SAP HANA Cloud, Data Lake Client Interfaces Overview

SAP HANA Cloud is composed of multiple components.

• SAP HANA is an in-memory, multi-model, column-based, relational database. For further details see Introduction to SAP HANA Cloud and the tutorial mission Use Clients to Query an SAP HANA Database.

• SAP HANA Cloud, data lake is composed of two components; a data lake Relational Engine and data lake Files.

  Data Lake Relational Engine is a disk-based, column-oriented relational database for storing and analyzing large amounts of infrequently updated data. It descends from SAP IQ, which was previously named Sybase IQ. Because of its heritage, there are commonalities with other Sybase products. Some of the client interface drivers are shared with SAP SQL Anywhere and SAP Adaptive Server Enterprise.

  Data Lake Files can be used to store and access unstructured data such as trace files and structured files like CSV, Parquet, or Delta table, or Iceberg table. Structured files can use SQL on Files, which enables SQL queries to be performed on them.

  Note, that the data lake Files component, is currently not available in free tier.

The SAP Business Technology Platform (SAP BTP) provides multiple runtime environments such as Cloud Foundry and Kyma. When a data lake instance is created, it can be created in an SAP BTP subaccount or in a Cloud Foundry space. SAP HANA Cloud Central can be used to provision and manage instances in the SAP BTP subaccount or in a Cloud Foundry space. In the screenshot below, there is an instance of a data lake that was provisioned in the SAP BTP subaccount (Other Environments) and one that was provisioned into Cloud Foundry.

SAP HANA Cloud Central can be accessed (once a subscription and setup is complete) under instances and subscriptions.

To complete the tutorials in this group, a SAP HANA Cloud, data lake instance is needed. Setup instructions on the SAP BTP trial are provided at Set Up Your SAP HANA Cloud, SAP HANA Database and Understand the Basics.

The following steps provide instructions on how to create a data lake instance in the SAP BTP trial using a free tier service plan. Additional content on this topic is available at Quick Start Tutorial for Data Lake.

There are multiple ways to create a data lake:

• A data lake can be created in step 6 of the SAP HANA Database creation wizard.

  

• A data lake can be added to an already created SAP HANA database that does not have a data lake already associated with it.

  

  When a data lake is created in either of the previous two methods, it is configured to be maximally compatible with an SAP HANA database.

• A data lake can be created that is independent (standalone) of a SAP HANA database by using the Create Instance button.

  

  On the Type page, select the option SAP HANA Cloud, data lake.

  

  A standalone data lake can be configured with additional options such a collation value of UTF8BIN and blank padding set to ON to be more compatible with an on-premise SAP IQ.

Perform the following steps to create a data lake Relational Engine.

1. Open SAP HANA Cloud Central.

2. If a data lake is not already present, add one using one of the three methods previously described.

   Take note that when creating the data lake Relational Engine instance, the administration user is HDLADMIN.

   The HDLADMIN user has a login policy that enforces the update of the password after 180 days.

3. If this instance is a free tier instance or a test instance, set allowed connections to Allow all IP addresses so that client applications can connect from any IP address.

   

4. Press the Refresh button or enable auto-refresh and wait for the status to change from CREATING to RUNNING.

   

   Important: SAP HANA Cloud, HANA data lake free tier instances are shut down overnight and will need to be restarted before working with them the next day.

Once the data lake has been created, its details can be examined.

1. Click on the instance to show its details.

   

2. Input your credentials. These will be stored by SAP HANA Cloud Central.

   

   After you enter your credentials, should you wish to use a different set of credentials, the current credentials can be updated using Sign in to the Instance.

   

In this step, a sample HOTEL dataset will be created comprising tables, a view, and a stored procedure.

1. From the action menu, select Open SQL Console.

   

2. Execute a few queries.

   SQL

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   SELECT CURRENT USER FROM DUMMY;
   SELECT * FROM sa_db_properties() WHERE UPPER(PropName) LIKE '%NAME%';
   SELECT * FROM SYS.SYSOPTIONS WHERE UPPER("option") LIKE '%VERIFY%';
   CALL sa_conn_properties(CONNECTION_PROPERTY('Number'));
   

   

   Additional details can be found at System Functions and Stored Procedures in Data Lake Relational Engine.

