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Letter from our CEO, Don DeLoach
As we ring in 2011, everyone is talking about Big Data.
The challenges surrounding Big Data — how to define it, how to manage and store it, how to make use of it — have commanded the attention of big industry players and start-ups alike, spawning cool new companies and open source projects
In the midst of all this activity, there is one growing class of data that has a particularly unique set of characteristics and database requirements: Machine-generated data.
Machine-generated data is being defined in different ways by different people....
New! Infobright Enterprise and Community Editions 3.5 now GA
We're pleased to announce the GA versions of both Infobright Enterprise Edition (IEE) and Infobright Community Edition (ICE) 3.5. These new releases feature significant performance enhancements, as well as some other new features that users have asked for. We are not quite finished with all the performance testing, so additional information will be available later this month.
ICE and IEE Query Performance Improvements
Tests performed by Infobright have demonstrated the following performance gains in both IEE and ICE.
|| Average Improvement Results
||Up to 400%
|Queries using AND, OR operators and IN clause
||20 – 400%
|Order by, Group by
|| 30 - 150%
|Union and Union all
||Details coming later this month
|Joins: – LEFT OUTER Joins
- Large – Large Joins
- Small – Large Joins
|Details coming later this month
|VarChar performance using Lookup
||Details coming later this month
Other IEE and ICE Features
Also delivered in both ICE and IEE is the ability to load data from a remote machine, which allows the offloading of potentially heavy ETL processing to a separate server. This saves significant time for network transfer of large LOAD files, which can be a key limiter of load speed. In addition, both ICE and IEE may optionally use the MySQL query cache, resulting in significant performance gains for repeated execution of queries.
New to IEE
High Availability Replication
Using MySQL statement-based replication, this feature provides support for scale-out, spreading the load among multiple slaves to improve concurrency and performance. It includes support for both master/slave, master/multi-slave configurations, delivering a simple approach to high-availability (HA) and redundancy. It also improves data security and backup, allowing the replication process to be paused for backup services to be run on the slave.
Enhanced Workload Management
Building on the multi-core query processing delivering in IEE 3.4, new workload management features include parallel scanning and execution of individual queries and automatic query throttling technology for load balancing data loads and query execution.
Improved Database Monitoring
This improves management by monitoring database operations and resource usage including per-second CPU usage, physical memory usage, disk I/O, cache directory size, and query concurrency.
||Windows XP (32bit)
|Windows Server 2003
||Windows Server 2003 (64 bit)
|Red Hat Enterprise Linux 5
||Red Hat Enterprise Linux 5 / CentOS 5.2 (64bit, 32bit)
|Novell SUSE Linux Enterprise 10, 11
||Debian 'Lenny' (64bit, 32bit)
|| Ubuntu 8.04 (32 bit)
||Fedora 9 (32 bit)
If you have questions, please post them to the forums. We encourage you to download the new version of ICE or the evaluation version of IEE. If you are an existing IEE customer, login and download the full IEE today.
Technical and Customer Webinars
Case Study: Analyzing Network Traffic Faster with Open Source at Nokia Siemen
Register for this webinar to learn how Nokia Siemens Networks stores and analyzes terabytes of network data faster and at lower cost, using open source Hadoop with Infobright’s columnar database.
February 9, 10AM ET, 16:00 CET
How USDA Saves Big, Does More with Open Source BI
In this webinar, hear from Joe Barbano of the USDA how the National Institute of Food and Agriculture (NIFA) is using open source technologies from Jaspersoft (BI), Infobright (Database) and Talend (data integration) to dramatically improve information sharing—including unstructured textual data—among key research institutions, internal research teams and the offices of the U.S. Congress.
February 10, 2 PM ET, 11AM PT, 20:00 CET
New! Infobright Enterprise and Community Editions 3.5
Register for this webinar to learn in more detail the improvements and enhancements in both the ICE and IEE versions of Infobright 3.5 GA release.
February 16 10AM ET, 16:00 CET
February 16 1PM ET, 10AM PT, 19:00 CET
Introduction to Infobright Columnar Database
Columnar databases are being broadly adopted for analytics and data warehousing. Attend this webinar to learn about the differences between row and column databases, and why leading organizations are turning to columnar databases like Infobright for faster queries, lower costs, and simplified administration.
February 17, 1 PM ET, 10AM PT, 19:00 CET
White Paper Downloads
For ISV/SaaS Providers: Read the Bloor Research report entitled "10 Rules: Embedding a Database for High Performance Reporting and Analytics"
Technical White Papers:
A Guide To Infobright For Microsoft Windows® Developers
Migration Guide: MySQL/MyISAM to Infobright
Migration Guide: SQL Server to Infobright for High Speed Analytics
Letter from Don, Continued...
Some say it is strictly data that is generated without any direct human intervention, and others say it is data that includes the machine tracking of human activities as well, such as web log data. But whether the definition of machine-generated data is precise or a little more open, certain key characteristics nearly always apply: new records are added with a high frequency, and the data itself is seldom if ever changed. Examples beyond web logs include all other types of logs, such as computer, network and security logs. Other examples include data coming from sensors. Finally, machine-generated data can also encompass call detail records, financial trading data, ATM transactions or RFID tags from shipping containers or cars on a toll way.
Infobright is particularly well suited to providing organizations the ability to analyze Machine Generated Data. Our customers tell me they are able to break through the performance and scalability wall posed by traditional databases without having to incur the time, money, and ongoing headaches associated with implementing a general purpose data warehouse.
Our goal is to provide the easiest, absolutely most cost effective option if you need to analyze Machine Generated Data. And we mean that both in terms of how easy and cost effective it is to get up and running, as well as how easy and cost effective it is to maintain the environment over time.
I’d be very interested in getting your perspective. Email me at email@example.com any time.
All the best,
47 Colborne Street, Suite 403, Toronto, ON M5E 1P8, Tel. 416 596 2483
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