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000-611 DB2 10.1 DBA for Linux UNIX and Windows

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000-611 exam Dumps Source : DB2 10.1 DBA for Linux UNIX and Windows

Test Code : 000-611
Test appellation : DB2 10.1 DBA for Linux UNIX and Windows
Vendor appellation : IBM
: 118 true Questions

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IBM DB2 10.1 DBA for

IBM Db2 question Optimization using AI | killexams.com true Questions and Pass4sure dumps

In September 2018, IBM introduced a recent product, IBM Db2 AI for z/OS. This synthetic intelligence engine monitors statistics entry patterns from executing SQL statements, uses computing device discovering algorithms to rule upon most fulfilling patterns and passes this counsel to the Db2 query optimizer for consume by means of subsequent statements.

laptop getting to know on the IBM z Platform

In may also of 2018, IBM announced version 1.2 of its machine gaining lore of for z/OS (MLz) product. here's a hybrid zServer and cloud software suite that ingests efficiency data, analyzes and builds models that symbolize the fitness repute of various indications, monitors them over time and provides true-time scoring capabilities.

a few elements of this product providing are aimed at assisting a community of model builders and managers. for instance:

  • It supports numerous programming languages akin to Python, Scala and R. This allows for statistics modelers and scientists to manufacture consume of a language with which they are general;
  • A graphical consumer interface referred to as the visible mannequin Builder guides model developers without requiring totally-technical programming abilities;
  • It includes distinctive dashboards for monitoring model results and scoring functions, in addition to controlling the device configuration.
  • This machine getting to know suite become at first geared toward zServer-based analytics functions. one of the first obvious choices become zSystem efficiency monitoring and tuning. equipment management Facility (SMF) data that are instantly generated by the working equipment supply the raw statistics for equipment aid consumption comparable to captious processor usage, I/O processing, reminiscence paging and so on. IBM MLz can assemble and store these records over time, and build and train models of device behavior, ranking those behaviors, determine patterns no longer effortlessly foreseen by means of people, extend key efficiency warning signs (KPIs) and then feed the mannequin outcomes returned into the device to bear an effect on system configuration changes that can enrich performance.

    The next step was to implement this suite to anatomize Db2 efficiency statistics. One solution, called the IBM Db2 IT Operational Analytics (Db2 ITOA) respond template, applies the computing device studying know-how to Db2 operational data to gain an realizing of Db2 subsystem fitness. it might dynamically build baselines for key performance symptoms, supply a dashboard of these KPIs and provides operational personnel true-time insight into Db2 operations.

    while regularly occurring Db2 subsystem efficiency is a vital ingredient in ordinary utility health and performance, IBM estimates that the DBA aid cadaver of workers spends 25% or greater of its time, " ... fighting access route issues which understanding efficiency degradation and service bear an effect on.". (See Reference 1).

    AI involves Db2

    agree with the plight of modern DBAs in a Db2 ambiance. In trendy IT world they bear to assist one or extra colossal data purposes, cloud application and database services, utility setting up and configuration, Db2 subsystem and application performance tuning, database definition and administration, catastrophe healing planning, and greater. question tuning has been in actuality due to the fact the origins of the database, and DBAs are continually tasked with this as neatly.

    The coronary heart of query route analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to access the statistics, reviews the places of the objects to live accessed and develops an inventory of candidate information entry paths. These entry paths can comprise indexes, table scans, numerous table live fragment of strategies and others. within the records warehouse and massive records environments there are usually further selections attainable. One of those is the actuality of summary tables (now and again called materialized query tables) that comprise pre-summarized or aggregated information, for that understanding enabling Db2 to retain away from re-aggregation processing. a different option is the starjoin entry path, ordinary in the data warehouse, where the order of desk joins is changed for efficiency explanations.

    The Optimizer then studies the candidate access paths and chooses the access path, "with the bottom can charge." cost in this context means a weighted summation of resource usage including CPU, I/O, memory and other substances. ultimately, the Optimizer takes the lowest charge access route, retailers it in memory (and, optionally, in the Db2 listing) and begins access direction execution.

    massive statistics and records warehouse operations now encompass software suites that enable the traffic analyst to consume a graphical interface to build and manipulate a miniature facts mannequin of the facts they exigency to analyze. The applications then generate SQL statements according to the clients’ requests.

