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This chapter is from the publication 

IBM data Studio is covered in each DB2 version. IBM facts Studio provides a sole integrated atmosphere for database administration and software development. you could achieve projects which are involving database modeling and design, establishing database purposes, administering and managing databases, tuning SQL performance, and monitoring databases multi role sole device. it is a powerful implement that may vastly edge a group ambiance with several roles and duties.

IBM information Studio comes in three favors: replete client, administration customer, and net console.

the replete client includes each the database administrative and the utility development capabilities. The development ambiance is Eclipse-based. This presents a collaborative development atmosphere with the aid of integrating with other advanced Eclipse-primarily based rig akin to InfoSphere information Architect and InfoSphere Optim pureQuery Runtime. subsist alert that one of the vital superior InfoSphere tools are handiest included within the DB2 advanced versions and the DB2 Developer version. that you would subsist able to besides separately purchase the advanced equipment.

The administration client is a subset of the all customer. It nonetheless provides a wide compass of database administrative performance similar to DB2 example management, object management, facts administration, and question tuning. primary application edifice tasks such as SQL Builder, query formatting, visual explain, debugging, enhancing, and working DB2 routines are supported. employ the total client for superior application development aspects.

The internet console, because the title implies, it's an internet-based browser interface that provides health monitoring, job management, and connection management.

IBM facts Studio Workspace and the assignment Launcher

you probably fill effectively installed the IBM information Studio, you are asked to deliver a workspace name. A workspace is a folder that saves your labor and tasks. It refers back to the computer construction environment, which is an Eclipse-primarily based conception.

task Launcher is displayed, which highlights the following category of projects:

  • Design
  • improve
  • Administer
  • Tune
  • monitor
  • each category is described in additional aspect in its personal tab. click on any tab, and you espy the well-known thing and first projects listed in the realm on the left. espy motif four.26 to accept an concept on the route to navigate the project Launcher.

    for instance, the motif suggests you the foster tasks. you could locate the well-known thing edifice initiatives on the left. On the perquisite right, it lists greater initiatives regarding development. On the backside correct, IBM records Studio gives just a few documentation links the position that you may subsist taught more about construction. where applicable, it besides suggests the superior rig available within the InfoSphere Optim portfolio that commemorate to the project you've got selected.

    Connection Profiles

    every task you were to role in opposition t a database requires to first establish a database connection. To connect with a database from IBM data Studio, open the Database Administration standpoint. On the exact amend nook, click the Open perspective icon and select Database Administration.

    On the Administration Explorer, correct-click the white space or beneath the new menu, select New Connection to a database. From the brand new Connection window, you espy that you should employ the IBM facts Studio to connect with different IBM facts sources, in addition to non-IBM data sources. select the database manager and enter the faultfinding connection parameters. determine four.28 shows an illustration.

    Figure 4.27

    determine four.27 Open the Database Administration point of view

    Pull down the JDBC driver drop-down menu, and you can select the class of JDBC driver to result employ of. JDBC classification four driver is used with the aid of default.

    Use the examine Connection button to result certain the connection information you enter is valid. click on conclude.

    At this point, you fill got created a connection profile. Connection profiles comprise guidance about how to hook up with a database akin to indicating the category of authentication to subsist used when connecting the database, specifying default schema, and configuring tracing options. other team participants can import the connection profiles to their personal IBM statistics Studio and subsist able to install a set of consistent connection settings.

    To supersede the connection profile, appropriate-click the database and select homes. houses for the database are displayed as proven in motif four.29.

    everyday Database Administration tools

    There are few other effectual administration tasks obtainable within the menu illustrated in determine four.29.

    The control Connection characteristic allows you to rename the connection profile, delete the connection profile, alternate the consumer identification and password, and replica the profile. The back Up and restoration characteristic enables you to setup a database or table house backups. within the commandeer editor, that you can specify the ilk of backup, district of the backup images, and performance alternatives for the backup. Database backup and recuperation is discussed in Chapter 10, “protecting, Backing Up, and getting better data.”

    The set up and Configure characteristic enables you to configure the database. Database configuration and this IBM records Studio characteristic are lined in component in Chapter 5. word from the menu, you could launch the Configure automatic preservation editor. DB2 offers automatic renovation capabilities for performing database backups, reorganizing tables and indexes, and updating the database data as vital. The editor enables you customize the automatic renovation policy (see determine 4.30).

    Figure 4.30

    figure 4.30 select the automatic protection policy alternate options

    The manage Database role enables you to start and desist the database. In DB2, that capacity activating and deactivating the database. Activating a database allocates All the indispensable database remembrance and capabilities or strategies required. Deactivating a database releases the reminiscence and prevents DB2 features and methods.

    The computer screen characteristic launches the IBM information Studio web Console. check with the area, “IBM facts Studio web Console,” for introduction of the device.

    The Generate DDL feature makes employ of the DB2 command-primarily based implement db2look to extract the information Definition Language (DDL) statements for the recognized database objects or the total database. This feature and gear foster light for those who want to mimic a database, a group of database objects, or the database statistics to a further database. because of the Generate DDL characteristic in IBM facts Studio or the DB2 command db2look, you obtain a DDL script. The script incorporates statements to re-create the database objects you've got chosen. espy motif four.31 for a reference of the styles of statements that you can generate the usage of the IBM information Studio.

    Figure 4.31

    determine four.31 Generate DDL feature within the IBM statistics Studio

    For complete alternatives for the DB2 command db2look, discuss with the DB2 recommendation center.

    The soar Tuning characteristic configures the database to allow query tuning. You may acquire a warning indicating that you simply requisite to activate the InfoSphere Optim question Workload Tuner (OQWT) license for superior tuning potential. subsist alert that IBM DB2 advanced enterprise Server edition comes with OQWT. comply with the guidelines to supervene the product license or click yes to configure the database server for tuning with the points complementary within the IBM statistics Studio.

    When the database is configured to result employ of the tuning advisors and equipment, you are introduced with the question Tuner Workflow Assistant, as shown in motif four.32.

