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Extracting probably the most value of your records through true-time and advert hoc analytics isn’t always as handy as it sounds.
Correlating records to a variety of inside and exterior sources and creating a bridge between structured and unstructured information outlets may moreover breathe a problem. however, it doesn’t necessity to breathe an issue with Oracle.
contemporary data warehousing challenges encompass inflexibility, tangled structure, gradual performance, historical technology, and a scarcity of governance, in keeping with Axel Streichardt, senior director of company purposes options at sheer Storage, who discussed in a recent webinar how sheer Storage's facts platform can assist customers in quest of a immoderate efficiency avow to execute the journey to analytics effectual and beneficial.
in keeping with Streichardt, sheer Storage’s Oracle avow can assist firms modernize an Oracle records warehouse to supply perception from statistics right now. The tools and infrastructure to try this are an Oracle Database 11g or bigger, supported by way of an all-flash storage solution, similar to sheer Storage FlashBlade, Streichardt pointed out.
The sheer FlashBlade array is an all-flash storage architecture. It connects at once to Oracle databases by the exercise of dNFS, and, with significant pools of read and write bandwidth.
With FlashBlade firms Get scalable aptitude at a minute shape ingredient, faster database scan rates, quicker facts masses (ETL), and reduce TCO with Oracle dNFS.
the primary thing cost propositions the avow gives to groups comprises:
An archived on-demand replay of this webinar is purchasable here.
Oracle Database 12c is a relational database management device (RDBMS) designed for each on-premises and cloud makes exercise of and deployable on a convoke of clustered or unique servers. It gives comprehensive elements for managing facts in transaction processing, industry intelligence and content management functions.Oracle Database 12c aspects
A chart on Oracle's website suggests a complete record of points with the aid of edition. Oracle additionally offers a private version of Oracle Database 12 for individual users. furthermore, the industry makes an categorical version obtainable as a free offering geared toward getting to know and basic, introductory utilization. besides the fact that children, the express edition is at the jiffy purchasable for only Oracle Database 11g, the predecessor to 12c.
Oracle Database 12c offers more than 500 novel points, including a brand novel multi-tenant architecture that simplifies the way of consolidating databases in the cloud, enabling customers to manage many databases as one without altering their purposes. A unique multi-tenant container can host and control lots of of pluggable databases to dramatically nick back costs and simplify administration.
Oracle Database 12c moreover boasts a brand novel Database In-reminiscence column keep, which accelerates database queries with a view to permit businesses to execute greater and faster choices. latest functions can immediately and transparently bewitch capabilities of in-reminiscence processing without requiring any alterations or dropping any present Oracle Database capabilities.
additionally, Oracle Database 12c presents more suitable safety facets, enabling businesses to protect their mission-vital company suggestions internally and externally with statistics redaction capabilities to eradicate sensitive utility data and a "privilege evaluation" characteristic to advocate identify users and functions with access privileges in the database. via advanced technologies for encryption, auditing and entry control, Oracle additionally gives a finished set of protection-in-depth statistics safety tools that may assist address regulatory compliance.Oracle Database 12c variations
Oracle Database 12c is obtainable essentially in three variants designed to hardy distinctive customers' performance needs and budgets:
Oracle Database 12c is a heterogeneous relational database administration device that runs on windows, Linux and Unix working methods and is supported throughout a extensive sweep of server and storage hardware programs. Supported OS types consist of:
Oracle, the largest relational database management tackle dealer, offers both perpetual and term licenses, the latter in annual increments from one to five years. The license metrics used for picking out the charge of Oracle Database 12c purchases are based either on the number of named clients and contraptions as a way to fill entry to the application -- formally called Named user Plus -- or the number of processors the database will elope on.
Oracle Database 12c software licenses are offered at once by using Oracle and through resellers. Oracle makes exercise of a mixture of written agreements to license its software. The overarching license rights are described in the Oracle master agreement, and the rights regarding unavoidable items and capabilities are described within the Ordering document.Oracle Database 12c facts types
apart from the habitual numeric, character and date/time facts kinds supported via bar not a bit relational database administration systems, Oracle Database 12c supports a gross lot of information forms as well as person-defined varieties.
