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

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

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Microsoft Implementing Data Models and

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

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extra turbulence is coming in the huge-records analytics market in 2019 | killexams.com true 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 well diverse from the business of even a brace of years ago, and it’s basically confident to subsist radically different in a few years’ time.

In 2018, they saw many transparent signs that the massive facts market that rose unexpectedly at the start of this decade is nascence to recrystallize in a extremely different kind. in the coming years, this market likely won’t even subsist called “massive facts.” That’s as a result of a gross lot of its evolution is towards ersatz intelligence, which, though records-driven at its core, doesn’t necessarily depend on mammoth amounts of records with the purpose to subsist 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 newfangled huge-records analytics boom alternatives

organisations are relocating extra of their mammoth 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 business 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 universal 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 exotic 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 newfangled era of cloud-native mammoth 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 subsist commercial enterprise necessities for scalable cloud-based file and kick storage is a huge fragment of the explanation why Cloudian bought Infinity Storage.

In 2019, they call that the Open Hybrid architecture Initiative will carry on its blueprint to modularize and containerize HDFS, MapReduce, HBase, Hive, Pig, YARN and different well-known Hadoop components. They additionally call that the top sponsors — Hortonworks Inc., soon to develop into fragment of Cloudera Inc., and IBM/crimson Hat — will deliver next-era business Hadoop platforms that accommodate 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 kick back and obstruct storage segments will remove 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 reply suppliers are in a warfare to win the hearts and minds of the brand newfangled era of builders engaged on AI projects.  a brand newfangled era of data science workbenches absorb near 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 mammoth facts analytics carriers comparable to IBM, Oracle, Cloudera, Alteryx and others absorb jumped into this district of interest, as absorb totality three fundamental public cloud companies.

The DataRobot, Tamr, and Immuta challenge capital funding rounds in 2018 simply trail at the incontrovertible fact that seemingly dozens of startups absorb taken root during this facts science workbench side over the past few years. In 2018, Wikibon has near 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 function engineering, hyperparameter optimization and statistics labeling. big-statistics analytics reply suppliers will configuration investments heavily in rig 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 embrace the means to automate initiatives over Kubernetes orchestration fabrics and additionally to containerize fashions for deployment into public and private clouds. This fashion will convey emerging requirements, corresponding to Kubeflow, into burgeoning facts science DevOps toolchain ecosystem.

Hadoop and Spark absorb 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 absorb been a foremost aspect in the back of Cloudera’s and Hortonworks’ contract to merge in 2018.

increasingly, Hadoop has considered its core exercise 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 nascence to subsist phased out in many commercial enterprise huge-statistics environments even in its core statistics-lake position in wish of distributed kick 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 feeling more and more dote a legacy technology in lots of TensorFlow-centric AI retail outlets. This vogue can subsist 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 near to the forefront.

huge statistics catalogs are revolve 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 large data cataloguing, governance, instruction and visualization options.

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

We also 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 mammoth statistics catalogs might subsist deployed throughout multiclouds, leveraging the brand newfangled technology of virtualization tools that existing a separate manage pane for managing disparate data assets throughout public and private clouds. And they call that the main public cloud providers — AWS, Microsoft, and Google — will roll out their own large-statistics catalogs for customers who determine to deploy these functions in hybrid public/inner most clouds.

facts lakes are evolving towards cloud kick storage and rush computing

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

Going forward, that fashion 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 final few years, one of the vital core BI trends has been the convergence of the technology’s established focal point on historical analytics with a brand newfangled generation of AI-infused predictive analytics, search and forecasting rig that enable any enterprise consumer to accomplish many issues that used to require a skilled facts scientist.

In 2019, extra BI providers will integrate a deep dose of AI to automate the distillation of predictive insights from knotty 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 subsist function in basically each segment of the mammoth information analytics market, accept as accurate 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 reply 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 effective 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 business 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 scholarship of rig to construct self sustaining AI-driven programs become an element motivating Microsoft to purchase Bonsai AI Inc.
  • The increasing commercial enterprise deployment of dispensed in-memory cloud databases to back knotty actual-time AI pipelines drove MariaDB Corp. Ab’s acquisition of Clustrix Inc.
  • an extra paramount fashion in mammoth 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 chain 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 estimable deal of the evolution in the huge statistics analytics market is towards part-dealing with, streaming, facts-in-action architectures, which don’t necessarily depend 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 fragment 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 newfangled facts platforms will near to market that combine mesh, streaming, in-memory, and blockchain capabilities. many of these newfangled disbursed information platforms might subsist 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 subsist normal, supplemented by means of stateful streaming fabrics that relieve 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 mammoth records analytics. i want to hear what my readers reckon might subsist the paramount trends in this market going forward.

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    Myths of large facts, Analytics & AI | killexams.com true 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 mammoth records, Analytics & AI and presents a practical method and some most trustworthy 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, anatomize and govern grasp facts of their solid using large facts, Cloud, Analytics and AI technologies. moreover, he is additionally involved in approach, business 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 multiply in facts, computing energy to procedure that records and the democratization of AI technologies in the cloud, any company can avail the benefits of mammoth facts, Analytics and AI to enrich their company results. however this may noiseless now not subsist considered as a “magic” reply that could transparent up any business rigor that an organization may have. this article addresses one of the crucial gauge 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 established applied sciences

    however the attract of BigData is that notwithstanding information is simply too massive or knotty it can subsist managed the usage of commodity hardware and open supply (therefore more cost-effective) technologies, the verity is far from this myth. It takes gigantic effort, faculty and substances to basically operationalize open-source BigData technologies to transparent up accurate world complications. organizations should noiseless additionally understand that BigData applied sciences don't appear to subsist 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 subsist preempt or imperative. 

