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C2090-461 - IBM InfoSphere Optim for Distributed Systems v9.1 Upgrade - Dump Information

Vendor : IBM
Exam Code : C2090-461
Exam Name : IBM InfoSphere Optim for Distributed Systems v9.1 Upgrade
Questions and Answers : 34 Q & A
Updated On : April 19, 2019
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C2090-461 Questions and Answers

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C2090-461 IBM InfoSphere Optim for Distributed Systems v9.1 Upgrade

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C2090-461 exam Dumps Source : IBM InfoSphere Optim for Distributed Systems v9.1 Upgrade

Test Code : C2090-461
Test Name : IBM InfoSphere Optim for Distributed Systems v9.1 Upgrade
Vendor Name : IBM
Q&A : 34 Real Questions

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IBM records Studio | Real Questions and Pass4sure dumps

This chapter is from the publication 

IBM data Studio is covered in each DB2 version. IBM facts Studio provides a single integrated atmosphere for database administration and software development. you could perform projects which are involving database modeling and design, establishing database purposes, administering and managing databases, tuning SQL performance, and monitoring databases multi function single device. it is a great tool that may vastly advantage a group ambiance with distinct roles and duties.

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

the full 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 equipment akin to InfoSphere information Architect and InfoSphere Optim pureQuery Runtime. be aware that one of the vital superior InfoSphere tools are handiest included within the DB2 advanced versions and the DB2 Developer version. that you would be able to also separately purchase the advanced equipment.

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

The internet console, because the name 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 have effectively installed the IBM information Studio, you are asked to deliver a workspace name. A workspace is a folder that saves your work 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 see the important thing and first projects listed in the field on the left. See figure four.26 to get an idea on the way to navigate the project Launcher.

    for instance, the figure suggests you the advance tasks. you could locate the important thing building initiatives on the left. On the right right, it lists greater initiatives regarding development. On the backside correct, IBM records Studio gives just a few documentation links the place that you may be taught more about construction. where applicable, it also suggests the superior equipment available within the InfoSphere Optim portfolio that observe to the project you've got selected.

    Connection Profiles

    every task you were to function 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 correct nook, click the Open perspective icon and choose Database Administration.

    On the Administration Explorer, correct-click the white space or beneath the new menu, choose New Connection to a database. From the brand new Connection window, you see that you should use the IBM facts Studio to connect with different IBM facts sources, in addition to non-IBM data sources. choose the database manager and enter the critical 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 choose the class of JDBC driver to make use of. JDBC classification four driver is used with the aid of default.

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

    At this point, you have 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 be 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 be able to install a set of consistent connection settings.

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

    everyday Database Administration tools

    There are few other effective 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 appropriate editor, that you can specify the type of backup, area 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 element 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 function enables you to start and stop the database. In DB2, that capacity activating and deactivating the database. Activating a database allocates all the indispensable database memory 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 use of the DB2 command-primarily based tool db2look to extract the information Definition Language (DDL) statements for the recognized database objects or the total database. This feature and gear come easy 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. See figure 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 advice center.

    The beginning Tuning characteristic configures the database to allow query tuning. You may acquire a warning indicating that you simply need to activate the InfoSphere Optim question Workload Tuner (OQWT) license for superior tuning potential. be aware that IBM DB2 advanced enterprise Server edition comes with OQWT. comply with the guidelines to follow 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 make use of the tuning advisors and equipment, you are introduced with the question Tuner Workflow Assistant, as shown in figure 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 place 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 have an interest and select exhibit SQL statement or Run Single-question Advisors and tools on the chosen statement.

    Run the question advisors and equipment on the chosen observation. which you could now enter the Invoke view. The device collects assistance and data and generates a data entry plan (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 figure 4.35. The tool documentation recommends gathering and re-accumulating all of primary records of the query.

    you can also overview the access plan graph generated by using the DB2 explain characteristic (see figure four.36 for an illustration). be aware 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. confer with Chapter eight, “enforcing security,” for details about privileges and database access controls.

    ordinary Database building 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 tool that helps you accomplish it.

