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Netezza to BigQuerySQL translation reference
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ContentsAbout this document 4
Introduction 4
Data types 5
Netezza data forma�ing 6
Timestamp and date type forma�ing 6
Query Syntax 7SELECT statement 7Comparison operators 8
Built-in SQL functions 8
Functions 9Aggregate functions 9Analytical functions 10Date/time functions 11String functions 13Math functions 15
DML syntax 16INSERT statement 16UPDATE statement 17DELETE, TRUNCATE statements 18MERGE statement 19
DDL syntax 19CREATE TABLE statement 19DROP statement 20Column options and a�ributes 20Temporary tables 20
Procedural SQL statements 21CREATE PROCEDURE statement 21Variable declaration and assignment 21Exception handlers 21Dynamic SQL statements 22Flow-of-control statements 22Other statements and procedural language elements 23
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Multi-statement and multi-line SQL statements 23
Other SQL statements 23
Consistency guarantees and transaction isolation 24Transactions 24Rollback 24
Database limits 25
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About this document
Highlights
Purpose To detail common similarities and di�erences in SQL syntaxbetween Netezza and BigQuery, so as to help accelerate theplanning and execution of moving a customer’s enterprise datawarehouse (EDW) to BigQuery.
Intended audience Enterprise architects, DBAs, application developers, and ITsecurity.
Key assumptions That the audience is familiar with Netezza and is looking forguidance on transitioning to BigQuery.
IntroductionNetezza data warehousing is designed to work with Netezza-speci�c SQL syntax. Netezza SQLis based on Postgres 7.2. SQL scripts wri�en for Netezza can’t be used in a BigQuery datawarehouse without alterations because the SQL dialects vary.
This document details the similarities and di�erences in SQL syntax between Netezza andBigQuery in the following areas:
● Data types● SQL language elements● Query syntax● DML● DDL● Stored procedures● Functions
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https://en.wikipedia.org/wiki/Netezza
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Data types
Netezza BigQuery Notes
INTEGER/INT/INT4 INT64
SMALLINT/INT2 INT64
BYTEINT/INT1 INT64
BIGINT/INT8 INT64
DECIMAL NUMERICThe DECIMAL data type in Netezza is an alias for the NUMERICdata type.
NUMERIC NUMERICINT64
NUMERIC(p,s) NUMERIC
The NUMERIC type in BigQuery does not enforce custom digitor scale bounds (constraints) like Netezza does. BigQuery has�xed 9 digits a�er the decimal, while Netezza allows a customsetup.
In Netezza, precision p can range from 1 to 38 and scale s from0 to the precision.
FLOAT(p) FLOAT64
REAL/FLOAT(6) FLOAT64
DOUBLEPRECISION/FLOAT(14) FLOAT64
CHAR/CHARACTER STRING
The STRING type in BigQuery is variable-length and does notrequire manually se�ing a max character length as the NetezzaCHARACTER and VARCHAR types require.
The default value of n in CHAR(n) is 1. The maximum characterstring size is 64,000.
VARCHAR STRING
The STRING type in BigQuery is variable-length and does notrequire manually se�ing a max character length as the NetezzaCHARACTER and VARCHAR types require.
The maximum character string size is 64,000.
NCHAR STRINGThe STRING type in BigQuery is stored as variable-length UTF-8encoded Unicode. The maximum length is 16,000 characters.
NVARCHAR STRINGThe STRING type in BigQuery is stored as variable-lengthUTF-8-encoded Unicode. The maximum length is 16,000characters.
VARBINARY BYTES
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ST_GEOMETRY GEOGRAPHY
BOOLEAN/BOOL BOOLThe BOOL type in BigQuery can only accept TRUE/FALSE, unlikethe BOOL type in Netezza, which can accept a variety of valueslike 0/1, yes/no, true/false, on/off.
DATE DATE
TIME TIME
TIMETZ/TIME WITHTIME ZONE TIME
Netezza stores the TIME data type in UTC and allows you topass an o�set from UTC using the WITH TIME ZONE syntax.The TIME data type in BigQuery represents a time that’sindependent of any date or time zone.
