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C2090-611 DB2 10.1 DBA for Linux, UNIX, and Windows

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C2090-611 exam Dumps Source : DB2 10.1 DBA for Linux, UNIX, and Windows

Test Code : C2090-611
Test name : DB2 10.1 DBA for Linux, UNIX, and Windows
Vendor name : IBM
: 118 true Questions

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IBM DB2 10.1 DBA for

IBM Db2 query Optimization the usage of AI | killexams.com true Questions and Pass4sure dumps

In September 2018, IBM announced a brand modern product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors data access patterns from executing SQL statements, uses desktop studying algorithms to settle upon most amenable patterns and passes this advice to the Db2 question optimizer to be used with the aid of subsequent statements.

computing device getting to know on the IBM z Platform

In can furthermore of 2018, IBM introduced version 1.2 of its computing device discovering for z/OS (MLz) product. here's a hybrid zServer and cloud application suite that ingests performance facts, analyzes and builds fashions that characterize the health popularity of quite a lot of warning signs, screens them over time and gives precise-time scoring capabilities.

a few facets of this product offering are aimed toward supporting a group of mannequin builders and managers. as an example:

  • It helps numerous programming languages reminiscent of Python, Scala and R. This allows facts modelers and scientists to develop expend of a language with which they are everyday;
  • A graphical user interface known as the visible mannequin Builder publications model builders devoid of requiring enormously-technical programming advantage;
  • It includes dissimilar dashboards for monitoring model results and scoring capabilities, as well as controlling the gear configuration.
  • This desktop discovering suite changed into at first aimed at zServer-based mostly analytics functions. some of the first evident choices become zSystem efficiency monitoring and tuning. gadget management Facility (SMF) records that are immediately generated through the working system deliver the uncooked facts for device aid consumption similar to censorious processor usage, I/O processing, memory paging etc. IBM MLz can compile and withhold these records over time, and construct and teach models of gadget conduct, score these behaviors, determine patterns not conveniently foreseen by means of people, enhance key efficiency indications (KPIs) and then feed the model effects again into the device to affect system configuration changes that can increase performance.

    The next step turned into to region into outcome this suite to research Db2 performance records. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) own template, applies the computing device discovering expertise to Db2 operational records to profit an understanding of Db2 subsystem fitness. it might probably dynamically build baselines for key performance warning signs, supply a dashboard of these KPIs and give operational cadaver of workers precise-time insight into Db2 operations.

    whereas customary Db2 subsystem efficiency is a vital component in universal utility health and performance, IBM estimates that the DBA back personnel spends 25% or greater of its time, " ... fighting access route problems which occasions performance degradation and service gain an outcome on.". (See Reference 1).

    AI comes to Db2

    trust the plight of modern DBAs in a Db2 environment. In modern day IT world they ought to aid one or extra big data applications, cloud application and database services, application setting up and configuration, Db2 subsystem and utility efficiency tuning, database definition and administration, catastrophe recovery planning, and greater. query tuning has been in existence on account that the origins of the database, and DBAs are always tasked with this as smartly.

    The coronary heart of query route analysis in Db2 is the Optimizer. It accepts SQL statements from functions, verifies authority to access the records, reviews the places of the objects to be accessed and develops a list of candidate information access paths. These entry paths can comprise indexes, table scans, a lot of desk unite strategies and others. in the records warehouse and big records environments there are usually further selections purchasable. One of these is the existence of summary tables (on occasion referred to as materialized question tables) that comprise pre-summarized or aggregated statistics, as a consequence allowing Db2 to withhold away from re-aggregation processing. a different option is the starjoin entry course, common in the statistics warehouse, the region the order of desk joins is changed for efficiency motives.

    The Optimizer then reports the candidate access paths and chooses the access path, "with the lowest cost." suffuse in this context skill a weighted summation of useful resource utilization including CPU, I/O, reminiscence and other elements. finally, the Optimizer takes the bottom cost entry path, shops it in reminiscence (and, optionally, within the Db2 listing) and starts off entry direction execution.

    big statistics and statistics warehouse operations now encompass utility suites that allow the enterprise analyst to expend a graphical interface to build and maneuver a miniature information mannequin of the information they need to analyze. The applications then generate SQL statements based on the users’ requests.

