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70-466 Implementing Data Models and Reports with Microsoft SQL Server

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70-466 exam Dumps Source : Implementing Data Models and Reports with Microsoft SQL Server

Test Code : 70-466
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: 173 actual Questions

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Microsoft Implementing Data Models and

Microsoft power BI Now integrated With Azure information Lake Storage | killexams.com actual Questions and Pass4sure dumps

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extra turbulence is coming in the huge-records analytics market in 2019 | killexams.com actual Questions and Pass4sure dumps

huge-information analytics has been one of the vital paramount tech developments of this decade and probably the most dynamic and imaginitive segments of the counsel expertise market. however these days’s huge facts analytics market is quite diverse from the industry of even a brace of years ago, and it’s basically certain to exist radically different in a few years’ time.

In 2018, they saw many clear signs that the massive facts market that rose unexpectedly at the start of this decade is birth to recrystallize in a extremely different kind. in the coming years, this market likely won’t even exist called “massive facts.” That’s as a result of a whole lot of its evolution is towards simulated intelligence, which, though records-driven at its core, doesn’t necessarily reckon on expansive amounts of records with the intention to exist valuable in lots of functions.

As Wikibon appears ahead to 2019, they foresee here paramount traits in large-information analytics:

Public cloud providers are absorbing most unusual huge-records analytics boom alternatives

organisations are relocating extra of their expansive records analytics workloads to public clouds and constructing greater greenfield applications for these environments.

In 2019, the three essential public cloud suppliers — Amazon web features Inc., Microsoft Azure and Google Cloud Platform — will step up their efforts to aid industry debts migrate their statistics lakes faraway from on-premises structures.

other public cloud suppliers will combat to cling onto their huge-records analytics market shares. In 2018, the challenges from the common public cloud leaders compelled IBM Corp. to purchase RedHat Inc. Going ahead, IBM, Oracle Corp. and other public cloud providers will emphasize hybrid cloud solutions that aid shoppers centralize management of massive-information assets dispensed between private and public clouds.

moreover, more huge-data public cloud providers — ceding the infrastructure-as-a-carrier and platform-as-a-provider segments to AWS, Microsoft Corp. and Google LLC — will shift to providing premium application-as-a-service analytics purposes for line-of-company and trade-particular opportunities. as an example, Snowflake Computing Inc. has been a unbelievable success in the cloud statistics warehousing market, touchdown $450 million funding in 2018 to preserve its boom trajectory.

The large-records analytics ecosystem goes deeply cloud-native

Kubernetes, the open-source software prevalent for managing application containers for applications that deserve to circulation with ease among clouds and on-premises statistics centers, is the groundwork for the unusual era of cloud-native expansive statistics. probably the most noteworthy vogue over the past yr during this market has been the recrystallization of the records ecosystem around Kubernetes.

The foster of cloud-native big-facts architectures drove loads of funding and mergers-and-acquisitions recreation in 2018. That explains why Pivotal, now focusing on dispensed in-reminiscence statistics in assorted clouds, fetched $555 million in its preparatory public offering of stock. The requisite for more straightforward tools for loading information into cloud information warehouses was at the back of Talend’s acquisition of sew. And starting to exist commercial enterprise necessities for scalable cloud-based file and object storage is a huge section of the explanation why Cloudian bought Infinity Storage.

In 2019, they prognosticate that the Open Hybrid architecture Initiative will carry on its procedure to modularize and containerize HDFS, MapReduce, HBase, Hive, Pig, YARN and different necessary Hadoop components. They additionally prognosticate that the top sponsors — Hortonworks Inc., soon to develop into section of Cloudera Inc., and IBM/crimson Hat — will deliver next-era industry Hadoop platforms that contain this architecture into their respective hybrid-cloud solution portfolios in early 2019, and that other cloud solution providers will supervene their lead totality over the yr.

identical containerization initiatives in the Spark, TensorFlow, streaming, disbursed object support and obscure storage segments will catch cling in 2019 because the complete large-data stack decouples for more agile deployment and administration in Kubernetes-based mostly DevOps environments.

every huge records analytics platform company is investing closely in information science toolchains

massive statistics analytics confess suppliers are in a warfare to win the hearts and minds of the brand unusual era of builders engaged on AI projects.  a brand unusual era of data science workbenches gain arrive to market in the past few years, together with Anaconda, Dataiku, DataKitchen, DataRobot, Dimensional Mechanics, Domino statistics Lab, H2O.ai, Hydrosphere.io, Kogentix, Pipeline.ai and Seldon. moreover, established expansive facts analytics carriers comparable to IBM, Oracle, Cloudera, Alteryx and others gain jumped into this district of interest, as gain totality three fundamental public cloud companies.