    SQL and XQuery Editor

    The SQL and XQuery editor helps you create and run SQL scripts that contain a couple 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 run 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 also very constructive. It lists all the current schemas in the database so that you can simply choose 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 run scripts towards multiple database connections.

    SQL question Builder

    The SQL query Builder allows for you to create a single SQL statement, but it surely doesn't support XQuery. because the identify implies, the device helps you construct an SQL observation. It helps you seem 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 good judgment at the database server instead of in software-stage code. Use of utility objects assist cut back overhead of SQL statements and the consequences that are handed during the community. stored tactics and UDFs are also called routines. IBM data Studio supports routines building and debugging.

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

    IBM focuses on information Governance with New software, functions | Real Questions and Pass4sure dumps

    IBM remaining week introduced two new items aimed toward helping organizations make 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 choose neighborhood of valued clientele. IBM also announced new features and a new center 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 building of latest items, like 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 join the Optim household of tools, which IBM got through its 2007 acquisition of Princeton Softech. The utility is designed to automatically admire and remove delicate content from documents and types. The utility may well be used with the aid of a financial institution, for example, to conceal a consumer’s credit rankings in a personal loan doc from an workplace clerk, while allowing it to be seen by means of a personal loan officer, according to IBM.

    It’s no longer clear no matter if Optim statistics Redaction will work at once with DB2/400; IBM didn't say and details of the product aren't yet accessible. If it’s like different Optim products, such as the archiving and test administration utility for JD Edwards EnterpriseOne that work 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 use it, until they have 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 work 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 part of the realm didn't make it into the aggregated database used for analysis, InfoSphere business display screen would alert the administrator to the problem, and steps may well be taken to repair it.

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

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

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    How Hadoop Keeps Even Small Businesses In the Loop for Big Data Analytics | real questions and Pass4sure dumps

    Hadoop is a software system developed by Apache that allows a company’s data science team to process for analytical purposes large 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 improve things like business performance and customer relationship management.  This unstructured data is known in the industry as big data.  Every company that conducts physical and electronic transactions has access to big data, but it was not until recently that corporate leaders began to fully recognize big data’s potential to help them to forecast trends needed to improve competitive advantage.  Large businesses were at an advantage because they could purchase specialized hardware and hire the human resources that are needed to prepare the diverse data for analysis.  Convenient features like Excel reporting in Hadoop allow small businesses to harness the power of big data analytics as even non-technical users are able to access large data sets from inexpensive, off the shelf servers for data analysis projects.  Here are some other reasons why Hadoop is considered a leading tool for corporate data science teams.

    Use Hadoop With Leading Storage TechnologyHadoop has leveled the playing field for companies that want to effectively use big 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 effective big data analysis.  Today, businesses of varying sizes use cloud storage options to expand their storage capabilities, and one of the most popular brands is Google Cloud Storage.  The value of Hadoop is well known in the information technology industry, and Google has responded by building a custom connector that integrates Google Cloud Storage with Hadoop.  Additionally, providers of storage area network and virtualization storage options have plans to integrate their products and services with Apache’s Hadoop.

    Tighten Up Big Data Security Using Third Party Tools and Add-OnsData security remains a hot 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 steal information or corrupt 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 experience productivity losses.  The storage and processing of big data by numerous companies just opens up a new path for cyber criminals because they have greater amounts of unsecured data to exploit.  Hadoop was not originally built with security mechanisms in place, but third party tools like IBM InfoSphere Optim Data Masking, Cloudera Sentry and DataStax Enterprise have 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 also allow for the encryption of the Hadoop file system and data access blocking.  Maintenance and customer support are additional benefits of purchasing these distributed, third party versions of Hadoop versus using the free, original Apache product.