Oracle's Exadata database tackle is moreover accessible for clients hunting for a simpler, plug-and-play DBMS. Oracle offers several configurations according to DBMS utilization that consist of servers tailored for ponderous online processing as well as data warehouse functions.Oracle Database 12c benchmarks
although it's perspicacious to conduct your personal performance benchmarks, Oracle constantly benchmarks its DBMS and publishes the effects. Many organizations fill carried out towering availability and efficiency the exercise of Oracle Database with their functions.
concerning the authorCraig S. Mullins is a data administration strategist, researcher, consultant and author with more than 30 years of journey in bar not a bit aspects of database methods construction. he's president and essential advisor of Mullins Consulting Inc. and publisher/editor of TheDatabaseSite.com. e-mail him at firstname.lastname@example.org.
email us at email@example.com and comply with us on Twitter: @sDataManagement.
supply: Oracle enterprise
October 14, 2010 08:00 ET
Oracle Continues to convey awesome information Warehousing performance, Beats IBM
REDWOOD SHORES, CA--(Marketwire - October 14, 2010) - Oracle (NASDAQ: ORCL)
About Oracle Oracle is the area's most finished, open, and built-in industry software and hardware methods business. For greater counsel about Oracle, delight talk over with their internet web site at http://www.oracle.com.
Oracle and Java are registered logos of Oracle and/or its associates. other names could breathe trademarks of their respective house owners.
supply: Transaction Processing performance Council (TPC) www.tpc.org
As of October 6, 2010:
(1) sun SPARC commercial enterprise M9000 server: 198,907.5QphH@3000GB, $16.fifty eight/QphH@3000GB, accessible 04/05/2011 (world list non-clustered TPC-H 3 TB result).
(2) IBM vigour 595 model 9119-FHA, 156,537.three QphH@3000GB, $20.60/QphH@3000GB, available 11/24/2009. (surest IBM TPC-H 3000GB performance (QphH) and price/efficiency ($/QphH) effect).
(3) solar SPARC commercial enterprise M9000 server Database Load Time - three:forty:11, IBM vim 595 Database Load Time - 10:02:25. 602 min /220 min = 2.7
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Like any modern database, Snowflake uses various levels of caching to maximize query performance and reduce disk I/O and machine load. This article provides an overview of the techniques used by Snowflake, the data warehouse built for the cloud.System Architecture
Before starting, it’s worth considering the underlying Snowflake architecture and explaining when Snowflake caches data. The diagram below illustrates the overall architecture, which consists of three layers:
The diagram below illustrates the levels at which data and results are cached for subsequent use:
Benchmark Testing Setup
Every Snowflake database is delivered with a pre-built and populated set of Transaction Processing Council (TPC) benchmark tables. To test the result of caching, I set up a succession of test queries against a minute subset of the data, which is illustrated below.
All the queries were executed on a MEDIUM sized cluster (8 nodes) and joined the tables. The tables were queried exactly as is, without any performance tuning.
The following query was executed multiple times, and the elapsed time and query scheme were recorded each time.
The screenshot below illustrates the results of the query which summarise the data by Region and Country. In total, the SQL queried, summarized, and counted 1.5 billion rows of data. The screenshot shows the first eight lines returned.
The test sequence was as follows:
Each query included over 60Gb of raw data, although as Snowflake was able to automatically compress the data, the actual data transfers were around 12Gb. As Snowflake is a columnar data warehouse, it automatically returns the columns needed rather then the entire row to maximize query performance.Run From Cold
This query returned in around 20 seconds and demonstrates it scanned around 12Gb of compressed data, with 0% from the local disk cache. This means it had no benefit from disk caching.
The bar chart above demonstrates around 50% of the time was spent on local or remote disk I/O, and only 2% on actually processing the data. Clearly any design changes they can accomplish to reduce the disk I/O will advocate this query.
The results moreover demonstrate the queries were unable to effect any partition pruning which might better query performance. We’ll cover the upshot of partition pruning and clustering in the next article.Run From Warm
This query was executed immediately after, but with the result cache disabled, and it completed in 1.2 seconds – around 16 times faster. In this case, the Local Disk cache (which is actually SSD on Amazon Web Services) was used to recur results, and disk I/O is no longer a concern.