    The issue for the DBA

    so as to effect respectable analytics for your numerous information shops you want a superb figuring out of the statistics requirements, an realizing of the analytical functions and algorithms attainable and a excessive-performance records infrastructure. regrettably, the quantity and placement of statistics sources is increasing (both in dimension and in geography), records sizes are transforming into, and purposes proceed to proliferate in number and complexity. How should soundless IT managers guide this ambiance, exceptionally with probably the most experienced and ripen cadaver of workers nearing retirement?

    be mindful additionally that a big fragment of reducing the full can charge of ownership of those methods is to rep Db2 purposes to dash sooner and greater efficaciously. This constantly translates into using fewer CPU cycles, doing fewer I/Os and transporting less statistics across the community. because it's often tricky to even identify which applications could capitalize from performance tuning, one route is to automate the detection and correction of tuning concerns. here's the Place computer discovering and synthetic intelligence will also live used to terrific impact.

    Db2 12 for z/OS and synthetic Intelligence

    Db2 edition 12 on z/OS makes consume of the computing device gaining lore of amenities mentioned above to collect and store SQL query textual content and entry route details, as well as precise efficiency-related ancient information such as CPU time used, elapsed instances and influence set sizes. This offering, described as Db2 AI for z/OS, analyzes and outlets the information in computer learning models, with the mannequin analysis consequences then being scored and made purchasable to the Db2 Optimizer. The subsequent time a scored SQL observation is encountered, the Optimizer can then consume the mannequin scoring information as enter to its access path option algorithm.

    The effect should live a reduction in CPU consumption because the Optimizer makes consume of mannequin scoring input to pick improved entry paths. This then lowers CPU prices and speeds application response instances. a significant competencies is that the consume of AI utility does not require the DBA to bear statistics science lore or abysmal insights into question tuning methodologies. The Optimizer now chooses the premiere entry paths based mostly now not best on SQL query syntax and information distribution statistics however on modelled and scored historic performance.

    This may also live specifically principal if you retain information in assorted locations. as an example, many analytical queries in opposition t colossal statistics require concurrent access to confident statistics warehouse tables. These tables are often referred to as dimension tables, and they contain the facts features usually used to handle subsetting and aggregation. for instance, in a retail environment accept as apt with a table known as StoreLocation that enumerates each deliver and its location code. Queries towards retain sales records might also want to aggregate or summarize income by using location; hence, the StoreLocation desk might live used by route of some big data queries. during this atmosphere it's ordinary to Take the dimension tables and copy them always to the colossal statistics application. in the IBM world this region is the IBM Db2 Analytics Accelerator (IDAA).

    Now respect about SQL queries from each operational purposes, data warehouse clients and big statistics traffic analysts. From Db2's standpoint, everything these queries are equal, and are forwarded to the Optimizer. youngsters, within the case of operational queries and warehouse queries they should soundless certainly live directed to entry the StoreLocation table within the warehouse. on the other hand, the query from the company analyst in opposition t huge data tables should soundless probably access the replica of the table there. This outcomes in a proliferations of lore entry paths, and more travail for the Optimizer. fortuitously, Db2 AI for z/OS can give the Optimizer the assistance it must manufacture smart access direction choices.

    the route it Works

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

  • throughout a bind, rebind, prepare or clarify operation, an SQL statement is handed to the Optimizer;
  • The Optimizer chooses the facts access course; because the alternative is made, Db2 AI captures the SQL syntax, entry direction option and question efficiency facts (CPU used, etc.) and passes it to a "studying project";
  • The gaining lore of assignment, which may also live achieved on a zIIP processor (a non-commonplace-intention CPU core that doesn't component into utility licensing costs), interfaces with the computer getting to know software (MLz mannequin services) to shop this information in a model;
  • as the amount of facts in each and every model grows, the MLz Scoring service (which also can live accomplished on a zIIP processor) analyzes the mannequin records and scores the conduct;
  • throughout the subsequent bind, rebind, prepare or clarify, the Optimizer now has access to the scoring for SQL fashions, and makes applicable alterations to access path selections.
  • There are also quite a lot of consumer interfaces that supply the administrator visibility to the popularity of the amassed SQL observation efficiency facts and mannequin scoring.

    abstract

    IBM's machine studying for zOS (MLz) providing is being used to super effect in Db2 edition 12 to enrich the performance of analytical queries in addition to operational queries and their associated applications. This requires management consideration, as you exigency to investigate that your company is prepared to consume these ML and AI conclusions. How will you measure the fees and advantages of using machine discovering? Which IT back group of workers exigency to live tasked to reviewing the outcome of mannequin scoring, and maybe approving (or overriding) the outcomes? How will you overview and warrant the assumptions that the software makes about entry direction decisions?