    The issue for the DBA

    with a view to enact decent analytics on your dissimilar data outlets you need an excellent understanding of the facts necessities, an understanding of the analytical functions and algorithms attainable and a excessive-performance facts infrastructure. sadly, the quantity and placement of records sources is increasing (both in dimension and in geography), statistics sizes are transforming into, and functions proceed to proliferate in number and complexity. How may noiseless IT managers back this atmosphere, especially with probably the most skilled and develope workforce nearing retirement?

    understand furthermore that a huge allotment of decreasing the whole suffuse of possession of these techniques is to entangle Db2 applications to Run sooner and more correctly. This usually interprets into using fewer CPU cycles, doing fewer I/Os and transporting less facts across the community. since it's frequently problematic to even determine which applications may odds from efficiency tuning, one strategy is to automate the detection and correction of tuning considerations. here is the region desktop gaining scholarship of and synthetic intelligence will furthermore be used to excellent effect.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 version 12 on z/OS uses the computing device researching facilities mentioned above to accumulate and withhold SQL question textual content and access direction particulars, as well as actual performance-related ancient guidance akin to CPU time used, elapsed times and outcomes set sizes. This providing, described as Db2 AI for z/OS, analyzes and outlets the facts in laptop studying models, with the mannequin evaluation consequences then being scored and made attainable to the Db2 Optimizer. The subsequent time a scored SQL commentary is encountered, the Optimizer can then expend the mannequin scoring statistics as enter to its entry path option algorithm.

    The outcome should noiseless be a discount in CPU consumption as the Optimizer uses mannequin scoring input to elect stronger entry paths. This then lowers CPU charges and speeds software response times. a major skills is that the expend of AI utility does not require the DBA to gain information science talents or profound insights into query tuning methodologies. The Optimizer now chooses the most amenable entry paths based mostly no longer simplest on SQL question syntax and facts distribution statistics but on modelled and scored ancient efficiency.

    This may furthermore be particularly censorious in case you reclaim information in varied places. for instance, many analytical queries against big facts require concurrent entry to certain information warehouse tables. These tables are often known as dimension tables, and they comprise the information aspects constantly used to handle subsetting and aggregation. for example, in a retail atmosphere accept as proper with a desk known as StoreLocation that enumerates every withhold and its location code. Queries towards store sales statistics might furthermore need to compund or summarize income by route of area; therefore, the StoreLocation desk will be used by some massive data queries. in this atmosphere it's ordinary to grasp the dimension tables and duplicate them continuously to the massive facts application. in the IBM world this location is the IBM Db2 Analytics Accelerator (IDAA).

    Now account about SQL queries from both operational applications, facts warehouse users and massive data company analysts. From Db2's perspective, utter these queries are equal, and are forwarded to the Optimizer. youngsters, in the case of operational queries and warehouse queries they may noiseless surely be directed to entry the StoreLocation table in the warehouse. nevertheless, the query from the business analyst towards big records tables should noiseless doubtless entry the replica of the desk there. This effects in a proliferations of skills entry paths, and extra travail for the Optimizer. luckily, Db2 AI for z/OS can supply the Optimizer the counsel it needs to develop sage access route decisions.

    the route it Works

    The sequence of movements in Db2 AI for z/OS (See Reference 2) is often here:

  • during a bind, rebind, region together or clarify operation, an SQL remark is passed to the Optimizer;
  • The Optimizer chooses the statistics entry course; as the option is made, Db2 AI captures the SQL syntax, access route option and question performance data (CPU used, and so on.) and passes it to a "studying assignment";
  • The learning project, which will furthermore be carried out on a zIIP processor (a non-frequent-purpose CPU core that doesn't aspect into software licensing expenses), interfaces with the desktop discovering utility (MLz model functions) to store this information in a model;
  • because the volume of statistics in each model grows, the MLz Scoring service (which can furthermore be executed on a zIIP processor) analyzes the mannequin facts and ratings the behavior;
  • throughout the next bind, rebind, prepare or clarify, the Optimizer now has entry to the scoring for SQL fashions, and makes acceptable changes to access direction selections.
  • There are additionally various consumer interfaces that provide the administrator visibility to the status of the accrued SQL remark performance information and mannequin scoring.

    abstract

    IBM's desktop studying for zOS (MLz) offering is getting used to high-quality outcome in Db2 version 12 to enhance the efficiency of analytical queries in addition to operational queries and their linked applications. This requires administration attention, as you must examine that your enterprise is ready to eat these ML and AI conclusions. How will you measure the prices and benefits of the usage of desktop gaining scholarship of? Which IT back workforce should be tasked to reviewing the result of model scoring, and perhaps approving (or overriding) the consequences? How will you assessment and warrant the assumptions that the application makes about entry path decisions?

    In different words, how well were you cognizant your information, its distribution, its integrity and your existing and proposed entry paths? this will verify the region the DBAs disburse their time in supporting analytics and operational software performance.