The DataRobot, Tamr, and Immuta challenge capital funding rounds in 2018 simply trace at the incontrovertible fact that seemingly dozens of startups gain taken root during this facts science workbench facet over the past few years. In 2018, Wikibon has arrive throughout a growing number of them primarily based in China and in other places within the some distance East.

In 2019, extra corporations will emphasize their offerings’ capacity to automate such historically manual projects as office engineering, hyperparameter optimization and statistics labeling. big-statistics analytics confess suppliers will get investments heavily in paraphernalia to accelerate deployment of educated AI fashions into production functions. as the big-facts analytics ecosystem shifts towards cloud-native architectures, greater facts science workbenches will include the means to automate initiatives over Kubernetes orchestration fabrics and additionally to containerize fashions for deployment into public and private clouds. This style will convey emerging requirements, corresponding to Kubeflow, into burgeoning facts science DevOps toolchain ecosystem.

Hadoop and Spark gain become legacy applied sciences

Hadoop has seen its position within the huge-records analytics enviornment steadily slash in the past a few years. knocking down growth potentialities in the Hadoop market gain been a foremost aspect in the back of Cloudera’s and Hortonworks’ contract to merge in 2018.

increasingly, Hadoop has considered its core consume situations narrow to a disbursed file device for unstructured records, a platform for batch information transformation, a huge-records governance repository and a queryable massive facts archive.

In 2019, Hadoop will battle to expand its utility attain to on-line analytical processing, company intelligence, information warehousing and other niches which are addressed by way of different open-supply initiatives. by way of the conclusion of the yr, Hadoop will birth to exist phased out in many commercial enterprise huge-statistics environments even in its core statistics-lake position in covet of distributed object shops, move-computing platforms and hugely scalable in-memory clusters.

Even Apache Spark, which developed within the core of this decade as a Hadoop choice, is emotion more and more relish a legacy technology in lots of TensorFlow-centric AI retail outlets. This vogue can exist considered through the information extract/seriously change/load niche into which Spark is increasingly being deployed, which may decline in value as schema-on-examine architectures arrive to the forefront.

huge statistics catalogs are turn into significant to data administration DevOps

clients’ means to impulsively search, discover, curate and govern data property is now the groundwork of digital enterprise success. during this regard, Looker information Sciences Inc. acquired $a hundred million in a sequence E funding to tackle the market for big data cataloguing, governance, instruction and visualization options.

In 2019, they anticipate to ogle greater businesses repurpose their records lakes into massive-records catalogs inside application infrastructures that accommodate the productivity of handicap workers, help the unusual generation of builders who're structure and training creation AI applications, and facilitate algorithmic transparency and e-discovery.

We besides are expecting vendors such as IBM, Cloudera/Hortonworks, Informatica, Collibra and others deepen their present massive-facts catalog structures’ potential to control more of the metadata, models, photos, containers and different artifacts that are the lifeblood of the AI DevOps workflow. greater expansive statistics catalogs might exist deployed throughout multiclouds, leveraging the brand unusual technology of virtualization tools that existing a single manage pane for managing disparate data assets throughout public and private clouds. And they prognosticate that the main public cloud providers — AWS, Microsoft, and Google — will roll out their own large-statistics catalogs for customers who select to deploy these functions in hybrid public/inner most clouds.

facts lakes are evolving towards cloud object storage and flow computing

In 2018, cloud object storage structures reminiscent of AWS S3 and Microsoft Azure statistics Lake Storage endured to supplant Hadoop in enterprise statistics lakes. They additionally saw task capitalists prioritize funding for established providers of multicloud records access, query and visualization options (e.g. Dremio, $25 million series B);  utility-described multicloud storage (e.g. Scality, $60 million collection E) and cloud object storage (e.g. Cloudian, $94 million series E).