    In different words, how neatly effect you know your facts, its distribution, its integrity and your latest and proposed entry paths? this will determine the Place the DBAs spend their time in aiding analytics and operational utility efficiency.

    # # #

    Reference 1

    John Campbell, IBM Db2 wonderful EngineerFrom "IBM Db2 AI for z/OS: enhance IBM Db2 application efficiency with laptop getting to know"https://www.worldofdb2.com/routine/ibm-db2-ai-for-z-os-increase-ibm-db2-utility-performance-with-ma

    Reference 2

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


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    See the all checklist of computing device discovering options

    final analysis

    RapidMiner may also now not bear the identify focus of AWS or Google, nonetheless it is a comprehensive data science platform. It aids groups in exploring, mixing and cleansing statistics, designing and refining predictive models through machine discovering and managing deployments. For organizations looking for a robust, expansive ML toolset, RapidMiner bears exploring.

    RapidMiner uses a unified interface to manipulate numerous projects although a graphical drag-and-drop strategy. It offers pre-described computer getting to know libraries however also incorporates a lot of third-party libraries. This includes a all bunch of add-ons encompassing computer studying, textual content analytics, predictive modeling, automation and manner manage.

    This produces a quick classification and regression analysis system for both supervised and unsupervised gaining lore of. The solution also helps split and go-validation methods that enhance the accuracy of predictive models. both Gartner and Forrester rank RapidMiner as a “leader.” The dealer additionally earned a Gartner consumer’s option 2018 award.

    Product Description

    RapidMiner strategies information science and desktop gaining lore of from a holistic viewpoint and presents a big number of equipment to handle myriad projects. The platform helps everything principal open supply facts science formats and provides greater than 60 connectors to control structured, unstructured and numerous sorts of massive information.

    https://o1.qnsr.com/log/p.gif?;n=203;c=204660772;s=9478;x=7936;f=201812281334210;u=j;z=TIMESTAMP;a=20403954;e=i

    RapidMiner boasts that it presents more than 1,500 desktop getting to know and statistics prep functions, and it supports greater than forty info forms, including SAS, ARFF, Stata and by route of URL. It helps NoSQL, MongoDB and Casandra, and its Radoop product extends information environments into the open supply Hadoop space.

    This makes it practicable to generate and re-use current R and Python code, and amalgamate and recombine current modules with recent extensions and modules. The platform also connects to foremost cloud storage functions similar to Amazon S3 and Dropbox. It writes to Qlik QVX or Tableau TDE files.

    Overview and lines person Base

    facts scientists, developers, traffic analysts and theme statistics scientists.

    Interface

    Graphical user interface.

    Scripting Languages supported

    Python, R and RapidMiner Studio

    codecs Supported

    more than 40 file varieties together with SAS, ARFF, Stata, and by means of URL. provides wizards for Microsoft excel and access, CSV, and database connections. presents access to NoSQL databases MongoDB and Cassandra.

    Integration

    guide for everything JDBC database connections together with Oracle, IBM DB2, Microsoft SQL Server, MySQL, Postgres, Teradata, Ingres, VectorWise, and others.

    Reporting and Visualization

    in-built visualization tools. extensive logging capabilities.

    Pricing

    $2,500 per user annually for the little version (one hundred,000 facts rows and a pair of rational processors), $5,000 per consumer yearly for the medium edition (1,000,000 records rows and 4 rational processors) and $10,000 per person yearly for unlimited entry.

    RapidMiner Overview and features at a glance:

    supplier and lines

    RapidMiner

    ML focal point

    enormously computerized ML platform example for businesses aiming to consume machine getting to know commonly.