    # # #

    Reference 1

    John Campbell, IBM Db2 unique EngineerFrom "IBM Db2 AI for z/OS: raise IBM Db2 software efficiency with computing device studying"https://www.worldofdb2.com/events/ibm-db2-ai-for-z-os-increase-ibm-db2-utility-performance-with-ma

    Reference 2

    Db2 AI for z/OShttps://www.ibm.com/support/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html


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    DB2 10.1 DBA for Linux, UNIX, and Windows

    Pass 4 certain C2090-611 dumps | Killexams.com C2090-611 true questions | http://morganstudioonline.com/

    Seven Surprising Findings About DB2 | killexams.com true questions and Pass4sure dumps

    I’ve just completed IBM DB2 for Linux, Unix and Windows (LUW) coverage here on expend The Index, Luke as preparation for an upcoming training I’m giving. This blog post describes the major differences I’ve create compared to the other databases I’m covering (Oracle, SQL Server, PostgreSQL and MySQL).

    Free & Easy

    Well, let’s physiognomy it: it’s IBM software. It has a pretty long history. You would probably not expect that it is light to install and configure, but in fact: it is. At least DB2 LUW Express-C 10.5 (LUW is for Linux, Unix and Windows, Express-C is the free community edition). That might be another surprise: there is a free community edition. It’s not open source, but it’s free as in free beer.

    No light Explain

    The first problem I stumbled upon is that DB2 has no light route to panoply an execution plan. No kidding. Here is what IBM says about it:

  • Explain a statement by prefixing it with warrant diagram for

    This stores the execution diagram in a set of tables in the database (you’ll need to create these tables first). This is pretty much fancy in Oracle.

  • Display a stored warrant diagram using db2exfmt

    This is a command line tool, not something you can plunge from an SQL prompt. To Run this appliance you’ll need shell access to a DB2 installation (e.g. on the server). That means, that you cannot expend this appliance over an regular database connection.

  • There is another command line appliance (db2expln) that combines the two steps from above. Apart from the fact that this procedure is not exactly convenient, the output you entangle an ASCII art:

    Access Plan: ----------- Total Cost: 60528.3 Query Degree: 1 Rows RETURN ( 1) Cost I/O | 49534.9 ^HSJOIN ( 2) 60528.3 68095 /-----+------\ 49534.9 10000 TBSCAN TBSCAN ( 3) ( 4) 59833.6 687.72 67325 770 | | 1.00933e+06 10000 TABLE: DB2INST1 TABLE: DB2INST1 SALES EMPLOYEES Q2 Q1

    Please note that this is just an excerpt—the plenary output of db2exfmt has 400 lines. Quite a lot information that you’ll hardly ever need. Even the information that you need utter the time (the operations) is presented in a pretty unreadable route (IMHO). I’m particularly thankful that utter the numbers you contemplate above are not labeled—that’s really the icing that renders this “tool” totally useless for the occasional user.

    However, according to the IBM documentation there is another route to panoply an execution plan: “Write your own queries against the warrant tables.” And that’s exactly what I did: I wrote a view called last_explained that does exactly what it’s name suggest: it shows the execution diagram of the eventual statement that was explained (in a non-useless formatting):

    Explain Plan ------------------------------------------------------------ ID | Operation | Rows | Cost 1 | revert | | 60528 2 | HSJOIN | 49535 of 10000 | 60528 3 | TBSCAN SALES | 49535 of 1009326 ( 4.91%) | 59833 4 | TBSCAN EMPLOYEES | 10000 of 10000 (100.00%) | 687 Predicate Information 2 - unite (Q2.SUBSIDIARY_ID = DECIMAL(Q1.SUBSIDIARY_ID, 10, 0)) unite (Q2.EMPLOYEE_ID = DECIMAL(Q1.EMPLOYEE_ID, 10, 0)) 3 - SARG ((CURRENT DATE - 6 MONTHS) < Q2.SALE_DATE) Explain diagram by Markus Winand - NO WARRANTY http://use-the-index-luke.com/s/last_explained

    I’m pretty certain many DB2 users will articulate that this presentation of the execution diagram is confusing. And that’s OK. If you are used to the route IBM presents execution plans, just stick to what you are used to. However, I’m working with utter kinds of databases and they utter gain a route to panoply the execution diagram similar to the one shown above—for me this format is much more useful. Further, I’ve made a useful selection of data to display: the row matter estimates and the predicate information.

    You can entangle the source of the last_explained view from here or from GitHub (direct download). I’m sedate about the no warranty part. Yet I’d fancy to know about problems you gain with the view.

    Emulating Partial Indexes is Possible

    Partial indexes are indexes not containing utter table rows. They are useful in three cases:

  • To preserve space when the index is only useful for a very minuscule fraction of the rows. Example: queue tables.

