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70-767 Implementing a SQL Data Warehouse

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70-767 exam Dumps Source : Implementing a SQL Data Warehouse

Test Code : 70-767
Test cognomen : Implementing a SQL Data Warehouse
Vendor cognomen : Microsoft
: 237 real Questions

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Microsoft Implementing a SQL Data

enforcing Stretch Database. | killexams.com real Questions and Pass4sure dumps

Micorosoft has interject a safe handicap to agencies of every bit of sizes with its Stretch Database offering in SQL Server 2016. Stretch Database permits agencies to exercise on-premises circumstances of SQL Server to "stretch" their records into the Microsoft Azure SQL Database. Stretch DB is relatively essential to configure and set into outcome on your atmosphere. It can be a manageable and low-budget alternative to off-load artic information remotely to reduce expenses with protection, storage, and so on. Your election to set into outcome Stretch DB is often according to Azure pricing in comparison to the charge of upgrading your storage potential. You may additionally moreover trust how regularly the artic information has to be accessed when you esteem that efficiency can be impacted by course of the latency created with the aid of having access to the far off facts. For tips on Azure Stretch Database pricing, that you would be able to examine SQL Server Stretch Database Pricing.

The article will parade you how to migrate the historical information transparently and securely to the Microsoft Azure cloud, which provides for low in cost availability of artic facts while maintaining it secure and unchanged. you'll observe a course to stretch your on-premises information to the cloud but silent maintain the skill to query the absolutely accessible and online desk and to monitor and manipulate the stretched tables.

To set into outcome a stretch database,you must permit the feature on each the instance and database. Let's enable the Stretch Database feature on the instance by course of working the beneath command.

--permit illustration for stretch EXEC sp_configure ‘far flung information archive’, ‘1’ passRECONFIGURE GO

After enabling the Stretch Database feature on the instance, you should enable the duty on database, as shown in Fig. 1. From SQL Server administration Studio (SSMS), confiscate click the database that holds the table(s) you need to stretch to Azure and select projects > Stretch > allow.

Fig. 1 - start the stretch wizard

Enabling this will open the permit Database for Stretch Wizard, as proven in Fig. 2. you could opt for the whole table contents otherwise you can choose particular rows to stretch.

Fig. 2 - The Stretch Database wizard

For this example, we've chosen the whole OrderTracking desk to be stretched, proven in Fig. 3.

Fig. 3 - choose the contents to stretch

The subsequent step is to Configure Azure. check in to Microsoft Azure with a Microsoft account and select the present Azure subscription and Azure space to gain exercise of for Stretch Database. Specify even if you wish to exercise an current server or create a original Azure server. we've chosen an current server during this illustration.

Fig. 4 - Enter credentials for Azure and settle a server

with the aim to stretch a database desk to Azure, the database maintain to maintain a database grasp key (DMK). Specify (and keep) the password for the DMK via growing the credential within the Wizard as follows on the cozy Credentials page as shown in Fig. 5.

Fig. 5 - Set a password for the DMK

On the choose IP manipulate page, you can select the subnet IP address range, or the public IP manipulate of your SQL Server, to create a firewall rule on Azure that lets SQL Server converse with the far flung Azure server.

The IP manipulate or addresses that you provide on this web page iterate the Azure server to allow incoming records, queries, and administration operations initiated by means of SQL Server to circulate throughout the Azure firewall. The wizard does not change anything else in the firewall settings on the SQL Server.

Fig. 6 - choose an IP tackle

After specifying the IP addresses, click on next for the abstract and consequences pages as proven in Fig. 7.

within the summary web page, evaluate the details, and then click conclude. this will beginning provisioning the database to the Azure SQL Stretch database server. When complete, the abstract page will exhibit a list of tasks and their popularity. be certain that every one initiatives maintain handed repute, and then click nigh to comprehensive the setup and shut the wizard. you've got correctly completed the tasks to install Stretch. 

Fig. 7 - ascertain the settings

manipulate and video parade Stretch Database

you maintain just enabled a SQL Stretch Database for the AdventureWorks2016 database. that you may now control and computer screen its activities the usage of the monitoring alternative in Stretch Database. This offers a dashboard view of tips on the faraway Microsoft Azure SQL Stretch database and tables the space your bloodless facts is saved. which you could moreover determine how many batches and rows of information maintain migrated. ultimately, that you can access particulars concerning the Stretch configured tables, corresponding to how many rows are in the neighborhood stored and how many maintain stretched to Microsoft Azure. 