TIMESTAMP TIMESTAMPThe Netezza TIMESTAMP type has microsecond precision(including leap seconds) and is usually associated with the UTCtime zone, the same as BigQuery. (Details)
ARRAYThere is no ARRAY data type in Netezza. The ARRAY type isinstead stored in a varchar �eld. (Details)
Netezza data forma�ingFor information about the default formats that Netezza SQL uses for each data type, see theNetezza data forma�ing documentation.
Timestamp and date type forma�ingFor more information about the date type forma�ing that Netezza SQL uses, see the Netezzadate/time template pa�erns documentation. For more information about the date/timefunctions, see the Netezza date/time functions documentation.
When you conve� date type forma�ing elements from Netezza to BigQuery standard SQL,you must pay pa�icular a�ention to time zone di�erences between TIMESTAMP and DATETIME,as summarized in the following table.
Netezza BigQueryCURRENT_TIMESTAMPCURRENT_TIME
TIME information inNetezza can havedi�erent time zoneinformation, which isde�ned using WITHTIME ZONE.
If possible, use CURRENT_TIMESTAMP, which isforma�ed correctly. However, the output format doesshow the UTC time zone (internally, BigQuery does nothave a time zone).
DATETIME in the bq command-line tool and CloudConsole is forma�ed using a T separator according toRFC 3339. However, in Python and Java JDBC, a spaceis used as a separator.
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Use the explicit FORMAT_DATETIME to de�ne the dateformat correctly. Otherwise, an explicit cast is made toa string, for example:
CAST(CURRENT_DATETIME() AS STRING)
This also returns a space separator.
CURRENT_DATE CURRENT_DATE
CURRENT_DATE-3 BigQuery does not suppo� arithmetic data operations.Instead, use DATE_ADD.
Query syntaxSELECT statementFor the most pa�, the Netezza SELECT statement is compatible with BigQuery. The followingtable contains a list of exceptions.
Netezza BigQuerySELECT without FROM clause Suppo�s special case such as:
SELECT 1 UNION ALL SELECT 2;
SELECT(subquery) AS flag,CASE WHEN flag = 1 THEN
...
In BigQuery, columns cannot reference the output of othercolumns de�ned within the same query. You must duplicate thelogic or move the logic into a nested query.
Option 1SELECT(subquery) AS flag,CASE WHEN (subquery) = 1 THEN ...
Option 2SELECTq.*,CASE WHEN flag = 1 THEN ...
FROM (SELECT(subquery) AS flag,...
) q
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Comparison operators
Netezza BigQuery Description
exp = exp2 exp = exp2 Equal
exp = exp2 Greater than or equal to
exp > exp2 exp > exp2 Greater than
Built-in SQL functions
Netezza BigQuery Description
CURRENT_DATE CURRENT_DATE Get the current date (year,month, and day).
CURRENT_TIME CURRENT_TIME Get the current time with fraction.
CURRENT_TIMESTAMP CURRENT_TIMESTAMP Get the current system date andtime to the nearest full second.
NOW CURRENT_TIMESTAMP Get the current system date andtime to the nearest full second.
COALESCE(exp, 0) COALESCE(exp, 0) Replace NULL with zero.
NVL(exp, 0) IFNULL(exp, 0) Replace NULL with zero.
EXTRACT(DOY FROMtimestamp_expression)
EXTRACT(DAYOFYEARFROMtimestamp_expression)
Return the number of days from thebeginning of the year.
ADD_MONTHS(date_expr,num_expr)
DATE_ADD(date,INTERVAL k MONTH)
Add months to a date.
DURATION_ADD(date, k) DATE_ADD(date,INTERVAL k DAY)
Pe�orm addition on dates.
DURATION_SUBTRACT(date, k) DATE_SUB(date,INTERVAL k DAY)
Pe�orm subtraction on dates.
str1 || str2 CONCAT(str1, str2) Concatenate strings.
For more information, see the BigQuery function documentation.
