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shivsoodshivsood
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add image file. Notebook does not still work with relative path for image
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‎samples/features/sql-big-data-cluster/spark/spark_to_sql/spark_to_sql_jdbc.ipynb‎

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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education-num| marital-status| occupation| relationship| race| sex|capital-gain|capital-loss|hours-per-week|native-country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows",
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"output_type": "stream"
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"text": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education-num| marital-status| occupation| relationship| race| sex|capital-gain|capital-loss|hours-per-week|native-country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows"
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}
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],
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"execution_count": 8
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows",
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"output_type": "stream"
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"text": "+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows"
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}
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],
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"execution_count": 9
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},
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{
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"cell_type": "code",
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"source": "#Write from Spark to SQL table using JDBC\r\nprint(\"Use build in JDBC connector to write to SQLServer master instance in Big data \")\r\n\r\nservername = \"jdbc:sqlserver://mssql-master-pool-0.service-master-pool\"\r\ndbname = \"MyTestDatabase\"\r\nurl = servername + \";\" + \"databaseName=\" + dbname + \";\"\r\n\r\nc = \"dbo.AdultCensus\"\r\nuser = \"sa\"\r\npassword = \"ShivTron007\"\r\n\r\nprint(\"url is \", url)\r\n\r\ntry:\r\n df.write \\\r\n .format(\"jdbc\") \\\r\n .mode(\"overwrite\") \\\r\n .option(\"url\", url) \\\r\n .option(\"dbtable\", dbtable) \\\r\n .option(\"user\", user) \\\r\n .option(\"password\", password)\\\r\n .save()\r\nexcept ValueError as error :\r\n print(\"JDBC Write failed\", error)\r\n\r\nprint(\"JDBC Write done \")\r\n\r\n\r\n",
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"source": "#Write from Spark to SQL table using JDBC\r\nprint(\"Use build in JDBC connector to write to SQLServer master instance in Big data \")\r\n\r\nservername = \"jdbc:sqlserver://mssql-master-pool-0.service-master-pool\"\r\ndbname = \"MyTestDatabase\"\r\nurl = servername + \";\" + \"databaseName=\" + dbname + \";\"\r\n\r\nc = \"dbo.AdultCensus\"\r\nuser = \"sa\"\r\npassword = \"****\"\r\n\r\nprint(\"url is \", url)\r\n\r\ntry:\r\n df.write \\\r\n .format(\"jdbc\") \\\r\n .mode(\"overwrite\") \\\r\n .option(\"url\", url) \\\r\n .option(\"dbtable\", dbtable) \\\r\n .option(\"user\", user) \\\r\n .option(\"password\", password)\\\r\n .save()\r\nexcept ValueError as error :\r\n print(\"JDBC Write failed\", error)\r\n\r\nprint(\"JDBC Write done \")\r\n\r\n\r\n",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Use build in JDBC connector to write to SQLServer master instance in Big data \nurl is jdbc:sqlserver://mssql-master-pool-0.service-master-pool;databaseName=MyTestDatabase;\nJDBC Write done",
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"output_type": "stream"
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"text": "Use build in JDBC connector to write to SQLServer master instance in Big data \nurl is jdbc:sqlserver://mssql-master-pool-0.service-master-pool;databaseName=MyTestDatabase;\nJDBC Write done"
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}
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],
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"execution_count": 10
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "read data from SQL server table \n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows",
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"output_type": "stream"
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"text": "read data from SQL server table \n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n|age| workclass|fnlwgt|education|education_num| marital_status| occupation| relationship| race| sex|capital_gain|capital_loss|hours_per_week|native_country|income|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\n| 39| State-gov| 77516|Bachelors| 13| Never-married| Adm-clerical|Not-in-family|White| Male| 2174| 0| 40| United-States| <=50K|\n| 50|Self-emp-not-inc| 83311|Bachelors| 13|Married-civ-spouse| Exec-managerial| Husband|White| Male| 0| 0| 13| United-States| <=50K|\n| 38| Private|215646| HS-grad| 9| Divorced|Handlers-cleaners|Not-in-family|White| Male| 0| 0| 40| United-States| <=50K|\n| 53| Private|234721| 11th| 7|Married-civ-spouse|Handlers-cleaners| Husband|Black| Male| 0| 0| 40| United-States| <=50K|\n| 28| Private|338409|Bachelors| 13|Married-civ-spouse| Prof-specialty| Wife|Black|Female| 0| 0| 40| Cuba| <=50K|\n+---+----------------+------+---------+-------------+------------------+-----------------+-------------+-----+------+------------+------------+--------------+--------------+------+\nonly showing top 5 rows"
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}
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],
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"execution_count": 13

‎samples/features/sql-big-data-cluster/spark/sparkml/Train_Score_Export_with_Spark.jpg‎

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