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

Test Code : 1Z0-515
Test name : Data Warehousing 11g Essentials
Vendor name : Oracle
real questions : 70 true Questions

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Oracle Data Warehousing 11g Essentials

Extracting Insights from Oracle Database | killexams.com true 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 furthermore exist a problem. however, it doesn’t necessity to exist an issue with Oracle.

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

in keeping with Streichardt, pure Storage’s Oracle answer can assist firms modernize an Oracle records warehouse to supply perception from statistics privilege 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 pure Storage FlashBlade, Streichardt  pointed out.

The pure 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 derive scalable skill at a minute profile ingredient, faster database scan rates, quicker facts masses (ETL), and reduce TCO with Oracle dNFS.

the notable thing cost propositions the answer gives to groups comprises:

  • Jobs getting performed faster with consistent high efficiency
  • Scalability in IOPs, BW, low latency and speedy deletes
  •  greater capacity/efficiency in much less area
  • high skill in minute 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 everyone over, effortless and Evergreen
  • An archived on-demand replay of this webinar is purchasable here.


    Oracle Database 12c relational database administration gadget overview | killexams.com true 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, trade 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 trade 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 unique points, including a brand unique 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 solitary multi-tenant container can host and control lots of of pluggable databases to dramatically nick back costs and simplify administration.

    Oracle Database 12c furthermore boasts a brand unique Database In-reminiscence column keep, which accelerates database queries with a view to permit businesses to accomplish greater and faster choices. latest functions can immediately and transparently rob 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 well distinctive customers' performance needs and budgets:

  • Oracle Database enterprise version presents unlimited CPU and reminiscence utilization (up to the OS highest), as well as everyone 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 exist 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 universal version is a lower-can imbue option with a greater constrained feature set for minute and medium-sized businesses or departmental functions. The commonplace edition can furthermore exist 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 universal edition One is an entry-stage providing that will furthermore exist used simplest on servers with up to two sockets. The different basic dissimilarity between it and the universal edition is that it does not aid 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 ambit 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 apparatus 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 imbue of Oracle Database 12c purchases are based either on the number of named clients and contraptions as a route to accommodate entry to the application -- formally called Named user Plus -- or the number of processors the database will race on.

    Oracle Database 12c software licenses are offered at once by using Oracle and through resellers. Oracle makes expend of a amalgam 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 customary numeric, character and date/time facts kinds supported via everyone relational database administration systems, Oracle Database 12c supports a entire lot of information forms as well as person-defined varieties.

    Oracle's Exadata database apparatus is furthermore accessible for clients hunting for a simpler, plug-and-play DBMS. Oracle offers sever configurations according to DBMS utilization that consist of servers tailored for cumbersome 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 accommodate carried out high 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 everyone 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 craig@craigmullins.com.

    email us at editor@searchdatamanagement.com 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 | killexams.com true Questions and Pass4sure dumps

    Oracle(R) Database 11g running on sun SPARC trade 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 trade M9000 server (1).
  • This result sets a unique 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 trade 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 result 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 vim 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 trade M9000 server," eminent 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 trade software and hardware methods business. For greater counsel about Oracle, gladden talk over with their internet web site at http://www.oracle.com.

    emblems

    Oracle and Java are registered logos of Oracle and/or its associates. other names could exist 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

    For a better sustain the expend of this website, gladden upgrade to a latest web browser.


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    Data Warehousing 11g Essentials

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    Performance Caching in a Snowflake Data Warehouse | killexams.com true 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 exist assumed to hold the result cache, a cached copy of the results of every query executed.
  • The Compute Layer actually does the cumbersome 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 flush is responsible for data resilience, which in the case of Amazon Web Services, means 99.999999999% durability, even in the event of an entire data headquarters 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 succession of test queries against a minute 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 contrivance 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 unique 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 capitalize 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 finish to reduce the disk I/O will aid this query.

    The results furthermore demonstrate the queries were unable to fulfill any partition pruning which might help query performance. We’ll cover the result 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.

    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 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 exist reused unless the underlying data (or SQL query) has changed. As a succession 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 circumscribe of 30 days, after which results query the remote disk.

    Summary

    The sequence of tests was designed purely to illustrate the result 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 withhold 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 trust and pains must exist made to ensure revise 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 discuss in the next article. badge up below for further details.

    Further Reading

    Thank you for reading this. If you establish this article piquant and would like to know more, you can read more about Snowflake, and Data Warehousing at www.analytics.today.

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

    Topics:

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


    Integration between SAP Data Services, trade Warehouse on the ascend | killexams.com true questions and Pass4sure dumps

    Because I specialize in the world of SAP trade Warehouse (BW), SAP Data Services was relatively alien 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 light 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 like BW could exist a powerful solution for many, though there are still a couple of pitfalls to exist sensible of.

    Improvements in DS 4.0, BW 7.3

    Integration with SAP sources including BW took a stout 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 exist extremely complex. Extractors act as semantic views on top of these tables and can provide additional capabilities like delta change capture. This functionality was introduced in Data Services 4.0, and parallel processing was improved in version 4.1.

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    SAP Data Services 4.1 furthermore introduces improved parallel loading when using BW's OpenHub tables as a source for Data Services. This is an notable functionality for many customers because efficiently pitiable data out of BW and into other systems for analysis and reporting can exist a challenge.

    Advantages Data Services can present now

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

    SAP trade 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 toil without interference even when simultaneously used for BW extraction, and can now exist 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 complex transformations may exist easier to near 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 accomplish custom development more reusable, while reducing the complexity of the BW environment.

    Data Services furthermore 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 exist 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 still has advantages over traditional databases when used as a Data Services data store, and current Data Services shops may want to reckon 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 still some rough edges in the Data Services integration with SAP applications and BW. Data Services support for trade content extractors is extensive, but it’s limited to extractors for "Operational Data Provisioning," which doesn’t cover every scenario. You can find the full 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 furthermore 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 skill to transform data by using Data Services in this scenario. This approach throws out much of Data Services' power in the name of simplicity.

    However, everyone 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 race 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 toil even if the target of the Data Services job appears to exist 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 furthermore risky because there are no restrictions forcing you to match structures, though the capability is likely to exist establish only by more audacious 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 accomplish expend of a lot more of the functions that both SAP applications and BW accommodate to offer. This should accomplish life a lot easier for those trying to expend Data Services in an environment with other SAP applications, and present some especially piquant unique options to BW shops. That said, this is a relatively unique functionality, so it suffers from bugs and implementation inconsistencies that can exist confusing. Remaining sensible 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 | killexams.com true 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 unique 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 cost 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 flush 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 remarkable metric to substantiate their investment in Oracle."
  • Supporting Resources

    About Oracle Oracle is the world's most complete, open, and integrated trade software and hardware systems company. For more information about Oracle, gladden visit their Web site at http://www.oracle.com.

    Trademarks

    Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may exist 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

    For a better sustain using this site, gladden upgrade to a modern web browser.



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