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FunctionsAggregate functions
Netezza BigQuery
ANY_VALUE
APPROX_COUNT_DISTINCT
APPROX_QUANTILES
APPROX_TOP_COUNT
APPROX_TOP_SUM
AVG AVG
intNand BIT_AND
intNnot bitwise not operator: ~
intNor BIT_OR
intNxor BIT_XOR
intNshl
intNshr
CORR CORR
COUNT COUNT
COUNTIF
COVAR_POP COVAR_POP
COVAR_SAMP COVAR_SAMP
GROUPING
LOGICAL_AND
LOGICAL_OR
MAX MAX
MIN MIN
MEDIAN PERCENTILE_CONT(x, 0.5)
STDDEV_POP STDDEV_POP
STDDEV_SAMP STDDEV_SAMP, STDDEV
STRING_AGG
SUM SUM
VAR_POP VAR_POP
VAR_SAMP VAR_SAMP, VARIANCE
9
https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#any_valuehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#approx_count_distincthttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#approx_quantileshttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#approx_top_counthttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#approx_top_sumhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_agg_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#avghttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_andhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bitwise-operatorshttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_orhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_xorhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_corr.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#corrhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_agg_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#counthttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#countifhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_covar_pop.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#covar_pophttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_covar_samp.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#covar_samphttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_grouping_sets.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#logical_andhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#logical_orhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_agg_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#maxhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_agg_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#minhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_agg_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#percentile_conthttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stddev_pophttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stddev_samphttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#string_agghttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#sumhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#var_pophttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#var_samp
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Analytical functions
Netezza BigQuery
ANY_VALUE
ARRAY_AGG
ARRAY_CONCAT ARRAY_CONCAT_AGG
ARRAY_COMBINE
ARRAY_COUNT
ARRAY_SPLIT
ARRAY_TYPE
AVG AVG
intNand BIT_AND
intNnot bitwise not operator: ~
intNor BIT_OR
intNxor BIT_XOR
intNshl
intNshr
CORR CORR
COUNT COUNT
COUNTIF
COVAR_POP COVAR_POP
COVAR_SAMP COVAR_SAMP
CUME_DIST CUME_DIST
DENSE_RANK DENSE_RANK
FIRST_VALUE FIRST_VALUE
LAG LAG
LAST_VALUE LAST_VALUE
LEAD LEAD
AND LOGICAL_AND
OR LOGICAL_OR
MAX MAX
MIN MIN
NTH_VALUE
NTILE NTILE
PERCENT_RANK PERCENT_RANK
10
https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#any_valuehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#array_agghttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_array_concat.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#array_concat_agghttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_array_combine.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_array_count.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_array_split.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_array_type.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#avghttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_andhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bitwise-operatorshttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_orhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#bit_xorhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_math_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_corr.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#corrhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#counthttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#countifhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_covar_pop.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#covar_pophttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.sqltk.doc/r_sqlext_covar_samp.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#covar_samphttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#cume_disthttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#dense_rankhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_first_last_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#first_valuehttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_lag_lead_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#laghttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_first_last_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#last_valuehttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_lag_lead_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#leadhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_operators.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#logical_andhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_operators.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#logical_orhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#maxhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#minhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#nth_valuehttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#ntilehttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#percent_rank