3. Execute the below SQL statements

   SQL

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   ---- drops the schema and all objects it contains
   -- SET SCHEMA HOTELS;
   -- DROP VIEW HOTEL_ROOMS_VIEW;
   -- DROP PROCEDURE SHOW_RESERVATIONS;
   -- DROP TABLE MAINTENANCE;
   -- DROP TABLE RESERVATION;
   -- DROP TABLE CUSTOMER;
   -- DROP TABLE ROOM;
   -- DROP TABLE HOTEL;
   -- DROP FUNCTION AVERAGE_PRICE;
   -- DROP SCHEMA HOTELS;
   
   -- DROP USER USER1;
   -- DROP USER USER2;
   -- DROP ROLE HOTEL_ADMIN;
   -- DROP ROLE HOTEL_READER;
   -- SET SCHEMA; --Workaround a bug where creating user fails with f_verify_pwd is no longer valid
   
   CREATE USER USER1 IDENTIFIED BY Password1;
   CREATE USER USER2 IDENTIFIED BY Password2;
   
   CREATE ROLE HOTEL_ADMIN;
   CREATE ROLE HOTEL_READER;
   
   --Further privileges will be granted later to the tables, views, functions, and procedures that are created below
   GRANT SET ANY CUSTOMER PUBLIC OPTION, READ FILE TO HOTEL_ADMIN, HOTEL_READER;
   GRANT EXECUTE ON sp_list_directory TO HOTEL_ADMIN, HOTEL_READER;
   GRANT EXECUTE ON sp_real_list_directory TO HOTEL_ADMIN, HOTEL_READER;
   
   GRANT ROLE HOTEL_ADMIN TO USER1;
   GRANT ROLE HOTEL_READER TO USER2;
   
   --Create a schema for the sample hotel dataset  
   CREATE SCHEMA HOTELS;
   
   --Specify the privileges for the roles on the schema HOTEL
   GRANT CREATE ANY, SELECT, UPDATE, INSERT, DELETE, EXECUTE PROCEDURE ON SCHEMA HOTELS TO HOTEL_ADMIN;
   GRANT SELECT ON SCHEMA HOTELS TO HOTEL_READER;
   
   SET SCHEMA HOTELS;
   SELECT CURRENT SCHEMA;
   --Create the objects in the HOTEL schema
   CREATE TABLE HOTEL(
       HNO INTEGER PRIMARY KEY,
       NAME VARCHAR(50) NOT NULL,
       ADDRESS VARCHAR(40) NOT NULL,
       CITY VARCHAR(30) NOT NULL,
       STATE VARCHAR(2) NOT NULL,
       ZIP VARCHAR(6)
   );
   
   CREATE TABLE ROOM(
       HNO INTEGER,
       TYPE VARCHAR(6),
       FREE NUMERIC(3),
       PRICE NUMERIC(6, 2),
       PRIMARY KEY (HNO, TYPE),
       FOREIGN KEY (HNO) REFERENCES HOTEL
   );
   
   CREATE TABLE CUSTOMER(
       CNO INTEGER PRIMARY KEY,
       TITLE VARCHAR(7),
       FIRSTNAME VARCHAR(20),
       NAME VARCHAR(40) NOT NULL,
       ADDRESS VARCHAR(40) NOT NULL,
       ZIP VARCHAR(6)
   );
   
   CREATE TABLE RESERVATION(
       RESNO INTEGER NOT NULL default autoincrement,
       RNO INTEGER NOT NULL,
       CNO INTEGER,
       HNO INTEGER,
       TYPE VARCHAR(6),
       ARRIVAL DATE NOT NULL,
       DEPARTURE DATE NOT NULL,
       PRIMARY KEY (
           "RESNO", "ARRIVAL"
       ),
       FOREIGN KEY(CNO) REFERENCES CUSTOMER,
       FOREIGN KEY(HNO) REFERENCES HOTEL
   );
   
   CREATE TABLE MAINTENANCE(
       MNO INTEGER PRIMARY KEY,
       HNO INTEGER,
       DESCRIPTION VARCHAR(100),
       DATE_PERFORMED DATE,
       PERFORMED_BY VARCHAR(40),
       FOREIGN KEY(HNO) REFERENCES HOTEL
   );
   
