Tutorial: Extract, transform, and load data using Apache Hive on Azure HDInsight
In this tutorial, you take a raw CSV data file, import it into an HDInsight cluster storage, and then transform the data using Apache Hive on Azure HDInsight. Once the data is transformed, you load that data into an Azure SQL database using Apache Sqoop. In this article, you use publicly available flight data.
The steps in this document require an HDInsight cluster that uses Linux. Linux is the only operating system used on Azure HDInsight version 3.4 or later. For more information, see HDInsight retirement on Windows.
This tutorial covers the following tasks:
- Download the sample flight data
- Upload data to an HDInsight cluster
- Transform the data using Hive
- Create a table in Azure SQL database
- Use Sqoop to export data to Azure SQL database
The following illustration shows a typical ETL application flow.
If you don't have an Azure subscription, create a free account before you begin.
A Linux-based Hadoop cluster on HDInsight. See Get started using Hadoop in HDInsight for steps on how to create a new Linux-based HDInsight cluster.
Azure SQL Database. You use an Azure SQL database as a destination data store. If you don't have a SQL database, see Create an Azure SQL database in the Azure portal.
Azure CLI 2.0. If you haven't installed the Azure CLI, see Install the Azure CLI for more steps.
An SSH client. For more information, see Connect to HDInsight (Hadoop) using SSH.
Download the flight data
On the page, select the following values:
Name Value Filter Year 2013 Filter Period January Fields Year, FlightDate, UniqueCarrier, Carrier, FlightNum, OriginAirportID, Origin, OriginCityName, OriginState, DestAirportID, Dest, DestCityName, DestState, DepDelayMinutes, ArrDelay, ArrDelayMinutes, CarrierDelay, WeatherDelay, NASDelay, SecurityDelay, LateAircraftDelay.
Clear all other fields.
Select Download. You get a .zip file with the data fields you selected.
Upload data to an HDInsight cluster
There are many ways to upload data to the storage associated with an HDInsight cluster. In this section, you use
scp to upload data. To learn about other ways to upload data, see Upload data to HDInsight.
Open a command prompt and use the following command to upload the .zip file to the HDInsight cluster head node:
scp <FILENAME>.zip <SSH-USERNAME>@<CLUSTERNAME>-ssh.azurehdinsight.net:<FILENAME.zip>
Replace FILENAME with the name of the .zip file. Replace USERNAME with the SSH login for the HDInsight cluster. Replace CLUSTERNAME with the name of the HDInsight cluster.
If you use a password to authenticate your SSH login, you're prompted for the password. If you use a public key, you might need to use the
-iparameter and specify the path to the matching private key. For example,
scp -i ~/.ssh/id_rsa FILENAME.zip USERNAME@CLUSTERNAME-ssh.azurehdinsight.net:.
After the upload has finished, connect to the cluster by using SSH. On the command prompt, enter the following command:
Use the following command to unzip the .zip file:
This command extracts a .csv file that is roughly 60 MB.
Use the following commands to create a directory on HDInsight storage, and then copy the .csv file to the directory:
hdfs dfs -mkdir -p /tutorials/flightdelays/data hdfs dfs -put <FILENAME>.csv /tutorials/flightdelays/data/
Transform data using a Hive query
There are many ways to run a Hive job on an HDInsight cluster. In this section, you use Beeline to run a Hive job. For information on other methods of running a Hive job, see Use Hive on HDInsight.
As part of the Hive job, you import the data from the .csv file into a Hive table named Delays.
From the SSH prompt that you already have for the HDInsight cluster, use the following command to create and edit a new file named flightdelays.hql:
Use the following text as the contents of this file:
DROP TABLE delays_raw; -- Creates an external table over the csv file CREATE EXTERNAL TABLE delays_raw ( YEAR string, FL_DATE string, UNIQUE_CARRIER string, CARRIER string, FL_NUM string, ORIGIN_AIRPORT_ID string, ORIGIN string, ORIGIN_CITY_NAME string, ORIGIN_CITY_NAME_TEMP string, ORIGIN_STATE_ABR string, DEST_AIRPORT_ID string, DEST string, DEST_CITY_NAME string, DEST_CITY_NAME_TEMP string, DEST_STATE_ABR string, DEP_DELAY_NEW float, ARR_DELAY_NEW float, CARRIER_DELAY float, WEATHER_DELAY float, NAS_DELAY float, SECURITY_DELAY float, LATE_AIRCRAFT_DELAY float) -- The following lines describe the format and location of the file ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' LINES TERMINATED BY '\n' STORED AS TEXTFILE LOCATION '/tutorials/flightdelays/data'; -- Drop the delays table if it exists DROP TABLE delays; -- Create the delays table and populate it with data -- pulled in from the CSV file (via the external table defined previously) CREATE TABLE delays AS SELECT YEAR AS year, FL_DATE AS flight_date, substring(UNIQUE_CARRIER, 2, length(UNIQUE_CARRIER) -1) AS unique_carrier, substring(CARRIER, 2, length(CARRIER) -1) AS carrier, substring(FL_NUM, 2, length(FL_NUM) -1) AS flight_num, ORIGIN_AIRPORT_ID AS origin_airport_id, substring(ORIGIN, 2, length(ORIGIN) -1) AS origin_airport_code, substring(ORIGIN_CITY_NAME, 2) AS origin_city_name, substring(ORIGIN_STATE_ABR, 2, length(ORIGIN_STATE_ABR) -1) AS origin_state_abr, DEST_AIRPORT_ID AS dest_airport_id, substring(DEST, 2, length(DEST) -1) AS dest_airport_code, substring(DEST_CITY_NAME,2) AS dest_city_name, substring(DEST_STATE_ABR, 2, length(DEST_STATE_ABR) -1) AS dest_state_abr, DEP_DELAY_NEW AS dep_delay_new, ARR_DELAY_NEW AS arr_delay_new, CARRIER_DELAY AS carrier_delay, WEATHER_DELAY AS weather_delay, NAS_DELAY AS nas_delay, SECURITY_DELAY AS security_delay, LATE_AIRCRAFT_DELAY AS late_aircraft_delay FROM delays_raw;