Going forward, that style will continue, but it’s within the system of being eclipsed over the next three to five years by way of stream computing backbones. Low-latency streaming structures comparable to Kafka, Flink and Spark Structured Streaming are getting as simple to enterprise facts infrastructures as relational facts architectures had been considering the Seventies.

business intelligence goes totality AI and totality in reminiscence

AI is remaking the enterprise intelligence market inside and out. over the ultimate few years, one of the vital core BI trends has been the convergence of the technology’s standard focal point on historical analytics with a brand unusual generation of AI-infused predictive analytics, search and forecasting paraphernalia that enable any enterprise consumer to accomplish many issues that used to require a skillful facts scientist.

In 2019, extra BI providers will integrate a abysmal dose of AI to automate the distillation of predictive insights from intricate records, while providing these refined aspects in solutions that supply self-carrier simplicity and guided next-greatest-motion prescriptions. They noticed a hallmark of this trend in 2018 with startup ThoughtSpot Inc. scoring $145 million in a collection D funding for its ingenious AI-augmented company analytics solution portfolio.

with esteem to AI’s turning out to exist office in basically each segment of the expansive information analytics market, accept as wonderful with these funding studies they saw in 2018:

  • undertaking capitalists fed an incredible amount of funding to AI startups — an expected $one hundred billion to U.S.-based mostly organizations by myself, with lots of that funding a proliferation of imaginative AI hardware-acceleration suppliers.
  • most of the biggest seed rounds this previous year were to based AI confess providers, together with AI automation workbench startup DataRobot Inc. with a $100 million collection D round, computerized records education enterprise Tamr Inc. with an $18 million sequence D and AI DevOps statistics privateness manage enterprise Immuta Inc. with a $20 million sequence B.
  • enterprises’ insatiable appetite for efficient AI-driven search technologies for sifting through transforming into piles of log facts explains why Elasticsearch B.V. turned into in a position to elevate $252 million in its IPO.
  • the ongoing industry shift towards self-provider statistics preparation in AI and enterprise analytics explains why Infogix Inc. purchased LavaStorm Analytics Inc.
  • The emerging marketplace for reinforcement gaining knowledge of paraphernalia to construct self sustaining AI-driven programs become an ingredient motivating Microsoft to purchase Bonsai AI Inc.
  • The increasing commercial enterprise deployment of dispensed in-memory cloud databases to support intricate actual-time AI pipelines drove MariaDB Corp. Ab’s acquisition of Clustrix Inc.
  • an extra paramount style in expansive records analytics has been in-reminiscence architectures. This explains why, in 2018, MemSQL Inc. scored $30 million in a collection D circular for in-reminiscence transactional analytics, InfluxData Inc. took in $35 million in a series C funding for precise-time efficiency monitoring the usage of a time-collection database, and Actian Corp. become obtained with the aid of fairness enterprise HCL on the power of its based in-reminiscence hybrid transaction/analytic platform.

    facet computing is radically remaking database architectures

    Databases as we’ve conventional them are within the process of being are deconstructed and reassembled for edge-dealing with deployments.

    a wonderful deal of the evolution in the huge statistics analytics market is towards part-dealing with, streaming, facts-in-action architectures, which don’t necessarily reckon upon massive storage architectures. That explains why they noticed terrific funding rounds in 2018 for scalable desktop-information storage, processing, and analytics (CrateDB, $11 million collection A) and streaming records pipeline integration, monitoring, and administration (StreamSets, $24 million sequence C).

    In 2019, corporations will set up streaming platforms to pressure low-latency DevOps pipelines that at totality times infuse cell, IoT, robotics, and different section purposes with proficient, choicest-fit laptop-researching fashions. online transactional analytic processing, statistics transformation, and facts governance workloads are additionally increasingly relocating towards low-latency, stateful streaming backbones.

    Over the arrival years, disruptive unusual facts platforms will arrive to market that combine mesh, streaming, in-memory, and blockchain capabilities. many of these unusual disbursed information platforms might exist optimized for continuous AI DevOps pipelines that require low-latency, scalable and automated statistics ingest, modeling, practicing and serving to district contraptions. Serverless interfaces to those analytic-pipeline capabilities should exist normal, supplemented by means of stateful streaming fabrics that help in-line suggestion engines, subsequent most efficient motion and different transactional workloads in aspect instruments on the rising 5G broadband wireless networks.