   --Note the use of the schema name in the FROM clause
   CREATE OR REPLACE VIEW HOTEL_ROOMS_VIEW AS
   SELECT
       H.NAME AS HOTEL_NAME,
       R.TYPE,
       R.FREE,
       R.PRICE
   FROM HOTELS.ROOM R
       LEFT JOIN HOTELS.HOTEL H ON R.HNO = H.HNO
           ORDER BY H.NAME;
   
   CREATE OR REPLACE FUNCTION AVERAGE_PRICE(room_type CHAR(6))
   RETURNS NUMERIC(6, 2)
   BEGIN
       DECLARE avg_price NUMERIC(6,2);  
       SELECT CAST(ROUND(sum(PRICE)/COUNT(*), 2) as NUMERIC(6,2)) INTO avg_price FROM ROOM WHERE TYPE = room_type GROUP BY TYPE;
       RETURN avg_price;
   END;
   
   CREATE OR REPLACE PROCEDURE SHOW_RESERVATIONS(
       IN IN_HNO INTEGER, IN IN_ARRIVAL DATE)
       RESULT (RESNO INTEGER, ARRIVAL DATE, NIGHTS INTEGER, HOTEL_NAME VARCHAR(50),TITLE VARCHAR(7), FIRST_NAME VARCHAR(20), LAST_NAME VARCHAR(40))
       BEGIN
           MESSAGE IN_HNO TO CLIENT;
           MESSAGE IN_ARRIVAL TO CLIENT;
       SELECT
           R.RESNO,
           R.ARRIVAL,
           DATEDIFF(DAY, R.ARRIVAL, R.DEPARTURE) as "Nights",
           H.NAME,
           CUS.TITLE,
           CUS.FIRSTNAME AS "FIRST NAME",
           CUS.NAME AS "LAST NAME"
       FROM
           RESERVATION AS R
           LEFT OUTER JOIN
           HOTEL AS H
           ON H.HNO = R.HNO
           LEFT OUTER JOIN
           CUSTOMER AS CUS
           ON CUS.CNO = R.CNO
           WHERE R.ARRIVAL = IN_ARRIVAL AND
           H.HNO = IN_HNO
           ORDER BY CUS.NAME ASC;
       END;
   

4. Open the database objects app. Select the instance, select Tables, and set the schema filter to be HOTELS to limit the returned tables to be those that were just created in the HOTELS schema.

   

   Additional details on the SQL used above can be found at CREATE TABLE Statement for Data Lake Relational Engine, CREATE VIEW Statement for Data Lake Relational Engine, and CREATE PROCEDURE Statement for Data Lake Relational Engine.

   Identifiers such as the column names in a table are case-insensitive.

For additional details on the SQL Console and database objects app, see the tutorials Query Databases Using the SQL Console in SAP HANA Cloud Central and Browse and Explore Catalog Objects with the Database Objects App, which showcases many of their features.

The data lake client install is available without included cryptographic libraries using the SAP Developer License agreement from SAP Development Tools. This version does not require the user to sign in prior to downloading the software. The software will use the cryptographic library found on the OS such as OpenSSL or SAP CommonCryptoLib. Note that it currently does not include the hdlfscli tool.

The data lake client install is also available from SAP for me. This version includes cryptographic libraries and does require a login and purchase of the software to access the download. To access it, navigate to Support Packages & Patches | By Alphabetical Index (A-Z) | H | HANA CLOUD CLIENTS | HANA CLOUD CLIENTS 1.0 | HANA DATALAKE CLIENT 1.0. Select the platform (Microsoft Windows or Linux) and download the latest version of the archive.

Either location can be used with this tutorial.

Further details on the download and install process can be found at Download the SAP HANA Data Lake Client.

Ensure that SAP JVM (Java Virtual Machine) 8.0 is installed before proceeding with the following steps if you wish to use the DBISQL tool.

1. Extract the archive.

   Shell (Linux)

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   tar -zxvf hdlclient-latest-linux-x64.tar.gz
   

2. Start the installer.

   Shell (Linux)

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   ./hdbinst
   

   

3. Configure the environment variables. This can be done by calling hdlclienv.sh manually or it can be added to the Bash shell by referencing it in .bashrc.

