Ngenxa yokubaluleka okukhulayo kokuhlaziywa kwedatha nokuphathwa kwedatha emabhizinisini, ukuqhathaniswa kwenkundla yedatha i-Snowflake ne-Databricks iyadingeka emakethe yanamuhla.
Izinhlangano zidinga indlela yokuqoqa yonke idatha eziyidingayo ukuze zihlolwe endaweni eyodwa lapho ingase ilungele ukumbiwa kwedatha njengoba inani ledatha okufanele ifundwe likhula kancane kancane.
Ngaphandle kokungabaza, izinhlelo zedatha ezisekelwe emafini ezihlonishwayo i-Snowflake ne-Databricks zombili zingabaholi bezimboni. Iyiphi inkundla yedatha, nokho, elungele inkampani yakho?
Inani, isivinini, kanye nekhwalithi edingwa izinhlelo zokusebenza zobuhlakani bebhizinisi konke kuhlinzekwa yi-Snowflake ne-Databricks.
Nakuba kukhona ukuhlukahluka, kukhona futhi ukufana okuningi. Zinomumo ohlukile, osobala lapho zihlolisiswa kahle.
Abasunguli be-Apache Spark basungule ibhizinisi lesoftware yebhizinisi iDatabricks.
Idume ngokuhlanganisa izici ezinhle kakhulu zedatha yamachibi kanye izindawo zokugcina idatha zibe i-lakehouse architecture.
Ibhizinisi lokugcinwa kwedatha I-Snowflake inikeza isitoreji esisekelwe efwini kanye nezinsizakalo zokufinyelela ezinobunzima obuncane. Isungula ukuma kwayo njengesixazululo esinikeza ukufinyelela okuphephile kudatha yakho kuyilapho idinga ukunakekelwa okuncane.
Lesi sihloko sikunikeza ukuqhathanisa okuningiliziwe kwe-Snowflake Vs. I-Databricks futhi ichaza izinzuzo zomkhiqizo ngamunye ukuze ukwazi ukunquma ukuthi yikuphi okungcono kakhulu kwebhizinisi lakho. Ake siqale ngesingeniso sabo.
Kuyini Snowflake?
I-Snowflake iyisevisi ephethwe ngokuphelele enikeza amakhasimende ukulinganisa cishe okungenamkhawulo kwemithwalo yemisebenzi efanayo ukuze kube lula ukuhlanganiswa kwedatha, ukulayishwa, ukuhlaziya, nokwabelana.
I-Data Lakes, Ubunjiniyela Bedatha, Ukuthuthukiswa Kohlelo Lokusebenza Kwedatha, Isayensi Yedatha, kanye nokusetshenziswa okuphephile kwedatha okwabelwana ngayo ngezinye zezinto ezisetshenziswayo ezivamile.
Ukwenza ikhompuyutha nokugcina kuhlukaniswa ngokwemvelo umklamo ohlukile we-Snowflake.
Ngosizo lwalesi sakhiwo, ungakwazi ukunikeza bonke abasebenzisi bakho nedatha yomsebenzi ukufinyelela ikhophi eyodwa yedatha yakho ngaphandle kokuthola imiphumela emibi yokusebenza.
Ukuze uthole okuhlangenwe nakho okungaguquki komsebenzisi, i-Snowflake ikuvumela ukuthi usebenzise isixazululo sedatha yakho ngokungabonakali ezindaweni ezihlukahlukene kanye namafu.
Ngokususa inkimbinkimbi yengqalasizinda ye-Cloud eyisisekelo, i-Snowflake yenza kube nokwenzeka.
I-Snowflake Data Marketplace, enikeza izinketho eziningi zokuxhumana nezinkulungwane zamakhasimende e-Snowflake, futhi ikuvumela ukuthi ufinyelele amasethi edatha okwabelwana ngawo kanye nezinsizakalo zedatha.
Izici
- Ukwenziwa kwezinqumo okushayelwa yidatha okusebenzayo okwengeziwe: Nge-Snowflake, ungasusa ama-silo wedatha futhi unikeze wonke umuntu ebhizinisini ukufinyelela kumininingwane ewusizo. Lesi isinyathelo sokuqala esibalulekile ekuthuthukiseni ubudlelwano bozakwethu, ukukhulisa amanani entengo, ukunciphisa izindleko ezihlobene nokusebenza, ukukhulisa impumelelo yokuthengisa, nezinye izinto eziningi.