  • To establish a specific row order in presence of constant non-equality predicates. Example: WHERE x IN (1, 5, 9) ORDER BY y. An index fancy the following can be used to avoid a sort operation:

    CREATE INDEX … ON … (y) WHERE x IN (1, 5, 9)
  • To implement unique constraints on a subset of rows (e.g. only those WHERE energetic = 'Y').

  • However, DB2 doesn’t back a where clause for indexes fancy shown above. But DB2 has many Oracle-compatibility features, one of them is EXCLUDE NULL KEYS: “Specifies that an index entry is not created when utter parts of the index key accommodate the null value.” This is actually the hard-wired behaviour in the Oracle database and it is commonly exploited to emulate partial indexes in the Oracle database.

    Generally speaking, emulating partial indexes works by mapping utter parts of the key (all indexed columns) to NULL for rows that should not discontinue up in the index. As an example, let’s emulate this partial index in the Oracle database (DB2 is next):

    CREATE INDEX messages_todo ON messages (receiver) WHERE processed = 'N'

    The solution presented in SQL Performance Explained uses a function to map the processed rows to NULL, otherwise the receiver value is passed through:

    CREATE OR REPLACE FUNCTION pi_processed(processed CHAR, receiver NUMBER) RETURN NUMBER DETERMINISTIC AS BEGIN IF processed IN ('N') THEN revert receiver; ELSE revert NULL; discontinue IF; END; /

    It’s a deterministic function and can thus be used in an Oracle function-based index. This won’t travail with DB2, because DB2 doesn’t allow user defined-functions in index definitions. However, let’s first complete the Oracle example.

    CREATE INDEX messages_todo ON messages (pi_processed(processed, receiver));

    This index has only rows WHERE processed IN ('N')—otherwise the function returns NULL which is not region in the index (there is no other column that could be non-NULL). Voilà: a partial index in the Oracle database.

    To expend this index, just expend the pi_processed function in the where clause:

    SELECT message FROM messages WHERE pi_processed(processed, receiver) = ?

    This is functionally equivalent to:

    SELECT message FROM messages WHERE processed = 'N' AND receiver = ?

    So far, so ugly. If you fade for this approach, you’d better need the partial index desperately.

    To develop this approach travail in DB2 they need two components: (1) the EXCLUDE NULL KEYS clause (no-brainer); (2) a route to map processed rows to NULL without using a user-defined function so it can be used in a DB2 index.

    Although the second one might appear to be hard, it is actually very simple: DB2 can enact expression based indexing, just not on user-defined functions. The mapping they need can be accomplished with regular SQL expressions:

    CASE WHEN processed = 'N' THEN receiver ELSE NULL END

    This implements the very identical mapping as the pi_processed function above. bethink that CASE expressions are first class citizens in SQL—they can be used in DB2 index definitions (on LUW just since 10.5):

    CREATE INDEX messages_not_processed_pi ON messages (CASE WHEN processed = 'N' THEN receiver ELSE NULL END) EXCLUDE NULL KEYS;

    This index uses the CASE expression to map not to be indexed rows to NULL and the EXCLUDE NULL KEYS feature to forestall those row from being stored in the index. Voilà: a partial index in DB2 LUW 10.5.

    To expend the index, just expend the CASE expression in the where clause and check the execution plan:

    SELECT * FROM messages WHERE (CASE WHEN processed = 'N' THEN receiver ELSE NULL END) = ?; Explain Plan ------------------------------------------------------- ID | Operation | Rows | Cost 1 | revert | | 49686 2 | TBSCAN MESSAGES | 900 of 999999 ( .09%) | 49686 Predicate Information 2 - SARG (Q1.PROCESSED = 'N') SARG (Q1.RECEIVER = ?)

    Oh, that’s a big disappointment: the optimizer didn’t grasp the index. It does a plenary table scan instead. What’s wrong?

    If you gain a very nigh watch at the execution diagram above, which I created with my last_explained view, you might contemplate something suspicious.

    Look at the predicate information. What happened to the CASE expression that they used in the query? The DB2 optimizer was smart enough rewrite the expression as WHERE processed = 'N' AND receiver = ?. Isn’t that great? Absolutely!…except that this smartness has just ruined my attempt to expend the partial index. That’s what I meant when I said that CASE expressions are first class citizens in SQL: the database has a pretty wonderful understanding what they enact and can transform them.

