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Data aggregation is the process of collecting and summarizing raw data for analysis. Though the term is typically associated with technical teams, nearly every employee engages in data aggregation at some point.
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What I Data Aggregation?(xample + Tool) 0 HAR G N k s A Data aggregation i the proce of collecting and ummarizing ra data for anali. Though the term i tpicall aociated ith technical team, nearl ever emploee engage in data aggregation at ome point. You’ve proal leveraged aggregated data ourelf: earl revenue, average cotper click, monthl organic viit. Data aggregation make data more informative, e몭cient, and uale, no matter hat our role i. ut data aggregation i o몭en framed in relation to ig data, analt and engineer, ho mut contend ith hundred of data ource, machine learning model, and other organizational data component. Thi guide ill de몭ne data aggregation, preent example ue cae, and ugget
Thi guide ill de몭ne data aggregation, preent example ue cae, and uggetThi guide ill de몭ne data aggregation, preent example ue cae, and ugget variou olution, including for nontechnical uer. Tale of Content 1.What i Data Aggregation? 2.Data Aggregation: Ho It Work a.Data Collection .Data ummarization 3.Data Aggregation for uine Uer 4.Data Aggregation: xample 5.Data Aggregation: Tool & olution a.River + no몭ake + Taleau .Datarick Lakehoue Platform + dt c.Coe몭cient + Google heet 6. Data Aggregation: Make Your Anali More 몭cient What i Data Aggregation? Data aggregation entail collecting ra data into a centralized ource, and ummarizing the data for anali. The term i tpicall de몭ned from the viepoint of a technical uer. In the cae of an analt or engineer, data aggregation involve ingeting ra data from variou dataae or data ource into a centralized dataae. The ra data i then comined to form aggregate value. A imple example of an aggregate value i a um or average. Thi aggregated data i ued in anali, reporting, dahoarding, and other data product to inform deciionmaking. Aggregating data can peed up timetoinight, improve e몭cienc, and deciion making. Data aggregation alo optimize ackend dataae performance reducing the numer of querie needed to tranform data. Data Aggregation: Ho It Work Here’ a cloer look at ho the to component of data aggregation — collection and ummarization — ork. 1. Data Collection Collecting data ha never een more challenging for modern enterprie. The average compan ue 110 aa application, and onl analze 12% of the data the have. ut companie mut centralize and organize all of thi data to perform data aggregation for tatitical model, AI/ML ork몭o, and more. To integrate all thee competing data ource, companie o몭en turn to TL/LT
To integrate all thee competing data ource, companie o몭en turn to TL/LTTo integrate all thee competing data ource, companie o몭en turn to TL/LT olution ith preuilt data pipeline. Thee olution enale companie to import ra data from their tem into a dataae (or data arehoue) ithout requiring them to uild pipeline from cratch. Centralized dataae and data arehoue ith QL architecture allo companie to colocate all of their diparate data ource for aggregation and anali. Popular dataae include MQL and PotgreQL, hile popular data arehoue include no몭ake, Redhi몭, and igQuer. 2. Data ummarization Once the ra data enter the dataae, QL querie are run to ummarize the data into aggregate value. entiall, ro (record) in dataae tale are comined into a ro of aggregate data. Thi ro of aggregate data repreent total or ummar tatitic, uch a um or average. Data aggregation peed up data anali and improve operational e몭cienc. It enale data and I pecialit to acce more data, fater, rather than comining and analzing cattered dataet. On the operation ide, data aggregation improve dataae performance eliminating the need to continuoul quer individual dataae record. Once the data i aggregated, the ummarized value are ued in data anali, dahoarding, modeling, and more, to generate inight. In addition to treamlining analtic, aggregated data alo o몭er a highlevel vie into important metric acro viualization and report. Thi provide deciionmaker ith the information the need to act deciivel.