    Your concepts?

    those are Wikibon’s year-conclusion retrospectives and look-aheads for expansive records analytics. i want to hear what my readers esteem might exist the paramount trends in this market going forward.

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    Myths of big facts, Analytics & AI | killexams.com actual Questions and Pass4sure dumps

    in this particular guest feature, Nikhil Bhatia, Director of Product administration at Riversand technologies, addresses one of the crucial usual myths and misconceptions across the areas of expansive records, Analytics & AI and presents a practical fashion and some most responsible practices to apply these applied sciences in today’s competitive world. Nikhil is among the key leads for conceptualization, design and construction of the Riversand records Platform and Apps which allow valued clientele to discover, control, resolve and govern grasp facts of their hard using big facts, Cloud, Analytics and AI technologies. moreover, he is additionally involved in approach, industry building, earnings and advertising and marketing services. His academic background comprises an MBA from one in totality India’s leading enterprise schools and levels in assistance know-how and IPR legislations.

    With the confluence of extend in facts, computing energy to procedure that records and the democratization of AI technologies in the cloud, any company can avail the benefits of expansive facts, Analytics and AI to enrich their company results. however this may still now not exist considered as a “magic” confess that could clear up any industry difficulty that an organization may have. this article addresses one of the crucial standard myths and misconceptions round these areas and gifts a practical approach and some top-quality practices to drill Analytics & AI in today’s competitive world.

    fantasy: BigData technologies are more advantageous and more affordable than standard applied sciences

    however the attract of BigData is that notwithstanding information is simply too massive or intricate it can exist managed the usage of commodity hardware and open supply (therefore more cost-effective) technologies, the truth is far from this myth. It takes gigantic effort, ability and substances to basically operationalize open-source BigData technologies to clear up wonderful world complications. organizations should still additionally understand that BigData applied sciences don't seem to exist for fixing totality types of issues. as a result of they are developed for massive scale, these applied sciences can’t definitely address smaller facts units. For some problems, a smaller records set is enough so applying BigData applied sciences wouldn’t exist usurp or imperative. 

    delusion: each statistics difficulty may besides exist solved simply through the consume of Analytics or AI

    The superior cost from Analytics and AI can exist realized after framing the usurp problem. The company cost of the problem needs to exist understood and without slow related to cost or profits for the organization. customarily, a problem which requires a expansive amount of time and application by means of the corporation deciphering counsel to profit handicap is a main candidate to generate cost the usage of Analytics and AI. That being referred to, on occasion the elementary confess can besides still exist to trade a fashion or approach of working which reduces the information itself in preference to automate the interpretation of it. direct an organization is amassing invoices or negotiating promotional phrases via email and needs to automate the reconciliation process. It could definitely exist superior and less complicated to implement a unusual collaboration tool to carry and manage invoices or promotions in preference to to consequence into consequence an AI solution to comb throughout the emails and instantly interpret this tips.

    myth: The stronger expertise you employ, the more desirable the value of AI you will realize 

    There are three fundamental components to AI: the statistics, customarily a mathematical mannequin and the application to generate and rush the mannequin. the way AI works is that via working records through the software a model has to exist found out and evolved. The utility items for AI these days aren't as packaged as one of the most typical software and therefore there's a plethora of tools and frameworks, each open supply vs. paid as well as developed by using software giants similar to Google and Microsoft vs. startups for consume to help the AI models. hence the main intention in sight should totality the time exist to create a transferable, packaged mannequin to resolve a specific company problem and not the technology to exist used.

    fantasy: AI and Analytics in and of themselves generate cost

    it is always AI and Analytics plus “whatever” which gives this value to a firm. direct a retail company discovers bottlenecks within the approval process of items they are looking to promote on-line by using Analytics and AI. That’s efficient tips and insights, however the ultimate confess is the consume of a higher workflow or expanding components or increasing automation within the approval technique – this is what goes to give the actual price. furthermore, value from AI and Analytics is not created on day 1 and may believe underwhelming originally. Any expense that is generated will usually exist more desirable than what the hard is doing nowadays, so it’s a delivery. These options accumulate superior over time and utilization, and therefore such initiatives accomplish require persistence and government sponsorship. AI is definitely math performed a different approach, so the remedy issue and privilege expectation is vital. in its set of finding a leap forward the company may still focal point on fixing functional day-to-day problems.