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PERCENTILE_CONT PERCENTILE_CONT
PERCENTILE_DISC PERCENTILE_DISC
RANK RANK
ROW_NUMBER ROW_NUMBER
STDDEV STDDEV
STDDEV_POP STDDEV_POP
STDDEV_SAMP STDDEV_SAMP
STRING_AGG
SUM SUM
VARIANCE VARIANCE
VAR_POP VAR_POP
VAR_SAMP VAR_SAMP / VARIANCE
WIDTH_BUCKET
Date/time functions
Netezza BigQuery
ADD_MONTHS DATE_ADD / TIMESTAMP_ADD
AGE
CURRENT_DATE CURRENT_DATE
CURRENT_DATETIME
CURRENT_TIME CURRENT_TIME
CURRENT_TIME(p)
CURRENT_TIMESTAMP CURRENT_TIMESTAMP
CURRENT_TIMESTAMP(p) DATEDATE_ADDDATE_DIFFDATE_FROM_UNIX_DATEDATE_SUB
DATE_TRUNC DATE_TRUNC
DATE_PART
DATETIME
DATETIME_ADD
DATETIME_DIFF
DATETIME_SUB
DATETIME_TRUNC
11
https://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_inverse_distribution_funcs_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#percentile_conthttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_inverse_distribution_funcs_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#percentile_dischttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#rankhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_ranking_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#row_numberhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stddevhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stddev_pophttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stddev_samphttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#string_agghttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#sumhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#variancehttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#var_pophttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_window_aggregation_family_syntax.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#var_samphttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/c_dbuser_example_width_bucket_func.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_addhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_val_funcs_keywords.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#current_datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#current_datetimehttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_val_funcs_keywords.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#current_timehttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_val_funcs_keywords.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_val_funcs_keywords.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#current_timestamphttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_val_funcs_keywords.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_addhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_diffhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_from_unix_datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_subhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_trunchttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_extract_datetime.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datetimehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datetime_addhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datetime_diffhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datetime_subhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#datetime_trunc
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DURATION_ADD
DURATION_SUBTRACT
EXTRACTEXTRACT (DATE), EXTRACT(TIMESTAMP)
FORMAT_DATE
FORMAT_DATETIME
FORMAT_TIME
FORMAT_TIMESTAMP
LAST_DAY DATE_SUB(DATE_TRUNC(DATE_ADD(date_expression,INTERVAL 1 MONTH
),MONTH
),INTERVAL 1 DAY
)
MONTHS_BETWEENDATE_DIFF(date_expression,date_expression, MONTH)
NEXT_DAY
NOW
NUMTODSINTERVAL
NUMTOYMINTERVAL
OVERLAPS PARSE_DATE
PARSE_DATETIME
PARSE_TIME
PARSE_TIMESTAMP
STRING
TIME
TIME_ADD
TIME_DIFF
TIME_SUB
TIME_TRUNC
TIMEOFDAY
TIMESTAMP TIMESTAMP
TIMESTAMP_ADD
12
https://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_extract_datetime.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#extracthttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#extract_1https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#format_datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#format_datetimehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#format_timehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#format_timestamphttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_subhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_trunchttp://date_addhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#date_diffhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://docs.teradata.com/reader/1DcoER_KpnGTfgPinRAFUw/5_mXJGOtuShvJLNjo16iewhttps://docs.teradata.com/reader/1DcoER_KpnGTfgPinRAFUw/RUNjJpIHhK3d8U1Z2c~8owhttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_datetimehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_timehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_timestamphttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#stringhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#time_addhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#time_diffhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#time_subhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#time_trunchttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamphttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_add
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TIMESTAMP_DIFF
TIMESTAMP_MICROS
TIMESTAMP_MILLIS
TIMESTAMP_SECONDS
TIMESTAMP_SUB
TIMESTAMP_TRUNC
TIMEZONE
TO_DATE PARSE_DATE
TO_TIMESTAMP PARSE_TIMESTAMP
UNIX_DATE
UNIX_MICROS
UNIX_MILLIS
UNIX_SECONDS
String functions