Open Stretch computer screen for the Stretch Database monitor choice, correct-click on Database AdventureWorks2016. choose project and Stretch, after which select parade screen as proven in Fig. eight.

Fig. 8 - Monitoring repute

determining the video parade option from the stretch stint will open the dashboard view of the database that has been stretched to Microsoft Azure. It offers details corresponding to Microsoft Azure SQL server name, database name, and database dimension as shown in Fig. 9.

Fig. 9 - Viewing a report for your information

which you could also assess the repute of the Stretch-enabled table (corresponding to stretch eligible row, variety of rows on-premises, and Azure SQL Stretch database) and examine the adventure fitness by clicking View Stretch Database health experience, or check adventure particulars for troubleshooting as proven in Fig. 10. event details includes the mistake particulars and state. which you can exercise this tips to diagnose the error.

For extra tips and documentation on the course to computer screen or troubleshoot Stretch Database, vanish to the MSDN web site: https://msdn.microsoft.com/en-us/library/mt484151.aspx.

Fig. 10 - Viewing health of the function

abstract

this article lined very primary tips on the course to deploy a Stretch Database.Its a simple course emigrate archive records to Microsoft Azure and allows you to dynamically stretch warm and artic transactional records from Microsoft SQL Server 2016 to Microsoft Azure. straightforward implementation, convenient monitoring are features that makes it a preferable solution for institutions that are required to retain historic statistics for a protracted and even indefinite epoch of time.


How marketers enjoy Macy’s exercise Cloud Synthesis To Optimize customer facts Intel | killexams.com real Questions and Pass4sure dumps

In short

during this excerpt from PSFK and Microsoft's Retail developments Playbook 2020, here's how three brands are enforcing cloud synthesis to maximise the advantages of facts-primarily based intelligence

In 2016, agents neglected out on $150 billion in salary because of terrible personalization. In a rush to tackle this loss, industry leaders invested in sophisticated, analytic algorithms devoid of constructing the infrastructure required to grasp and partake the gathered information most simply: fifty two% of businesses puss challenges connecting the dots between information saved across diverse components of their firm.

retailers are sitting on a wealth of records, but these advantageous insights commonly range away between disparate teams and siloed channels. information-driven communication channels can assist optimize quick and responsive communique between factories, warehouses, shops and other give chain features, sharing key counsel between companions via cloud infrastructures and retaining every bit of and sundry on tempo to deliver excellence. during this excerpt from PSFK’s Retail tendencies Playbook 2020, made in partnership with Microsoft, here are three examples of main manufacturers the usage of cloud-based options to maximise the benefits of client records intelligence:

NordstromThe luxurious sustain chain Nordstrom leverages the cloud platform NuOrder to streamline and democratize insights gathered across its wholesale purchasing methods between the retailer and its suppliers. shifting faraway from its previous system that resulted in too lots leftover inventory waste, the cloud-primarily based strategy permits consumers to examine patterns, establish gaps in patterns and collaborate between teams.

Macy’s dealer DirectDepartment retailer Macy’s created the seller Direct program, which presents an extended product assortment online with the aid of enabling third birthday party carriers to fulfill and ship orders at once to consumers. The program allows Macy’s to present a much broader product assortment without worrying about warehousing, which allows them to refine their inventory extra nimbly and curate their product option in line with purchasers’ buy history and location. It additionally drives traffic to Macy’s physical outlets through in-shop pickup.

Merck KGaAThe German pharmaceuticals firm Merck KGaA is planning to incorporate AI and predictive analytics every bit of through its complete deliver chain by using the conclusion of 2019. presently, the enterprise is conducting a pilot software that makes exercise of analytics software from Aera know-how Inc. to divine require spikes, establish bottlenecks and alleviate supply shortages for 100 items. The software collects deliver chain facts from Merck KGaA’s different planning techniques and, after the facts is uploaded to Aera’s cloud infrastructure, is analyzed via computing device getting to know algorithms. These algorithms deliver ideas enjoy supply alterations or require forecasts.