To save the file, press Esc and then enter
To start Hive and run the flightdelays.hql file, use the following command:
beeline -u 'jdbc:hive2://localhost:10001/;transportMode=http' -f flightdelays.hql
After the flightdelays.hql script finishes running, use the following command to open an interactive Beeline session:
beeline -u 'jdbc:hive2://localhost:10001/;transportMode=http'
When you receive the
jdbc:hive2://localhost:10001/>prompt, use the following query to retrieve data from the imported flight delay data:
INSERT OVERWRITE DIRECTORY '/tutorials/flightdelays/output' ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t' SELECT regexp_replace(origin_city_name, '''', ''), avg(weather_delay) FROM delays WHERE weather_delay IS NOT NULL GROUP BY origin_city_name;
This query retrieves a list of cities that experienced weather delays, along with the average delay time, and saves it to
/tutorials/flightdelays/output. Later, Sqoop reads the data from this location and exports it to Azure SQL Database.
To exit Beeline, enter
!quitat the prompt.
Create a SQL database table
This section assumes that you have already created an Azure SQL database. If you don't already have a SQL database, use the information in Create an Azure SQL database in the Azure portal to create one.
If you already have a SQL database, you must get the server name. To find the server, name in the Azure portal, select SQL Databases, and then filter on the name of the database that you choose to use. The server name is listed in the Server name column.
There are many ways to connect to SQL Database and create a table. The following steps use FreeTDS from the HDInsight cluster.
To install FreeTDS, use the following command from an SSH connection to the cluster:
sudo apt-get --assume-yes install freetds-dev freetds-bin
After the installation finishes, use the following command to connect to the SQL Database server. Replace serverName with the SQL Database server name. Replace adminLogin and adminPassword with the login for SQL Database. Replace databaseName with the database name.
TDSVER=8.0 tsql -H <serverName>.database.windows.net -U <adminLogin> -p 1433 -D <databaseName>
When prompted, enter the password for the SQL Database admin login.
You receive output similar to the following text:
locale is "en_US.UTF-8" locale charset is "UTF-8" using default charset "UTF-8" Default database being set to sqooptest 1>
1>prompt, enter the following lines:
CREATE TABLE [dbo].[delays]( [origin_city_name] [nvarchar](50) NOT NULL, [weather_delay] float, CONSTRAINT [PK_delays] PRIMARY KEY CLUSTERED ([origin_city_name] ASC)) GO
GOstatement is entered, the previous statements are evaluated. This query creates a table named delays, with a clustered index.
Use the following query to verify that the table has been created:
SELECT * FROM information_schema.tables GO
The output is similar to the following text:
TABLE_CATALOG TABLE_SCHEMA TABLE_NAME TABLE_TYPE databaseName dbo delays BASE TABLE
1>prompt to exit the tsql utility.
Export data to SQL database using Sqoop
In the previous sections, you copied the transformed data at
/tutorials/flightdelays/output. In this section, you use Sqoop to export the data from '/tutorials/flightdelays/output` to the table you created in Azure SQL database.
Use the following command to verify that Sqoop can see your SQL database:
sqoop list-databases --connect jdbc:sqlserver://<serverName>.database.windows.net:1433 --username <adminLogin> --password <adminPassword>
This command returns a list of databases, including the database in which you created the delays table earlier.
Use the following command to export data from hivesampletable to the delays table:
sqoop export --connect 'jdbc:sqlserver://<serverName>.database.windows.net:1433;database=<databaseName>' --username <adminLogin> --password <adminPassword> --table 'delays' --export-dir '/tutorials/flightdelays/output' --fields-terminated-by '\t' -m 1
Sqoop connects to the database that contains the delays table, and exports data from the
/tutorials/flightdelays/outputdirectory to the delays table.
After the sqoop command finishes, use the tsql utility to connect to the database:
TDSVER=8.0 tsql -H <serverName>.database.windows.net -U <adminLogin> -P <adminPassword> -p 1433 -D <databaseName>
Use the following statements to verify that the data was exported to the delays table:
SELECT * FROM delays GO
You should see a listing of data in the table. The table includes the city name and the average flight delay time for that city.
exitto exit the tsql utility.
In this tutorial, you learned how to perform extract, transform, and load data operations using an Apache Hadoop cluster in HDInsight. Advance to the next tutorial to learn how to create HDInsight Hadoop clusters on-demand using Azure Data Factory.
To learn more ways to work with data in HDInsight, see the following articles: