This article provides information about configuring PostgreSQL Repository connection
Click OptionsClick PlusEnter connection nameSelect typeEnter username and passwordClick ODBC manager and create new ODBC DSNSelect newly created DSNMake sure that connection actually works
Great news Visual Importer ETL works now directly with PostgreSQL databases. The direct connection gives it a massive performance boost so you can load more than 10000 records per second.
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And here is what our customers think about it:
The latest version of Visual Importer ETL offers full support for PostgreSQL. Data can be imported from Flat files, Excel, MS Access, Oracle, MySQL, Interbase, Firebird, PostgreSQL, OleDB, ODBC and DBF files
Full support for Unicode
All versions of PostgreSQL are supported including version 9.0.1
For every database, file, data source we use the best possible way of importing data
PostgreSQL is a powerful, open-source object-relational database system. It has more than 15 years of active development and a proven architecture that has earned it a strong reputation for reliability, data integrity, and correctness. It runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows. It is fully ACID compliant, has full support for foreign keys, joins, views, triggers, and stored procedures (in multiple languages). It includes most SQL:2008 data types, including INTEGER, NUMERIC, BOOLEAN, CHAR, VARCHAR, DATE, INTERVAL, and TIMESTAMP. It also supports storage of binary large objects, including pictures, sounds, or video. It has native programming interfaces for C/C++, Java, .Net, Perl, Python, Ruby, Tcl, ODBC, among others, and exceptional documentation.
An enterprise-class database, PostgreSQL boasts sophisticated features such as Multi-Version Concurrency Control (MVCC), point in time recovery, tablespaces, asynchronous replication, nested transactions (savepoints), online/hot backups, a sophisticated query planner/optimizer, and write-ahead logging for fault tolerance. It supports international character sets, multibyte character encodings, Unicode, and it is locale-aware for sorting, case-sensitivity, and formatting. It is highly scalable both in the sheer quantity of data it can manage and in the number of concurrent users, it can accommodate. There are active PostgreSQL systems in production environments that manage in excess of 4 terabytes of data. Some general PostgreSQL limits are included in the table below.
More information about PostgreSQL
This SQL script will create and populate the Time dimension for PostgreSQL based data warehouse
CREATE TABLE time_dim(time_key integer NOT NULL,time_value character(5) NOT NULL,hours_24 character(2) NOT NULL,hours_12 character(2) NOT NULL,hour_minutes character (2) NOT NULL,day_minutes integer NOT NULL,day_time_name character varying (20) NOT NULL,day_night character varying (20) NOT NULL,CONSTRAINT time_dim_pk PRIMARY KEY (time_key))WITH (OIDS=FALSE);COMMENT ON TABLE time_dim IS 'Time Dimension';COMMENT ON COLUMN time_dim.time_key IS 'Time Dimension PK';insert into time_dimSELECT cast(to_char(minute, 'hh24mi') as numeric) time_key,to_char(minute, 'hh24:mi') AS tume_value,-- Hour of the day (0 - 23)to_char(minute, 'hh24') AS hour_24, -- Hour of the day (0 - 11)to_char(minute, 'hh12') hour_12,-- Hour minute (0 - 59)to_char(minute, 'mi') hour_minutes,-- Minute of the day (0 - 1439)extract(hour FROM minute)*60 + extract(minute FROM minute) day_minutes,-- Names of day periodscase when to_char(minute, 'hh24:mi') BETWEEN '06:00' AND '08:29'then 'Morning'when to_char(minute, 'hh24:mi') BETWEEN '08:30' AND '11:59'then 'AM'when to_char(minute, 'hh24:mi') BETWEEN '12:00' AND '17:59'then 'PM'when to_char(minute, 'hh24:mi') BETWEEN '18:00' AND '22:29'then 'Evening'else 'Night'end AS day_time_name,-- Indicator of day or nightcase when to_char(minute, 'hh24:mi') BETWEEN '07:00' AND '19:59' then 'Day'else 'Night'end AS day_nightFROM (SELECT '0:00'::time + (sequence.minute || ' minutes')::interval AS minute FROM generate_series(0,1439) AS sequence(minute)GROUP BY sequence.minute) DQORDER BY 1
Based on on information provided here
+ Up to 2 times faster data extraction from SQL Server + Up to 2 times faster data extraction from ODBC sources + Up to 40 percent faster loading data into SQL Server + Up to 40 percent faster loading data into ODBC + Up to 10 percent faster QVX files creation + Up to 10 percent faster loading data into PostgreSQL - Various bugs fixes and improvements
The Greenplum Database builds on the foundations of the open source database PostgreSQL. It primarily functions as a data warehouse and utilizes a shared-nothing, massively parallel processing (MPP) architecture. In this architecture, data is partitioned across multiple segment servers, and each segment owns and manages a distinct portion of the overall data; there is no disk-level sharing nor data contention among segments.
Unlike PostgreSQL, the Greenplum database does not support the binary option of the copy command
Select the Text Mode option to load data into Greenplum
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