Cloud synthesis is amongst many different initiatives being taken to radically change facts into superior client experiences. For the entire document, down load PSFK and Microsoft’s Retail trends Playbook 2020 right here.

Lead graphic: stock pictures from Monkey enterprise pictures/Shutterstock

In 2016, sellers overlooked out on $a hundred and fifty billion in earnings as a result of negative personalization. In a rush to manipulate this loss, trade leaders invested in refined, analytic algorithms devoid of constructing the infrastructure required to hold and partake the accrued guidance most without difficulty: fifty two% of agencies puss challenges connecting the dots between data kept across distinctive constituents of their company.

retailers are sitting on a wealth of records, but these advantageous insights frequently range away between disparate teams and siloed channels. facts-driven verbal exchange channels can succor optimize quickly and responsive conversation between factories, warehouses, shops and other supply chain aspects, sharing key information between companions via cloud infrastructures and maintaining every person on tempo to bring excellence. in this excerpt from PSFK’s Retail traits Playbook 2020, made in partnership with Microsoft, listed below are three examples of main brands the exercise of cloud-primarily based options to maximize the benefits of buyer information intelligence:


10 Keys to Microsoft groups Governance Success | killexams.com real Questions and Pass4sure dumps

wall of keys image: Chunlea Ju

A multi-function communication platform enjoy Microsoft teams is a necessary tool for today’s travail environment — no longer just a intricate piece of tech. practically three-quarters (70 percent) of specialists globally telecommute at the least sooner or later a week, while forty three p.c of U.S. employees achieve this every so often. These workers, and their groups, require seamless 24/7 access to the very information to linger successful.

however managing a company-spanning Microsoft groups atmosphere is no handy assignment. fundamental governance measures should be in region to involve sprawl, at ease inner facts and gain certain efficiency. 

listed below are the ten most critical elements to esteem when constructing your Microsoft teams governance.

1. The introduction manner for original workplace 365 companies and Microsoft teams

starting from scratch with your governance plan means marvelous the confiscate balance. The greater possession stakeholders maintain over workplace 365 agencies and Microsoft groups, the greater a success your implementation should be. however this moreover must be a controlled manner. The capability to create collaboration areas should silent handiest be afforded to certain individuals, which might moreover encompass:

  • Your IT department, which may certify the features obtainable to each and every subset of clients.
  • enterprise house owners, who maintain yardstick responsibility to your enterprise’s facts safety.
  • while content house owners and stakeholders recall their procedures and collaboration spaces, they shouldn’t always be depended on to manage integrated functions enjoy Microsoft groups.

    linked Article: office 365 Governance: set up Your crew

    2. The purpose in the back of original corporations and teams

    As with the introduction technique, it’s finest to locality restrictions across the standards behind collaboration spaces. They at AvePoint maintain seen groups with 2,000 users that maintain 2,500 workplace 365 businesses — a recipe for multiplied haphazard and charges, and a number of difficult clutter. Chaos already plagues some distance too many groups — 32 p.c of personnel throughout distinctive industries, and 37 percent of those in IT in particular, maintain avoided sharing documents as a result of they feared they might by no means determine them once again. 

    clients should justify their businesses and teams to evade these logjams, nonetheless it additionally shouldn’t believe enjoy an authoritarian manner. travail to gain an constrict with users and stakeholders around most efficient practices for original collaboration spaces.

    three. The Roles and Stakeholders Who verify These standards

    The past you identify and win over stakeholders and interior understanding leaders to champion build and profit business-huge buy-in for governance tactics, the more suitable. each person to your organization benefits from a thorough figuring out of not just the what in the back of your original guidelines, but the why. You’ll want the confiscate partners to succor communicate that.

    speak with a wide selection of stakeholders to set up maintain assurance and profit perception into the struggles faced through conclusion clients. These individuals will esteem preferred and understood, and will disseminate that goodwill every bit of over the corporation for correct, long-lasting adherence to processes.

    related Article: Microsoft teams: The respectable, The bad, The 'Is it capable?'