    From the query Tuner Workflow Assistant, that you can reap a statement from numerous sources and tune the commentary. within the capture view, it gives you a listing of sources the position you could capture the statements. determine four.33 indicates an instance on capturing the SQL statements from the kit Cache. This example captures over 100 statements. right-click on the observation wherein you fill an interest and select exhibit SQL statement or accelerate Single-question Advisors and tools on the chosen statement.

    Run the question advisors and rig on the chosen observation. which you could now enter the Invoke view. The device collects assistance and data and generates a data entry map (see determine 4.34).

    When the question tuning actions are comprehensive, you are brought to the evaluate view. It presents you the evaluation consequences and an consultant advice, such as the one proven in motif 4.35. The implement documentation recommends gathering and re-accumulating All of primary records of the query.

    you can besides overview the access map graph generated by using the DB2 clarify characteristic (see motif four.36 for an illustration). subsist alert to store the analysis for future references and examine them if vital.

    The manage Privileges characteristic means that you can furnish database privileges to the users. parley with Chapter eight, “enforcing security,” for details about privileges and database access controls.

    ordinary Database edifice equipment

    IBM statistics Studio consolidates the database administration and database construction capabilities. From the project Launcher – advance, you discover an inventory of key development projects reminiscent of growing and operating SQL statements, debugging saved procedures, and person-described services (UDFs). every task brings you to a implement that helps you accomplish it.

    SQL and XQuery Editor

    The SQL and XQuery editor helps you create and accelerate SQL scripts that hold a yoke of SQL and XQuery statements. To launch the editor, open the facts mission Explorer; under SQL Scripts opt for New > SQL or XQuery Script. As shown in determine 4.37, a pattern SQL script is entered. that you could configure the accelerate options for the script.

    The editor codecs the SQL statements nicely and offers syntax highlights for easier reading as you enter the SQL statements. The performance content aid is besides very constructive. It lists All the current schemas in the database so that you can simply select one from the drop-down menu. The editor additionally parses the observation and validates the commentary syntax. that you could validate the syntax in scripts with numerous database parsers and accelerate scripts towards multiple database connections.

    SQL question Builder

    The SQL query Builder allows for you to create a sole SQL statement, but it surely doesn't uphold XQuery. because the identify implies, the device helps you construct an SQL observation. It helps you appear on the underlying database schema or build an expression, as proven in determine four.38.

    Database Routines Editor and Debugger

    kept methods and consumer-described capabilities (UDFs) are database software objects that encapsulate utility safe judgment at the database server instead of in software-stage code. employ of utility objects assist reduce back overhead of SQL statements and the consequences that are handed during the community. stored tactics and UDFs are besides called routines. IBM data Studio supports routines edifice and debugging.

    From the information task Explorer, create a new facts edifice project. in the assignment, you can create a variety of sorts of database software objects reminiscent of saved processes and UDFs (see motif four.39). To debug a events, appropriate-click on the events and select Debug.


    IBM focuses on information Governance with New software, functions | killexams.com true Questions and Pass4sure dumps

    IBM remaining week introduced two new items aimed toward helping organizations result certain that guidelines and policies concerning entry to tips are enforced. each products, Optim records Redaction and IBM InfoSphere business tips display screen, will become attainable in March. InfoSphere best will develop into purchasable to a select neighborhood of valued clientele. IBM besides announced new features and a new heart of Excellence dedicated to counsel governance.

    New laws, such because the these days bolstered HIPAA and the hello-Tech Act, are putting better restraints on how agencies–principally organizations within the healthcare enterprise–manage sensitive facts. IBM has moved aggressively to fulfill these new requirements during the edifice of latest items, fancy the new Optim and InfoSphere tools, and acquisitions, similar to remaining week’s introduced acquisition of provoke, a developer of facts integrity utility for organizations in the healthcare and executive industries.

    Optim facts Redaction is the newest product to connect the Optim household of tools, which IBM got through its 2007 acquisition of Princeton Softech. The utility is designed to automatically admire and remove fine content from documents and types. The utility may well subsist used with the aid of a pecuniary institution, for example, to conceal a consumer’s credit rankings in a personal loan doc from an workplace clerk, while allowing it to subsist seen by means of a personal loan officer, according to IBM.

    It’s no longer lucid no matter if Optim statistics Redaction will labor at once with DB2/400; IBM didn't enlighten and details of the product aren't yet accessible. If it’s fancy different Optim products, such as the archiving and test administration utility for JD Edwards EnterpriseOne that labor with DB2/four hundred and that i/OS only through “toleration support”, then it’s doubtful a system i shop would want to soar through the hoops to employ it, until they fill got loads of other records to give protection to on Unix, home windows, Linux, and mainframe techniques.

    IBM mentioned that the upcoming InfoSphere enterprise computer screen product would labor with All DB2 facts, including, most likely, DB2/400 (which IBM formally calls DB2 for i), moreover other main DBMSes, business intelligence techniques, and ERP methods. The software is designed to alert administrators when surprising breaks within the circulation of facts raise the probability of errors establishing in the records.

    IBM gives the example of a health insurance business that is inspecting earnings margins across distinctive product strains and geographies. If the information feed from one a fraction of the realm didn't result it into the aggregated database used for analysis, InfoSphere business display screen would alert the administrator to the problem, and steps may well subsist taken to repair it.

    IBM says InfoSphere company display screen is based mostly in fraction on technology developed through Guardium, a database safety application company that IBM bought closing fall. Guardium’s products gained DB2/400 uphold final spring.

    big Blue’s global services unit besides introduced the foundation of a brand new organization dedicated to assisting valued clientele with their assistance governance needs. called the IBM world company services’ recommendation Governance heart of Excellence (COE), the corporation will subsist able to tap more than 250 IBM professionals with learning within the design, development, and deployment of guidance governance projects.

    related reports

    facts protecting device from Camouflage Now helps DB2/400

    IBM Beefs Up Database safety with Guardium buy

    facts protecting device from dataguise to accept DB2/400 aid

    IBM can provide Optim Archiving and examine utility for JDE, but Goofs Up i OS assist

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    Guardium adds DB2/four hundred assist to Database safety tool

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    Hadoop is a software system developed by Apache that allows a company’s data science team to process for analytical purposes great sets of data that are located on distributed servers.  The software framework is mainly used by those companies that want the capability of extracting unstructured data to ameliorate things fancy business performance and customer relationship management.  This unstructured data is known in the industry as tremendous data.  Every company that conducts physical and electronic transactions has access to tremendous data, but it was not until recently that corporate leaders began to fully recognize tremendous data’s potential to uphold them to forecast trends needed to ameliorate competitive advantage.  great businesses were at an edge because they could purchase specialized hardware and hire the human resources that are needed to prepare the diverse data for analysis.  Convenient features fancy outdo reporting in Hadoop allow petite businesses to harness the power of tremendous data analytics as even non-technical users are able to access great data sets from inexpensive, off the shelf servers for data analysis projects.  Here are some other reasons why Hadoop is considered a leading implement for corporate data science teams.

