The ultimate database test data generation tool for PostgreSQL is better than ever.

Version 5 of Datanamic Data Generator for PostgreSQL has been redesigned. The tool has a complete new user interface. The new version generates test data faster and generates better sample data, has a new "test run" option and has new generator settings such as the "synchronized selection" option for generators which get data from external sources. Test data generation for MS SQL Server 2012 and SQLite (version 3) databases is supported now also.

This is not all. There is more. A complete list of changes can be found at on our website at:

Datanamic Data Generator for PostgreSQL product page.

SQL Maestro Group announces the release of AnySQL Maestro 13.2, a powerful tool for managing any database engine accessible via ODBC driver or OLE DB provider (PostgreSQL, MySQL, SQL Server, Oracle, Access, etc).

The new version is immediately available at
http://www.sqlmaestro.com/products/anysql/maestro/.

AnySQL Maestro comes in both Freeware and Professional editions.

Top 10 new features:

1. The software has been optimized to work with some popular DBMS including PostgreSQL.
2. Updated Connection String Reference.
3. Enhanced Get SQL Dump wizard.
4. New Start Page with quick access to recently connected databases and latest company news.
5. Improved SQL Editor.
6. Highlighted key and required columns and multi-column lookup editors in the data grids.
7. Data import from CSV files containing multi-line strings.
8. Data Export: support for custom separators.
9. Colored profiles in Database Explorer.
10. New modern progress windows.

There are also some other useful things. Full press release is available at the SQL Maestro Group website.

What does parallel processing bring to the table? Obviously the possibility, but not the guarantee, of reduced execution time. What does it cost? That depends on the system but for OLTP systems it usually creates more work than it accomplishes. Read on to learn more.

As a company that for many years has been committed to delivering end-to-end data platform technologies to our customers, we’re excited about all the attention big data is receiving from press, analysts and the industry. While all the pundits are in general agreement that big data is the next greatest IT trend, the definition of big data varies depending on the source.

What do we mean when we say big data? We mean data that is so large, so complex or collected at such a fast pace that it challenges the capabilities of traditional data management systems. Any organization looking at their current data infrastructure and seeing a significant shift the volume, variety or velocity of data is experiencing big data.

• Data volume is growing by 10 times every 5 years, According to a recent IDC report, the volume of digital records is forecasted to hit 1.2M zetabytes (1021 bytes) this year – and predicted to grow 44 times over the next decade.
• The variety of data is also challenging traditional data storage technologies -- up to 85% of new data is unstructured, such as Facebook posts, tweets, photos, videos search engine queries, etc.
• Finally, the velocity of data creates new challenges as organizations now nearly instantaneously capture vast amounts of data from websites, machine generated data like sensors on smart grids, point-of-sale devices and other sources.

The real promise of big data is to enable every type of organization to harness all their data and discover insights that help them do whatever they do – better. We’re here to help with tools and solutions to help make sense of all that big data – helping our customers make faster, better decisions.

We’ll have more to share on big data trends and solutions over the next few weeks and months – stay tuned!

Today, the Precog team has released a free implementation of Precog for PostgreSQL. Precog for PostgreSQL empowers users to easily perform data science on PostgreSQL.

This release bundles the core Precog analysis technology into a completely free package that anyone can download and deploy on their existing PostgreSQL database. Precog for PostgreSQL gives you the ability to analyze all the data in your PostgreSQL database, without forcing you to export data into another tool or write any custom code.

Precog for PostgreSQL comes bundled with Labcoat, a high-level analysis tool that lets users analyze data using Quirrel, the statistically-oriented query language. To get started, visit this page to download the zipped file (includes JAR, scripts and config file).

To get started….

1. Download the zip file
2. Configure Precog
3. Launch Precog
4. Try the API
5. Try Labcoat
6. Analyze your data!

See the read me for the complete installation and configuration instructions. We provide full support for this release so if you run into any trouble, please contact the Precog team at support@precog.com.

Have fun analyzing your data on your PostgreSQL database and let us know what you think!

2ndQuadrant is proud to announce the release of version 1.2.0 of Barman, Backup and Recovery Manager for PostgreSQL.

This major release introduces automated support for retention policies based on redundancy of periodical backups or recovery window.

Retention policies are integrated by a safety mechanism that allows administrators to specify a minimum number of periodical backups that must exist at any time for a server.

For a complete list of changes, see the "Release Notes" section below.