    Key features and capabilities

    presents greater than 1,500 computing device discovering and facts prep services, and it helps more than 40 files forms. Connects to Amazon S3 and Dropbox.

    user comments

    among the highest rated data science and ML options. clients record it as effectual and “revolutionary” notwithstanding there are complaints concerning the lack of GPU help. 

    Pricing and licensing

    Tiered pricing ranging from $2,500 per consumer per year to upwards of $10,000 per consumer per 12 months.


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    DB2 10.1 DBA for Linux UNIX and Windows

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    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 consume 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 pan it: it’s IBM software. It has a pretty long history. You would probably not expect that it is simple 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 live another surprise: there is a free community edition. It’s not open source, but it’s free as in free beer.

    No simple Explain

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

  • Explain a statement by prefixing it with define purpose for

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

  • Display a stored define purpose using db2exfmt

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

  • There is another command line tool (db2expln) that combines the two steps from above. Apart from the fact that this procedure is not exactly convenient, the output you rep 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 full output of db2exfmt has 400 lines. Quite a lot information that you’ll hardly ever need. Even the information that you exigency everything the time (the operations) is presented in a pretty unreadable route (IMHO). I’m particularly thankful that everything the numbers you behold 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 pomp an execution plan: “Write your own queries against the define tables.” And that’s exactly what I did: I wrote a view called last_explained that does exactly what it’s appellation suggest: it shows the execution purpose of the final statement that was explained (in a non-useless formatting):

    Explain Plan ------------------------------------------------------------ ID | Operation | Rows | Cost 1 | recrudesce | | 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 - link (Q2.SUBSIDIARY_ID = DECIMAL(Q1.SUBSIDIARY_ID, 10, 0)) link (Q2.EMPLOYEE_ID = DECIMAL(Q1.EMPLOYEE_ID, 10, 0)) 3 - SARG ((CURRENT DATE - 6 MONTHS) < Q2.SALE_DATE) Explain purpose by Markus Winand - NO WARRANTY http://use-the-index-luke.com/s/last_explained

    I’m pretty confident many DB2 users will Say that this presentation of the execution purpose 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 everything kinds of databases and they everything bear a route to pomp the execution purpose 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 signify estimates and the predicate information.

    You can rep the source of the last_explained view from here or from GitHub (direct download). I’m solemn about the no warranty part. Yet I’d dote to know about problems you bear with the view.

    Emulating Partial Indexes is Possible

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

  • To preserve space when the index is only useful for a very little 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 dote the following can live 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 active = 'Y').

  • However, DB2 doesn’t advocate a where clause for indexes dote 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 everything parts of the index key contain 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 everything parts of the key (all indexed columns) to NULL for rows that should not conclude 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 role 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 recrudesce receiver; ELSE recrudesce NULL; conclude IF; END; /

    It’s a deterministic role and can thus live 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 role returns NULL which is not establish in the index (there is no other column that could live non-NULL). Voilà: a partial index in the Oracle database.

    To consume this index, just consume the pi_processed role 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 Go for this approach, you’d better exigency the partial index desperately.

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

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

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

    This implements the very identical mapping as the pi_processed role above. recollect that CASE expressions are first class citizens in SQL—they can live 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 live 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 consume the index, just consume 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 | recrudesce | | 49686 2 | TBSCAN MESSAGES | 900 of 999999 ( .09%) | 49686 Predicate Information 2 - SARG (Q1.PROCESSED = 'N') SARG (Q1.RECEIVER = ?)

    Oh, that’s a colossal disappointment: the optimizer didn’t Take the index. It does a full table scan instead. What’s wrong?

    If you bear a very close search at the execution purpose above, which I created with my last_explained view, you might behold 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 consume 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 grand understanding what they effect and can transform them.

    We exigency a route to apply their magic NULL-mapping but they can’t consume functions (can’t live indexed) nor can they consume CASE expressions, because they are optimized away. Dead-end? Au contraire: it’s pretty simple to fuddle an optimizer. everything you exigency to effect 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 consume this expression in the index and the query, I rep this execution plan:

    ID | Operation | Rows | Cost 1 | recrudesce | | 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 conclude = ?) 5 - START ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL conclude = ?) quit ( CASE WHEN (Q1.PROCESSED = 'N') THEN (Q1.RECEIVER + 0) ELSE NULL conclude = ?)

