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1Z0-515 Data Warehousing 11g Essentials

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1Z0-515 exam Dumps Source : Data Warehousing 11g Essentials

Test Code : 1Z0-515
Test cognomen : Data Warehousing 11g Essentials
Vendor cognomen : Oracle
braindumps : 70 actual Questions

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Oracle Data Warehousing 11g Essentials

Extracting Insights from Oracle Database | actual Questions and Pass4sure dumps

Jul 7, 2017

Stephanie Simone

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 also be a problem. however, it doesn’t need to be an issue with Oracle.

contemporary data warehousing challenges encompass inflexibility, intricate structure, gradual performance, historical technology, and a scarcity of governance, in keeping with Axel Streichardt, senior director of company purposes options at unadulterated Storage, who discussed in a recent webinar how unadulterated Storage's facts platform can assist customers in quest of a exorbitant efficiency respond to obtain the journey to analytics effectual and beneficial.

in keeping with Streichardt, unadulterated Storage’s Oracle respond can assist firms modernize an Oracle records warehouse to supply perception from statistics perquisite now. The tools and infrastructure to try this are an Oracle Database 11g or bigger, supported by route of an all-flash storage solution, similar to unadulterated Storage FlashBlade, Streichardt  pointed out.

The unadulterated FlashBlade array is an all-flash storage architecture. It connects at once to Oracle databases by the expend of dNFS, and, with significant pools of read and write bandwidth.

With FlashBlade firms Get scalable faculty at a wee profile ingredient, faster database scan rates, quicker facts masses (ETL), and reduce TCO with Oracle dNFS.

the necessary thing cost propositions the respond gives to groups comprises:

  • Jobs getting performed faster with consistent tall efficiency
  • Scalability in IOPs, BW, low latency and speedy deletes
  •  greater capacity/efficiency in much less area
  • high skill in wee 4U profile component drastically reduces footprint
  •  primary to manipulate
  • handy HW install, productive/automatic system startup
  •  Future proof architecture
  • New up to date inventive architecture
  •  Low Environmental working requirements
  • reduce vigour, lessen cooling
  •  enterprise and Procurement model
  • smart Storage – speedy total over, effortless and Evergreen
  • An archived on-demand replay of this webinar is purchasable here.

    Oracle Database 12c relational database administration gadget overview | actual Questions and Pass4sure dumps

    Oracle Database 12c is a relational database management device (RDBMS) designed for each on-premises and cloud makes expend of and deployable on a convoke of clustered or solitary servers. It gives comprehensive elements for managing facts in transaction processing, business 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 business 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 minute purchasable for only Oracle Database 11g, the predecessor to 12c.

    Oracle Database 12c offers more than 500 modern points, including a brand modern multi-tenant architecture that simplifies the route of consolidating databases in the cloud, enabling customers to manage many databases as one without altering their purposes. A solitary multi-tenant container can host and control lots of of pluggable databases to dramatically sever back costs and simplify administration.

    Oracle Database 12c also boasts a brand modern Database In-reminiscence column keep, which accelerates database queries with a view to permit businesses to obtain greater and faster choices. latest functions can immediately and transparently snare 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 support 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 wholesome distinctive customers' performance needs and budgets:

  • Oracle Database enterprise version presents unlimited CPU and reminiscence utilization (up to the OS highest), as well as total of the aspects of Oracle Database 12c, however well-nigh 20 of them -- together with the Multitenant and Database In-reminiscence technologies -- are not obligatory add-ons that should be purchased one after the other. listing expenditures for the commercial enterprise edition delivery at $90 for a Named person Plus license or $forty seven,500 for a processor license.
  • Oracle Database general version is a lower-can suffuse option with a greater constrained feature set for wee and medium-sized businesses or departmental functions. The commonplace edition can also be licensed handiest on servers with a highest potential of four sockets. If Named person Plus licensing is used, not less than 5 licenses is required.
  • Oracle general edition One is an entry-stage providing that will also be used simplest on servers with up to two sockets. The different basic inequity between it and the general edition is that it does not animate Oracle's actual utility Clusters and automated workload administration applied sciences.
  • Oracle Database 12c OSes