    Improve Big Data Processing Through Hadoop Integration With Popular IT System BrandsA great advantage of using Hadoop over other business intelligence software is the capability that it provides to developers and analysts to quickly extract and process large groupings of data.  The efficiency of processing is dependent on many factors including the location of the data and the server platform used.  Many businesses trust Microsoft’s brand and have outfitted their organization with the company’s servers, operating system and application software.  Although Microsoft’s products have been known not to be compatible with competing software systems,  the computing giant has taken great strides to update their flagship MS SQL Server product so that it and its Parallel Data Warehouse utility connects with Hadoop.  Microsoft Office applications like Excel have also 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 also allows Hadoop users to view, analyze, graph and update data from multiple sources using a web based spreadsheet; IBM’s plan was to make their version of Hadoop the preferred one for business users. The fact that Hadoop can be implemented on these many platforms, and the many resources available to those learning it for the first time, make it the ideal 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 do not have to be Java programming experts to make modifications to the software framework. Database developers can use SQL similar scripting languages like 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 | real questions and Pass4sure dumps

    Julian Stuhler shares his pick of the most important 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 have some sort of impact on data management professionals during the next 12-18 months.


    The Greek philosopher Heraclitus is credited with the saying "Nothing endures but change". Two millennia later those words still 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 important 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 have some sort of impact on data management professionals during the next 12-18 months.

    1. Living on a Smarter Planet

    You don't have to be an IT professional to see that the world around us is getting smarter. Let's just take a look at a few examples from the world of motoring: we've become used to our 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 make our lives easier and safer by using real-time data to make smart decisions.

    However, all of this is just the beginning: everywhere you look the world is getting more "instrumented", and clever technologies are being adopted to use the real-time data to make things safer, quicker and greener. Smart electricity meters in homes are giving consumers the ability to monitor their energy usage in real time and make informed decisions on how they use it, resulting in an average reduction of 10% in a recent US study. Sophisticated traffic management systems in our 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 our systems will have to manage (see trend #2 below) but the IT impact goes a lot deeper than that. The very infrastructure that we run our IT systems on is also getting smarter. Virtualization technologies allow server images to be 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 be more accurately measured and predicted so that capacity can be dynamically adjusted to cope. With up to 85% of server capacity typically sitting idle on distributed platforms, the ability to virtualize and consolidate multiple physical servers can save an enormous amount of power, money and valuable IT center 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 be a new way of thinking about your server environment. Either way, most of us will be managing our 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 our 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 end 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 move 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 move towards instrumenting our planet (see trend #1 above) is only going to make things worse.

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

    3. Hardware Assist

    OK, so this is not a new trend: some of the earliest desktop PCs had the option to fit coprocessors to speed 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, use of special hardware is becoming ever more important on all of the major computing platforms.

    In 2004, IBM introduced the zAAP (System z Application Assist Processor), a special type 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, work can be offloaded from the general-purpose processors to improve 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" have been a critical 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: we can expect the theme of workload optimization through dedicated hardware to continue.

    On the distributed computing platform, things have 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 run 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) have already been proven and the mainframe database vendors won't be 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 pure functionality. In that kind of environment, it's a real treat 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 part of DB2 10 for z/OS qualify on both counts.

    Many IT systems need to keep some form of historical information in addition to the current status for a given business object. For example, a financial institution may need to retain the previous addresses of a customer as well as the one they are currently living at, and know what address applied at any given time. Previously, this would have required the DBA and application developers to spend valuable time creating the code and database design to support the historical perspective, while minimizing any performance impact.

    The new temporal data support in DB2 10 for z/OS provides this functionality as part of the core database engine. All you need to do is indicate which tables/columns require temporal support, and DB2 will automatically maintain the history whenever an update is made to the data. Elegant SQL support allows the developer to query the database with an "as of" date, which will return 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 expect other databases (both IBM and non-IBM) to implement this feature sooner rather than later.