- Thuthukisa Isivinini Nekhwalithi Yezibalo: Ungakwazi ukuqinisa ipayipi lakho lezibalo nge-Snowflake ngokushintsha kusukela ekulayishweni kwenqwaba yasebusuku uye ekusakazweni kwedatha yesikhathi sangempela. Ngokuvumela wonke umuntu osebhizinisini lakho ukufinyelela okuphephile, ngesikhathi esisodwa, nokulawulwayo endaweni yakho yokugcina idatha, ungathuthukisa ikhwalithi yezibalo emsebenzini. Lokhu kunciphisa izindleko nomsebenzi wezandla, okwenza amafemu akwazi ukusabalalisa izinsiza ngendlela efanele ukuze kwandiswe imali engenayo.
- Ukushintshaniswa kwedatha nokwenza ngokwezifiso: Ungakwazi ukudala ukushintshana kwedatha yakho nge-Snowflake, okukuvumela ukuthi udlulise idatha ebukhoma, elawulwayo ngendlela ephephile. Ukwengeza, isebenza njengesisusa sokuthuthukisa ukuxhumana kwedatha okuqinile nozakwethu, amaklayenti, namanye amayunithi ebhizinisi. Ifinyelela lokhu ngokuthola umbono we-360-degree womthengi wakho, okunikeza ulwazi ngezici ezibalulekile zekhasimende ezihlanganisa izinto ozithandayo, umsebenzi, nokunye okuningi.
- Umkhiqizo Omkhulu Nomuzwa Womsebenzisi: Ungakwazi ukuqonda ukuziphatha komsebenzisi nokusetshenziswa komkhiqizo kangcono nge-Snowflake endaweni. Ukwengeza, ungasebenzisa yonke isethi yedatha ukuze wanelise amakhasimende, uthuthukise kakhulu ulayini womkhiqizo wakho, futhi ukhuthaze ukusungulwa kwesayensi yedatha.
- Ukuphepha okuqinile: Yonke idatha yokuthobela kanye ne-cybersecurity ingafakwa endaweni eyodwa echibini ledatha elivikelekile. Ukusabela kwesigameko esisheshayo kuqinisekiswa amachibi edatha ye-snowflake. Ukuhlanganisa amanani amakhulu edatha yelogi endaweni eyodwa nokuhlola ngokushesha idatha yelogu yenani leminyaka, kukwenza ukwazi ukuthola isithombe esigcwele sesenzeko. Amalogi anesakhiwo esincane kanye nedatha yebhizinisi ehlelekile manje ingahlanganiswa echibini ledatha elilodwa. Ngaphandle kwanoma yikuphi ukukhomba, i-Snowflake ikuvumela ukuthi ungenise unyawo lwakho emnyango ngenkathi ikwenza kube lula ukuhlela nokushintsha idatha uma isingenisiwe.
Kuyini izitini idatha?
I-Databricks iyinkundla yedatha esekwe emafini eqhutshwa yi-Apache Spark. Igxile ku-Big Data Analytics kanye nokusebenzisana kakhulu.
Ungahlinzeka ngendawo yokusebenza ye-Data Science ephelele Abahlaziyi bezebhizinisi, Ososayensi Bedatha, Nonjiniyela Bedatha ukuze basebenzisane kusetshenziswa Isikhathi Sokufunda Somshini Sedathabricks, Ukugeleza Kwe-ML okulawulwayo, kanye Namabhuku Amanothi Asebenzisanayo.
Ama-dataframes kanye nemitapo yolwazi ye-Spark SQL, ekuvumela ukuthi ubhekane nedatha ehlelekile, igcinwe kwa-Databricks.
Ngaphezu kokukusiza ukuthi udale Ukuhlakanipha okungekhona okwangempela izixazululo, I-Databricks yenza kube lula ukuthola iziphetho kudatha yakho yamanje.
Ngaphezu kwalokho, i-Databricks inikeza imitapo yolwazi ehlukahlukene ukufunda imishini, okuhlanganisa i-Tensorflow, i-Pytorch, nezinye, zokwakha nokuqeqesha amamodeli okufunda emishini.