    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 scope of server and storage hardware programs. Supported OS types consist of:

  • Microsoft windows x64
  • Linux x86-sixty four and zLinux64
  • Oracle Solaris (for each SPARC and x86 programs)
  • HP-UX Itanium
  • IBM AIX (PPC64)
  • zLinux sixty four
  • Oracle Database 12c licensing

    Oracle, the largest relational database management paraphernalia 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 suffuse of Oracle Database 12c purchases are based either on the number of named clients and contraptions as a route to hold entry to the application -- formally called Named user Plus -- or the number of processors the database will hasten on.

    Oracle Database 12c software licenses are offered at once by using Oracle and through resellers. Oracle makes expend of a admixture of written agreements to license its software. The overarching license rights are described in the Oracle master agreement, and the rights regarding certain items and capabilities are described within the Ordering document.

    Oracle Database 12c facts types

    apart from the customary numeric, character and date/time facts kinds supported via total relational database administration systems, Oracle Database 12c supports a gross lot of information forms as well as person-defined varieties.

    Oracle's Exadata database paraphernalia is also accessible for clients hunting for a simpler, plug-and-play DBMS. Oracle offers distinct configurations according to DBMS utilization that consist of servers tailored for heavy online processing as well as data warehouse functions.

    Oracle Database 12c benchmarks

    although it's sagacious to conduct your personal performance benchmarks, Oracle constantly benchmarks its DBMS and publishes the effects. Many organizations hold carried out tall availability and efficiency the expend 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 total aspects of database methods construction. he's president and essential advisor of Mullins Consulting Inc. and publisher/editor of e-mail him at

    email us at and comply with us on Twitter: @sDataManagement.

    Oracle® Database 11g operating on solar SPARC commercial enterprise M9000 Server sets World checklist TPC-H Three Terabyte Non-Clustered Benchmark influence | actual Questions and Pass4sure dumps

    Oracle(R) Database 11g running on sun SPARC business M9000 Server units World listing TPC-H Three Terabyte Non-Clustered Benchmark effect

    supply: Oracle enterprise

    Oracle Corporation

    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)

    news facts

  • today, Oracle introduced an international list non-clustered TPC-H three TB benchmark influence demonstrating the advanced scalability of Oracle® Database 11g working on Oracle's sun SPARC business M9000 server (1).
  • This consequence sets a modern checklist, outperforming the top-rated IBM TPC-H 3 TB outcomes, providing 27 percent greater performance with a 19 p.c more suitable rate/performance (2).
  • Benchmark details

  • running on a solar SPARC business M9000 server, equipped with 32 SPARC64 VII 2.88 GHz processors, and sun Storage 6180 arrays, Oracle Database 11g with Oracle Solaris executed a world record TPC-H 3 TB non-clustered performance consequence of 198,907.5 QphH@3000GB with a value of $16.58/QphH@3000GB. 
  • additionally, Oracle Database 11g running on the sun SPARC commercial enterprise M9000 server loaded the total database 2.7 times sooner than the IBM vigor 595 equipment(3) while holding the maximum degree of storage redundancy.
  • assisting charges

  • "This benchmark outcome clearly demonstrates the advanced records warehousing performance and value delivered via the combination of Oracle Database 11g and Oracle's sun SPARC business M9000 server," preeminent Juan Loaiza, senior vice president, methods know-how, Oracle. "This benchmark result provides purchasers with a superb metric to substantiate their funding in Oracle."
  • aiding substances

    About Oracle Oracle is the area's most finished, open, and built-in business software and hardware methods business. For greater counsel about Oracle, please talk over with their internet web site at


    Oracle and Java are registered logos of Oracle and/or its associates. other names could be trademarks of their respective house owners.

    supply: Transaction Processing performance Council (TPC)

    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 vigor 595 Database Load Time - 10:02:25. 602 min /220 min = 2.7

    For a better experience the expend of this website, please upgrade to a latest web browser.