   Open the .bashrc.

   Shell (Linux)

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   pico ~/.bashrc
   

   This tutorial uses notepad and pico as default text editors, but any text editor will do.
   Pico can be installed on SUSE Linux with

   Shell (Linux SUSE)

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   sudo zypper install pico
   

   Add the following line to point to the location where the SAP data lake client is installed.

   Shell (Linux) .bash_profile

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   source ~/sap/hdlclient/bin64/hdlclienv.sh
   

   Test the change by running:

   Shell (Linux)

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   source ~/.bashrc
   

   The following command should display the install location of the data lake client.

   Shell (Linux)

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   echo $HDL_CLIENT_HOME
   

   In the case that the data lake client needs to be uninstalled, run the hdbuninst file located in the directory ~/sap/hdlclient/install.

Ensure that SAP JVM (Java Virtual Machine) 8.0 is installed before proceeding with the following steps if you wish to use the DBISQL tool.

1. Run hdbsetup.exe.

   

2. Examine the installation log and take note if the required crypto libraries were located in the machine’s path.

   

   Select View Log and search for crypto

   

   If these libraries were not found but are on your machine perhaps as part of the git client, add that folder to your path (For example, C:\Git\mingw64\bin).

3. After the installation process is completed, open Microsoft Windows, click the Start icon and search for Edit the system environment variables and press the “Environment Variables” button under the “Advanced” tab.

   

4. It is also possible to run a batch file that will temporarily set the environment variables. This can be done by calling hdlclienv.bat from within a command prompt.

   

   Then once called, the variables set can be seen by calling SET.

   

5. In the case the data lake client needs to be uninstalled, run the hdbuninst file located in the directory C:\SAP\hdlclient\install.

The data lake client install includes dbisql Interactive SQL Utility, which can be used to connect and query a data lake Relational Engine. The following steps will provide instructions on how to connect to the data lake Relational Engine using DBISQL and then populate the previously created tables with sample data.

1. DBISQL requires SAP JVM 8.0. Verify that you have this installed by entering java -version as shown below.

   Shell

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   java -version
   

   

   If you do not have this version on Microsoft Windows, it can be downloaded and configured as shown below

   • Download SAP JVM 8.0 and unzip it to c:\SAP\SAPJVM8

   • In your environment variables, add the bin subdirectory of JAVA_HOME to the existing Path environment variable.

     

   • Open a new command prompt and test the change.

     Shell (Microsoft Windows)

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     java -version
     dbisql
     

     The Data Lake Relational Engine should start, this may take a moment. If the location has changed since the install was run, you may need to edit C:\SAP\hdlclient\bin64\dbisql.ini.

   If you do not have this version on Linux, it can be downloaded and installed as shown below.

   • Download and install SAP JVM 8.0

     Shell (Linux)

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     unzip sapjvm-8.1.102-linux-x64.zip
     mv ./sapjvm_8 ~/
     

   • Add the following to your bashrc.

     Shell (Linux)

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     export JAVA_HOME=~/sapjvm_8
     export PATH=$PATH:$JAVA_HOME/bin
     

   • Apply and test the changes.

     Shell (Linux)

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     source ~/.bashrc
     java -version
     dbisql
     

   If a further error occurs such as libXext.so.6: cannot open shared object file, install the missing components.

   ```Shell (Linux)
   sudo zypper install libXext6
   sudo zypper install libXrender1
   sudo zypper install libXtst6
   sudo zypper install libXi6
   ```
   

   If an error occurs mentioning that saip17.jar file has moved or has been deleted, examine
   C:\Users\Public\Documents\DBISQL 17.1.6\dbisql_64.rep and optionally comment out with # the plugins that are not loading.

2. Specify the connection type.

   

   The Connect window may appear enlarged on the screen. This can be adjusted by lowering the Scale and layout value in the device display settings.

3. Provide the connection details. See below on how to obtain the instance ID and landscape values.

   

   SAP HANA Cloud Central can be used to get the instance ID and landscape value. The landscape value can be obtained from the SQL Endpoint by removing the instance ID from the start and port number from the end.

   

   A failure to connect could be caused by the allowed connections list, which is editable in SAP HANA Cloud Central.