    5. The Rise of XML and Spatial Data

    Most relational databases have been able to store "unstructured" data such as photographs and scanned images for a while now, in the form of BLOBS (Binary Large OBjects). This has proven useful in some situations, but most businesses use specialized applications such as IBM Content Manager to handle this information more effectively than a general-purpose database. These kind of applications typically do not have to perform 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 need to be fully understood by the database system so that it can be integrated with structured data and queried using the full 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 form of XML as the primary means of data interchange, both internally between applications and externally when communicating with third-parties. As the volume of critical XML business documents increases, so too does the need to properly store and retrieve those documents alongside other business information. DB2's pureXML feature allows XML documents to be 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 beginning to actually make use of pureXML within their systems. The ability to offload some XML parsing work to a zAAP coprocessor (see trend #3) is certainly helping.

    Nearly all of our existing applications contain a wealth of spatial data (customer addresses, supplier locations, store locations, etc): the trouble is we're unable to use it properly as it's in the form of simple text fields. The spatial abilities within DB2 allow that data to be "geoencoded" in a separate column, so that the full power of SQL can be 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 plain or high crime area? All of this and much more is possible with simple SQL queries. Again, this is not a brand new feature but more and more organizations are beginning to see the potential and design applications to exploit this feature.

    6. Application Portability

    Despite the relative maturity of the relational database marketplace, there is still fierce competition for overall market share 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 brave souls that undertook migration projects in the past faced a difficult process, often entailing significant effort and risk to port the database and associated applications to run on the new platform. This made large-scale migrations relatively rare, even when there were compelling cost or functionality reasons to move to another platform.

    Two trends are changing this and making porting projects more common. The first is the rise of the packaged ERP/CRM solution from companies such as SAP and Siebel. These applications have been written to be largely database agnostic, with the core business logic isolated from the underlying database by an "I/O layer". So, while there may still be good reasons to be on a specific vendor's database in terms of functionality or price, the pain of moving 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 have 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 support for competitor's database APIs. DB2 for LUW version 9.7 includes a host of new Oracle compatibility features that makes it possible to run the vast majority of Oracle applications natively against DB2 with little or no change required to the code. IBM has also announced the "DB2 SQL Skin" feature, which provides similar capabilities for Sybase ASE applications to run against DB2. With these features greatly reducing the cost and risk of changing the application code to work 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 see 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 return the favor if they begin to lose significant market share as a result.

    7. Scalability and Availability

    The ability to provide unparalleled scalability and availability for DB2 databases is not new: high-end mainframe users have 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 run mission-critical systems with 24x7 availability and no single point of failure, with only a minimal performance penalty. Major increases in workload can be accommodated by adding additional members to the data sharing group, providing an easy way to scale.

    Two developments have resulted in this making my top 10 trends list. Firstly, I'm seeing a significant number of mainframe customers who had not previously taken advantage of data sharing begin to take 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 be big news over the next year is DB2 pureScale: the implementation of the same data sharing shared-disk concepts on the DB2 for LUW platform. It's difficult to overstate the potential impact this could have on distributed DB2 customers that run high volume mission critical applications. Before pureScale, those customers had to rely on features such as HADR to provide failover support to a separate server (which could require many seconds to take over in the event of a failure) or go 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 same ballpark as DB2 for z/OS in terms of scalability and availability, and I'm expecting a lot of customer activity in this area over the next year.

    8. Stack 'em high...

    For some time now, it has been possible for organizations to take 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 building skills and experience in specific vendor's products, thereby reducing support costs.

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

    Two acquisitions during the past 12 months have 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 single stroke, Oracle was able to offer potential customers a completely integrated hardware/software/application stack.

    The jury is still out on the potential impact 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 choose to integrate the Sybase database technology into the SAP product. That will still leave them dependent on other vendors such as IBM for the hardware and operating system, but it would be a major step forward in any integration strategy they may have.

    Older readers of this article may see some startling similarities to the bad old days of vendor lock-in prevalent in the 1970s and 1980s. IBM's strategy to support other vendor's database APIs (see trend # 6) is in direct contrast to this, and it will be interesting to see how far customers are willing to go down the single 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 decision support application for IMS databases. The ability to build a warehouse within the same environment as your operational data resides (and thereby avoid the expensive and time-consuming process of moving that data to another platform for analysis) is attractive to many customers.