    4. Managing entry and ownership

    preserve special statistics of which adult or individuals maintain entry and administrative rights to your office 365 agencies and Microsoft groups to evade “shadow IT” traps — tasks that are managed outside of, and without the handicap of, the IT department. examine yourself:

  • Does your department maintain the confiscate materials to video parade and hold song of roles and access inside groups and groups?
  • Do they esteem the energy of admins (new), house owners, contributors and external individuals in Microsoft groups?
  • Are stakeholders, as well because it, prison and safety teams, capable of be certain guidance is silent in the acceptable purposes and collaboration areas?
  • 5. The applications and features users Are Allowed so as to add

    A plethora of purposes can be institute that can multiply cloud functions and systems. but you must additionally agree with the elevated risk of third-party capabilities.

    happily, IT departments manipulate the purposes and integrations that can be introduced to their Microsoft teams on the crew level. for instance, you might moreover now not need to enable external sharing. may silent clients then be able to relate their Microsoft teams to other cloud storage solutions? examine the confiscate questions, and plan your guidelines and procedures cautiously.

    linked Article: Why Your Microsoft groups Governance plan needs a Lifecycle mannequin

    6. Structuring and implementing houses and Naming Conventions

    it could actually believe overly restrictive to manipulate how users identify individual businesses and teams, however cogitate lower back to these employees who battle to find a document. The equal situation occurs when clients can’t find the group or group they need. And your IT arm can’t apply guidelines or retain information lifecycles in the event that they don’t be vigilant of why a team exists or what assistance it holds. You’ll store time ultimately by using automating houses, naming conventions and lifecycle management from the very beginning — in spite of the fact that it makes you suppose enjoy a Grinch.

    7. policies for Saving, Archiving and Deleting content material in teams

    whereas Microsoft teams is a fairly original format for collaboration, americans will partake info and files, so that they can nevertheless be saved in the team’s SharePoint websites. You need to manipulate natural questions around content material lifecycle, records management and facts insurance policy/statistics loss protection. Your plan should silent involve language around:

  • correct document labeling.
  • Enforcement of content material-degree safety, taxonomy, disposition and automation.
  • Classifications and labels that reflect the information inside files and files.
  • office 365 equipment such because the safety and Compliance focus and the SharePoint information management core can aid with these approaches, and third-celebration providers in the Microsoft ecosystem moreover can deliver sheperd right here.

    linked Article: Why You need a data Archiving method

    eight. Which previous company approaches Microsoft groups Will supplant and enhance

    while it could now not hold the identical cognomen recognition for your crew, Microsoft groups will quickly exchange Skype for business. As employees collaborate greater in Microsoft groups and sustain in intellect its performance, they'll ameliorate to elements enjoy chatbots and AI, as well as workflow and PowerPoint integrations.

    before unleashing the entire vigour of teams, set into outcome a metamorphosis administration approach for training users on original company strategies that raise efficiency and reduce risk. crew members will suppose empowered and be able to sustain with the swift tempo of change.

    9. training original clients on These techniques

    practising is only as critical as system when it involves a hit implementation. build out your inner working towards program early, and develop relationships with enterprise clients and stakeholders. they'll revolve into an expert evangelists for original rollouts and champion you swipe into account the needs of daily users, making life more straightforward for each person.

    10. integrated purposes That reduce IT and industry consumer Burden

    When it involves training users and imposing company procedures, giving clients control over how they can securely interact with know-how reduces the affliction of IT worker's to swipe note and conform to each enterprise workflow.

    applications enjoy circulate, kinds and Sway gain it even more straightforward for clients to create primary workflows, ration counsel and collaborate. When incorporated with Microsoft teams, these options create a less complicated, streamlined and effortless adventure.

    With the entire tools provided with the aid of office 365, the implementation of protection and governance controls is the winning run in facilitating a successful, scalable adoption and governance strategy.

    Hunter Willis is a product advertising supervisor at AvePoint and the president of the Richmond SharePoint user neighborhood, MSCA O365. He has been in internet building, SEO and gregarious media advertising and marketing for over a decade, and entered the SharePoint house in 2016.


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    Implementing a SQL Data Warehouse

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    Data Warehousing Tip #9 – Test at Volume | killexams.com real questions and Pass4sure dumps

    My next tip is to test your BI solution with the volume of data that you are expecting.  This is paramount to pile a successful system.  You need to ensure that you can not only report on your data in a timely manner but moreover be able to load the volumes and complexity of data that you are expecting.  I would bicker that if you haven’t tested at volume you haven’t really tested at all.