    We need a route to apply their magic NULL-mapping but they can’t expend functions (can’t be indexed) nor can they expend CASE expressions, because they are optimized away. Dead-end? Au contraire: it’s pretty light to befuddle an optimizer. utter you need to enact is to obfuscate the CASE expression so that the optimizer doesn’t transform it anymore. Adding zero to a numeric column is always my first attempt in such cases:

    CASE WHEN processed = 'N' THEN receiver + 0 ELSE NULL END

    The CASE expression is essentially the same, I’ve just added zero to the RECEIVER column, which is numeric. If I expend this expression in the index and the query, I entangle this execution plan:

    ID | Operation | Rows | Cost 1 | revert | | 13071 2 | FETCH MESSAGES | 40000 of 40000 | 13071 3 | RIDSCN | 40000 of 40000 | 1665 4 | SORT (UNQIUE) | 40000 of 40000 | 1665 5 | IXSCAN MESSAGES_NOT_PROCESSED_PI | 40000 of 999999 | 1646 Predicate Information 2 - SARG ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL discontinue = ?) 5 - START ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL discontinue = ?) cease ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL discontinue = ?)

    The partial index is used as intended. The CASE expression appears unchanged in the predicate information section.

    I haven’t checked any other ways to emulate partial indexes in DB2 (e.g., using partitions fancy in more recent Oracle versions).

    As always: just because you can enact something doesn’t denote you should. This approach is so ugly—even more hideous than the Oracle workaround—that you must desperately need a partial index to warrant this maintenance nightmare. Further it will cease working whenever the optimizer becomes smart enough to optimize +0 away. However, then you just need region an even more hideous obfuscation in there.

    INCLUDE Clause Only for Unique Indexes

    With the comprise clause you can add extra columns to an index for the sole purpose to allow in index-only scan when these columns are selected. I knew the comprise clause before because SQL Server offers it too, but there are some differences:

  • In SQL Server comprise columns are only added to the leaf nodes of the index—not in the root and arm nodes. This limits the repercussion on the B-tree’s depth when adding many or long columns to an index. This furthermore allows to bypass some limitations (number of columns, total index row length, allowed data types). That doesn’t appear to be the case in DB2.

  • In DB2 the comprise clause is only convincing for unique indexes. It allows you to implement the uniqueness of the key columns only—the comprise columns are just not considered when checking for uniqueness. This is the identical in SQL Server except that SQL Server supports comprise columns on non-unique indexes too (to leverage the above-mentioned benefits).

  • Almost No NULLS FIRST/LAST Support

    The NULLS FIRST and NULLS eventual modifiers to the order by clause allow you to specify whether NULL values are considered as larger or smaller than non-NULL values during sorting. Strictly speaking, you must always specify the desired order when sorting nullable columns because the SQL benchmark doesn’t specify a default. As you can contemplate in the following chart, the default order of NULL is indeed different across various databases:

    Figure A.1. Database/Feature Matrix

    In this chart, you can furthermore contemplate that DB2 doesn’t back NULLS FIRST or NULLS LAST—neither in the order by clause no in the index definition. However, note that this is a simplified statement. In fact, DB2 accepts NULLS FIRST and NULLS eventual when it is in line with the default NULLS order. In other words, ORDER BY col ASC NULLS FIRST is valid, but it doesn’t change the result—NULLS FIRST is anyways the default. identical is proper for ORDER BY col DESC NULLS LAST—accepted, but doesn’t change anything. The other two combinations are not convincing at utter and capitulate a syntax error.

    SQL:2008 FETCH FIRST but not OFFSET

    DB2 supports the fetch first … rows only clause for a while now—kind-of impressive considering it was “just” added with the SQL:2008 standard. However, DB2 doesn’t back the offset clause, which was introduced with the very identical release of the SQL standard. Although it might watch fancy an capricious omission, it is in fact a very sage ride that I deeply respect. offset is the root of so much evil. In the next section, I’ll warrant how to live without offset.

    Side node: If you gain code using offset that you cannot change, you can noiseless activate the MySQL compatibility vector that makes restrict and offset available in DB2. humorous enough, combining fetch first with offset is then noiseless not viable (that would be benchmark compliant).