Uhlu olubanzi lwamakhasimende ebhizinisi asebenzisa i-Databricks ukwenza izinqubo zokukhiqiza ezinkulu ezimweni eziningi zokusetshenziswa nemikhakha, okuhlanganisa Ukunakekelwa Kwezempilo, Imidiya Nokuzijabulisa, Izinsizakalo Zezezimali, Ukudayisa, nokunye okuningi.
Izici
- Delta Lake: I-Databricks inesendlalelo sesitoreji sokuhweba esiwumthombo ovulekile futhi esidizayinelwe ukusetshenziswa kuwo wonke umjikelezo wempilo wedatha. Lesi sendlalelo singasetshenziselwa ukuhlinzeka ngokulinganisa kwedatha nokuthembeka echibini lakho ledatha lamanje.
- Interactive Notebooks: Ungakwazi ukufinyelela ngokushesha idatha yakho, uyihlaziye, wakhe amamodeli nabanye, futhi wabelane ngemininingwane emisha, ewusizo uma unamathuluzi nolimi olulungile. I-Scala, i-R, i-SQL, ne-Python yizilimi ezimbalwa nje ezisekelwa yi-Databricks.
- Ukufunda komshini: Ngosizo lwezinhlaka ezisezingeni eliphezulu njenge-Tensorflow, i-Scikit-Learn, ne-Pytorch, i-Databricks ikunikeza ukufinyelela ngokuchofoza okukodwa ezindaweni ezilungiselelwe kusengaphambili Zokufunda Ngomshini. Ungakwazi ukwabelana futhi ugade izivivinyo, uphathe amamodeli ndawonye, futhi uphindaphinde kusebenza konke kusuka kunqolobane eyodwa emaphakathi.
- Injini Ye-Spark Ethuthukisiwe: Ungathola izinguqulo zakamuva kakhulu ze-Apache Spark usebenzisa i-Databricks. Imitapo yolwazi ehlukahlukene ye-Open-source ingabuye ihlanganiswe ngaphandle komthungo ne-Databricks. Ungakwazi ukusetha amaqoqo ngokushesha futhi udale indawo ephethwe ngokugcwele ye-Apache Spark uma ukwazi ukufinyelela ekutholakaleni nasekulinganiseni kwabahlinzeki besevisi bamafu abambalwa. Amaqoqo angacushwa, asethwe, futhi acutshungulwe kahle ngeDathabricks ngaphandle kwesidingo sokuqapha okuqhubekayo ukuze kugcinwe ukusebenza okuphezulu nokwethembeka.
Umehluko Oyinhloko phakathi kwe-Snowflake & Databricks
Architecture
I-Snowflake iyisistimu engenaseva esekelwe ku-ANSI SQL enesitoreji esihluke ngokuphelele futhi ihlanganisa izendlalelo zokucubungula.
Indlu ngayinye yokugcina impahla ebonakalayo (okungukuthi, iqoqo lekhompyutha) ku-Snowflake igcina isethi engaphansi yayo yonke idatha esethwe endaweni kuyilapho kusetshenziswa ukucubungula okuhambisana kakhulu (MPP) ukwenza imibuzo.
Ukuze uthole ukuhlelwa kwedatha yangaphakathi nokuthuthukisa ibe ifomethi yekholomu ecindezelwe engagcinwa emafini, i-Snowflake isebenzisa ama-partitions amancane.
Iqiniso lokuthi i-Snowflake igcina zonke izici zokuphathwa kwedatha, okuhlanganisa usayizi wefayela, ukucindezelwa, ukwakheka, imethadatha, izibalo, nezinye izinto zedatha ezingabonakali ngokushesha kubasebenzisi futhi ezingafinyelelwa kuphela ngemibuzo ye-SQL, yenza konke lokhu kwenzeke. ngokuzenzakalelayo.
Ama-warehouses abonakalayo, okungamaqoqo ekhompuyutha akhiwe amanodi amaningi e-MPP, asetshenziselwa ukwenza konke ukucubungula ngaphakathi kwe-Snowflake.
I-Snowflake ne-Databricks zombili izixazululo ze-SaaS, nokho, ukwakheka kweDathabricks kuhluke kakhulu ngoba yakhelwe ku-Spark.
