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70-767 | Implementing a SQL Data Warehouse

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70-767 - Implementing a SQL Data Warehouse - braindump

Vendor Microsoft
Exam Number 70-767
Exam Name Implementing a SQL Data Warehouse
Questions 237 Q & A
Recent Update March 14, 2019
Free PDF Download 70-767 Brain Dump
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70-767 exam Dumps Source : Implementing a SQL Data Warehouse

Test Code : 70-767
Test Name : Implementing a SQL Data Warehouse
Vendor Name : Microsoft
Q&A : 237 Real Questions

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Microsoft Implementing a SQL Data

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

Micorosoft has introduce a good advantage to agencies of all sizes with its Stretch Database offering in SQL Server 2016. Stretch Database permits agencies to use on-premises circumstances of SQL Server to "stretch" their records into the Microsoft Azure SQL Database. Stretch DB is relatively essential to configure and put into effect on your atmosphere. It can be a manageable and low-budget alternative to off-load cold information remotely to cut expenses with protection, storage, and so on. Your choice to put into effect Stretch DB is often according to Azure pricing in comparison to the charge of upgrading your storage potential. You may additionally also trust how regularly the cold information has to be accessed when you consider that efficiency can be impacted by way 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 display you how to migrate the historical information transparently and securely to the Microsoft Azure cloud, which provides for low in cost availability of cold facts while maintaining it secure and unchanged. you'll see a way to stretch your on-premises information to the cloud but still have the ability to query the absolutely accessible and online desk and to monitor and manipulate the stretched tables.

To put into effect 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 way 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 function on database, as shown in Fig. 1. From SQL Server administration Studio (SSMS), appropriate 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 place to make use of for Stretch Database. Specify even if you wish to use an current server or create a new Azure server. we've chosen an current server during this illustration.

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

with the intention to stretch a database desk to Azure, the database have to have 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 handle page, you can select the subnet IP address range, or the public IP handle of your SQL Server, to create a firewall rule on Azure that lets SQL Server speak with the far flung Azure server.

The IP handle or addresses that you provide on this web page tell 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 sure that every one initiatives have handed repute, and then click close 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 display Stretch Database

you have 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 place your bloodless facts is saved. which you could also determine how many batches and rows of information have 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 have 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 display screen as proven in Fig. eight.

Fig. 8 - Monitoring repute

determining the video display option from the stretch task 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 error particulars and state. which you can use this tips to diagnose the error.

For extra tips and documentation on the way to computer screen or troubleshoot Stretch Database, go 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 way to deploy a Stretch Database.Its a simple way emigrate archive records to Microsoft Azure and allows you to dynamically stretch warm and cold 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 period of time.


How marketers Like Macy’s Use 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, business leaders invested in sophisticated, analytic algorithms devoid of constructing the infrastructure required to grasp and share the gathered information most simply: fifty two% of businesses face 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 wander 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 advice between companions via cloud infrastructures and retaining all 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 keep 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 all through its complete deliver chain by using the conclusion of 2019. presently, the enterprise is conducting a pilot software that makes use of analytics software from Aera know-how Inc. to foretell demand 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 like supply alterations or demand 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 handle this loss, trade leaders invested in refined, analytic algorithms devoid of constructing the infrastructure required to hold and share the accrued guidance most without difficulty: fifty two% of agencies face 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 wander away between disparate teams and siloed channels. facts-driven verbal exchange channels can help 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 use 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 like Microsoft teams is a necessary tool for today’s work environment — no longer just a elaborate 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 same information to stay successful.

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

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

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

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

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

    linked Article: office 365 Governance: set up Your crew

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

    As with the introduction technique, it’s finest to area restrictions across the standards behind collaboration spaces. We at AvePoint have seen groups with 2,000 users that have 2,500 workplace 365 businesses — a recipe for multiplied chance 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, have avoided sharing documents as a result of they feared they might by no means discover them once again. 

    clients should justify their businesses and teams to evade these logjams, nonetheless it additionally shouldn’t believe like an authoritarian manner. Work to reach an contract with users and stakeholders around most efficient practices for new collaboration spaces.

    three. The Roles and Stakeholders Who verify These standards

    The past you identify and win over stakeholders and interior idea leaders to support 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 new guidelines, but the why. You’ll want the appropriate partners to help communicate that.

    speak with a wide selection of stakeholders to set up have confidence and benefit perception into the struggles faced through conclusion clients. These individuals will consider preferred and understood, and will disseminate that goodwill all 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 have 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 advantage of, the IT department. Ask yourself:

  • Does your department have the appropriate materials to video display and hold song of roles and access inside groups and groups?
  • Do they consider 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 still 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 found that can increase cloud functions and systems. but you must additionally agree with the elevated risk of third-party capabilities.

    happily, IT departments handle the purposes and integrations that can be introduced to their Microsoft teams on the crew level. for instance, you might also now not need to enable external sharing. may still clients then be able to join their Microsoft teams to other cloud storage solutions? Ask the appropriate 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 handle how users identify individual businesses and teams, however think 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 branch can’t apply guidelines or retain information lifecycles in the event that they don’t be aware 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 like a Grinch.