    IBM is making significant efforts to make this an attractive proposition for more of their mainframe customers. The Cognos tools have been available for zLinux for a couple of years now, and the DB2 for z/OS development team have been steadily adding BI-related functions to the core database engine for years. Significant portions of a typical BI workload can also be 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 also begun to talk about the Smart Analytics Optimizer - a high performance appliance-like blade for System z capable of handling intensive BI query workloads with minimal impact to CPU.

    IBM is serious about BI on the mainframe, and is building an increasingly compelling cost and functionality case to support 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 same time, there has been an increasing focus on data quality: bad data can result in bad business decisions, which no one can afford in today's competitive markets. There has also 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 beginning to come together under the general heading of data governance. As our database systems get 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: Run Oracle applications on DB2 9.7 for Linux, Unix, and WindowspureScaleIBM Smart Analytics OptimizeIBM Smart Analytics System 9600IBM Data governance

    » See All Articles by Columnist Julian Stuhler

    Big Data Security: The Evolution of Hadoop’s Security Model | real questions and Pass4sure dumps

    One of the biggest concerns in our present age revolves around the security and protection of sensitive information. In our current era of Big Data, our 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 same time, more and more organizations are being required to enforce 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 be detected, and those affected are paying the price. Organizations that have 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 found that the average organizational cost of one security breach in the United States is 5.4 million dollars1. Another recent study shows that the cost of cybercrime in the U.S. economy alone is 140 billion dollars per year.
  • One of the largest breaches in recent history involved Sony’s Playstation Network in 2011, and experts estimate Sony’s costs related to the breach to be somewhere between 2.7 and 24 billion dollars (a wide range, but the breach was so large, it is almost impossible to quantify).2
  • Netflix and AOL have already faced (and in some cases, settled) millions of dollars in lawsuits over their management of large 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 have experienced such incidents report that the fallout from a data breach results in a diminished trust of the organization and a damaged reputation that could put a company out of business.4
  • Simply put - Without ensuring that proper security controls are in place, Big Data can easily become a Big Problem with a Big Price Tag.

    What does this mean for organizations processing Big Data? The more data you have, the more important it is that you protect it. It means that not only must we provide effective security controls on data leaving our networks, but we also must control access to data within our networks. Depending on the sensitivity of the data, we may need to make certain that our data analysts have permission to see the data that they are analyzing, and we have to understand the ramifications of the release of the data and resulting analysis. The Netflix data breach alone shows us that even when you attempt to “anonymize” data sets, you may also release unintentional information – something that is addressed in the field of differential privacy.

    One of the most popular platforms for Big Data processing is Apache Hadoop. Originally designed without security in mind, Hadoop’s security model has continued to evolve. Its rise in popularity has brought much scrutiny, and as security professionals have continued to point out potential security vulnerabilities and Big 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 Big Data Protector for Hadoop, Revelytix Loom, Zettaset Secure Data Warehouse, and the list could go on. At the same 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) promise that big changes are coming to Hadoop itself.

    The great 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 use cases of Hadoop revolved around managing large 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 be 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 make mistakes (e.g. deleting massive amounts of data within seconds with a distributed delete). All users and programmers had the same level 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 make his job complete faster – or worse, kill the other jobs.