    Use Hadoop With Leading Storage TechnologyHadoop has leveled the playing realm for companies that want to effectively employ tremendous data to optimize their business processes.  For example, many medical companies collecting genetic data for advanced personalized medicine initially lacked the storage capacity needed for effectual tremendous data analysis.  Today, businesses of varying sizes employ cloud storage options to expand their storage capabilities, and one of the most favorite brands is Google Cloud Storage.  The value of Hadoop is well known in the information technology industry, and Google has responded by edifice a custom connector that integrates Google Cloud Storage with Hadoop.  Additionally, providers of storage district network and virtualization storage options fill plans to integrate their products and services with Apache’s Hadoop.

    Tighten Up tremendous Data Security Using Third Party Tools and Add-OnsData security remains a flaming button issue for many companies, non profit organizations and government agencies.  It seems that no organization is immune to attacks by hackers who want to snitch information or debauch the integrity of stored data.  As a result, many businesses are forced to pay fines or legal reparations for not adequately protecting the information entrusted to them, and other businesses flavor productivity losses.  The storage and processing of tremendous data by numerous companies just opens up a new path for cyber criminals because they fill greater amounts of unsecured data to exploit.  Hadoop was not originally built with security mechanisms in place, but third party tools fancy IBM InfoSphere Optim Data Masking, Cloudera Sentry and DataStax Enterprise fill incorporated authentication and data privacy features into their versions of Hadoop.  Many of these tools provide for the authentication of Hadoop processes, services and users; they besides allow for the encryption of the Hadoop file system and data access blocking.  Maintenance and customer uphold are additional benefits of purchasing these distributed, third party versions of Hadoop versus using the free, original Apache product.

    Improve tremendous Data Processing Through Hadoop Integration With favorite IT System BrandsA powerful edge of using Hadoop over other business intelligence software is the capability that it provides to developers and analysts to quickly extract and process great groupings of data.  The efficiency of processing is conditional on many factors including the location of the data and the server platform used.  Many businesses faith Microsoft’s brand and fill outfitted their organization with the company’s servers, operating system and application software.  Although Microsoft’s products fill been known not to subsist compatible with competing software systems,  the computing giant has taken powerful strides to update their flagship MS SQL Server product so that it and its Parallel Data Warehouse utility connects with Hadoop.  Microsoft Office applications fancy outdo fill besides been updated to integrate with the Apache product; this functionality allows Hadoop users to import data analysis output into a spreadsheet format.  The distributed version of Hadoop that is used by IBM’s InfoSphere BigInsights system besides allows Hadoop users to view, analyze, graph and update data from multiple sources using a web based spreadsheet; IBM’s map was to result their version of Hadoop the preferred one for business users. The fact that Hadoop can subsist implemented on these many platforms, and the many resources available to those learning it for the first time, result it the model product to use.

    Modify Hadoop To Extend FunctionalityAlthough the development team for the original Apache Hadoop software positively responds to the user community with value added updates, many businesses want to customize the open source software to quickly meet their organization’s’ unique needs.  Hadoop is Java based, but developers carry out not fill to subsist Java programming experts to result modifications to the software framework. Database developers can employ SQL similar scripting languages fancy Hive and Pig that are exclusively associated with Hadoop to add structure to data sets and import value added customizations into Hadoop.

    Author: Lindsey Patterson

    Lindsey Patterson is a freelance writer and entrepreneur who specializes in business technology, employee appreciation, and management. She loves music, poetry, and researching the latest trends.… View full profile ›

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    Top 10 IBM Information Management Trends | killexams.com true questions and Pass4sure dumps

    Julian Stuhler shares his pick of the most well-known current trends in the world of IBM Information Management. Some are completely new and some are evolutions of existing technologies, and he's betting that every one of them will fill some sort of repercussion on data management professionals during the next 12-18 months.

    Introduction

    The Greek philosopher Heraclitus is credited with the motto "Nothing endures but change". Two millennia later those words silent ring true, and nowhere more so than within the IT industry. Each year brings exciting new technologies, concepts and buzzwords for us to assimilate. Here is my pick of the most well-known current trends in the world of IBM Information Management. Some are completely new and some are evolutions of existing technologies, but I'm betting that every one of them will fill some sort of repercussion on data management professionals during the next 12-18 months.

    1. live on a Smarter Planet

    You don't fill to subsist an IT professional to espy that the world around us is getting smarter. Let's just tangle a espy at a few examples from the world of motoring: we've become used to their in-car GPS systems giving us real-time traffic updates, signs outside car parks telling us exactly how many spaces are free, and even the cars themselves being smart enough to brake individual wheels in order to control a developing skid. All of these result their lives easier and safer by using real-time data to result smart decisions.

    However, All of this is just the beginning: everywhere you espy the world is getting more "instrumented", and clever technologies are being adopted to employ the real-time data to result things safer, quicker and greener. Smart electricity meters in homes are giving consumers the talent to monitor their energy usage in true time and result informed decisions on how they employ it, resulting in an average reduction of 10% in a recent US study. Sophisticated traffic management systems in their cities are reducing congestion and improving fuel efficiency, with an estimated reduction in journey delays of 700,000 hours in another study covering 439 cities around the world.