Netezza BigQuery
ASCII TO_CODE_POINTS(string_expr)[OFFSET(0)]
BYTE_LENGTH
TO_HEX
CHAR_LENGTH
CHARACTER_LENGTH
CODE_POINTS_TO_BYTES
BTRIM
CHR CODE_POINTS_TO_STRING([numeric_expr])
CONCAT
DBL_MP
DLE_DST ENDS_WITH
FORMAT
FROM_BASE32
FROM_BASE64
FROM_HEX
HEX_TO_BINARY
HEX_TO_GEOMETRY
INITCAP
13
https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_diffhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_microshttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_millishttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_secondshttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_subhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#timestamp_trunchttps://www.ibm.com/support/knowledgecenter/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_date_time_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_datehttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_functions.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#parse_timestamphttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#unix_datehttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#unix_microshttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#unix_millishttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#unix_secondshttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_char_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#to_code_pointshttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#byte_lengthhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#to_hexhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#char_lengthhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#character_lengthhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#code_points_to_byteshttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_char_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_char_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#code_points_to_stringhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#concathttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.0.3/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_phonetic_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.0.3/com.ibm.nz.dbu.doc/r_dbuser_functions_expressions_phonetic_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#ends_withhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#formathttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#from_base32https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#from_base64https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#from_hexhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_conversion_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_conversion_funcs.htmlhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_char_funcs.html
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INSTR
INT_TO_STRING
LE_DST
LENGTH LENGTH
LOWER LOWER
LPAD LPAD
LTRIM LTRIM
NORMALIZE
NORMALIZE_AND_CASEFOLD
PRI_MP REGEXP_CONTAINS
REGEXP_EXTRACT REGEXP_EXTRACT
REGEXP_EXTRACT_ALL REGEXP_EXTRACT_ALL
REGEXP_EXTRACT_ALL_SP
REGEXP_EXTRACT_SP
REGEXP_INSTR STRPOS(col, REGEXP_EXTRACT())
REGEXP_LIKE
REGEXP_MATCH_COUNT
REGEXP_REPLACE REGEXP_REPLACE
REGEXP_REPLACE_SP IF(REGEXP_CONTAINS,1,0)
REGEXP_EXTRACT
REPEAT REPEAT
REPLACE
REVERSE REVERSE
RPAD RPAD
RTRIM RTRIM
SAFE_CONVERT_BYTES_TO_STRING
SCORE_MP
SEC_MP
SOUNDEX SPLIT
STARTS_WITH
STRING_TO_INT
STRPOS STRPOS
SUBSTR SUBSTR
TO_BASE32
TO_BASE64
14
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doc/r_dbuser_ntz_sql_extns_char_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#strposhttps://www.ibm.com/support/knowledgecenter/en/SSULQD_7.2.1/com.ibm.nz.dbu.doc/r_dbuser_ntz_sql_extns_char_funcs.htmlhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#substrhttps://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#to_base32https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators#to_base64
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TO_CHAR
TO_DATE
TO_NUMBER
TO_TIMESTAMP TO_CODE_POINTS
TO_HEX
TRANSLATE
TRIM TRIM
UPPER UPPER
UNICODE
UNICODES
Math functions
Netezza BigQuery
ABS ABS
ACOS ACOS
ACOSH
ASIN ASIN
ASINH
ATAN ATAN
ATAN2 ATAN2
ATANH
CEILDCEIL
CEILCEILING
COS COS
COSH
COT
DEGREES DIV
EXP EXP
FLOORDFLOOR FLOOR
GREATEST GREATEST
IEEE_DIVIDE
IS_INF
IS_NAN
15
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LEAST LEAST
LN LN
LOG LOG
LOG10
MOD MOD
NULLIF(expr, 0)
PI ACOS(-1)
POWFPOW
POWER / POW
RADIANS
RANDOM RAND
ROUND ROUND
SAFE_DIVIDE
SETSEED
SIGN SIGN
SIN SIN
SINH
SQRTNUMERIC_SQRT SQRT
TAN TAN
TANH
TRUNC TRUNC
IFNULL(expr, 0)
DML syntaxINSERT statement
Netezza BigQueryINSERT INTO table VALUES(...);
INSERT INTO table (...) VALUES (...);
Netezza o�ers a DEFAULT keyword and other constraints forcolumns. In BigQuery, omi�ing column names in the INSERTstatement is valid only if all columns are given.
16
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INSERT INTO table (...)VALUES (...);INSERT INTO table (...)VALUES (...);
INSERT INTO table VALUES (), ()
BigQuery imposes DML quotas, which restrict the number of DMLstatements you can execute daily. To make good use of yourquota, consider the following approaches:
● Combine multiple rows in a single INSERT statement,instead of one row per INSERT.
● Combine multiple DML statements (including INSERT)using a MERGE statement.
● Use CREATE TABLE ... AS SELECT to create andpopulate new tables.
DML scripts in BigQuery have slightly di�erent consistency semantics than the equivalentstatements in Netezza. Also note that BigQuery does not o�er constraints and DEFAULT apa�from NOT NULL.
For an overview of snapshot isolation and session and transaction handling, see theConsistency guarantees and transaction isolation section.
UPDATE statementIn Netezza, the WHERE clause is optional, but in BigQuery it is necessary.