Injini yezilimi eziningi ebizwa ngokuthi Spark ingafakwa efwini futhi isekelwe kumanodi noma amaqoqo angawodwa. I-Databricks njengamanje isebenzisa i-AWS, i-GCP, ne-Azure, njenge-Snowflake.
Indiza yokulawula kanye nendiza yedatha yenza isakhiwo sayo. Yonke idatha ecutshunguliwe iqukethwe endizeni yedatha, kuyilapho zonke izinsiza ezingemuva eziphethwe i-Databricks Serverless computing zitholakala endizeni yokulawula.
I-Serverless Computing inika amandla abalawuli ukuthi bakhe izindawo zokugcina ze-SQL ezingenaseva eziphethwe ngokugcwele yi-Databricks futhi zinikeze ngekhompyutha esheshayo.
Ngenkathi izinsiza zokubala zezibalo eziningi ze-Databricks zabelwa ngaphakathi kwe-akhawunti yefu noma indiza yedatha evamile, lezi zinsiza zabiwa endizeni yedatha ye-Serverless.
Ukwakhiwa kweDatabricks kwenziwa izingxenye ezimbalwa ezibalulekile:
- I-Databricks Delta Lake
- I-Databricks Delta Engine
- MLFlow
Isakhiwo sedatha
Womabili amafayela anesakhiwo esincane nahlelekile angalondolozwa futhi alayishwe kusetshenziswa i-Snowflake ngaphandle kwesidingo sethuluzi le-ETL lokuhlela kuqala idatha ngaphambi kokuyingenisa ku-EDW.
I-Snowflake iguqulela ngokushesha idatha kufomethi yayo yangaphakathi, ehlelekile lapho idatha ithunyelwa. Ngokungafani ne-Data Lake, i-Snowflake ayidingi ukuthi unikeze isakhiwo kudatha yakho engahlelekile ngaphambi kokuthi uyilayishe futhi uxhumane nayo.
Izinhlobo zedatha zingasetshenziswa zonke ne-Databricks ngefomethi yazo yoqobo. Ukuze unikeze ukwakheka kwedatha yakho engahlelekile ukuze ikwazi ukusetshenziswa ngamanye amathuluzi afana ne-Snowflake, ungasebenzisa futhi i-Databricks njengethuluzi le-ETL..
Empikiswaneni phakathi kwe-Databricks ne-Snowflake, i-Databricks inqoba i-Snowflake ngokuya ngeSakhiwo Sedatha.
Ubunikazi Bedatha
Izendlalelo zokucubungula nokugcina zihlukaniswa nge-Snowflake, ezivumela ukuthi zikhule ngokuzimela emafini. Lokhu kukhombisa ukuthi zonke zingakala ngokuzimela Efwini ngokuya ngezidingo zakho.
Izimali zakho zizozuza kulokhu. Ukwengeza, ubunikazi bezendlalelo zombili buyagcinwa. I-Snowflake ivikela ukufinyelela kudatha nezinsiza zomshini kusetshenziswa indlela yokulawula ukufinyelela okusekelwe kundima (RBAC).
Izendlalelo zokucubungula nokugcinwa kwedatha ze-Databricks zihlukaniswe ngokuphelele, ngokungafani nezendlalelo ezihlukanisiwe ku-Snowflake.
Abasebenzisi bangabeka idatha yabo noma yikuphi kunoma iyiphi ifomethi, futhi i-Databricks izoyiphatha ngempumelelo ngoba umgomo wayo oyinhloko uwuhlelo lokusebenza lwedatha.
I-Databricks iwinile ngokusobala kunkulumompikiswano phakathi kwe-Databricks ne-Snowflake njengoba ungakwazi ukuvele uyisebenzisele ukucubungula idatha.
Ukuvikelwa kwedatha
I-Time Travel kanye ne-Fail-safe yizici ezimbili ezikhethekile ze-Snowflake. Umsebenzi Wokuhamba Kwesikhathi we-Snowflake ugcina idatha isesimweni ngaphambi kwesibuyekezo.
Nakuba amaklayenti e-Enterprise angakhetha ibanga lesikhathi elifika ezinsukwini ezingu-90, Ukuhamba Kwesikhathi kuvame ukukhawulelwa osukwini olulodwa. Imininingo egciniwe, izikimu, namathebula konke kungasebenzisa leli khono.