    In conclusion, there's a misconception out there that anybody and everyone can catch a bunch of open-source tools and create an AI confess a wonderful way to give tremendous value and absolutely alternate the style a hard operates. It’s first-rate that americans are talking about and getting excited in regards to the expertise for AI. youngsters, the truth is that operationalizing AI without needing a finished facts platform is going to exist very nearly not possible.  companies requisite to gain a knowledge platform basis that may scale, is hybrid in nature and has the skill to devour totality types and volumes of information. one of the crucial greatest concerns is that however the core processing technologies for AI and Analytics may well exist quickly and scalable, the relaxation of the pipeline to devour and stream information remains behind and this bottleneck does not permit us to obtain the consequences in wonderful time. eighty% of the work in Analytics and AI is around collecting, cleansing, making ready and munging facts. until the very facts outlets and pipelines are being used by way of every person in the company, the varied, separate efforts being executed via different groups for several purposes is simply a waste of time and substances.  now not to mention that the company will never recognize the actual profit and value that AI can give when done competently. groups requisite to map out their statistics approach first (and substantiate they gain a bizarre records basis) earlier than they try to embark on the promises of BigData, AI and Analytics.

    register for the free insideBIGDATA publication.


    70-466 Implementing Data Models and Reports with Microsoft SQL Server

    Study pilot Prepared by Killexams.com Microsoft Dumps Experts


    Killexams.com 70-466 Dumps and actual Questions

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    70-466 exam Dumps Source : Implementing Data Models and Reports with Microsoft SQL Server

    Test Code : 70-466
    Test appellation : Implementing Data Models and Reports with Microsoft SQL Server
    Vendor appellation : Microsoft
    : 173 actual Questions

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    Implementing Data Models and Reports with Microsoft SQL Server

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    Implementing Data Models & Reports with Microsoft SQL Server | killexams.com actual questions and Pass4sure dumps

    Are you a industry intelligence (BI) developer, focusing on creating BI solutions? If your solutions require implementing multidimensional data models, implementing and maintaining OLAP cubes, and creating information displays used in industry decision-making, this course is for you. Learn from the experts, and accumulate lots of practical information and tips you can consume privilege away.


    Implementing Tabular Data Models: Microsoft Virtual Academy | killexams.com actual questions and Pass4sure dumps

    Implementing Tabular Data Models

    Date: November 11, 2014Time: 9am?5pm PSTWhere: Live, online virtual classroom:http://www.microsoftvirtualacademy.com/liveevents/implementing-tabular-data-modelsCost: Free!

    Register today! (linkto: http://www.microsoftvirtualacademy.com/liveevents/implementing-tabular-data-models)

    What will you accomplish with totality that data?

    What about structure corporate BI solutions, creating self-service analysis, and setting up custom reporting? In “Implementing Tabular Data Models,” on November 11, we’ll catch a minute ogle at in-memory databases, known as tabular data models, supported by SQL Server Analysis tools. accumulate practical insights from the experts, including Julie Koesmarno, fresh from the SQL PASS Summit 2014, and current author Patrick LeBlanc.

    Learn to enrich the data model to meet analytical requirements, accumulate the details on DAX queries for custom reporting, and hear tips and techniques for near real-time solutions. Watch a step-by-step build of an actual tabular data model, and hear about common pitfalls. Let us help you get sense of totality that data!

    Course outline

  • Introduction to Developing Tabular Model in SQL Server Data Tools (SSDT)?
  • Designing the Data Model in Tabular
  • Enriching the Data Model with Calculated Measures and Data Analysis in DAX
  • Overview of Querying Data Model with DAX
  • Administering and Securing Tabular Model
  • Near Real-Time Strategy in Tabular Model
  • Gotchas, Tips, and Tricks in Tabular Model Development
  • Talk to you soon,

    Patrick LeBlanc


    Managing data with Enterprise Integration Management and SQL Server | killexams.com actual questions and Pass4sure dumps

    Microsoft has been pushing its Enterprise Integration Management (EIM) technologies as a way to manage data across the enterprise. Built on SQL Server 2012 components, Microsoft's EIM offers IT professionals and data stewards tools to integrate system and application data, cleanse and standardize data, manage data governance and gain insights that lead to better industry decisions.