    All of this has some obvious implications for the volume of data their systems will fill to manage (see trend #2 below) but the IT repercussion goes a lot deeper than that. The very infrastructure that they accelerate their IT systems on is besides getting smarter. Virtualization technologies allow server images to subsist created on demand as capacity increases, and just as easily torn down again when the demand reduces. More extensive instrumentation and smarter analysis allows the peaks and troughs in demand to subsist more accurately measured and predicted so that capacity can subsist dynamically adjusted to cope. With up to 85% of server capacity typically sitting idle on distributed platforms, the talent to virtualize and consolidate multiple physical servers can deliver an immense amount of power, money and valuable IT heart floor space.

    If you live in the mainframe space, virtualization is an established technology that you've been working with for many years. If not, this might subsist a new route of thinking about your server environment. Either way, most of us will subsist managing their databases on virtual servers running on a more dynamic infrastructure in the near future.

    2. The Information Explosion

    As IT becomes ever more prevalent in nearly every aspect of their lives, the amount of data generated and stored continues to grow at an astounding rate. According to IBM, worldwide data volumes are currently doubling every two years. IDC estimates that 45GB of data currently exists for each person on the planet: that's a mind-blowing 281 billion gigabytes in total. While a mere 5 percent of that data will terminate up on enterprise data servers, it is forecast to grow at a staggering 60 percent per year, resulting in 14 exabytes of corporate data by 2011.

    Major industry trends such as the slip towards packaged ERP and CRM applications, increased regulatory and audit requirements, investment in advanced analytics and major company mergers and acquisitions are All contributing to this explosion of data, and the slip towards instrumenting their planet (see trend #1 above) is only going to result things worse.

    As the custodians of the world's corporate data, they are at the sharp terminate of this particular trend. We're being forced to accept more inventive with database partitioning schemes to reduce the performance and operational repercussion of increased data volumes. Archiving strategies, usually an afterthought for many new applications, are becoming increasingly important. The slip to a 64-bit remembrance model on All major computing platforms allows us to design their systems to hold much more data in remembrance rather than on disk, further reducing the performance impact. As volumes continue to enlarge and new types of data such as XML and geospatial information are integrated into their corporate data stores (see trend #5), we'll fill to accept even more inventive.

    3. Hardware Assist

    OK, so this is not a new trend: some of the earliest desktop PCs had the option to apt coprocessors to accelerate up floating point arithmetic, and the mainframe has used many types of supplementary hardware over the years to boost specific functions such as sort and encryption. However, employ of special hardware is becoming ever more well-known on All of the major computing platforms.

    In 2004, IBM introduced the zAAP (System z Application Assist Processor), a special ilk of processor aimed at Java workloads running under z/OS. Two years later, it introduced the zIIP (System z Integrated Information Processor) which was designed to offload specific types of data and transaction processing workloads for business intelligence, ERP and CRM, and network encryption. In both cases, labor can subsist offloaded from the general-purpose processors to ameliorate overall capacity and significantly reduce running costs (as most mainframe customers pay according to how much CPU they burn on their general-purpose processors). These "specialty coprocessors" fill been a faultfinding factor in keeping the mainframe cost-competitive with other platforms, and allow IBM to easily tweak the overall TCO proposition for the System z platform. IBM has previewed its Smart Analytics Optimizer blade for System z (see trend #9) and is about to release details of the next generation of mainframe servers: they can anticipate the theme of workload optimization through dedicated hardware to continue.

    On the distributed computing platform, things fill taken a different turn. The GPU (graphics processing unit), previously only of interest to CAD designers and hard-core gamers, is gradually establishing itself as a formidable computing platform in its own right. The capability to accelerate hundreds or thousands of parallel processes is proving valuable for All sorts of applications, and a new movement called CPGPU (General-Purpose computation on Graphics Processing Units) is rapidly gaining ground. It is very early days, but many database operations (including joins, sorting, data visualization and spatial data access) fill already been proven and the mainframe database vendors won't subsist far behind.

    4. Versioned/Temporal Data

    As the major relational database technologies continue to mature, it's getting more and more difficult to distinguish between them on the basis of sheer functionality. In that benevolent of environment, it's a true deal when a vendor comes up with a major new feature, which is both fundamentally new and immediately useful. The temporal data capabilities being delivered as fraction of DB2 10 for z/OS qualify on both counts.

    Many IT systems requisite to maintain some configuration of historical information in addition to the current status for a given business object. For example, a pecuniary institution may requisite to retain the previous addresses of a customer as well as the one they are currently live at, and know what address applied at any given time. Previously, this would fill required the DBA and application developers to expend valuable time creating the code and database design to uphold the historical perspective, while minimizing any performance impact.

    The new temporal data uphold in DB2 10 for z/OS provides this functionality as fraction of the core database engine. All you requisite to carry out is attest which tables/columns require temporal support, and DB2 will automatically maintain the history whenever an update is made to the data. Elegant SQL uphold allows the developer to query the database with an "as of" date, which will revert the information that was current at the specified time.

    With the ongoing focus on improving productivity and reducing time-to-market for key new IT systems, you can anticipate other databases (both IBM and non-IBM) to implement this feature sooner rather than later.

    5. The soar of XML and Spatial Data

    Most relational databases fill been able to store "unstructured" data such as photographs and scanned images for a while now, in the configuration of BLOBS (Binary great OBjects). This has proven useful in some situations, but most businesses employ specialized applications such as IBM Content Manager to maneuver this information more effectively than a general-purpose database. These benevolent of applications typically carry out not fill to achieve any significant processing on the BLOB itself - they merely store and retrieve it according to externally defined index metadata.

    In contrast, there are some kinds of non-traditional data that requisite to subsist fully understood by the database system so that it can subsist integrated with structured data and queried using the replete power of SQL. The two most powerful examples of this are XML and spatial data, supported as special data types within the latest versions of both DB2 for z/OS and DB2 for LUW.

    More and more organizations are coming to rely on some configuration of XML as the primary means of data interchange, both internally between applications and externally when communicating with third-parties. As the volume of faultfinding XML business documents increases, so too does the requisite to properly store and retrieve those documents alongside other business information. DB2's pureXML feature allows XML documents to subsist stored natively in a specially designed XML data store, which sits alongside the traditional relational engine. This is not a new feature any more, but the trend I've observed is that more organizations are soar to actually result employ of pureXML within their systems. The talent to offload some XML parsing labor to a zAAP coprocessor (see trend #3) is certainly helping.