   Paste the following code in the console and click run.

   SQL

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   SELECT * FROM SYS.SYSINFO;
   

   

   DBISQL can also be started without a GUI.

   Shell (Microsoft Windows)

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   dbisql -c "uid=USER1;pwd=Password1;host=XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX.iq.hdl.xxxx-xxxx.hanacloud.ondemand.com:443;ENC=TLS(tls_type=rsa;direct=yes)" -nogui
   

   In a Bash shell, strings in double quotes versus single quotes are treated differently.

   Shell (Linux)

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   dbisql -c 'uid=USER1;pwd=Password1;host=XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX.iq.hdl.trial-XXXX.hanacloud.ondemand.com:443;ENC=TLS(tls_type=rsa;direct=yes)' -nogui
   

   NOTE:-If you encounter the error message “error while loading shared libraries: libnsl.so.1: cannot open shared object file: No such file or directory,” you will need to install libnsl.so.1.

   

1. Execute the following insert statements to provide some sample data.

   If you do not wish to use the GUI mode, paste the insert statements into a file first and then run dbisql -c "uid..." sql.sql.

   SQL

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   SET SCHEMA HOTELS;
   INSERT INTO HOTEL VALUES(10, 'Congress', '155 Beechwood St.', 'Seattle', 'WA', '20005');
   INSERT INTO HOTEL VALUES(11, 'Regency', '477 17th Avenue', 'Seattle', 'WA', '20037');
   INSERT INTO HOTEL VALUES(12, 'Long Island', '1499 Grove Street', 'Long Island', 'NY', '11788');
   INSERT INTO HOTEL VALUES(13, 'Empire State', '65 Yellowstone Dr.', 'Albany', 'NY', '12203');
   INSERT INTO HOTEL VALUES(14, 'Midtown', '12 Barnard St.', 'New York', 'NY', '10019');
   INSERT INTO HOTEL VALUES(15, 'Eighth Avenue', '112 8th Avenue', 'New York', 'NY', '10019');
   INSERT INTO HOTEL VALUES(16, 'Lake Michigan', '354 OAK Terrace', 'Chicago', 'IL', '60601');
   INSERT INTO HOTEL VALUES(17, 'Airport', '650 C Parkway', 'Rosemont', 'IL', '60018');
   INSERT INTO HOTEL VALUES(18, 'Sunshine', '200 Yellowstone Dr.', 'Clearwater', 'FL', '33575');
   INSERT INTO HOTEL VALUES(19, 'Beach', '1980 34th St.', 'Daytona Beach', 'FL', '32018');
   INSERT INTO HOTEL VALUES(20, 'Atlantic', '111 78th St.', 'Deerfield Beach', 'FL', '33441');
   INSERT INTO HOTEL VALUES(21, 'Long Beach', '35 Broadway', 'Long Beach', 'CA', '90804');
   INSERT INTO HOTEL VALUES(22, 'Indian Horse', '16 MAIN STREET', 'Palm Springs', 'CA', '92262');
   INSERT INTO HOTEL VALUES(23, 'Star', '13 Beechwood Place', 'Hollywood', 'CA', '90029');
   INSERT INTO HOTEL VALUES(24, 'River Boat', '788 MAIN STREET', 'New Orleans', 'LA', '70112');
   INSERT INTO HOTEL VALUES(25, 'Ocean Star', '45 Pacific Avenue', 'Atlantic City', 'NJ', '08401');
   INSERT INTO HOTEL VALUES(26, 'Bella Ciente', '1407 Marshall Ave', 'Longview', 'TX', '75601');
   