    As Hadoop became a more popular 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 be analyzed. Anyone could submit a job to a JobTracker and it could be 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 - meaning that implementers of web applications and web consoles could implement their own authentication mechanism for HTTP connections. This could include (but was not limited to) HTTP SPNEGO authentication.
  • Enforcement of HDFS file permissions – Access control to files in HDFS could be 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 Block When access to data blocks were needed, the NameNode would make an access control decision based on HDFS file permissions and would issue Block access tokens (using HMAC-SHA1) that could be sent to the DataNode for block access requests. Because DataNodes have no concept of files or permissions, this was necessary to make the connection between the HDFS permissions and access to the blocks of data.
  • Job Tokens to Enforce Task Authorization - Job tokens are created by the JobTracker and passed onto TaskTrackers, ensuring that Tasks could only do work on the jobs that they are assigned. Tasks could also be configured to run 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 have 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 be better to use Kerberos consistently, since Kerberos authentication was already being used for RPC connections (users, applications, and Hadoop services). Now, Hadoop web consoles are configured to use HTTP SPNEGO Authentication, an implementation of Kerberos for web consoles. This provided some much-needed consistency.
  • Network Encryption - Connections utilizing SASL can be configured to use a Quality of Protection (QoP) of confidential, enforcing encryption at the network level – this includes connections using Kerberos RPC and subsequent authentication using delegation tokens. Web consoles and MapReduce shuffle operations can be encrypted by configuring them to use SSL. Finally, HDFS File Transfer can also be configured for encryption.
  • Since the security redesign, Hadoop’s security model has by and large stayed the same. Over time, some components of the Hadoop ecosystem have 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 book that I have written with Boris Lublinsky and Alexey Yakubovich, we 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 do you enforce authentication for users and applications on all types of clients (e.g. web consoles and processes)?
  • How do you make sure that rogue services aren’t impersonating real services (e.g. rogue TaskTrackers and Tasks, unauthorized processes presenting block IDs to DataNodes to get access to data blocks, etc?)
  • How do you enforce 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) be implemented?
  • How can Hadoop integrate with existing enterprise security services?
  • How do you control who is authorized to access, modify and stop MapReduce jobs?
  • How can you encrypt data in transit?
  • How do you encrypt data at rest?
  • How can you keep track of, and audit events & keep track of data provenance?
  • What are the best network approaches for protecting my Hadoop cluster on the network?
  • Many of these can currently be answered by Hadoop’s current capabilities, but many of them cannot, leading to the proliferation of Hadoop security-complementing tools that we see 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 rest on HDFS. For organizations with strict security requirements related to the encryption of their data in Hadoop clusters, they are forced to use 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 separate authentication system in the enterprise.

    3. Limited Authorization Capabilities – Although Hadoop can be configured to perform authorization based on user and group permissions and Access Control Lists (ACLs), this may not be enough for every organization. Many organizations use flexible and dynamic access control policies based on XACML and Attribute-Based Access Control. Although it is certainly possible to perform these level 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 use of delegation tokens, block tokens, and job tokens. For network encryption, there are also three encryption mechanisms that must be configured – Quality of Protection for SASL mechanisms, and SSL for web consoles, HDFS Data Transfer Encryption. All of these settings need to be separately configured – and it is easy to make mistakes.

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

    Big Changes Coming

    At the beginning of 2013, Intel launched an open source effort called Project Rhino to improve 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 effort are to support 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 have been documented in JIRA for Hadoop, MapReduce, HBase, and Zookeeper, and highlights are shown below:

  • Encrypted Data at Rest - 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 ability to support encryption and decryption of files on HDFS, and the second focuses on a key distribution and management framework for MapReduce to be 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 be 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 perform encryption and decryption. The requirements that they have developed include different options for the different stages of MapReduce jobs, and support a flexible way of retrieving keys. In a somewhat related task, ZOOKEEPER-1688 will provide the ability for transparent encryption of snapshots and commit 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 Single Sign-On) and HADOOP-9466 (Unified Authorization Framework) are also 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 support a flexible authorization enforcement engine that aims to replace (but be backwards compatible with) the current ACL approaches for access control. For the token-based authentication framework, the first task plans to support 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 support an advanced authorization model, focusing on Attribute 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 promise to address security concerns for organizations that have these security requirements.


    In our fast-paced and connected world where Big Data is king, it is critical to understand the importance of security as we process and analyze massive amounts of data. This starts with understanding our data and associated security policies, and it also revolves around understanding the security policies in our organizations and how they need to be 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 book Professional Hadoop Solutions, as well as Applied SOA: Service-Oriented Architecture and Design Strategies, The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management and many others. He can be reached at


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

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

    2 Business Insider, “Playstation Network Crisis May Cost Sony Billions”, 

    3 For more information see “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 Big Data Breach?”, March 2013. 

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