    Nearly All of their existing applications hold a wealth of spatial data (customer addresses, supplier locations, store locations, etc): the pains is we're unable to employ it properly as it's in the configuration of simple text fields. The spatial abilities within DB2 allow that data to subsist "geoencoded" in a sever column, so that the replete power of SQL can subsist unleashed. Want to know how many customers live within a 10-mile radius of your new store? Or if a property you're about to insure is within a known flood modest or elevated crime area? All of this and much more is viable with simple SQL queries. Again, this is not a brand new feature but more and more organizations are soar to espy the potential and design applications to exploit this feature.

    6. Application Portability

    Despite the relative maturity of the relational database marketplace, there is silent fierce competition for overall market participate between the top three vendors. IBM, Oracle and Microsoft are the main protagonists, and each company is constantly looking for new ways to tempt their competitor's customers to defect. Those dauntless souls that undertook migration projects in the past faced a difficult process, often entailing significant ail and risk to port the database and associated applications to accelerate on the new platform. This made large-scale migrations relatively rare, even when there were compelling cost or functionality reasons to slip to another platform.

    Two trends are changing this and making porting projects more common. The first is the soar of the packaged ERP/CRM solution from companies such as SAP and Siebel. These applications fill been written to subsist largely database agnostic, with the core business logic isolated from the underlying database by an "I/O layer". So, while there may silent subsist safe reasons to subsist on a specific vendor's database in terms of functionality or price, the smart of poignant from one to another is vastly reduced and the process is supported by the ERP solution vendor with additional tooling. Over 100 SAP/Oracle customers are known to fill switched to DB2 during the past 12 months for example, including huge organizations such as Coca-Cola.

    The second and more recent trend is direct uphold for competitor's database APIs. DB2 for LUW version 9.7 includes a host of new Oracle compatibility features that makes it viable to accelerate the vast majority of Oracle applications natively against DB2 with itsy-bitsy or no change required to the code. IBM has besides announced the "DB2 SQL Skin" feature, which provides similar capabilities for Sybase ASE applications to accelerate against DB2. With these features greatly reducing the cost and risk of changing the application code to labor with a different database, All that is left is to physically port the database structures and data to the new platform (which is a relatively straightforward process that is well supported by vendor tooling). There is a huge amount of excitement about these new features and IBM is expecting to espy a significant number of Oracle customers switch to DB2 in the coming year. I'm expecting IBM to continue to pursue this strategy by targeting other databases such as SQL Server, and Oracle and Microsoft may well revert the favor if they start to lose significant market participate as a result.

    7. Scalability and Availability

    The talent to provide unparalleled scalability and availability for DB2 databases is not new: high-end mainframe users fill been enjoying the benefits of DB2 Data Sharing and Parallel Sysplex for more than 15 years. The shared-disk architecture and advanced optimizations employed in this technology allow customers to accelerate mission-critical systems with 24x7 availability and no sole point of failure, with only a minimal performance penalty. Major increases in workload can subsist accommodated by adding additional members to the data sharing group, providing an light route to scale.

    Two developments fill resulted in this making my top 10 trends list. Firstly, I'm seeing a significant number of mainframe customers who had not previously taken edge of data sharing start to tangle the plunge. There are various reasons for this, but we've definitely moved away from the days when DB2 for z/OS data sharing customers were a minority group huddling together at conferences and speaking a different language to everyone else.

    The second reason that this is set to subsist tremendous intelligence over the next year is DB2 pureScale: the implementation of the identical data sharing shared-disk concepts on the DB2 for LUW platform. It's difficult to overstate the potential repercussion this could fill on distributed DB2 customers that accelerate elevated volume mission faultfinding applications. Before pureScale, those customers had to rely on features such as HADR to provide failover uphold to a sever server (which could require many seconds to tangle over in the event of a failure) or ebb to external suppliers such as Xkoto with their Gridscale solution (no longer an option since the company was acquired by Teradata and the product was removed from the market). pureScale brings DB2 for LUW into the identical ballpark as DB2 for z/OS in terms of scalability and availability, and I'm expecting a lot of customer activity in this district over the next year.

    8. Stack 'em high...

    For some time now, it has been viable for organizations to tangle a "pick and mix" approach to their IT infrastructure, selecting the best hardware, operating system, database and even packaged application for their needs. This allowed IT staff to concentrate on edifice skills and flavor in specific vendor's products, thereby reducing uphold costs.

    Recent acquisitions fill begun to achieve this environment under threat. Oracle's previous purchase of ERP vendors such as Peoplesoft, Siebel and JD Edwards had already resulted in tremendous pressure to employ Oracle as the back-end database for those applications (even if DB2 and other databases are silent officially supported). That reinforced SAP's alliance with IBM and the shove to accelerate their applications on DB2 (again, other databases are supported but not encouraged).

    Two acquisitions during the past 12 months fill further eroded the "mix and match" approach, and started a trend towards single-vendor end-to-end solution "stacks" comprising hardware, OS, database and application. The first and most significant of these was Oracle's acquisition of Sun Microsystems in January 2010. This gave the company access to Sun's well-respected server technology and the Solaris OS that runs on it. At a sole stroke, Oracle was able to proffer potential customers a completely integrated hardware/software/application stack.

    The jury is silent out on the potential repercussion of the second acquisition: SAP's purchase of Sybase in May 2010. Although the official SAP position is that the Sybase technology has been purchased for the enhanced mobile and in-memory computing technologies that Sybase will bring, there is the possibility that SAP will select to integrate the Sybase database technology into the SAP product. That will silent leave them conditional on other vendors such as IBM for the hardware and operating system, but it would subsist a major step forward in any integration strategy they may have.

    Older readers of this article may espy some startling similarities to the irascible archaic days of vendor lock-in prevalent in the 1970s and 1980s. IBM's strategy to uphold other vendor's database APIs (see trend # 6) is in direct contrast to this, and it will subsist spellbinding to espy how far customers are willing to ebb down the sole vendor route.