    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 dote in more recent Oracle versions).

    As always: just because you can effect something doesn’t live substantive you should. This approach is so ugly—even more grisly than the Oracle workaround—that you must desperately exigency a partial index to warrant this maintenance nightmare. Further it will quit working whenever the optimizer becomes smart enough to optimize +0 away. However, then you just exigency establish an even more grisly 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 impact on the B-tree’s depth when adding many or long columns to an index. This also allows to bypass some limitations (number of columns, total index row length, allowed data types). That doesn’t appear to live 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 final 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 behold 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 also behold that DB2 doesn’t advocate 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 final 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 apt for ORDER BY col DESC NULLS LAST—accepted, but doesn’t change anything. The other two combinations are not convincing at everything and yield 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 advocate the offset clause, which was introduced with the very identical release of the SQL standard. Although it might search dote an capricious omission, it is in fact a very sapient sail that I deeply respect. offset is the root of so much evil. In the next section, I’ll define how to live without offset.

    Side node: If you bear code using offset that you cannot change, you can soundless activate the MySQL compatibility vector that makes confine and offset available in DB2. humorous enough, combining fetch first with offset is then soundless not practicable (that would live benchmark compliant).

    Decent Row-Value Predicates Support

    SQL row-values are multiple scalar values grouped together by braces to contour a sole rational 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 poverty-stricken advocate 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 dote 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 exigency to rep the next few ones. For that you’d consume the time_stamp value of the final entry you’ve got for the bind value (?). The query then just recrudesce the rows from there on. But what if there are two rows with the very identical time_stamp value? Then you exigency a tiebreaker: a second column—preferably a unique column—in the order by and where clauses that unambiguously marks the Place till where you bear the result. This is where row-value predicates arrive 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 manufacture confident 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 live 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 practicable to apply the identical logic without row-value predicates—but that’s rather inconvenient and simple to rep wrong.

    Even if a database understands the row-value predicate, it’s not necessarily understanding these predicates grand enough to manufacture proper consume of indexes that advocate the order by clause. This is where MySQL fails—although it applies the logic correctly and delivers the prerogative result, it does not consume 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 advocate row-value predicates in the route it should be.

    The fact that DB2 LUW has everything you exigency for convenient keyset pagination is also the understanding why there is absolutely no understanding to complain about the missing offset functionality. In fact I contemplate that offset should not bear been added to the SQL benchmark and I’m satisfied to behold a vendor that resisted the press to add it because its became fragment of the standard. Sometimes the benchmark is wrong—just sometimes, not very often ;) I can’t change the standard—all I can effect is teaching how to effect it prerogative and start campaigns dote #NoOffset.

    Figure A.2. Database/Feature Matrix

    If you dote my route of explaining things, you’ll adore my engage “SQL Performance Explained”.


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

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

    Let's start with Amanda. Amanda is the most current open source data protection product in the market today, at least based on the number of free downloads: 250,000 or more. dote most free downloads, these usually arrive from universities -- both students and IT folks -- and scientific labs. But, they also 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 dash 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 bear contributed to its development, bug fixes and its universal supervision and feeding. As a result, the usage of the product has continued to climb dramatically over the past few years.

    You can consume Amanda for free. You can modify it and establish it back in the ether for free. But, dote everything open source software, if the software just stopped running in the middle of the night because your client application server was not yet supported, grand luck trying to rep support. Or anything else. Your best stake would live to Place 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 dote RedHat and SUSE, which is now Novell) or Linux-based databases (where there are companies dote mySQL), Amanda did not bear a "for profit" sponsor until recently. In late 2005, a newly-formed company was charged with working to manufacture Amanda a more usable product that would live able to advocate enterprises of everything 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 drop in the enterprise-class data protection software category. Even within the final six months, Amanda has arrive a long way. But, it also has a long route to Go before I would respect it a full member of this class. Should you therefore ignore it? No. However, the understanding I am writing this column is to manufacture you awake that, under the prerogative set of circumstances, Amanda is worth considering.