    Decent Row-Value Predicates Support

    SQL row-values are multiple scalar values grouped together by braces to figure a sole logical value. IN-lists are a common use-case:

    WHERE (col_a, col_b) IN (SELECT col_a, col_b FROM…)

    This is supported by pretty much every database. However, there is a second, hardly known use-case that has pretty impecunious back in today’s SQL databases: key-set pagination or offset-less pagination. Keyset pagination uses a where clause that basically says “I’ve seen everything up till here, just give me the next rows”. In the simplest case it looks fancy this:

    SELECT … FROM … WHERE time_stamp < ? ORDER BY time_stamp DESC FETCH FIRST 10 ROWS ONLY

    Imagine you’ve already fetched a bunch of rows and need to entangle the next few ones. For that you’d expend the time_stamp value of the eventual entry you’ve got for the bind value (?). The query then just revert the rows from there on. But what if there are two rows with the very identical time_stamp value? Then you need a tiebreaker: a second column—preferably a unique column—in the order by and where clauses that unambiguously marks the region till where you gain the result. This is where row-value predicates attain in:

    SELECT … FROM … WHERE (time_stamp, id) < (?, ?) ORDER BY time_stamp DESC, id DESC FETCH FIRST 10 ROWS ONLY

    The order by clause is extended to develop certain there is a well-defined order if there are equal time_stamp values. The where clause just selects what’s after the row specified by the time_stamp and id pair. It couldn’t be any simpler to express this selection criteria. Unfortunately, neither the Oracle database nor SQLite or SQL Server understand this syntax—even though it’s in the SQL benchmark since 1992! However, it is viable to apply the identical logic without row-value predicates—but that’s rather inconvenient and light to entangle wrong.

    Even if a database understands the row-value predicate, it’s not necessarily understanding these predicates wonderful enough to develop proper expend of indexes that back the order by clause. This is where MySQL fails—although it applies the logic correctly and delivers the right result, it does not expend an index for that and is thus rather slow. In the end, DB2 LUW (since 10.1) and PostgreSQL (since 8.4) are the only two databases that back row-value predicates in the route it should be.

    The fact that DB2 LUW has everything you need for convenient keyset pagination is furthermore the reason why there is absolutely no reason to complain about the missing offset functionality. In fact I reason that offset should not gain been added to the SQL benchmark and I’m elated to contemplate a vendor that resisted the urge to add it because its became allotment of the standard. Sometimes the benchmark is wrong—just sometimes, not very often ;) I can’t change the standard—all I can enact is teaching how to enact it right and start campaigns fancy #NoOffset.

    Figure A.2. Database/Feature Matrix

    If you fancy my route of explaining things, you’ll admire my bespeak “SQL Performance Explained”.


    Don't overlook Amanda for your storage data protection needs | killexams.com true questions and Pass4sure dumps

    Chances are, you gain never heard of Amanda… in the sense of open source that is. And if you gain not heard of Amanda, then chances are you gain not heard of Zmanda either. I will warrant both, and I will give you my view of why it is essential for you to at least be cognizant of these products and their relation to data protection. Whether you should invest in either depends on many factors that will become clear shortly.

    Let's start with Amanda. Amanda is the most common open source data protection product in the market today, at least based on the number of free downloads: 250,000 or more. fancy most free downloads, these usually attain from universities -- both students and IT folks -- and scientific labs. But, they furthermore comprise individuals from corporations that are experimenting with open source. In a nutshell, Amanda is a client/server data protection software that runs on a Linux server (backup server) and protects clients that Run Windows, Linux or Unix (only a few variants at the moment). It was developed originally at the University of Maryland and then dropped into the world of open source. Since it was distributed to the open source community, hundreds of programmers gain contributed to its development, bug fixes and its common care and feeding. As a result, the usage of the product has continued to climb dramatically over the past few years.

    You can expend Amanda for free. You can modify it and region it back in the ether for free. But, fancy utter open source software, if the software just stopped running in the middle of the night because your client application server was not yet supported, wonderful luck trying to entangle support. Or anything else. Your best pot would be to region your request on one of many Web sites where users and developers back each other out.

    But, unlike Linux operating systems (where there are companies fancy RedHat and SUSE, which is now Novell) or Linux-based databases (where there are companies fancy mySQL), Amanda did not gain a "for profit" sponsor until recently. In late 2005, a newly-formed company was charged with working to develop Amanda a more usable product that would be able to back enterprises of utter sizes. In keeping with the open source model, Zmanda has grabbed leadership of this space and is feverishly encouraging additional programmers -- some internal to the company, but most belonging to other companies/organizations -- to enhance Amanda so it can effectively compete with Symantec NetBackup, EMC Networker, CommVault Galaxy, Tivoli and others that plunge in the enterprise-class data protection software category. Even within the eventual six months, Amanda has attain a long way. But, it furthermore has a long route to fade before I would account it a plenary member of this class. Should you therefore ignore it? No. However, the reason I am writing this column is to develop you cognizant that, under the right set of circumstances, Amanda is worth considering.

    Enter Zmanda. The company has released a specific version of Amanda (two versions, actually) that they back under the classic open source subscription model. You pay only for subscription and back and not for the product itself, just fancy any other open source product. Of course, the whole credence is to price it such that the total cost of ownership is significantly (as in one-half to one-fourth the cost) lower than other commercial products.