    9. BI on the Mainframe

    The concept of running business Intelligence applications on the mainframe is not new: DB2 was originally marketed as a back-end determination uphold application for IMS databases. The talent to build a warehouse within the identical environment as your operational data resides (and thereby avoid the expensive and time-consuming process of poignant that data to another platform for analysis) is attractive to many customers.

    IBM is making significant efforts to result this an attractive proposition for more of their mainframe customers. The Cognos tools fill been available for zLinux for a yoke of years now, and the DB2 for z/OS development team fill been steadily adding BI-related functions to the core database engine for years. Significant portions of a typical BI workload can besides subsist offloaded to a zIIP coprocessor (see trend # 3), reducing the CPU costs.

    More recently, IBM unveiled its Smart Analytics System 9600 - an integrated, workload balanced bundle of hardware, software and services based on System z and DB2 for z/OS. It has besides begun to talk about the Smart Analytics Optimizer - a elevated performance appliance-like blade for System z capable of handling intensive BI query workloads with minimal repercussion to CPU.

    IBM is staid about BI on the mainframe, and is edifice an increasingly compelling cost and functionality case to uphold it.

    10. Data Governance

    Ensuring that sensitive data is properly secured and audited has always been a concern, but this has received more attention in recent years due to legislation such as Sarbanes-Oxley, HIPAA and others. At the identical time, there has been an increasing focus on data quality: irascible data can result in irascible business decisions, which no one can afford in today's competitive markets. There has besides been an increasing awareness of data as both an asset and a potential liability, making archiving and lifecycle management more important.

    All of these disciplines and more and soar to foster together under the common heading of data governance. As their database systems accept smarter and more self-managing, database professionals are increasingly morphing from data administrators to data governors. A new generation of tools is being rolled out to help, including Infosphere Information Analyser, Guardium and the Optim data management products.

    Additional Resources

    IBM's Smarter Planet initiativeIBM's zIIP Home PageDatabase operations using the GPUDB2 10 for z/OSpureXMLDB2 9.7: accelerate Oracle applications on DB2 9.7 for Linux, Unix, and WindowspureScaleIBM Smart Analytics OptimizeIBM Smart Analytics System 9600IBM Data governance

    » espy All Articles by Columnist Julian Stuhler


    Big Data Security: The Evolution of Hadoop’s Security Model | killexams.com true questions and Pass4sure dumps

    One of the biggest concerns in their present age revolves around the security and protection of sensitive information. In their current era of tremendous Data, their organizations are collecting, analyzing, and making decisions based on analysis of massive amounts of data sets from various sources, and security in this process is becoming increasingly more important. At the identical time, more and more organizations are being required to implement access control and privacy restrictions on these data sets to meet regulatory requirements such as HIPAA and other privacy protection laws. Network security breaches from internal and external attackers are on the rise, often taking months to subsist detected, and those affected are paying the price. Organizations that fill not properly controlled access to their data sets are facing lawsuits, negative publicity, and regulatory fines.

    Consider the following eye-opening statistics:

  • A study released this year by Symantec and the Ponemon Institute organize that the average organizational cost of one security infringement in the United States is 5.4 million dollars1. Another recent study shows that the cost of cybercrime in the U.S. economy solitary is 140 billion dollars per year.
  • One of the largest breaches in recent history involved Sony’s Playstation Network in 2011, and experts rate Sony’s costs related to the infringement to subsist somewhere between 2.7 and 24 billion dollars (a wide range, but the infringement was so large, it is almost impossible to quantify).2
  • Netflix and AOL fill already faced (and in some cases, settled) millions of dollars in lawsuits over their management of great sets of data and their protection of personal information – even data that they had “anonymized” and released for research.3
  • Beyond quantifiable costs related to security breaches (loss of customers and business partners, lawsuits, regulatory fines), organizations that fill experienced such incidents report that the fallout from a data infringement results in a diminished faith of the organization and a damaged reputation that could achieve a company out of business.4
  • Simply achieve - Without ensuring that proper security controls are in place, tremendous Data can easily become a tremendous Problem with a tremendous cost Tag.

    What does this stand for for organizations processing tremendous Data? The more data you have, the more well-known it is that you protect it. It means that not only must they provide effectual security controls on data leaving their networks, but they besides must control access to data within their networks. Depending on the sensitivity of the data, they may requisite to result certain that their data analysts fill permission to espy the data that they are analyzing, and they fill to understand the ramifications of the release of the data and resulting analysis. The Netflix data infringement solitary shows us that even when you attempt to “anonymize” data sets, you may besides release unintentional information – something that is addressed in the realm of differential privacy.

    One of the most favorite platforms for tremendous Data processing is Apache Hadoop. Originally designed without security in mind, Hadoop’s security model has continued to evolve. Its soar in popularity has brought much scrutiny, and as security professionals fill continued to point out potential security vulnerabilities and tremendous Data Security risks with Hadoop, this has led to continued security modifications to Hadoop. There has been explosive growth in the “Hadoop security” marketplace, where vendors are releasing “security-enhanced” distributions of Hadoop and solutions that compliment Hadoop security. This is evidenced by such products as Cloudera Sentry, IBM InfoSphere Optim Data Masking, Intel's secure Hadoop distribution, DataStax Enterprise, DataGuise for Hadoop, Protegrity tremendous Data Protector for Hadoop, Revelytix Loom, Zettaset Secure Data Warehouse, and the list could ebb on. At the identical time, Apache projects, such as Apache Accumulo provide mechanisms for adding additional security when using Hadoop. Finally, other open source projects, such as Knox Gateway (contributed by HortonWorks) and Project Rhino (contributed by Intel) engage that tremendous changes are coming to Hadoop itself.

    The powerful demand for Hadoop to meet security requirements is resulting in ongoing changes to Hadoop, which is what I will focus on in this article.

    A (Brief) History of Hadoop Security

    It is a well-known fact that security was not a factor when Hadoop was initially developed by Doug Cutting and Mike Cafarella for the Nutch project. As the initial employ cases of Hadoop revolved around managing great amounts of public web data, confidentiality was not an issue. For Hadoop's initial purposes, it was always assumed that clusters would consist of cooperating, trusted machines used by trusted users in a trusted environment.