    Testing at volume is vitally essential to your solution, and the earlier you enact it the easier your job will be.  It’s about understanding the challenges of the volume of data you’re dealing with as early as practicable in the evolution process, and actually pile the solution to manipulate that volume of data efficiently both in terms of read and write performance.

    How many records are you intending to load per day?  Whether its one million or one billion, gain certain you’ve tested loading that volume of data, and not at the conclude of the evolution process but right at the beginning.  Can you load it?  Can you query it?  The volume of data you are loading has to shape the solution that you build.

    There is no point in pile a data warehouse to cope with processing one billion original records a day when you actually only need to process one hundred thousand.  You’ll conclude up spending a lot of time and a lot of money pile a system that completely dwarfs your needs so sustain to spec.  But if you don’t know how it’s going to fulfill processing one hundred thousand rows enact you just vanish and set everything in space that you can cogitate of?  No.  You actually start processing one hundred thousand rows from the word go.

    So you might not know your exact throughput, but you’ll probably maintain a safe idea, or a worst case estimate (worst case being the highest practicable throughput).  You may be in the position where every bit of of your source data is already available.  If this is the case there really is no excuse.  When you start to build your solution load every bit of of it.  If you don’t maintain the source data available then mock it up.  If it is coming from flat files then create test files to the exact spec that you are going to receive.  If it’s coming from a DB or from an API enact the very thing.

    The most extreme issue resulting from not testing at volume that I’ve witnessed is where a company I was working with performed an upgrade for one of their clients.  The solution was dealing a couple of thousand inserts a day.  No history was retained as allotment of the upgrade, so it was a brand original blank database.  Within days of the upgrade going live queries were timing out and the system was unusable for the conclude user.

    I was asked to maintain a look.  The problem was that they’d built a scalar duty to parse some cognomen and value pairs out of some very big text fields.  The duty was being called by stored procedures being executed at query time.  Now SQL Server isn’t considerable at parsing cognomen and value pairs out of unstructured data, especially when it’s having to enact it row-by-painstaking-row.  This duty in itself wasn’t greatly efficient either.  Parsing the very value multiple times rather than just storing the value retrieved initially in a variable and reusing it.

    What was even better was that they were calling this duty not just once per record, but multiple times for each record as there were numerous cognomen and value pairs to retrieve.  Awesome.  When questioned about why they took this approach they said it had worked fine in testing…

    Most queries, however inefficient, will travail fine when you test them with a tiny data set.  The system was minuscule in database terms and had gone from zero to only a few thousand records before it had become unusable.  It was an upgrade that went badly wrong, and problems with the system that should never maintain seen the light of day were discovered by the conclude user.

    The proper solution to the problem would maintain been to parse these cognomen and value pairs from within the source system that was moreover allotment of the solution, giving the database the data as it wanted it.  Because the issue needed fixing immediately, and the proper solution would swipe longer to implement, I set in a better workaround within SQL Server.  I rebuilt the duty making it as efficient as possible, and then did the parsing as allotment of an instead of insert statement.  Catching the insert of the unstructured text and parsing it and loading the parsed data into a original structured table.  Performing the parsing once on insert rather than every time that the data was queried.  With the database holding data in the course that it was to be consumed by the UI queries returned instantaneously.

    Another situation I was involved in was one where I was asked to scale up a system from 500 thousand inserts per day to 100 million.  Seriously.  Now, this in itself wasn’t a problem with not testing at volume as the 100 million wasn’t a requirement until every bit of of a sudden it was.  However, imagine you knew the anticipated volume but you were testing the system with 0.5% of the planned daily inserts.  What is the point in that?  What are you actually testing?  It was one of the first BI solutions that I was involved with and getting it to travail almost broke me.  I did learn a hell of a lot in the process though.

    The one safe thing about this situation was that getting the data wasn’t a problem.  They switched the additional feed on almost immediately.  Then I watched everything grind to a halt.  At first I couldn’t derive the data into the data warehouse.  They moved from SQL Server yardstick Edition to Enterprise so that I could exercise table partitioning.  I added daily partitions and made other improvements.  I could load the data.