    At the heart of Microsoft's EIM lies a set of SQL Server components. SQL Server Integration Services (SSIS) transforms and consolidates the data, Data quality Services (DQS) matches and cleanses the data and Master Data Services (MDS) manages and organizes the data. Together these components get it viable to work with data from a wide sweep of sources stored in a variety of formats.

    SQL Server Integration Services

    If there's one component that keeps the EIM heart pumping, it's SSIS, a workflow and data flow engine that can achieve a variety of extract, transform and load (ETL) operations. With SSIS, you can automate tasks relish importing data, managing files, performing look-ups, cleansing data or sending notification emails.

    Built into SSIS is the ability to set up ETL operations that can integrate data from various systems: SQL Server databases, SQL Database, SQL Server Analysis Services (SSAS), other database management systems, enterprise applications, cloud services and data files. During your ETL operations, you can merge, consolidate, aggregate, pivot and transform the data in several ways.

    SSIS besides provides workflow capabilities that let you control your data flow operations and how they're integrated with other tasks, which includes running T-SQL statements, sending emails, touching files or managing database objects. In addition, SSIS includes features that let you extend its built-in capabilities so you can better integrate with and transform data from non-standard sources, relish a legacy application or data stores.

    Data quality Services

    In some cases, the data cleansing capabilities in SSIS might not exist enough. Data coming from multiple heterogeneous sources can exist full of inconsistencies and errors. Standards, constraints and formats can vary from one system to the next, resulting in incorrect or duplicated data. For example, a customer's appellation might exist spelled differently in different systems within the enterprise -- DQS can help address those issues.

    New to SQL Server 2012, DQS provides the tools that data stewards and IT professionals requisite to resolve inaccuracies, inconsistencies and duplications. DQS takes a knowledge based approach to defining the types of supported data and the validation rules needed to cleanse and standardize that data.

    For example, a industry user at an automobile retailer might create a knowledge foundation that defines the domains -- fields -- commonly used in automobile records. The knowledge foundation would include such domains as make, model, year, color and so on. For each domain, the industry user can define validation rules that restrict what values can exist included in a particular domain. For instance, a validation rule on the year domain might confine the permitted values to the sweep 1960 through 2016.

    A data steward can besides identify synonyms -- "Chevy" and "Chevrolet" --and designate one of those synonyms as the lead value, that is, the remedy one to consume in set of totality others. In addition, the industry user can create matching policies that help identify duplicate records. DQS has besides been integrated into SSIS so your ETL operations can include a component that applies DQS rules to the data during the data flow process.

    Master Data Services

    In many enterprises, data is spread across multiple systems and stored in various formats without esteem to integrity and consistency, resulting in inaccurate reports and data analytics. MDS can help to bring order to the chaos by providing the platform necessary to establish and maintain data governance. Data can exist imported from a wide sweep of sources and managed within a common unified infrastructure.

    At the core of MDS is a central hub where industry users can implement and maintain the infrastructure as well as validate and standardize data supported by that infrastructure. Users start by creating data models that reflect the company's industry entities. Each entity contains a set of attributes that define the type of data that can exist stored in that entity, similar to how a database table is made up of related columns.

    Business users can then organize the entities into hierarchical relationships. In addition, they can create industry rules that validate the imported data. The MDS system serves as the definitive master record for each industry entity, ensuring consistency across the enterprise.

    Business users can implement an MDS solution on any type of industry entity. They can designate an existing data store as the master system of record or create a master hub from scratch. MDS adheres to a relational database architecture, with SQL Server in the background and SSIS providing the data flow. MDS besides includes an outstrip add-in so that outstrip can exist used to manage the data.

    Together, SSIS, DQS and MDS provide a set of tools for implementing an EIM solution, but these services don't work in a vacuum. Other components can play an necessary role in an EIM solution. The SQL Server database engine, for example, makes it viable to warehouse and manage the data. SQL Server Analysis Services supports sophisticated data analytics and mining. SQL Server Reporting Services lets you generate a wide sweep of reports that present data in multiple formats. Then you gain SharePoint Server, PowerPivot, Power View and Microsoft Office, totality of which let you bring the data to a variety of stakeholders. Together, these components get it viable to implement a comprehensive EIM solution that incorporates a wide sweep of enterprise data.

    About the author: Robert Sheldon is a technical consultant and the author of numerous books, articles and training material related to Microsoft Windows, various relational database management systems and industry intelligence design and implementation.



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