Backup retention policies

A backup retention policy is an user-defined policy that determines how long backups and related archive logs (Write Ahead Log segments in PostgreSQL) need to be retained for recovery procedures.

Through the 'retention_policy' configuration option, Barman retains the periodical backups required to satisfy the current retention policy, and any archived WAL files required for the complete recovery of those backups.

Barman users can define a retention policy in terms of backup redundancy (how many periodical backups, e.g. 5) or a recovery window (how long, e.g. 3 months).

Minimum redundancy safety --

Through the 'minimum_redundancy' configuration option, Barman controls the minimum number of backups available at any time in the catalogue for a specific server. This feature will protect users from accidental delete operations.

Links --

• Website
• Download
• Documentation
• Support

Sponsors --

The open-source development of retention policies under GPL has been sponsored by a large European company that opted to remain anonymous.

Release Notes --

• Added the "retention_policy_mode" global/server option which defines the method for enforcing retention policies (currently only "auto")
• Added the "minimum_redundancy" global/server option which defines the minimum number of backups to be kept for a server
• Added the "retention_policy" global/server option which defines retention policies management based on redunancy (e.g. REDUNDANCY 4) or recovery window (e.g. RECOVERY WINDOW OF 3 MONTHS)
• Added retention policy support to the logging infrastructure, the "check" and the "status" commands
• The "check" command now integrates minimum redundancy control
• Added retention policy states (valid, obsolete and potentially obsolete) to "show-backup" and "list-backup" commands
• The 'all' keyword is now forbidden as server name
• Added basic support for Nagios plugin output to the 'check' command through the --nagios option
• Barman now requires argh => 0.21.2 and argcomplete
• Minor bug fixes

About Barman --

Barman (Backup and Recovery Manager) is an open-source administration tool for disaster recovery of PostgreSQL servers written in Python. It allows your organisation to perform remote backups of multiple servers in business critical environments and help DBAs during the recovery phase. Barman most wanted features include backup catalogues, retention policies, remote recovery, archiving and compression of WAL files and backups.

Built on top of PostgreSQL's robust and reliable Point-In-Time-Recovery technology, Barman allows database administrators to manage the backup and recovery phases of several PostgreSQL database servers from a centralised location, using an intuitive command interface. Barman is distributed under GNU GPL 3.

Some good news for MySQL users; version 5.6 includes native full-text support. If you’re not using MySQL, maybe this will be just the impetus you need to make the switch. In today’s article, Rob Gravelle shows you how to take advantage of MySQL 5.6’s Full-text search capabilities.

Beginning with DB2 10.1 for Linux, UNIX, and Windows, DB2 has supported RDF data and SPARQL application development. In this article, learn about important enhancements for RDF application development that were added in DB2 10.1 Fix Pack 2.

Are you wondering where to find the information you need to learn about and start using the real-time monitoring information available in the DB2 for Linux, UNIX, and Windows product? This article sets out a path through the documentation that will help you deal with this question by providing direct links to the appropriate topics in the Information Center.

The DB2 for Linux, UNIX, and Windows data compression feature allows you to store your data in a compact form. There are two known benefits of this approach: first, reduction of storage space, and second, improvement of performance. In this article we describe a case study showing the third benefit: reduction of electricity consumption per unit of work. As a result, data compression reduces the cost of database maintenance and makes your database greener.

Installation of DB2 10 requires configuring z/OS Storage Management Subsystem (SMS) for DB2 system objects, and migrating the system data sets to SMS to exploit its benefits. Since the migration to DB2 10 is possible from DB2 8, some customers, upon reaching DB2 10 new-function mode (NFM), can benefit from many features related to z/OS SMS, including those introduced in DB2 9. This article will help you in reorganizing DB2 catalog and directory objects.

The PostgreSQL Global Development Group has released a security update to all current versions of the PostgreSQL database system, including versions 9.2.3, 9.1.8, 9.0.12, 8.4.16, and 8.3.23. This update fixes a denial-of-service (DOS) vulnerability. All users should update their PostgreSQL installations as soon as possible.

The security issue fixed in this release, CVE-2013-0255, allows a previously authenticated user to crash the server by calling an internal function with invalid arguments. This issue was discovered by independent security researcher Sumit Soni this week and reported via Secunia SVCRP, and we are grateful for their efforts in making PostgreSQL more secure.

Today's update also fixes a performance regression which caused a decrease in throughput when using dynamic queries in stored procedures in version 9.2. Applications which use PL/pgSQL's EXECUTE are strongly affected by this regression and should be updated. Additionally, we have fixed intermittent crashes caused by CREATE/DROP INDEX CONCURRENTLY, and multiple minor issues with replication.