    delusion: each statistics rigor may also subsist solved simply through the exercise of Analytics or AI

    The superior cost from Analytics and AI can subsist realized after framing the preempt problem. The company cost of the problem needs to subsist understood and without retard related to cost or profits for the organization. customarily, a problem which requires a mammoth amount of time and endeavor by means of the corporation deciphering counsel to capitalize handicap is a main candidate to generate cost the usage of Analytics and AI. That being referred to, on occasion the elementary reply can also noiseless subsist to trade a method or approach of working which reduces the information itself in preference to automate the interpretation of it. impart an organization is amassing invoices or negotiating promotional phrases via email and needs to automate the reconciliation process. It could definitely subsist superior and less complicated to implement a newfangled collaboration implement to carry and manage invoices or promotions in preference to to establish into result 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 Hurry the mannequin. the way AI works is that via working records through the software a model has to subsist institute 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 exercise to better the AI models. hence the main purpose in sight should totality the time subsist to create a transferable, packaged mannequin to resolve a specific company problem and not the technology to subsist 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. impart 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 effective tips and insights, however the ultimate reply is the exercise 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 cost that is generated will usually subsist more desirable than what the solid is doing nowadays, so it’s a delivery. These options gain 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 reform issue and right expectation is vital. in its space of finding a leap forward the company may noiseless focal point on fixing functional day-to-day problems.

    In conclusion, there's a misconception out there that anybody and everyone can remove a bunch of open-source tools and create an AI reply a estimable way to give tremendous value and absolutely alternate the style a solid operates. It’s first-rate that americans are talking about and getting excited in regards to the expertise for AI. youngsters, the verity is that operationalizing AI without needing a finished facts platform is going to subsist very nearly not possible.  companies requisite to absorb a scholarship 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 subsist quickly and scalable, the relaxation of the pipeline to devour and stream information remains deliberate and this bottleneck does not permit us to obtain the consequences in accurate time. eighty% of the travail in Analytics and AI is around collecting, cleansing, making ready and munging facts. until the identical facts outlets and pipelines are being used by way of every person in the company, the varied, part efforts being executed via different groups for distinct purposes is simply a squander of time and substances.  now not to mention that the company will never recognize the true capitalize and value that AI can give when done competently. groups requisite to map out their statistics approach first (and validate they absorb a exotic 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 true Questions

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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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    Implementing Data Models and Reports with Microsoft SQL Server

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

    Are you a business 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 business decision-making, this course is for you. Learn from the experts, and gain lots of practical information and tips you can exercise right away.


    Implementing Tabular Data Models: Microsoft Virtual Academy | killexams.com true 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 edifice corporate BI solutions, creating self-service analysis, and setting up custom reporting? In “Implementing Tabular Data Models,” on November 11, we’ll remove a particular ogle at in-memory databases, known as tabular data models, supported by SQL Server Analysis tools. gain practical insights from the experts, including Julie Koesmarno, fresh from the SQL PASS climax 2014, and accepted author Patrick LeBlanc.

    Learn to enrich the data model to meet analytical requirements, gain 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 relieve you configuration 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 true 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 business 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 configuration it feasible to travail with data from a wide scope 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 rush engine that can discharge a variety of extract, transform and load (ETL) operations. With SSIS, you can automate tasks dote importing data, managing files, performing look-ups, cleansing data or sending notification emails.

    Built into SSIS is the faculty 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 also provides workflow capabilities that let you control your data rush operations and how they're integrated with other tasks, which includes running T-SQL statements, sending emails, moving 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, dote a legacy application or data stores.

    Data quality Services

    In some cases, the data cleansing capabilities in SSIS might not subsist enough. Data coming from multiple heterogeneous sources can subsist plenary 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 denomination might subsist spelled differently in different systems within the enterprise -- DQS can relieve 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 scholarship based approach to defining the types of supported data and the validation rules needed to cleanse and standardize that data.

    For example, a business user at an automobile retailer might create a scholarship ground that defines the domains -- fields -- commonly used in automobile records. The scholarship ground would embrace such domains as make, model, year, color and so on. For each domain, the business user can define validation rules that restrict what values can subsist included in a particular domain. For instance, a validation rule on the year domain might circumscribe the permitted values to the scope 1960 through 2016.

    A data steward can also identify synonyms -- "Chevy" and "Chevrolet" --and designate one of those synonyms as the lead value, that is, the reform one to exercise in space of totality others. In addition, the business user can create matching policies that relieve identify duplicate records. DQS has also been integrated into SSIS so your ETL operations can embrace a component that applies DQS rules to the data during the data rush 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 relieve to bring order to the chaos by providing the platform necessary to establish and maintain data governance. Data can subsist imported from a wide scope of sources and managed within a common unified infrastructure.

    At the core of MDS is a central hub where business 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 business entities. Each entity contains a set of attributes that define the kind of data that can subsist 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 business rules that validate the imported data. The MDS system serves as the definitive master record for each business entity, ensuring consistency across the enterprise.

    Business users can implement an MDS solution on any kind of business 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 also includes an outstrip add-in so that outstrip can subsist 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 travail in a vacuum. Other components can play an well-known role in an EIM solution. The SQL Server database engine, for example, makes it feasible 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 scope of reports that present data in multiple formats. Then you absorb SharePoint Server, PowerPivot, Power View and Microsoft Office, totality of which let you bring the data to a variety of stakeholders. Together, these components configuration it feasible to implement a comprehensive EIM solution that incorporates a wide scope 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 business intelligence design and implementation.



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