   INSERT INTO ROOM VALUES(10, 'single', 20, 135.00);
   INSERT INTO ROOM VALUES(10, 'double', 45, 200.00);
   INSERT INTO ROOM VALUES(12, 'single', 10, 70.00);
   INSERT INTO ROOM VALUES(12, 'double', 13, 100.00);
   INSERT INTO ROOM VALUES(13, 'single', 12, 45.00);
   INSERT INTO ROOM VALUES(13, 'double', 15, 80.00);
   INSERT INTO ROOM VALUES(14, 'single', 20, 85.00);
   INSERT INTO ROOM VALUES(14, 'double', 35, 140.00);
   INSERT INTO ROOM VALUES(15, 'single', 50, 105.00);
   INSERT INTO ROOM VALUES(15, 'double', 230, 180.00);
   INSERT INTO ROOM VALUES(15, 'suite', 12, 500.00);
   INSERT INTO ROOM VALUES(16, 'single', 10, 120.00);
   INSERT INTO ROOM VALUES(16, 'double', 39, 200.00);
   INSERT INTO ROOM VALUES(16, 'suite', 20, 500.00);
   INSERT INTO ROOM VALUES(17, 'single', 4, 115.00);
   INSERT INTO ROOM VALUES(17, 'double', 11, 180.00);
   INSERT INTO ROOM VALUES(18, 'single', 15, 90.00);
   INSERT INTO ROOM VALUES(18, 'double', 19, 150.00);
   INSERT INTO ROOM VALUES(18, 'suite', 5, 400.00);
   INSERT INTO ROOM VALUES(19, 'single', 45, 90.00);
   INSERT INTO ROOM VALUES(19, 'double', 145, 150.00);
   INSERT INTO ROOM VALUES(19, 'suite', 60, 300.00);
   INSERT INTO ROOM VALUES(20, 'single', 11, 60.00);
   INSERT INTO ROOM VALUES(20, 'double', 24, 100.00);
   INSERT INTO ROOM VALUES(21, 'single', 2, 70.00);
   INSERT INTO ROOM VALUES(21, 'double', 10, 130.00);
   INSERT INTO ROOM VALUES(22, 'single', 34, 80.00);
   INSERT INTO ROOM VALUES(22, 'double', 78, 140.00);
   INSERT INTO ROOM VALUES(22, 'suite', 55, 350.00);
   INSERT INTO ROOM VALUES(23, 'single', 89, 160.00);
   INSERT INTO ROOM VALUES(23, 'double', 300, 270.00);
   INSERT INTO ROOM VALUES(23, 'suite', 100, 700.00);
   INSERT INTO ROOM VALUES(24, 'single', 10, 125.00);
   INSERT INTO ROOM VALUES(24, 'double', 9, 200.00);
   INSERT INTO ROOM VALUES(24, 'suite', 78, 600.00);
   INSERT INTO ROOM VALUES(25, 'single', 44, 100.00);
   INSERT INTO ROOM VALUES(25, 'double', 115, 190.00);
   INSERT INTO ROOM VALUES(25, 'suite', 6, 450.00);
   
   INSERT INTO CUSTOMER VALUES(1000, 'Mrs', 'Jenny', 'Porter', '1340 N. Ash Street, #3', '10580');
   INSERT INTO CUSTOMER VALUES(1001, 'Mr', 'Peter', 'Brown', '1001 34th St., APT.3', '48226');
   INSERT INTO CUSTOMER VALUES(1002, 'Company', NULL, 'Datasoft', '486 Maple St.', '90018');
   INSERT INTO CUSTOMER VALUES(1003, 'Mrs', 'Rose', 'Brian', '500 Yellowstone Drive, #2', '75243');
   INSERT INTO CUSTOMER VALUES(1004, 'Mrs', 'Mary', 'Griffith', '3401 Elder Lane', '20005');
   INSERT INTO CUSTOMER VALUES(1005, 'Mr', 'Martin', 'Randolph', '340 MAIN STREET, #7', '60615');
   INSERT INTO CUSTOMER VALUES(1006, 'Mrs', 'Sally', 'Smith', '250 Curtis Street', '75243');
   INSERT INTO CUSTOMER VALUES(1007, 'Mr', 'Mike', 'Jackson', '133 BROADWAY APT. 1', '45211');
   INSERT INTO CUSTOMER VALUES(1008, 'Mrs', 'Rita', 'Doe', '2000 Humboldt St., #6', '97213');
   INSERT INTO CUSTOMER VALUES(1009, 'Mr', 'George', 'Howe', '111 B Parkway, #23', '75243');
   INSERT INTO CUSTOMER VALUES(1010, 'Mr', 'Frank', 'Miller', '27 5th St., 76', '95054');
   INSERT INTO CUSTOMER VALUES(1011, 'Mrs', 'Susan', 'Baker', '200 MAIN STREET, #94', '90018');
   INSERT INTO CUSTOMER VALUES(1012, 'Mr', 'Joseph', 'Peters', '700 S. Ash St., APT.12', '92714');
   INSERT INTO CUSTOMER VALUES(1013, 'Company', NULL, 'TOOLware', '410 Mariposa St., #10', '20019');
   INSERT INTO CUSTOMER VALUES(1014, 'Mr', 'Antony', 'Jenkins', '55 A Parkway, #15', '20903');
   