    But before you jump into the fray, quiz yourself the following questions:

  • Does the current product gain back for my systems?
  • Does it gain the features I need?
  • Does the product gain back for my applications (e.g., Oracle, SQLserver, and DB2?)
  • Does it gain adequate disk support?
  • What about archiving?
  • I am certain that as you watch into these options you will gain other questions that are specific to your organization's needs. Version 2.50 of Zmanda does gain back for Windows and Linux, but not for utter common flavors of Unix. It should back databases and other applications in the future but does not right now. It furthermore lacks a GUI and does not yet back utter the modern innovations that they gain seen in the world of disk back (like VTL and CDP). But, it does gain disk support. It furthermore has some features that I wish they had in the other commercial offerings, fancy a non-proprietary data format and fancy having the capacity to enact a recovery without requiring the vendor's software. Of course, its Linux back is excellent.

    In my view, true innovation occurs when there is a monetary incentive and there is a discontinuity in the technology curve. That is why they gain seen the massive transformation in data protection software in the past five years. SATA was the technology that opened up opportunities that just were not available before. But, before that, one could develop a pretty reasonable controversy that data protection software from utter the major vendors had become pretty bloated, and the rate of innovation was very slow. Adding back for a modern tape library does not matter as innovation in my book. It is precisely at such times, when differentiation between vendors' products is low, that open source starts to develop a lot of sense. Thousands of programmers start developing and creating a simpler, less cumbersome product with adequate functionality for many companies that don't need it all. Also, they are cost-sensitive and fancy the freedom.

    That is how mySQL and, of course, Linux itself got going. Now it is Zmanda. But unlike the other segments, data protection is now experiencing phenomenal innovation. So, Amanda's (and therefore, Zmanda's) challenge will be to not only create the old tape-based functionality but furthermore to add utter the modern juicy disk-based functionality that is coming in waves currently. I suspect it is up for the challenge but at least be cognizant that there could be a lag before you contemplate utter of these features.

    It was bound to happen. If database, J2EE, server virtualization and security tools got an open source counterpart, how far behind could data protection be? If you gain simpler needs, cost is a major issue and you crave that freedom from the big vendor -- for whatever reason -- then you should check out this modern space. But my advice: enact not Run a production environment without the back that comes with Zmanda. Amanda may be free, but she can be application without the support.

    About the author: Arun Taneja is the founder and consulting analyst for the Taneja Group. Taneja writes columns and answers questions about data management and related topics.


    IT Skills Poised To Pay | killexams.com true questions and Pass4sure dumps

    In-Depth

    IT Skills Poised To Pay

    Advances in mobility, cloud, big Data, DevOps and digital delivery, plus the shift to more rapid release cycles of software and services, are enabling businesses to become more agile. IT workforce research and analyst firm Foote Partners assesses the IT skills gap these trends are creating, their repercussion on salaries and where the exact for expertise is headed.

  • By David Foote
  • 12/05/2016
  • It's difficult to find an employer not struggling to attain up with a unique tech staffing model that balances three things: the urgencies of modern digital innovation strategies, combating ever deepening security threats, and keeping integrated systems and networks running smoothly and efficiently. The staffing challenge has moved well beyond simply having to elect between contingent workers, full-time tech professionals, and a variety of cloud computing and managed services options (Infrastructure as a Service [IaaS], Platform as a Service [PaaS], Software as a Server [SaaS]). Over the next few years, managers will continue to be tasked with leading a massive transformation of the technology and tech-business hybrid workforce to focus on quickly and predictably delivering a wide variety of operational and revenue-generating infrastructure solutions involving Internet of Things (IoT) products and services, big Data advanced analytics, cybersecurity, and modern mobile and cloud computing capabilities. Consequently, tech professionals and developers must align their skills and interests accordingly to back their employers meet existing and forthcoming digital transformation imperatives that are forcing deep, accelerated changes in technology organizations.

    As cloud infrastructure becomes more capable of economically delivering performance and data at capacities and speeds once never imagined, organizations of utter sizes are seeking tech professionals and developers with the proper skills, knowledge, and competencies to create more agile and responsive environments.