Data Aggregation for uine Uer Hoever, thi narro de몭nition of “data aggregation” tpicall exclude the kind that occur ever da among uine uer. aleOp manager aggregate data all the time. And their ojective i the ame: to create report and dahoard that in몭uence deciionmaking. ut aleOp uer don’t kno QL, and the don’t have ideranging acce to the data tack. That’ h the prefer to perform data aggregation in preadheet. For intance, e uilt an entire ale dahoard template in Google heet (it’ free to ue, the a). The prolem, though, i that aleOp manager cannot import realtime data into their preadheet. Hitoricall, uine uer have coppated their data into
their preadheet. Hitoricall, uine uer have coppated their data intotheir preadheet. Hitoricall, uine uer have coppated their data into preadheet. ut the proce i timeconuming, and the data ecome tale rapidl, making dahoard intantl outofdate. Neverthele, aleOp uer till 몭nd preadheet preferale to aiting in the data team’ queue. And ith the advent of olution uch a Coe몭cient, uine uer can no import realtime data directl into their preadheet, eliminating manual cop andpating, and hare alalive dahoard ith their team. Data Aggregation: xample ector a divere a healthcare, 몭nancial ervice, and ecommerce all rel on data aggregation for toplevel anali, core analtical function, and datoda tak. With uch road application, data aggregation i ued acro all indutrie in man divere a, including: A aa compan comining lat ear’ overall revenue, average deal ize, and deal per rep to forecat ale A 몭ntech leveraging average date to default, default rate income, and det per cutomer to model rik for loan origination An algorithm on an advertiing platform uing click per uer, average ear of education, and median age to create campaign for peci몭c demographic An advanced tatitical model harneing total population, minimum da ick, and maximum da ick to viualize the viralit of a dieae Thee are jut a fe example of data aggregation in action. In practice, jut aout ever compan utilize data aggregation to ome degree. Data Aggregation: Tool & olution Additional tool and olution can help companie ith data aggregation, epeciall if the maintain man di몭erent data ource, and need to analze large volume of data. Here are ome of the top option for di몭erent ue cae.
Here are ome of the top option for di몭erent ue cae.Here are ome of the top option for di몭erent ue cae. River + no몭ake + Taleau River i an TL/LT tool ith over 150+ preuilt data connector. no몭ake i the premier aa cloud data arehoue, o몭ering engineer unlimited compute and torage, ith a upport capacit a ell. Pull data into no몭ake ith River’ data connector, and then ummarize the data uing River’ data tranformation capailitie. Viualize the ummarized data uing the I platform Taleau. Thi con몭guration make ene for ig data ue cae ith a I component. Datarick Lakehoue Platform + dt The Datarick Lakehoue Platform uni몭e data arehouing and AI ue cae in a
The Datarick Lakehoue Platform uni몭e data arehouing and AI ue cae in aThe Datarick Lakehoue Platform uni몭e data arehouing and AI ue cae in a ingle platform. The Lakehoue Platform i a hrid comination of data arehoue and data lake, enaling trong data governance, ut ith poerful upport for untructured data and AI/ML ork몭o. Ue dt tranformation to aggregate data, and deplo the output 몭exil, for analtic, ML model, or anthing in eteen. Coe몭cient + Google heet Mot uine uer, uch a aleOp manager, ill never need to deplo AI or analze illion of data point. Hoever, the do need to aggregate and viualize data on a neardail ai. In man cae, Google heet ha the analtical and dahoarding functionalit the need. ut the can’t acce realtime data in Google heet, turning their preadheet into a minidata ilo. ut Coe몭cient change thi tatu quo enaling aleOp manager to acce live data directl in Google heet. Coe몭cient can connect to an uine tem, pull realtime data into heet, and update the data automaticall. With thi added laer of connectivit, aleOp uer can utilize the familiar analtical function in heet to eail ummarize data and viualize the aggregate in dahoard. aleOP manager can even run QLlike querie againt the data uing the QURY function in Google heet. Data Aggregation: Make Your Anali More 몭cient In a modern enterprie, mot team memer ill leverage aggregated data to olve prolem and make deciion. For man uine uer, the preadheet remain the tool of choice for data aggregation. The can eail calculate and viualize um, average, and other data ummarie directl from the preadheet interface. Hoever, at the level of advanced tatitical anali, data cience, and ig data, a preadheet i not u몭cient for data aggregation. In thi cenario, data profeional mut ue their coding kill and modern data tack to treamline anali and optimize
mut ue their coding kill and modern data tack to treamline anali and optimizemut ue their coding kill and modern data tack to treamline anali and optimize ackend quer performance againt illion of record. ut no matter the ue cae, the main ene몭t of data aggregation remain the ame. Data aggregation enale all uer to etter undertand the data the’re orking ith, and make data anali more e몭cient. Tr Coe몭cient’ free plan no to connect live data from all our tem to heet, and peed up our data anali. Wait, there' more! Connect an tem to Google heet in jut econd. Get tarted Free 50,000+ uer on Google Marketplace Truted thouand of companie
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