Netezza BigQueryUPDATE tblSETtbl.col1=val1;
Not suppo�ed without the WHERE clause. UseWHERE true to update all rows.
UPDATE ASETy = B.y,z = B.z + 1
FROM BWHERE A.x = B.xAND A.y IS NULL;
UPDATE ASETy = B.y,z = B.z + 1
FROM BWHERE A.x = B.xAND A.y IS NULL;
UPDATE A aliasSET x = x + 1WHERE f(x) IN (0, 1)
UPDATE ASET x = x + 1WHERE f(x) IN (0, 1)
UPDATE ASET z = B.zFROM BWHERE A.x = B.xAND A.y = B.y
UPDATE ASET z = B.zFROM BWHERE A.x = B.xAND A.y = B.y
See UPDATE DML examples in BigQuery.
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Because of DML quotas, you should prefer larger MERGE statements over multiple singleUPDATE and INSERT statements. DML scripts in BigQuery have slightly di�erent consistencysemantics than equivalent statements in Netezza. For an overview on snapshot isolation andsession and transaction handling, see the Consistency guarantees and transaction isolationsection.
DELETE, TRUNCATE statementsThe DELETE and TRUNCATE statements are both ways to remove rows from a table withouta�ecting the table schema or indexes. The TRUNCATE command has the same e�ect as theDELETE command, but is much faster than the DELETE command for large tables. TRUNCATE issuppo�ed in Netezza but not suppo�ed in BigQuery. However, you can use DELETE statementsin both Netezza and BigQuery.
In BigQuery, the DELETE statement must have a WHERE clause. In Netezza, the WHERE clause isoptional. If the WHERE clause is not speci�ed, all the rows in the Netezza table are deleted.
Netezza BigQuery Description
BEGIN;LOCK TABLE A INEXCLUSIVE MODE;DELETE FROM A;INSERT INTO ASELECT * FROM B;COMMIT;
Replacing the contents of a table withquery output is the equivalent of atransaction. You can do this with either aquery or a copy operation.
bq query --replace--destination_table tableA'SELECT * FROM tableB WHERE...'bq cp -f tableA tableB
Replace the contents of a table withthe results of a query.
DELETE FROMdatabase.table
DELETE FROM table WHERE TRUE; In Netezza, when a DELETEstatement is run, the rows are notdeleted physically but only markedfor deletion. Running GROOM TABLEor nzreclaim later on removes therows marked for deletion andreclaims the corresponding diskspace.
GROOM TABLE Netezza uses GROOM TABLEcommand to reclaim disk space byremoving rows marked for deletion.
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MERGE statementMERGE must match at most one source row for each target row. DML scripts in BigQuery haveslightly di�erent consistency semantics than the equivalent statements in Netezza. For anoverview on snapshot isolation and session and transaction handling, see Consistencyguarantees and transaction isolation section.
See MERGE DML Examples in BigQuery and MERGE DML Examples in Netezza.
DDL syntaxCREATE TABLE statement
Netezza BigQuery Description
TEMPTEMPORARY
With BigQuery’s DDL suppo�, youcan create a table from the resultsof a query and specify itsexpiration at creation time. Forexample, for three days:
CREATE TABLE`fh-bigquery.public_dump.vtemp` OPTIONS(expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(), INTERVAL 3 DAY) )
Create tables temporary to a session.
ZONE MAPS Not suppo�ed. Quick search for the WHERE condition.
DISTRIBUTE ON PARTITION BY Pa�itioning.
ORGANIZE ON CLUSTER BY BigQuery clustering is available only inpa�itioned tables.
Both Netezza and BigQuery suppo� up to fourkeys for clustering.
Netezza clustered base tables (CBT) provideequal precedence to each of the clusteringcolumns.
BigQuery gives precedence to the �rst columnon which the table is clustered, followed by thesecond column, and so on.
ROW SECURITY Authorized View Row-level security.
CONSTRAINT Not suppo�ed. Check constraints.
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DROP statement
Netezza BigQuery
DROP TABLE DROP TABLE
DROP DATABASE DROP DATABASE
DROP VIEW DROP VIEW
Column options and a�ributes
Netezza BigQuery Description
NULLNOT NULL
NULLABLEREQUIRED
Specify if the column is allowed to contain NULLvalues.