    Then I couldn’t load the Analysis Services multidimensional model.  The incremental load started off okay on an void measure group but very quickly degraded as the measure group got bigger.  This led me to partitioned measure groups with the partitions aligned with the DW table partitions.  I could then load my measure groups.

    Finally I couldn’t actually query the data within any reasonable timeframe.  They had a web based UI and they wanted results in seconds rather than minutes.  Numerous improvements were needed in the multidimensional model.  Profiling the queries and finding that they were silent hitting every partition even when we’d specified the date compass was just one problem.  I pulled the model apart.  Stuff that maybe wasn’t considerable at 500 thousand records a day but didn’t reason a problem at those volumes needed reworking.  Which in hindsight is what you’d expect when scaling up a solution to manipulate 200 times the initial throughput.

    This is every bit of stuff that they could maintain tackled up front had they known that was the requirement.  If you know the requirement up front, you don’t maintain any excuse if you find yourself in that situation.  It could silent be a immense challenge, but we’re here to resolve challenges.  You can’t tackle it if you aren’t vigilant of it.  Floating along, oblivious to it.

    We did derive there in the end.  Just in time for them to settle that they didn’t actually want the system after all.

    These are both extreme examples, but you never want to be in the position of not testing your system at volume until you vanish live with it.  Testing at volume from as early as practicable in the evolution side will succor you identify and tackle challenges before they become problems.  You are pile a solution that is apt for purpose.

    If you aren’t testing at volume you are leaving the performance of your solution to chance.  You may well maintain built a lot of solutions and built up a lot of learning and experience, but no amount of learning and sustain can compensate for proper volume testing.

    Testing at volume will parade you where there are immediate issues.  Where you need to rethink something completely, and where you need to tweak something else.  be it the ETL/ELT, schema design, indexing, partitioning strategies, you cognomen it.  It will be anything that could demean as you push more data through the data warehouse.  Which within a data warehouse or indeed any database, is pretty much everything.

    The post Data Warehousing Tip #9 – Test at Volume appeared first on BI Design.

    I’m a Microsoft Certified Solution Expert specialising in the design and evolution of BI solutions using the complete SQL Server BI stack. I’ve been working with SQL Server since 2003 and maintain been developing BI solutions since 2007. I’ve been running my own independent consultancy, based in Southampton, UK, since 2015.


    Seven Considerations When pile a Data Warehouse in the Cloud | killexams.com real questions and Pass4sure dumps

    For the past decade, most companies maintain resisted implementing a data warehouse (DW) in the cloud, largely due to concerns about security. In addition, few maintain had sustain using cloud-based applications. But times maintain changed. Read this report to learn why companies are flocking to the cloud for data warehousing and industry intelligence/analytics.

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    How a Data Warehouse Solved a Snack Company’s Data Problems | killexams.com real questions and Pass4sure dumps

    Developing and implementing a data warehousing solution is no effortless undertaking. A lot of companies are discovering that a data warehouse can resolve their data and reporting needs. Unfortunately, lofty costs, time, and resources leave technologists with an uphill battle. With today’s modern data warehousing solutions, implementing the right data warehouse that meets your organization’s needs could be one of the most cost efficient and invaluable products you deploy.

    In this post, I’ll interpret how they used a cloud-based data warehouse to resolve their data needs. I’ll walk you through the major challenges they faced, the benefits of a data warehouse, and the implementation process. It’s my hope that this article will succor provide answers for individuals exploring data warehouse options.

    Our Challenges Disparate Data Sources and Data Inconsistencies

    Disparate data sources and informal data governance can lead to a very lofty cognitive load for domain experts. As an organization grows, the skill for people to escape independent analyses increases as the number of employees that maintain access to these data increases. However, if the data required to escape these analyses are disparate and only a select few understand the system schemas, data lifecycles, and workflow processes, then bottlenecks and inefficiencies will arise. Individual analysts will grow impatient and attempt to familiarize themselves with the data the best they can and extract the confiscate data. In the best case scenario, the analyst can extract the data themselves, escape the analysis, and report to the stakeholder with tiny to no trouble. However, this is seldom the case. What typically tends to occur is two or more analysts travail on similar reports, they extract different data sets, derive different results, and report back to divorce stakeholders. These stakeholders then find out later on that their numbers don’t align with their colleague’s numbers and mistrust sets in. These reporting inconsistencies reason miscommunication between departments and can be costly to resolve.