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(100, 1000, 11, 'single', '2020-12-24', '2020-12-27');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(110, 1001, 11, 'double', '2020-12-24', '2021-01-03');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(120, 1002, 15, 'suite', '2020-11-14', '2020-11-18');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(130, 1009, 21, 'single', '2019-02-01', '2019-02-03');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(150, 1006, 17, 'double', '2019-03-14', '2019-03-24');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(140, 1013, 20, 'double', '2020-04-12', '2020-04-30');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(160, 1011, 17, 'single', '2020-04-12', '2020-04-15');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(170, 1014, 25, 'suite', '2020-09-01', '2020-09-03');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(180, 1001, 22, 'double', '2020-12-23', '2021-01-08');
   INSERT INTO RESERVATION(rno, cno, hno, type, arrival, departure) VALUES(190, 1013, 24, 'double', '2020-11-14', '2020-11-17');
   
   INSERT INTO MAINTENANCE VALUES(10, 24, 'Replace pool liner and pump', '2019-03-21', 'Discount Pool Supplies');
   INSERT INTO MAINTENANCE VALUES(11, 25, 'Renovate the bar area.  Replace TV and speakers', '2020-11-29', 'TV and Audio Superstore');
   INSERT INTO MAINTENANCE VALUES(12, 26, 'Roof repair due to storm', null, null);
   COMMIT;
   

   Additional details on the SQL used above can be found at INSERT Statement for Data Lake Relational Engine. Moreover, the LOAD TABLE Statement for Data Lake Relational Engine can be used for efficient mass insertion into a database table from a file with ASCII or binary data.

   Autocommit is set to on in the SQL console of the database explorer, while in DBISQL it is set to off. A series of insert statements will run quicker in the SQL console if they are surrounded with begin and end or if autocommit is set to off.

   SQL

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   begin
   INSERT INTO HOTELS.ROOM VALUES(11, 'double', 13, 190.00);
   INSERT INTO HOTELS.ROOM VALUES(11, 'single', 15, 175.00);
   end;
   

   SQL

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   set temporary option auto_commit= 'off';
   INSERT INTO HOTELS.ROOM VALUES(11, 'triple', 7, 235.00);
   INSERT INTO HOTELS.ROOM VALUES(11, 'quad', 5, 275.00);
   set temporary option auto_commit= 'on';
   

   Autocommit can also be set via the connection settings dialog.

   

   

2. Notice that pressing ctrl-space brings up auto complete (GUI mode only).

   

   Query a table, a view, invoke a function, and call a stored procedure.

   SQL

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   SET SCHEMA HOTELS;
   SELECT * FROM HOTEL;
   SELECT * FROM HOTEL_ROOMS_VIEW;
   SELECT AVERAGE_PRICE('single'), AVERAGE_PRICE('double'), AVERAGE_PRICE('suite') FROM DUMMY;
   CALL SHOW_RESERVATIONS(11, '2020-12-24');
   

   

3. DBISQL can also execute SQL from the command line or from a provided file. A few examples are shown below.

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   dbisql -c "uid=USER1;pwd=Password1;host=XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX.iq.hdl.trial-XXXX.hanacloud.ondemand.com:443;ENC=TLS(tls_type=rsa;direct=yes)" "select * from HOTELS.CUSTOMER;"
   dbisql -c "uid=USER1;pwd=Password1;host=XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX.iq.hdl.trial-XXXX.hanacloud.ondemand.com:443;ENC=TLS(tls_type=rsa;direct=yes)" sql.sql
   

   

   See Connection Parameters for additional documentation on the parameters used to connect.

Congratulations! You have created and connected to a data lake Relational Engine. In the following tutorials, the client interfaces will be used to connect from ODBC, JDBC and Node.js.