    At the identical time, they're grappling to ensure reliability of existing infrastructure where any amount of downtime is less acceptable than ever. Along with that is an onslaught of cybersecurity attacks occurring more frequently that gain many IT managers aphorism they can't find adequate labor to back them protect their existing networks and endpoints. The latest reminder was in the spotlight following the most powerful denial of service (DoS) storm to date in late October resulting from unprotected endpoints on surveillance cameras. IoT, machine-to-machine communications and telematics gain introduced modern complexities ranging from the need to better secure the devices and the delivery points to which they connect. Meanwhile, the growing IoT landscape is unleashing an exponential flood of modern data from hundreds of millions of devices, and organizations need to blend their IT and operational systems and find people with big Data analytics skills to handle the cloud-based machine learning infrastructure that's now emerging. This generational shift in IT will region a premium on, or create a baseline requirement for, IT professionals willing to result the money and contemplate where their skills will be most applicable. Whether you're a manager looking to ensure your staff can deliver on these changes or an IT professional deciding on a career direction, workforce requirements and customer expectations are changing.

    If you're in the latter camp, it's essential to understand that the supply-and-demand aspect that drives compensation is furthermore a poignant target. IT pay has a long history of volatility and in 2016 they gain seen even sharper swings in those premiums. Based on hiring patterns, the following overriding trends will drive market exact for IT professionals who gain the experience, drive and skills to deliver solutions:

  • Cybersecurity: The need to protect traditional infrastructure from pervasive and ongoing attacks from a growing number of vectors and sophistication. Evidence suggests pay premiums for cybersecurity will continue to be sturdy for the coming years as the threat landscape continues to become more complicated and confounding. The elimination of traditional boundaries brought about by cloud computing and mobility and a massive modern influx of data generated by IoT devices will only exacerbate this need. More than 25 percent of identified attacks will involve IoT, according to Gartner Inc.
  • Cloud: IT infrastructure over time is transitioning to an all-cloud model, whether provided by a services provider, in the datacenter or a hybrid coalesce of the two. The ride to these elastic infrastructures and op-ex approach to IT is furthermore enabling high-performance computing and storage capacity that's ushering in the capacity to perform workloads and software-defined automation not viable with traditional client server or Web application tier infra­structures. Likewise, the ride to cloud service-based apps such as Salesforce, Office 365 and Workday, to name just a few, is shifting the need for those with skills in building and managing traditional packaged software to those proficient in these modern SaaS-based solutions. The amount spent on cloud this year was forecast at $111 billion, according to Gartner. By 2020, that spending is expected to climb to $216 billion.
  • Big Data Analytics/Machine Learning: The ride toward digital transformation is utter about empowering users to develop quick decisions based on an overwhelmingly massive groundswell of data to be curated from modern sources such as IoT endpoints using the cloud infrastructure and enabling predictive analytics utilizing the machine learning conversational computing frameworks that Amazon Web Services Inc. (AWS), Google Inc., IBM Corp. and Microsoft are developing.
  • DevOps: The drive to bring together IT operations and progress is taking hold as the ride to digital transformation, or at least the diagram to enact so, means organizations must be more agile. A more rapid release cadence in software delivery -- from Windows and Office to open source environments and vertical applications -- requires that IT shops can build, deliver and manage systems with these dynamics. Likewise, modern programming environments and frameworks such as containers and micro-services are enabling modern classes of cloud-native applications designed for modern classes of devices and quick-witted and modern infrastructure.
  • Digital business Transformation: This is the discontinue goal of many organizations that fear, rightfully so, their business models are at risk unless they can become digital businesses. This is the culmination of the four areas just celebrated but furthermore includes the capacity to leverage advances in UX and UI design and the capacity to lever­age IT to back companies build modern products, services and back that's tuned to the digital era.
  • Selected DevOps Skills & Certifications: Pay Premiums Performance DevOps Area Tool Median Pay Premium Noncertified Skills Build and packaging tools Apache Maven 11% Build and packaging tools Ant 10% Cloud computing AWS 11% Cloud computing OpenStack 11% Configuration management Puppet 9% Configuration management Chef 9% Configuration management Ansible 9% Configuration management Salt 9% Continuous integration tools Jenkins 11% Database Couchbase Server 13% Database CouchDB 12% Debugging Go language (Golang) 13% Debugging Node.js 8% Hosting environments AWS cloud tools and solutions  11% Methodology Agile software development 10% Open source databases MySQL 9% Open source databases PostgreSQL 11% Open source databases Redis 12% SCM tools Git/GitHub 9% Scripting languages Ruby on Rails/Ruby 12% Scripting languages Python 9% Virtualization Vagrant 8% Certifications Cloud computing AWS Certified DevOps Engineer - Professional 11% Cloud computing AWS Certified Solutions Architect - Associate (Cloud)      8% Cloud computing AWS Certified Solutions Architect - Professional (Cloud)     12% Cloud computing AWS Certified SysOpsAdministrator - Associate (Cloud) 8% Configuration management Red Hat Certified Architect - DevOps 8%


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