REFERENCES Not suppo�ed. Specify column constraint.
UNIQUE Not suppo�ed. Each value in the column must be unique.
DEFAULT Not suppo�ed. Default value for all values in the column.
Temporary tablesNetezza suppo�s TEMPORARY tables that exist for the duration of a session.
To build a temporary table in BigQuery, do the following:
1. Create a dataset that has a sho� time to live (for example, 12 hours).
2. Create the temporary table in the dataset, with a table name pre�x of temp. Forexample, to create a table that expires in one hour, do this:
CREATE TABLE temp.name (col1, col2, ...)OPTIONS(expiration_timestamp=TIMESTAMP_ADD(CURRENT_TIMESTAMP(),INTERVAL 1 HOUR));
With scripting and procedures, the expiration_timestamp value can be a script-widevariable.
3. Sta� reading and writing from the temporary table.
More o�en, users prefer to remove duplicates independently in order to �nd errors indownstream systems.
Note that BigQuery does not suppo� DEFAULT and IDENTITY (sequences) columns.
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Procedural SQL statementsNetezza uses the NZPLSQL scripting language to work with stored procedures. NZPLSQL isbased on Postgres PL/pgSQL language. This section describes how to conve� procedural SQLstatements used in stored procedures, functions, and triggers from Netezza to BigQuery.
CREATE PROCEDURE statementNetezza suppo�s stored procedures, but BigQuery does not.
Netezza BigQuery DescriptionCREATE PROCEDURE Not suppo�ed.
() Not suppo�ed. Name of procedure.Parameters can be passed tothe procedure inside thebrackets.
RETURNS datatype Not suppo�ed. Returns either a unique valueor a REFTABLE result set in theform of tables.
LANGUAGE Not suppo�ed. Language used by the storedprocedure (NZPLSQL).
Multiple BEGIN END statements suppo�edinside the BEGIN_PROC END_PROC block.
Not suppo�ed. Number of returned resultsets.
Declarations are inside BEGIN END block. Not suppo�ed.
Variable declaration and assignmentBigQuery scripting is a solution to replace procedural SQL statements. However, it does notprovide an explicit transaction boundary or sessions, and with scripting, all commands have tobe executed within a single network call.
Netezza BigQuery Description
DECLARE var datatype(len)[DEFAULT value];
Scripting andprocedure.
Declare variable.
SET var = value; Not suppo�ed. Assign value to variable.
Exception handlersNetezza suppo�s exception handlers that can be triggered for ce�ain error conditions.BigQuery does not suppo� condition handlers.
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Netezza BigQuery Description
EXCEPTION Not suppo�ed. Declare SQL exception handlerfor general errors.
Dynamic SQL statementsNetezza suppo�s dynamic SQL queries inside stored procedures. The scripting feature inBigQuery suppo�s dynamic SQL statements like those shown in the following table.
Netezza BigQuery Description
EXECUTE IMMEDIATE sql_str; EXECUTE IMMEDIATE Execute dynamic SQL.
Flow-of-control statementsBigQuery scripting enables you to use control �ow statements such as IF and WHILE.
Netezza BigQuery Description
IF THEN ELSE STATEMENTIF condition THEN...ELSE...END IF;
IF condition THENstmts ELSE stmtsEND IF
Execute conditionally.
Iterative ControlFOR var AS SELECT ...DO stmts END FOR;
FOR var AS cur CURSORFOR SELECT ...DO stmts END FOR;
Not suppo�ed. Iterate over a collection of rows.
Iterative ControlLOOP stmts END LOOP;
LOOP
sql_statement_listEND LOOP;
Loop block of statements.
EXIT WHEN BREAK Exit a procedure.
WHILE condition LOOP WHILE condition DOstmts END WHILE
Execute a loop of statements until a whilecondition fails.
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Other statements and procedural language elements
Netezza BigQuery Description
CALL proc(param,...) Not suppo�ed. Execute a procedure.
EXEC proc(param,...) Not suppo�ed. Execute a procedure.
EXECUTE proc(param,...) Not suppo�ed. Execute a procedure.