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    Performance Caching in a Snowflake Data Warehouse | actual questions and Pass4sure dumps

    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:

    Image title

  • The Service Layer accepts SQL requests from users, coordinates queries, and manages transactions and results. Logically, this can be assumed to hold the result cache, a cached copy of the results of every query executed.
  • The Compute Layer actually does the heavy lifting. This is where the actual SQL is executed across the nodes of a Virtual Data Warehouse. This layer holds a cache of data queried, and is often referred to as Local Disk I/O, although in reality this is implemented using SSD storage. All data in the compute layer is temporary and only held as long as the virtual warehouse is active.
  • The Storage Layer provides long-term storage of results. This is often referred to as Remote Disk and is currently implemented on either Amazon S3 or Microsoft Blob storage.
  • Snowflake Caching Layers

    The diagram below illustrates the levels at which data and results are cached for subsequent use:

  • The Result Cache holds the results of every query executed in the past 24 hours. These are available across virtual warehouses, so query results returned to one user are available to every user in the system.
  • The Local Disk Cache caches data used in queries. Whenever data is needed for a given query, it's retrieved from the Remote Disk storage, and cached for subsequent queries here.
  • The Remote Disk holds the long-term storage. This smooth is accountable for data resilience, which in the case of Amazon Web Services, means 99.999999999% durability, even in the event of an entire data seat failure.
  • Image title

    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 chain of test queries against a wee subset of the data, which is illustrated below.

    Image title

    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 draw were recorded each time.

    Image title

    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.

    Image title

    Test Sequence

    The test sequence was as follows:

  • Run from cold: Starting a modern virtual warehouse (with no local disk caching), and executing the query.
  • Run from warm: Disabling the result caching and repeating the query. This made expend of the local disk caching.
  • Run from hot: Again, repeated the query, but with the result caching switched on.
  • 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 profit from disk caching.

    Image title

    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 carry out to reduce the disk I/O will animate this query.

    The results also demonstrate the queries were unable to fulfill any partition pruning which might ameliorate query performance. We’ll cover the consequence 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 revert results, and disk I/O is no longer a concern.

    Image title

    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.

    Image title

    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 chain 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 chain of tests demonstrated the result cache will be reused unless the underlying data (or SQL query) has changed. As a chain of additional tests demonstrated inserts, updates and deletes which don't move 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 restrict of 30 days, after which results query the remote disk.


    The sequence of tests was designed purely to illustrate the consequence of data caching on Snowflake. The tests included:-

  • Raw Data: Including over 1.5 billion rows of TPC generated data, a total of over 60Gb of raw data
  • Initial Query: Took 20 seconds to complete, and ran entirely from the remote disk.  Quite impressive.
  • Second Query: Was 16 times faster at 1.2 seconds and used the Local Disk (SSD) cache.
  • Result Set Query: Returned results in 130 milliseconds from the result cache (intentionally disabled on the prior query).  
  • To apportion the above results in context, I repeatedly ran the same 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 care and exertion must be made to ensure revise partitioning, indexing, stats gathering and data compression, Snowflake caching is entirely automatic and available by default. Absolutely no exertion was made to tune either the queries or the underlying design, although there are options available, which I'll contend in the next article. token up below for further details.

    Further Reading

    Thank you for reading this. If you organize this article consuming and would enjoy to know more, you can read more about Snowflake, and Data Warehousing at

    One of the most favorite articles I’ve written describes my experience as an Oracle warehouse architect and compares this to how Snowflake would handle the problems I encountered. You can read more at The pattern Data Warehouse Architecture.


    big data ,caching ,database ,performance ,performance optimization ,queries ,performance caching

    Integration between SAP Data Services, business Warehouse on the surge | actual questions and Pass4sure dumps

    Because I specialize in the world of SAP business Warehouse (BW), SAP Data Services was relatively foreign to me until a couple 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 enjoy BW could be a powerful solution for many, though there are soundless a couple of pitfalls to be watchful of.