    Inefficient and complicated Reporting Workflows

    Without integrating these disparate data sources, individuals become contingent on inefficient and complicated workflows to resolve the most yardstick industry reporting needs. Having many disparate data sources labeled as the “source of truth” makes reporting manual and inefficient.

    Imagine trying to determine whether product feedback ratings and frequent product changes to a membership was a sign that a particular customer wasn’t gratified with your service, indicating potential churn. From the analysts point of view, this workflow would consist of manually extracting customer data, revenue transaction data, and product feedback data from three different systems, reading these data into their tool of election (Excel, Python or R), merging these data together, manipulating and cleaning these data, developing a model, and reporting the insights back to the stakeholder. An analyst might be able to enact this in a couple of hours, but what if the stakeholder comes back and requests that this become a monthly report? This takes hours out of the analyst’s calendar every month to focus on one metric as opposed to uncovering original insights.

    I’m certain most of you can imagine this workflow. We’ve every bit of been there at one point in their careers. It isn’t fun, but it gives us the unique chance to understand the benefits of a data warehouse.

    As your company grows, more people join, original departments form, and the need for an efficient reporting and more organized data infrastructure becomes more critical to scale and swipe your data and insights to the next level.

    Solving our Problems Centralized Storage and Unified Reporting

    The presence of disparate data sources can lead to inefficient, manual reporting workflows and data inconsistencies across departments with similar metrics but different reporting methodologies. They eliminated these entirely with their data warehouse. Their users only vanish to other systems as a eventual resort now as the data warehouse provides us with one space to query and merge every bit of their data together efficiently. They are now able to tailor and serve information to the end-users based on their reporting needs.

    Data and Reporting Automation

    The skill to merge, aggregate, and manipulate data before running an analysis removes the dependency on manual reporting workflows. As long as the necessary data are in the data warehouse, you can leverage SQL in tandem with BI tools or other scripting languages and open source tools to automate the most repetitive analyses and multiply efficiencies of other data workflows.

    At SnackNation, they leverage SQL and Tableau to deliver automated reports and lofty flush dashboards to end-users in various departments. Creating custom SQL queries and visualizations for each report takes time to set up, but once they build the dashboards, they can set it and forget it. Their end-users don’t need to email the analysts asking for the report or worry about the hours of travail it would swipe them to escape the very analysis each month. With automation, we’re moreover able to avoid wee human errors that are made from time-to-time in manual reporting.

    To be clear, what I’m calling their data warehouse currently exists as a data lake. The main contrast between a data warehouse and a data lake is that a data lake takes the raw data from every bit of enterprise systems and loads it into a unique place. There is not much transformation that happens — it usually reflects the source system’s existing schema and their exercise cases are not yet defined. We’re not transforming or processing the data during its initial ingestion from their respective sources to match their industry model or specific purposes. lawful data transformation is happening after the fact when they reshape the data to the definition of the business, simplify certain processes, or gain a subset of data more specific to an individual or a team. They leverage open source tools enjoy RStudio to automate data extraction, build machine learning models, and manipulate advanced ETL processes that are then pushed back to the data warehouse to create data marts. A data mart is a custom subset of data created for specific industry units and their reporting needs. Data marts allow for more efficient querying since the data is already manipulated and merged to meet your needs. Transforming the data into the industry domain reduces the cognitive load of the end-user since they don’t maintain to understand the systems and how they work — they only maintain to understand the industry as the data reflects it.

    Another course to cogitate of it is that we’re now getting their data from a “System of Record” (SoR)for every bit of automated reporting as opposed to the “Source of Truth” (SoT). The contrast between the two is important. The SoT is the root of a specific data point or its original entry point into your organization. A SoR is ordained as the space that provides the most complete and accurate data that observes your businesses operational means because it has been cleaned and processed. For example, a customer’s billing address is first entered in your main CRM during sales prospecting, but then, through system integrations, syncs to your billing system. That data is now stored twice, but your CRM would be the SoT for that data point, right? Well, what happens when that sync fails and only the record in your billing system reflects the truth? There is now a discrepancy. Your billing system is now the SoT considering it has the most up-to-date information, and it makes sense logically. The system in which they exercise to bill customers should hold the correct billing address. Analysts creating the automated reports or their stakeholders don’t need to know where the SoT is; only the people using it directly or the people who maintain to troubleshoot discrepancies should.