This release is expected to be the final update for version 8.3, which is now End-of-Life (EOL). Users of version 8.3 should plan to upgrade to a later version of PostgreSQL immediately. For more information, see our Versioning Policy.

This update release also contains fixes for many minor issues discovered and patched by the PostgreSQL community in the last two months, including:

• Prevent unnecessary table scans during vacuuming
• Prevent spurious cached plan error in PL/pgSQL
• Allow sub-SELECTs to be subscripted
• Prevent DROP OWNED from dropping databases or tablespaces
• Make ECPG use translated messages
• Allow PL/Python to use multi-table trigger functions (again) in 9.1 and 9.2
• Fix several activity log management issues on Windows
• Prevent autovacuum file truncation from being cancelled by deadlock_timeout
• Make extensions build with the .exe suffix automatically on Windows
• Fix concurrency issues with CREATE/DROP DATABASE
• Reject out-of-range values in to_date() conversion function
• Revert cost estimation for large indexes back to pre-9.2 behavior
• Make pg_basebackup tolerate timeline switches
• Cleanup leftover temp table entries during crash recovery
• Prevent infinite loop when COPY inserts a large tuple into a table with a large fillfactor
• Prevent integer overflow in dynahash creation
• Make pg_upgrade work with INVALID indexes
• Fix bugs in TYPE privileges
• Allow Contrib installchecks to run in their own databases
• Many documentation updates
• Add new timezone "FET".

As with other minor releases, users are not required to dump and reload their database or use pg_upgrade in order to apply this update release; you may simply shut down PostgreSQL and update its binaries. Users who have skipped multiple update releases may need to perform additional, post-update steps; see the Release Notes for details.

Links:

• Download
• Release Notes

Hello SQL Server community! My name is Eron Kelly and I recently took the role of General Manager of Product Marketing for the Data Platform at Microsoft. I’ve been at Microsoft for over 12 years in various product marketing roles on BizTalk, Exchange, Office 365 and Windows Azure. I’m excited that my first blog post in my new role is to announce Microsoft’s positioning as a Leader the Magic Quadrant for Data Warehouse Database Management Systems that was published on January 31, 2013.

First off, thank you to all the customers who spoke to Gartner on our behalf for this Magic Quadrant. Your engagement with us on SQL Server has made it a better product for data warehousing, and we believe that our position in the Leaders Quadrant in this report reflects the core values of our platform: ease of use, high scale, high availability and market leading TCO. This placement recognizes our strategy to provide customers with data warehouses of all sizes – from the mid-market to the largest mission critical, tier one deployments – that have the best price for performance in market.

This is different from the approach of other vendors in the data warehousing space, who can deliver similar performance at a much higher price. Customers, like Hy-Vee who saw a 100X query performance improvement, or AMD who adds a terabyte of data to their data warehouse each week, are seeing great performance with the SQL Server Parallel Data Warehouse Appliance.

We are pleased that Gartner has recognized us as a leader in Data Warehouse Database Management Systems and that customers are affirming our approach. In the coming year, we will continue to focus on delivering the highest value to our customers through innovations in mission critical scale, performance, and high availability while continuing to deliver on lowering the total cost of ownership of the data platform. Thank you for your continued support and I look forward to talking with you more in the future.

Eron Kelly
General Manager
SQL Server

* Disclaimer: Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Management Tool Engineered for Oracle’s Sun ZFS Storage Appliance Reduces Steps Required for Data Protection and Secondary Processing by 88 Percent1 /us/corporate/press/1904809 en

London, UK, 4th February 2013 - Actian Corp. (“Actian”), a leader in next generation big data management, today announced that Differentia Consulting has signed a reseller agreement with the company to offer enterprises the benefits of Actian Vectorwise, the innovative, record-breaking analytic database for big data.

Active since 2002, Differentia Consulting provides consulting, solutions, resourcing, support and training services to its clients and is a leading QlikView Solution Provider in Europe. Differentia Consulting has chosen to offer Actian Vectorwise to reply to the demand from its QlikView clients who want to go beyond the confines of the technology and now analyze and report on bigger, more complex data sets.

With Actian Vectorwise, Differentia Consulting can now offer its customers a joint solution of QlikView and Vectorwise that allows its clients to broaden their analytics and benefit from better performance and simplicity.