    Enter Zmanda. The company has released a specific version of Amanda (two versions, actually) that they advocate under the classic open source subscription model. You pay only for subscription and advocate and not for the product itself, just dote any other open source product. Of course, the all sentiment is to expense 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, inquire yourself the following questions:

  • Does the current product bear advocate for my systems?
  • Does it bear the features I need?
  • Does the product bear advocate for my applications (e.g., Oracle, SQLserver, and DB2?)
  • Does it bear adequate disk support?
  • What about archiving?
  • I am confident that as you search into these options you will bear other questions that are specific to your organization's needs. Version 2.50 of Zmanda does bear advocate for Windows and Linux, but not for everything current flavors of Unix. It should advocate databases and other applications in the future but does not prerogative now. It also lacks a GUI and does not yet advocate everything the recent innovations that they bear seen in the world of disk advocate (like VTL and CDP). But, it does bear disk support. It also has some features that I wish they had in the other commercial offerings, dote a non-proprietary data format and dote having the ability to effect a recovery without requiring the vendor's software. Of course, its Linux advocate 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 bear 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 manufacture a pretty reasonable controversy that data protection software from everything the major vendors had become pretty bloated, and the rate of innovation was very slow. Adding advocate for a recent tape library does not signify as innovation in my book. It is precisely at such times, when differentiation between vendors' products is low, that open source starts to manufacture 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 exigency it all. Also, they are cost-sensitive and dote 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 live to not only create the former tape-based functionality but also to add everything the recent juicy disk-based functionality that is coming in waves currently. I suspect it is up for the challenge but at least live awake that there could live a lag before you behold everything 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 bear simpler needs, cost is a major issue and you crave that license from the colossal vendor -- for whatever understanding -- then you should check out this recent space. But my advice: effect not dash a production environment without the advocate that comes with Zmanda. Amanda may live free, but she can live exertion 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, colossal 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 impact 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 arrive up with a unique tech staffing model that balances three things: the urgencies of recent 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 pick 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 live 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, colossal Data advanced analytics, cybersecurity, and recent 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 everything 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 bear many IT managers saw 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) assail to date in late October resulting from unprotected endpoints on surveillance cameras. IoT, machine-to-machine communications and telematics bear introduced recent complexities ranging from the exigency to better secure the devices and the delivery points to which they connect. Meanwhile, the growing IoT landscape is unleashing an exponential flood of recent data from hundreds of millions of devices, and organizations exigency to blend their IT and operational systems and find people with colossal Data analytics skills to handle the cloud-based machine learning infrastructure that's now emerging. This generational shift in IT will establish a premium on, or create a baseline requirement for, IT professionals willing to supervene the money and behold where their skills will live 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 principal to understand that the supply-and-demand aspect that drives compensation is also a moving target. IT pay has a long history of volatility and in 2016 they bear seen even sharper swings in those premiums. Based on hiring patterns, the following overriding trends will drive market exact for IT professionals who bear the experience, drive and skills to deliver solutions:

  • Cybersecurity: The exigency 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 live stalwart for the coming years as the threat landscape continues to become more complex and confounding. The elimination of traditional boundaries brought about by cloud computing and mobility and a massive recent 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 amalgamate of the two. The sail to these elastic infrastructures and op-ex approach to IT is also enabling high-performance computing and storage capacity that's ushering in the ability to fulfill workloads and software-defined automation not practicable with traditional client server or Web application tier infra­structures. Likewise, the sail to cloud service-based apps such as Salesforce, Office 365 and Workday, to appellation just a few, is shifting the exigency for those with skills in building and managing traditional packaged software to those skilled in these recent 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 sail toward digital transformation is everything about empowering users to manufacture quick decisions based on an overwhelmingly massive groundswell of data to live curated from recent 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 evolution is taking hold as the sail to digital transformation, or at least the purpose to effect so, means organizations must live more agile. A more rapid release cadence in software delivery -- from Windows and Office to open source environments and perpendicular applications -- requires that IT shops can build, deliver and manage systems with these dynamics. Likewise, recent programming environments and frameworks such as containers and micro-services are enabling recent classes of cloud-native applications designed for recent classes of devices and knowing and modern infrastructure.
  • Digital traffic Transformation: This is the conclude goal of many organizations that fear, rightfully so, their traffic models are at risk unless they can become digital businesses. This is the culmination of the four areas just famed but also includes the ability to leverage advances in UX and UI design and the ability to lever­age IT to back companies build recent products, services and advocate 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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