Multi-statement and multi-line SQL statementsNetezza suppo�s transactions (sessions) and therefore suppo�s statements separated bysemicolons that are consistently executed together.
Netezza BigQuery Description
; // SEMICOLON Suppo�ed in scripting and storedprocedures.Separate one statement fromanother.
Other SQL statements
Netezza BigQuery Description
GENERATE STATISTICS Generate statistics for all the tablesin the current database.
GENERATE STATISTICS ONtable_name
Generate statistics for a speci�ctable.
GENERATE STATISTICS ONtable_name(col1,col4)
Either use statistical functions likeMIN, MAX, and AVG, use the CloudConsole, or use Cloud Data LossPrevention.
Generate statistics for speci�ccolumns in a table.
GENERATE STATISTICS ONtable_name
APPROX_COUNT_DISTINCT(col) Show the number of unique valuesfor columns.
INSERT INTO table_name INSERT INTO table_name Inse� a row.
LOCK TABLE table_nameFOR EXCLUSIVE;
Not suppo�ed. Lock a row.
SET SESSIONCHARACTERISTICS ASTRANSACTION ISOLATIONLEVEL ...
BigQuery uses snapshot isolation.For details, see Consistencyguarantees in this document.
De�ne the transaction isolationlevel.
BEGIN TRANSACTION
END TRANSACTION
COMMIT
BigQuery uses snapshot isolation.For details, see Consistencyguarantees in this document.
De�ne the transaction boundary formulti-statement requests.
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EXPLAIN ... Not suppo�ed.
Similar features are the query planexplanation in the Cloud Consoleand the slot allocation and auditlogging in Cloud Monitoring.
Show query plan for a SELECTstatement.
User Views metadata
System Views metadata
SELECT* EXCEPT(is_typed)FROMmydataset.INFORMATION_SCHEMA.TABLES
BigQuery INFORMATION_SCHEMA.
Query objects in the database.
Consistency guarantees and transaction isolationBoth Netezza and BigQuery are atomic—that is, ACID compliant on a per-mutation level acrossmany rows. For example, a MERGE operation is completely atomic, even with multiple inse�edvalues. BigQuery has no concept of a session or of an explicit transaction boundary andtherefore does not suppo� multi-statement transactions or index consistency.
TransactionsNetezza syntactically accepts all four modes of ANSI SQL transaction isolation. But regardlessof what mode is speci�ed, only SERIALIZABLE is used to maximize consistency and avoid di�y,nonrepeatable, and phantom reads between concurrent transactions. Netezza does not useconventional locking to enforce consistency. Instead, it uses serialization dependencychecking, a form of optimistic concurrency control, to automatically roll back the latesttransaction when two transactions a�empt to modify the same data.
BigQuery suppo�s optimistic concurrency control (�rst to commit wins) with snapshotisolation (the query sees the last commi�ed data before it sta�ed). This approach guaranteesthe same level of consistency on a per-row, per-mutation basis and across rows within thesame DML statement. The concurrency limitations of DML in BigQuery essentially guaranteeno con�icts between two updates. However, BigQuery does not provide an explicit transactionboundary or session.
RollbackNetezza suppo�s ROLLBACK to abo� the current transaction and roll back all the changesmade in the transaction. There is no concept of an explicit rollback in BigQuery. Theworkarounds are table decorators or using FOR SYSTEM_TIME AS OF.
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Database limitsThe following table shows Netezza database maximums.
Limit Netezza BigQueryTables per database 32,000 Unrestricted
Columns per table 1600 10,000
Maximum row size 64 KB 100 MB
Column and tablename length
128 bytes 16,384 Unicode characters
Rows per table Unlimited Unlimited
Maximum SQLrequest length
1 MB (maximum unresolved standard SQLquery length).
12 MB (maximum resolved legacy and standardSQL query length).
Streaming:10 MB (HTTP request size limit)10,000 (maximum rows per request)
Maximum requestand response size
10 MB (request) and 10 GB (response) orvi�ually unlimited if using pagination or theBigQuery Storage API.
Maximum number ofconcurrent sessions
63 concurrent read-writetransactions. 2,000concurrent connections tothe server.
100 concurrent queries (can be raised with slotreservation). 300 concurrent API requests peruser.
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