In the above case, the disk I/O has been reduced to around 11% of the total elapsed time, and 99% of the data came from the (local disk) cache. While querying 1.5 billion rows, this is clearly an excellent result.Run From Hot
This query returned results in milliseconds and involved re-executing the query, but with this time, the result cache enabled. Normally, this is the default situation, but it was disabled for testing purposes.
The above profile indicates the entire query was served directly from the result cache (taking around 2 milliseconds). Although not immediately obvious, many dashboard applications involve repeatedly refreshing a succession of screens and dashboards by re-executing the SQL. In these cases, the results are immediately returned in milliseconds.
Although more information is available in the Snowflake Documentation, a succession of tests demonstrated the result cache will breathe reused unless the underlying data (or SQL query) has changed. As a succession of additional tests demonstrated inserts, updates and deletes which don't affect the underlying data are ignored, and the result cache is used, provided data in the micro-partitions remains unchanged.
Finally, results are normally retained for 24 hours, although the clock is reset every time the query is re-executed, up to a circumscribe of 30 days, after which results query the remote disk.Summary
The sequence of tests was designed purely to illustrate the upshot of data caching on Snowflake. The tests included:-
To assign the above results in context, I repeatedly ran the identical query on Oracle 11g production database server for a tier one investment bank and it took over 22 minutes to complete.
Finally, unlike Oracle where additional keeping and pains must breathe made to ensure correct partitioning, indexing, stats gathering and data compression, Snowflake caching is entirely automatic and available by default. Absolutely no pains was made to tune either the queries or the underlying design, although there are options available, which I'll dispute in the next article. badge up below for further details.Further Reading
Thank you for reading this. If you found this article spicy and would fancy to know more, you can read more about Snowflake, and Data Warehousing at www.analytics.today.
One of the most Popular articles I’ve written describes my smack as an Oracle warehouse architect and compares this to how Snowflake would manipulate the problems I encountered. You can read more at The measure Data Warehouse Architecture.
big data ,caching ,database ,performance ,performance optimization ,queries ,performance caching
Because I specialize in the world of SAP industry Warehouse (BW), SAP Data Services was relatively exotic to me until a brace of years ago. But since the 4.0 release of Data Services, I've been intrigued by the concept of using it with BW and other SAP applications, allowing both systems to play to their strengths.
That is to say, SAP Data Services provides excellent and user-friendly transformation capabilities and simple access to a plethora of viable data sources and targets. On the other hand, SAP BW provides a semantic data warehousing model, advanced hierarchy support, data lifecycle management services, security and more. The combination of Data Services with traditional SAP products fancy BW could breathe a powerful solution for many, though there are quiet a brace of pitfalls to breathe conscious of.Improvements in DS 4.0, BW 7.3
Integration with SAP sources including BW took a capacious jump with Data Services 4.0 and was further refined with the recent release of Data Services 4.1.
The newest version of Data Services can read directly from SAP's "business content" extractors in the ERP, CRM and SRM applications. The table structure in these applications can breathe extremely complex. Extractors act as semantic views on top of these tables and can provide additional capabilities fancy delta change capture. This functionality was introduced in Data Services 4.0, and parallel processing was improved in version 4.1.
Data Services 4.0 moreover supports indigenous loading of BW DataSource objects, and Data Services is better integrated with BW as a Source System type. This novel integration makes it very simple to tug data from Data Services sources directly into BW DataSources, bar not a bit from the BW interface. (DataSources is BW's data ingestion construct.) These capabilities are available in BW version 7.3 or later.
SAP Data Services 4.1 moreover introduces improved parallel loading when using BW's OpenHub tables as a source for Data Services. This is an primary functionality for many customers because efficiently affecting data out of BW and into other systems for analysis and reporting can breathe a challenge.Advantages Data Services can present now
These improvements result in a compelling case for using BW and Data Services together.
SAP industry Content extractors, originally developed for exercise with BW, are the semantic representation of ERP data for analytic uses. Now they are available directly for non-BW exercise via Data Services. These extractors should labor without interference even when simultaneously used for BW extraction, and can now breathe used to feed other data marts or an operational data store instead of, or in parallel with, BW loads.