    Initially, there was no security model – Hadoop didn’t authenticate users or services, and there was no data privacy. As Hadoop was designed to execute code over a distributed cluster of machines, anyone could submit code and it would subsist executed. Although auditing and authorization controls (HDFS file permissions) were implemented in earlier distributions, such access control was easily circumvented because any user could impersonate any other user with a command line switch. Because impersonation was prevalent and done by most users, the security controls that did exist were not really effective.

    Back then, organizations concerned about security segregated Hadoop clusters onto private networks, and restricted access to authorized users. However, because there were few security controls within Hadoop, many accidents and security incidents happened in such environments. Well-intended users can result mistakes (e.g. deleting massive amounts of data within seconds with a distributed delete). All users and programmers had the identical plane of access to All of the data in the cluster, any job could access any data in the cluster, and any user could potentially read any data set. Because MapReduce had no concept of authentication or authorization, a mischievous user could lower the priorities of other Hadoop jobs in order to result his job complete faster – or worse, exterminate the other jobs.

    As Hadoop became a more favorite platform for data analytics and processing, security professionals began to express concerns about the insider threat of malicious users in a Hadoop cluster. A malicious developer could easily write code to impersonate other users’ Hadoop services (e.g. writing a new TaskTracker and registering itself as a Hadoop service, or impersonating the hdfs or mapred users, deleting everything in HDFS, etc.). Because DataNodes enforced no access control, a malicious user could read arbitrary data blocks from DataNodes, bypassing access control restrictions, or writing garbage data to DataNodes, undermining the integrity of the data to subsist analyzed. Anyone could submit a job to a JobTracker and it could subsist arbitrarily executed.

    Because of these security concerns, the Hadoop community realized that more robust security controls were needed, and as a result, a team at Yahoo! decided to focus on authentication, and chose Kerberos as the authentication mechanism for Hadoop, documented in their 2009 white paper.

    The release of the .20.20x distributions of Hadoop accomplished their goals, by utilizing the following:

  • Mutual Authentication with Kerberos RPC (SASL/GSSAPI) on RPC connections – SASL/GSSAPI was used to implement Kerberos and mutually authenticate users, their processes, and Hadoop services on RPC connections.
  • “Pluggable” Authentication for HTTP Web Consoles - import that implementers of web applications and web consoles could implement their own authentication mechanism for HTTP connections. This could involve (but was not limited to) HTTP SPNEGO authentication.
  • Enforcement of HDFS file permissions – Access control to files in HDFS could subsist enforced by the NameNode based on file permissions - Access Control Lists (ACLs) of users and groups.
  • Delegation Tokens for Subsequent Authentication checks - These were used between the various clients and services after their initial authentication in order to reduce the performance overhead and load on the Kerberos KDC after the initial user authentication. Specifically, delegation tokens are used in communication with the NameNode for subsequent authenticated access without using the Kerberos Servers.
  • Block Access Tokens for Access Control to Data obscure When access to data blocks were needed, the NameNode would result an access control determination based on HDFS file permissions and would issue obscure access tokens (using HMAC-SHA1) that could subsist sent to the DataNode for obscure access requests. Because DataNodes fill no concept of files or permissions, this was necessary to result the connection between the HDFS permissions and access to the blocks of data.
  • Job Tokens to implement task Authorization - Job tokens are created by the JobTracker and passed onto TaskTrackers, ensuring that Tasks could only carry out labor on the jobs that they are assigned. Tasks could besides subsist configured to accelerate as the user submitting the job, making access control checks simpler.
  • Putting it All together, this provided a significant step forward for Hadoop. Since then, a few notable modifications fill been implemented:
  • From “Pluggable Authentication” to HTTP SPNEGO Authentication – Although the 2009 security design of Hadoop focused on pluggable authentication, the Hadoop developer community decided that it would subsist better to employ Kerberos consistently, since Kerberos authentication was already being used for RPC connections (users, applications, and Hadoop services). Now, Hadoop web consoles are configured to employ HTTP SPNEGO Authentication, an implementation of Kerberos for web consoles. This provided some much-needed consistency.
  • Network Encryption - Connections utilizing SASL can subsist configured to employ a property of Protection (QoP) of confidential, enforcing encryption at the network plane – this includes connections using Kerberos RPC and subsequent authentication using delegation tokens. Web consoles and MapReduce shuffle operations can subsist encrypted by configuring them to employ SSL. Finally, HDFS File Transfer can besides subsist configured for encryption.
  • Since the security redesign, Hadoop’s security model has by and great stayed the same. Over time, some components of the Hadoop ecosystem fill applied their own security as a layer over Hadoop – for example, Apache Accumulo provides cell-level authorization, and HBase provides access controls at the column and family level.

    Today’s Hadoop Security Challenges

    There are number of security challenges for organizations securing Hadoop, and in a new engage that I fill written with Boris Lublinsky and Alexey Yakubovich, they dedicate two chapters to securing Hadoop – one focused on Hadoop’s capabilities, and the other focused on strategies for complementing Hadoop security.

    Common security questions are:

  • How carry out you implement authentication for users and applications on All types of clients (e.g. web consoles and processes)?
  • How carry out you result certain that rogue services aren’t impersonating true services (e.g. rogue TaskTrackers and Tasks, unauthorized processes presenting obscure IDs to DataNodes to accept access to data blocks, etc?)
  • How carry out you implement access control to the data, based on existing access control policies and user credentials?
  • How can Attribute-Based Access Control (ABAC) or Role-Based Access Control (RBAC) subsist implemented?
  • How can Hadoop integrate with existing enterprise security services?
  • How carry out you control who is authorized to access, modify and desist MapReduce jobs?
  • How can you encrypt data in transit?
  • How carry out you encrypt data at rest?
  • How can you maintain track of, and audit events & maintain track of data provenance?
  • What are the best network approaches for protecting my Hadoop cluster on the network?
  • Many of these can currently subsist answered by Hadoop’s current capabilities, but many of them cannot, leading to the proliferation of Hadoop security-complementing tools that they espy in the industry. Just a few reasons that vendors are releasing security products that complement Hadoop are:

    1. No “Data at Rest” Encryption. Currently, data is not encrypted at ease on HDFS. For organizations with strict security requirements related to the encryption of their data in Hadoop clusters, they are forced to employ third-party tools for implementing HDFS disk-level encryption, or security-enhanced Hadoop distributions (like Intel’s distribution from earlier this year).