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

    whereas Microsoft teams is a fairly new format for collaboration, americans will share info and files, so that they can nevertheless be saved in the team’s SharePoint websites. You need to handle normal questions around content material lifecycle, records management and facts insurance policy/statistics loss protection. Your plan should still include 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 center and the SharePoint information management core can aid with these approaches, and third-celebration providers in the Microsoft ecosystem also can deliver guide right here.

    linked Article: Why You need a data Archiving method

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

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

    before unleashing the entire vigour of teams, put into effect a metamorphosis administration approach for training users on new 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 New clients on These techniques

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

    10. integrated purposes That reduce IT and business 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 burden of IT worker's to take note and adapt to each enterprise workflow.

    applications like circulate, kinds and Sway make it even more straightforward for clients to create primary workflows, distribute counsel and collaborate. When incorporated with Microsoft teams, these options create a less complicated, streamlined and easy adventure.

    With the entire tools provided with the aid of office 365, the implementation of protection and governance controls is the winning move 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 social 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 building a successful system.  You need to ensure that you can not only report on your data in a timely manner but also be able to load the volumes and complexity of data that you are expecting.  I would argue that if you haven’t tested at volume you haven’t really tested at all.

    Testing at volume is vitally important to your solution, and the earlier you do 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 possible in the development process, and actually building the solution to handle 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, make sure you’ve tested loading that volume of data, and not at the end of the development 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 building a data warehouse to cope with processing one billion new records a day when you actually only need to process one hundred thousand.  You’ll end up spending a lot of time and a lot of money building a system that completely dwarfs your needs so keep to spec.  But if you don’t know how it’s going to perform processing one hundred thousand rows do you just go and put everything in place that you can think 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 have a good idea, or a worst case estimate (worst case being the highest possible throughput).  You may be in the position where all 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 all of it.  If you don’t have 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 do the same 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 part of the upgrade, so it was a brand new blank database.  Within days of the upgrade going live queries were timing out and the system was unusable for the end user.

    I was asked to have a look.  The problem was that they’d built a scalar function to parse some name and value pairs out of some very large text fields.  The function was being called by stored procedures being executed at query time.  Now SQL Server isn’t great at parsing name and value pairs out of unstructured data, especially when it’s having to do it row-by-painstaking-row.  This function in itself wasn’t greatly efficient either.  Parsing the same 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 function not just once per record, but multiple times for each record as there were numerous name 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 work 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 have seen the light of day were discovered by the end user.

    The proper solution to the problem would have been to parse these name and value pairs from within the source system that was also part of the solution, giving the database the data as it wanted it.  Because the issue needed fixing immediately, and the proper solution would take longer to implement, I put in a better workaround within SQL Server.  I rebuilt the function making it as efficient as possible, and then did the parsing as part of an instead of insert statement.  Catching the insert of the unstructured text and parsing it and loading the parsed data into a new structured table.  Performing the parsing once on insert rather than every time that the data was queried.  With the database holding data in the way 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 all 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 work almost broke me.  I did learn a hell of a lot in the process though.

    The one good 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 get the data into the data warehouse.  We moved from SQL Server Standard Edition to Enterprise so that I could use 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 empty 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.  We had a web based UI and we wanted results in seconds rather than minutes.  Numerous improvements were needed in the multidimensional model.  Profiling the queries and finding that they were still hitting every partition even when we’d specified the date range was just one problem.  I pulled the model apart.  Stuff that maybe wasn’t great at 500 thousand records a day but didn’t cause a problem at those volumes needed reworking.  Which in hindsight is what you’d expect when scaling up a solution to handle 200 times the initial throughput.

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

    We did get there in the end.  Just in time for them to decide 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 go live with it.  Testing at volume from as early as possible in the development phase will help you identify and tackle challenges before they become problems.  You are building a solution that is fit for purpose.

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

    Testing at volume will show 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 name it.  It will be anything that could degrade 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 development of BI solutions using the full SQL Server BI stack. I’ve been working with SQL Server since 2003 and have been developing BI solutions since 2007. I’ve been running my own independent consultancy, based in Southampton, UK, since 2015.


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

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

    Download PDF

    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 easy undertaking. A lot of companies are discovering that a data warehouse can solve their data and reporting needs. Unfortunately, high 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 effective and invaluable products you deploy.

    In this post, I’ll explain how we used a cloud-based data warehouse to solve our data needs. I’ll walk you through the major challenges we faced, the benefits of a data warehouse, and the implementation process. It’s my hope that this article will help 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 high cognitive load for domain experts. As an organization grows, the ability for people to run independent analyses increases as the number of employees that have access to these data increases. However, if the data required to run 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 appropriate data. In the best case scenario, the analyst can extract the data themselves, run the analysis, and report to the stakeholder with little to no trouble. However, this is seldom the case. What typically tends to happen is two or more analysts work on similar reports, they extract different data sets, get different results, and report back to separate stakeholders. These stakeholders then find out later on that their numbers don’t align with their colleague’s numbers and uncertainty sets in. These reporting inconsistencies cause miscommunication between departments and can be costly to resolve.