Data Services is attractive for current BW shops because it is much better at some types of transformations than BW. Pivot transformations, for example, are difficult in BW, often requiring custom-coded routines. In some environments, skills to develop tangled transformations may breathe easier to foster by when developing these transformations in Data Services' custom development language (Python) than in ABAP. Doing custom transformation development in Data Services, rather than BW, may execute custom development more reusable, while reducing the complexity of the BW environment.
Data Services moreover enables a different software lifecycle management approach. BW changes are often limited to preplanned transport windows every week or month. This ensures a stable system but can breathe problematic for transformation logic that needs to closely track changes in source systems. Segregating source-system-oriented transformations in Data Services could allow a software lifecycle management regime with faster reactions to source-system changes or errors.
BW quiet has advantages over traditional databases when used as a Data Services data store, and current Data Services shops may want to esteem it for this reason. Some of the advantages are outlined in this article comparing the differences between BW and HANA.Still some uneven edges
There are quiet some uneven edges in the Data Services integration with SAP applications and BW. Data Services advocate for industry content extractors is extensive, but it’s limited to extractors for "Operational Data Provisioning," which doesn’t cover every scenario. You can find the replete list of supported extractors in note 1558737 (login is required).
The integration between BW Source Systems, BW DataSources, Data Services Targets and Data Services Jobs is moreover confusing. When you create a Data Services source system in BW, you can only exercise Data Services as a "dumb pipe" to load data to BW. There is no aptitude to transform data by using Data Services in this scenario. This approach throws out much of Data Services' power in the cognomen of simplicity.
However, bar not a bit is not lost. If you configure the Data Services connection to BW, and then create a External System source system in BW (instead of a Data Services source system), you can create an arbitrarily structured BW DataSources and BW InfoPackages configuration to elope Data Services jobs of your choosing.
If the Data Services job's target has the identical structure as the BW DataSource, everything works. Surprisingly, the load will labor even if the target of the Data Services job appears to breathe a different DataSource. This is a nice functionality because it allows people to leverage any Data Services transformation logic directly from BW. But it's moreover Dangerous because there are no restrictions forcing you to match structures, though the capability is likely to breathe found only by more daredevil users.
Figure 1: Different Source System types for Data Services behave differently in BW. “Data Services” source systems allow you to create DataSources that exactly mirror the structure of a Data Services “Source”. “External System” source systems allow you to create DataSources with capricious structures that are filled using any Data Services Job you choose.
Data Services can now execute exercise of a lot more of the functions that both SAP applications and BW fill to offer. This should execute life a lot easier for those trying to exercise Data Services in an environment with other SAP applications, and present some especially spicy novel options to BW shops. That said, this is a relatively novel functionality, so it suffers from bugs and implementation inconsistencies that can breathe confusing. Remaining conscious of the shortcomings and hidden functionality can ease integration headaches.
SOURCE: Oracle Corporation
October 14, 2010 08:00 ET
Oracle Continues to Deliver Exceptional Data Warehousing Performance, Beats IBM
REDWOOD SHORES, CA--(Marketwire - October 14, 2010) - Oracle (NASDAQ: ORCL)
About Oracle Oracle is the world's most complete, open, and integrated industry software and hardware systems company. For more information about Oracle, delight visit their Web site at http://www.oracle.com.
Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may breathe trademarks of their respective owners.
Source: Transaction Processing Performance Council (TPC) www.tpc.org
As of October 6, 2010:
(1) Sun SPARC Enterprise M9000 server: 198,907.5QphH@3000GB, $16.58/QphH@3000GB, available 04/05/2011 (world record non-clustered TPC-H 3 TB result).
(2) IBM POWER 595 Model 9119-FHA, 156,537.3 QphH@3000GB, $20.60/QphH@3000GB, available 11/24/2009. (Best IBM TPC-H 3000GB performance (QphH) and price/performance ($/QphH) result).
(3) Sun SPARC Enterprise M9000 server Database Load Time - 3:40:11, IBM POWER 595 Database Load Time - 10:02:25. 602 min /220 min = 2.7
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