    Improvements in DS 4.0, BW 7.3

    Integration with SAP sources including BW took a stupendous 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 be extremely complex. Extractors act as semantic views on top of these tables and can provide additional capabilities enjoy 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 also supports autochthonous loading of BW DataSource objects, and Data Services is better integrated with BW as a Source System type. This modern integration makes it very simple to haul data from Data Services sources directly into BW DataSources, total 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 also introduces improved parallel loading when using BW's OpenHub tables as a source for Data Services. This is an necessary functionality for many customers because efficiently piteous data out of BW and into other systems for analysis and reporting can be a challenge.

    Advantages Data Services can present now

    These improvements result in a compelling case for using BW and Data Services together.

    SAP business Content extractors, originally developed for expend with BW, are the semantic representation of ERP data for analytic uses. Now they are available directly for non-BW expend via Data Services. These extractors should work without interference even when simultaneously used for BW extraction, and can now be 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 intricate transformations may be easier to arrive by when developing these transformations in Data Services' custom progress language (Python) than in ABAP. Doing custom transformation progress in Data Services, rather than BW, may obtain custom progress more reusable, while reducing the complexity of the BW environment.

    Data Services also 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 be 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 soundless has advantages over traditional databases when used as a Data Services data store, and current Data Services shops may want to consider it for this reason. Some of the advantages are outlined in this article comparing the differences between BW and HANA.

    Still some rough edges

    There are soundless some rough edges in the Data Services integration with SAP applications and BW. Data Services support for business 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 also confusing. When you create a Data Services source system in BW, you can only expend Data Services as a "dumb pipe" to load data to BW. There is no faculty 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, total 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 hasten Data Services jobs of your choosing.

    If the Data Services job's target has the same structure as the BW DataSource, everything works. Surprisingly, the load will work even if the target of the Data Services job appears to be 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 also uncertain because there are no restrictions forcing you to match structures, though the capability is likely to be organize only by more adventuresome 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 obtain expend of a lot more of the functions that both SAP applications and BW hold to offer. This should obtain life a lot easier for those trying to expend Data Services in an environment with other SAP applications, and present some especially consuming modern options to BW shops. That said, this is a relatively modern functionality, so it suffers from bugs and implementation inconsistencies that can be confusing. Remaining watchful of the shortcomings and hidden functionality can ease integration headaches.

    Oracle® Database 11g Running on Sun SPARC Enterprise M9000 Server Sets World Record TPC-H Three Terabyte Non-Clustered Benchmark Result | actual questions and Pass4sure dumps

    Oracle(R) Database 11g Running on Sun SPARC Enterprise M9000 Server Sets World Record TPC-H Three Terabyte Non-Clustered Benchmark Result

    SOURCE: Oracle Corporation

    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)

    News Facts

  • Today, Oracle announced a world record non-clustered TPC-H 3 TB benchmark result demonstrating the superior scalability of Oracle® Database 11g running on Oracle's Sun SPARC Enterprise M9000 server (1).
  • This result sets a modern record, outperforming the best IBM TPC-H 3 TB result, delivering 27 percent more performance with a 19 percent better price/performance (2).
  • Benchmark Details

  • Running on a Sun SPARC Enterprise M9000 server, equipped with 32 SPARC64 VII 2.88 GHz processors, and Sun Storage 6180 arrays, Oracle Database 11g with Oracle Solaris achieved a world record TPC-H 3 TB non-clustered performance result of 198,907.5 QphH@3000GB with a price of $16.58/QphH@3000GB. 
  • Additionally, Oracle Database 11g running on the Sun SPARC Enterprise M9000 server loaded the entire database 2.7 times faster than the IBM Power 595 system(3) while maintaining the highest smooth of storage redundancy.
  • Supporting Quotes

  • "This benchmark result clearly demonstrates the superior data warehousing performance and value delivered by the combination of Oracle Database 11g and Oracle's Sun SPARC Enterprise M9000 server," said Juan Loaiza, senior vice president, Systems Technology, Oracle. "This benchmark result provides customers with a much metric to substantiate their investment in Oracle."
  • Supporting Resources

    About Oracle Oracle is the world's most complete, open, and integrated business software and hardware systems company. For more information about Oracle, please visit their Web site at


    Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

    Source: Transaction Processing Performance Council (TPC)

    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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