Previously, in very high data volume scenarios, users had to build and manage numerous linked QlikView documents supported by QVD files. These QVD files are a local QlikView data storage mechanism and in order to achieve the required performance with very large data sets it was necessary to create a hierarchy of aggregation. This approach is less than ideal as it introduces constraints in the flexibility of the analysis possible.

QlikView now incorporates Direct Discovery functionality which permits it to leverage the very high performance of the Vectorwise database for calculating aggregates on-the-fly over very large datasets. This approach removes the requirement to build pre-calculated aggregates within QVDs and permits the highly flexible analysis approach which has made QlikView so popular.

Differentia Consulting believes that Vectorwise offers a superior level of manageability, simplicity as well as analytic performance when compared to other database solutions. Furthermore, thanks to the QlikView Direct Discovery functionality, users can extend their use of QlikView applications by implementing Actian Vectorwise as the underlying analytic database in high volume usage cases that demand; speed, agility and system governance.

“I see Vectorwise as the enabler of Big Data access from our clients’ perspective. We needed an analytic database that we could offer our clients where large data volumes were making deploying QlikView non viable. For us, Vectorwise was the logical solution; being agile and rapid is key,” commented Adrian Parker, vice-president strategy and marketing at Differentia Consulting.

“Differentia Consulting is a key solution provider in the European BI marketplace and has been successful in helping businesses benefit from QlikView technology,” commented Sean Jackson, marketing director EMEA at Actian. “By offering Vectorwise, Differentia Consulting is now extending their reach and helping more enterprises benefit from faster and simpler big data analytics and reporting. No longer do QlikView users need to be constrained by the amount of data they can analyze; with Vectorwise, the amount of data can grow exponentially, which means that users can analyze and report on more data than ever before. We look forward to working with Differentia Consulting to take this proposition to the QlikView installed base.”

About Actian: Take Action on Big Data
Actian Corporation enables organizations to transform big data into business value with data management solutions to transact, analyze, and take automated action across their business operations. Actian helps 10,000 customers worldwide take action on their big data with Action Apps, Vectorwise, the analytical database, and Ingres, an independent mission-critical OLTP database. Actian is headquartered in California with offices in New York, London, Paris, Frankfurt, Amsterdam and Melbourne. Stay connected with Actian Corporation on Facebook, Twitter and LinkedIn.

Actian, Cloud Action Platform, Action Apps, Ingres and Vectorwise are trademarks of Actian Corporation. All other trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.

About Differentia Consulting
Differentia Consulting has provided consulting, solutions, resourcing, support and training services to many different clients since 2002. The company is a long-standing partner of IBM and Oracle with JD Edwards, and a QlikView Elite Solution Provider with QlikView customers from all sectors and core technologies (JD Edwards, Oracle, SAP, Infor, IFS, Microsoft, SAGE, Siebel, SalesForce, SugarCRM etc). Differentia Consulting has an ERP heritage; however, the company has expanded into the agile analytics space both from a software resale and value-add consulting perspective. For more information, go to www.differentia.co

Contact:
Jamie Stevenson
Axicom
Jamie.stevenson@axicom.com
t:  02083924095
m: 07769640202

New database release provides performance and scalability gains

Curious about the differences in approaching performance and scalability in SQL Server vs. Windows Azure SQL Database (formerly SQL Azure)? Check out this new paper that brings together insights from the Microsoft SQL Engineering and Customer Advisory Teams (CAT) to detail the differences between on-premises SQL Server and Azure SQL Database tuning and monitoring techniques and best practices for database performance testing. If you’re an on-premises SQL Server whiz and currently integrating the cloud or considering a move to Platform as a Service, this paper is a must read!

Paper Overview

While SQL Server and Windows Azure SQL Databases have large and important similarities, they are not identical, and while the differences are relatively small, they affect the way that applications perform on SQL Database compared to SQL Server. As a result, the application architecture and performance evaluation techniques for each platform also differ.

This document explains these performance differences and their causes and includes real-world customer wisdom from experience troubleshooting performance on production customer SQL Databases. This document also examines common SQL Server performance evaluation techniques that do not work on SQL Database.

Read the full paper.

Oracle Announces General Availability of MySQL 5.6 -- New Version of the World’s Most Popular Open Source Database Designed to Power Next Generation Web, Cloud and Mobile Applications /us/corporate/press/1904335 en

New Version of the World’s Most Popular Open Source Database Designed to Power Next Generation Web, Cloud and Mobile Applications

Greg Larsen explores the new SQL Server 2012 date and time functions and shows you how to exploit these functions in new application code.