    2. A Kerberos-Centric Approach – Hadoop security relies on Kerberos for authentication. For organizations utilizing other approaches not involving Kerberos, this means setting up a sever authentication system in the enterprise.

    3. Limited Authorization Capabilities – Although Hadoop can subsist configured to achieve authorization based on user and group permissions and Access Control Lists (ACLs), this may not subsist enough for every organization. Many organizations employ supple and dynamic access control policies based on XACML and Attribute-Based Access Control. Although it is certainly viable to achieve these plane of authorization filters using Accumulo, Hadoop’s authorization credentials are limited

    4. Complexity of the Security Model and Configuration. There are a number of data flows involved in Hadoop authentication – Kerberos RPC authentication for applications and Hadoop Services, HTTP SPNEGO authentication for web consoles, and the employ of delegation tokens, obscure tokens, and job tokens. For network encryption, there are besides three encryption mechanisms that must subsist configured – property of Protection for SASL mechanisms, and SSL for web consoles, HDFS Data Transfer Encryption. All of these settings requisite to subsist separately configured – and it is light to result mistakes.

    Implementers requiring security capabilities that Hadoop does not provide today fill had to spin to integration of third-party tools, employ a vendor’s security-enhanced Hadoop distribution, or foster up with other creative approaches.

    Big Changes Coming

    At the soar of 2013, Intel launched an open source ail called Project Rhino to ameliorate the security capabilities of Hadoop and the Hadoop ecosystem, and contributed code to Apache. This promises to significantly enhance Hadoop’s current offering. The overall goals for this open source ail are to uphold encryption and key management, a common authorization framework beyond ACLs of users and groups that Hadoop currently provides, a common token based authentication framework, security improvements to HBase, and improved security auditing. These tasks fill been documented in JIRA for Hadoop, MapReduce, HBase, and Zookeeper, and highlights are shown below:

  • Encrypted Data at ease - JIRA Tasks HADOOP-9331 (Hadoop Crypto Codec Framework and Crypto Codec Implementation) and MAPREDUCE-5025 (Key Distribution and Management for Supporting Crypto Codec in MapReduce) are directly related. The first focuses on creating a cryptography framework and implementation for the talent to uphold encryption and decryption of files on HDFS, and the second focuses on a key distribution and management framework for MapReduce to subsist able to encrypt and decrypt data during MapReduce operations. In order to achieve this, a splittable AES codec implementation is being introduced to Hadoop, allowing distributed data to subsist encrypted and decrypted from disk. The key distribution and management framework will allow the resolution of key contexts during MapReduce operations so that MapReduce jobs can achieve encryption and decryption. The requirements that they fill developed involve different options for the different stages of MapReduce jobs, and uphold a supple route of retrieving keys. In a relatively related task, ZOOKEEPER-1688 will provide the talent for transparent encryption of snapshots and confide logs on disk, protecting against the leakage of sensitive information from files at rest.

  • Token-Based Authentication & Unified Authorization Framework - JIRA Tasks HADOOP-9392 (Token-Based Authentication and sole Sign-On) and HADOOP-9466 (Unified Authorization Framework) are besides related. The first task presents a token-based authentication framework that is not tightly-coupled to Kerberos. The second task will utilize the token based framework to uphold a supple authorization enforcement engine that aims to supersede (but subsist backwards compatible with) the current ACL approaches for access control. For the token-based authentication framework, the first task plans to uphold tokens for many authentication mechanisms such as LDAP username/password authentication, Kerberos, X.509 Certificate authentication, SQL authentication (based on username/password combinations in SQL databases), and SAML. The second task aims to uphold an advanced authorization model, focusing on impute Based Access Control (ABAC) and the XACML standard.

  • Improved Security in HBase - The JIRA task HBASE-6222 (Add Per-KeyValue Security) adds cell-level authorization to HBase – something that Apache Accumulo has but HBase does not. HBASE-7544 builds on the encryption framework being developed, extending it to HBase, providing transparent table encryption.
  • These are major changes to Hadoop, but engage to address security concerns for organizations that fill these security requirements.

    Conclusion

    In their fast-paced and connected world where tremendous Data is king, it is faultfinding to understand the importance of security as they process and dissect massive amounts of data. This starts with understanding their data and associated security policies, and it besides revolves around understanding the security policies in their organizations and how they requisite to subsist enforced. This article provided a brief history of Hadoop Security, focused on common security concerns, and it provided a snapshot of the future, looking at Project Rhino.

    About the Author

    Kevin T. Smith is the Director of Technology Solutions and Outreach for the Applied Mission Solutions division of Novetta Solutions, where he provides strategic technology leadership and develops innovative, data-focused and highly-secure solutions for customers. A frequent speaker at technology conferences, he is the author of numerous technology articles and he has authored many technology books, including the upcoming engage Professional Hadoop Solutions, as well as Applied SOA: Service-Oriented Architecture and Design Strategies, The Semantic Web: A sheperd to the Future of XML, Web Services, and learning Management and many others. He can subsist reached at KSmith@Novetta.com.

    Acknowledgements

    Special thanks to Stella Aquilina, Boris Lublinsky, Joe Pantella, Ralph Perko, Praveena Raavicharla, outspoken Tyler, and Brian Uri for their review and observation on some of the content of this article. besides - thanks to Chris Bailey for the “Abbey Road” picture of the evolving Hadoop elephant.

    1 Ponemon Institute, “2013 Cost of Data infringement Study: Global Analysis”, May 2013, 

    2 business Insider, “Playstation Network crisis May Cost Sony Billions”, 

    3 For more information espy “CNN/Money – 5 Data Breaches – From Embarrassing to Deadly”, and Wikipedia’s page on the AOL search data leak on anonymized records

    4 Ponemon Institute, “Is Your Company Ready for a tremendous Data Breach?”, March 2013. 



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