    Inefficient and Complex Reporting Workflows

    Without integrating these disparate data sources, individuals become dependent on inefficient and complex workflows to solve the most standard business 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 happy 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 choice (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 do 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 new insights.

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

    As your company grows, more people join, new departments form, and the need for an efficient reporting and more organized data infrastructure becomes more critical to scale and take 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. We eliminated these entirely with our data warehouse. Our users only go to other systems as a last resort now as the data warehouse provides us with one place to query and merge all our data together efficiently. We are now able to tailor and serve information to the end-users based on their reporting needs.

    Data and Reporting Automation

    The ability 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 increase efficiencies of other data workflows.

    At SnackNation, we leverage SQL and Tableau to deliver automated reports and high level dashboards to end-users in various departments. Creating custom SQL queries and visualizations for each report takes time to set up, but once we build the dashboards, we can set it and forget it. Our end-users don’t need to email the analysts asking for the report or worry about the hours of work it would take them to run the same analysis each month. With automation, we’re also able to avoid small human errors that are made from time-to-time in manual reporting.

    To be clear, what I’m calling our data warehouse currently exists as a data lake. The main difference between a data warehouse and a data lake is that a data lake takes the raw data from all enterprise systems and loads it into a single place. There is not much transformation that happens — it usually reflects the source system’s existing schema and their use cases are not yet defined. We’re not transforming or processing the data during its initial ingestion from their respective sources to match our business model or specific purposes. True data transformation is happening after the fact when we reshape the data to the definition of the business, simplify certain processes, or make a subset of data more specific to an individual or a team. We leverage open source tools like RStudio to automate data extraction, build machine learning models, and handle 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 business 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 business domain reduces the cognitive load of the end-user since they don’t have to understand the systems and how they work — they only have to understand the business as the data reflects it.

    Another way to think of it is that we’re now getting our data from a “System of Record” (SoR)for all automated reporting as opposed to the “Source of Truth” (SoT). The difference between the two is important. The SoT is the origin of a specific data point or its original entry point into your organization. A SoR is ordained as the place 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 we use 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 have to troubleshoot discrepancies should.

    Implementing our Data Warehouse Solution

    Before 2018, I had little working experience with data warehousing. It’s a complex subject and hard to jump into without some technology and database knowledge. Having worked in data-heavy roles throughout my career, I’m lucky to have experience with relational databases. This helped me get up to speed with the technical details of data warehousing. I found plenty of resources covering key concepts and technical definitions, but I had trouble finding articles describing the actual implementation process. This was a bit shocking to me given that data warehousing is such a popular concept nowadays.

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

    Things to consider:

  • Scalability — How simple and flexible is it to add/remove data sources and get them up and running to match our business specific needs?
  • Integration — How many data sources will our 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 kind of queries will we be running? How much data will we 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 have a variable cost based off execution and data storage. The volume and frequency in which you sync your data could increase your overall cost.
  • Security — How will we manage end-user access and permissions to the data warehouse? How will we secure our data and all of the access points?
  • Data Quality — How will we validate the initial data ingestion? How will we ensure the data we are ingesting into the data warehouse is clean and accurate? The last thing we want is to transfer bad data from one system to another.
  • Goal — What are all the business needs and goals of the data warehouse? What are we trying to solve? What are some initial products that are now possible with a data warehouse? For us it was data automation, machine learning models, and improving reporting efficiencies. Without a clear goal, it will be difficult to show the ROI after implementation. Make sure there is a clear strategy.
  • The entire data warehouse project took a few months from ideation to the first data ingestion. We went back and forth between a few tools, but we 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 like 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 full data warehouse solution. Since Panoply has data ingestion and a large selection of integrations built-in, getting it up and running was simple. It also prevented us from having to create ad-hoc solutions and alleviated work from engineers. For integrations that weren’t supported, we leveraged other 3rd party tools like Stitch Data and SnapLogic to manage ETL processes. The actual data ingestion and data validation part only took a few days before we were up and running!

    Cost was an important 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 also a huge factor in our decision making. Building reliable, fast, and near real-time dashboards that report on business KPIs is a huge part of any analyst’s role. Panoply uses machine learning algorithms to analyze query performance and caches and materializes popular queries to increase the speed 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 our data warehouse, we had our analysts spending 30+ hours a month on repetitive tasks and reports. There wasn’t time to tackle new projects or solve new problems. With our data warehousing solution, we removed these manual and repetitive workflows, and are able to uncover insights faster, solve business problems more efficiently, and leverage our data in new, innovative ways that we were never able to before. We’ve only begun to scratch the surface in the ways we can expand our data and insights with our data warehouse.

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



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    Box.net : https://app.box.com/s/8xb4os3t1wrsyvhdn80maoqarvanj3gg
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    Calameo : http://en.calameo.com/books/004923526bbca7806f5eb






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