tables. The InsertIntoHiveTable. Function tHiveOutput connects to a given Hive database and writes data it receives into a Hive table or a directory you specify. To get Spark to utilize this Hive context, you will need to copy the hive-site. Data Science and Data Analytics Using Spark | R | Python a new or existing Hive table, Insert or update data from HDFS into a table in a relational database. Apache Hive - Transactions in Hive (Insert, update and delete) itversity. The default location of Hive table is overwritten by using LOCATION. It provide much better performance btter to running a query in Hive console. UPDATE kudu_table SET c3 = upper(c3), c4 = FALSE, c5 = 0 WHERE c6 = TRUE; The following examples show how to perform an update using the FROM keyword with a join clause:. Follow the below steps: Step 1: Sample table in Hive. Spark SQL, part of Apache Spark big data framework, is used for structured data processing and allows running SQL like queries on Spark data. when receiving/processing records via Spark Streaming. Getting some CSV data to populate into Hive. --incremental lastmodified will import the updated and new. The list of such applications includes Spark. Step 3: Create temporary Hive Table and Load data. We can then create an external table in hive using hive SERDE to analyze this data in hive. We do this by creating two dataframes and joining on a particular key. A Phoenix table is created through the CREATE TABLE command and can either be:. Those are Parquet file, JSON document, HIVE. x, this node requires Hive Warehouse Connector to be part of the Spark classpath and correctly configured. Here, we will be using the JDBC data source API to fetch data from MySQL into Spark. So Hive queries can be run against this data. UPDATE kudu_table SET c3 = 'impossible' WHERE 1 = 0; -- Change the values of multiple columns in a single UPDATE statement. engine=tez;. I had to use sbt or Maven to build a project for this purpose but it works. Hive Command Examples for Exporting, Importing, and Querying Data in DynamoDB. We can sqoop the data from RDBMS tables into Hadoop Hive table without using SQOOP. Each Hive recipe runs in a separate Hive environment (called a metastore). Its pretty simple writing a update statement will work out UPDATE tbl_name SET upd_column = new_value WHERE upd_column = current_value; But to do updates in Hive you must take care of the following: Minimum requisite to perform Hive CRUD using ACI. Sync Hive Table into Kylin. I am able to do it successfully. Spark, Scala & Hive Sql simple tests.