    Implementing their Data Warehouse Solution

    Before 2018, I had tiny working sustain with data warehousing. It’s a complicated theme and arduous to jump into without some technology and database knowledge. Having worked in data-heavy roles throughout my career, I’m lucky to maintain sustain with relational databases. This helped me derive up to quicken with the technical details of data warehousing. I institute plenty of resources covering key concepts and technical definitions, but I had concern finding articles describing the actual implementation process. This was a bit shocking to me given that data warehousing is such a favorite concept nowadays.

    Our Technology Team kicked off the project in the beginning of 2018. They started by reviewing the necessary requirements and scope of travail that would deliver the most appropriate, cost-effective, and scalable solution for their business. Spoiler alert: there’s no one size fits every bit of solution.

    Things to consider:

  • Scalability — How simple and springy is it to add/remove data sources and derive them up and running to match their industry specific needs?
  • Integration — How many data sources will their data warehouse be housing and where will it be coming from? What BI tools will be reading data from the data warehouse?
  • Cost and Performance — What benevolent of queries will they be running? How much data will they query at a time?
  • Timeliness — How often will you sync data into the data warehouse? Different reports are more time sensitive and require near real-time data. Some data warehousing solutions maintain a variable cost based off execution and data storage. The volume and frequency in which you sync your data could multiply your overall cost.
  • Security — How will they manage end-user access and permissions to the data warehouse? How will they secure their data and every bit of of the access points?
  • Data Quality — How will they validate the initial data ingestion? How will they ensure the data they are ingesting into the data warehouse is antiseptic and accurate? The eventual thing they want is to transfer flagrant data from one system to another.
  • Goal — What are every bit of the industry needs and goals of the data warehouse? What are they trying to solve? What are some initial products that are now practicable with a data warehouse? For us it was data automation, machine learning models, and improving reporting efficiencies. Without a limpid goal, it will be difficult to parade the ROI after implementation. gain certain there is a limpid strategy.
  • The entire data warehouse project took a few months from ideation to the first data ingestion. They went back and forth between a few tools, but they ended up choosing Panoply, a cloud-based data warehousing solution that sits on RedShift. Panoply is a managed solution that does most of the heavy lifting tasks enjoy data ingestions, data transformation, re-indexing, and schema modeling. Having someone dedicated to managing this would be a full-time job for most data engineers.

    Our biggest motivation was to minimize the overhead of learning a complete data warehouse solution. Since Panoply has data ingestion and a big selection of integrations built-in, getting it up and running was simple. It moreover prevented us from having to create ad-hoc solutions and alleviated travail from engineers. For integrations that weren’t supported, they leveraged other 3rd party tools enjoy Stitch Data and SnapLogic to manage ETL processes. The actual data ingestion and data validation allotment only took a few days before they were up and running!

    Cost was an essential factor in going with Panoply. Panoply has a fixed cost model that charges you monthly. Most data warehousing solutions carry variable costs that charge you based on usage and execution.

    Query performance was moreover a huge factor in their decision making. pile reliable, fast, and near real-time dashboards that report on industry KPIs is a huge allotment of any analyst’s role. Panoply uses machine learning algorithms to resolve query performance and caches and materializes favorite queries to multiply the quicken of repeated queries.

    In Summary

    The ROI of a data warehouse is not just the value of the insights your team can deliver. There’s incredible value in freeing up your employees time by removing manual, inefficient reporting workflows and minimizing data inconsistencies. That time once spent compiling data and running manual analyses on a weekly or monthly basis adds up! Prior to deploying their data warehouse, they had their analysts spending 30+ hours a month on repetitive tasks and reports. There wasn’t time to tackle original projects or resolve original problems. With their data warehousing solution, they removed these manual and repetitive workflows, and are able to uncover insights faster, resolve industry problems more efficiently, and leverage their data in new, innovative ways that they were never able to before. We’ve only begun to scratch the surface in the ways they can expand their data and insights with their data warehouse.

    Let me know what you cogitate in the comments below! What are some of the ways a data warehouse solved your data problems?



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