Uma isikhathi sokugcinwa kwe-Time Travel siphelelwa yisikhathi, kuqala isikhathi esiyizinsuku ezingu-7, esiklanyelwe ukuvikela nokubuyisela idatha yangaphambilini.
I-Databricks Ngokufanayo nendlela isici se-Snowflake's Time Travel esisebenza ngayo, ne-Delta Lake's nayo yenza kanjalo. Idatha egcinwe e-Delta Lake iguqulelwa ngokuzenzakalelayo, okuvumela abasebenzisi ukuthi bathole izinguqulo zangaphambili zedatha ukuze zisetshenziswe esikhathini esizayo.
I-Databricks isebenza ku-Spark, futhi njengoba i-Spark yakhelwe endaweni yokugcina izinto, i-Databricks ayilokothi igcine noma iyiphi idatha.
Lokhu kungenye yezinzuzo zayo eziyinhloko. Lokhu futhi kusho ukuthi i-Databricks ingase isingathe amacala okusetshenziswa kumasistimu asendaweni.
Security
Yonke idatha ibethelwa ngokuzenzakalelayo lapho uphumule ngaphakathi kwe-Snowflake.
Konke ukuxhumana phakathi kwendiza elawulayo nendiza yedatha kwenzeka ngaphakathi kwenethiwekhi yangasese yomhlinzeki wamafu, futhi yonke idatha elondolozwe ngaphakathi kwe-Databricks ivikelekile.
Zombili izinketho zinikeza i-RBAC (ukulawula ukufinyelela okusekelwe endimeni). I-Snowflake kanye ne-Databricks kuthobela imithetho nezitifiketi ezimbalwa, okuhlanganisa i-SOC 2 Type II, ISO 27001, HIPAA, kanye ne-GDPR.
Kodwa-ke, njengoba i-Databricks isebenza phezulu kwesitoreji sezinga lezinto njenge-AWS S3, i-Azure Blob Storage, Ifu le-Google Isitoreji, njll., asinaso isendlalelo sesitoreji ngokungafani ne-Snowflake.
Ukusebenza
Mayelana nokusebenza, i-Snowflake ne-Databricks yizixazululo ezihluke kakhulu kangangokuthi kuyinselele kakhulu ukuziqhathanisa.
Kungenzeka ukuguqula ibhentshimakhi ngayinye ukwethula inganekwane ehluke kancane. Isibonelo esiphelele salokhu yi- ucwaningo lwamuva eyenziwa yi-Databricks mayelana nebhentshimakhi ye-TPC-DS.
Ngokuqhathanisa nekhanda nekhanda, i-Snowflake ne-Databricks isekela izimo zokusetshenziswa ezihluke kancane, futhi akukho neyodwa edlula enye ngokwemvelo.
I-snowflake, nokho, ingahle ibe inketho ekhethwayo yemibuzo ephendulanayo njengoba ithuthukisa sonke isitoreji sokufinyelela idatha ngesikhathi sokufakwa.
Sebenzisa Icala
Amacala okusetshenziswa kwe-BI ne-SQL asekelwa kahle yi-Databricks ne-Snowflake.
I-Snowflake inikeza izishayeli ze-JDBC ne-ODBC okulula ukuzihlanganisa nenye isofthiwe.
Uma kubhekwa ukuthi amakhasimende akudingeki alawule uhlelo, ludume kakhulu ngezimo zalo zokusetshenziswa ku-BI kanye namabhizinisi akhetha inkundla yokuhlaziya eqondile.
I-Delta Lake yomthombo ovulekile ekhishwe yi-Databricks yengeza isendlalelo esengeziwe sokuzinza kwi-Data Lake yabo okwamanje. Amakhasimende angathumela imibuzo ye-SQL e-Delta Lake ngokusebenza okuhle.
Uma kubhekwa ukuhlukahluka nobuchwepheshe bazo obusezingeni eliphezulu, i-Databricks yaziwa kakhulu ngamakesi ayo okusebenzisa anciphisa ukukhiya kwabathengisi, ifaneleka kangcono imithwalo yemisebenzi ye-ML, futhi isiza ama-tech giants.
Zamanani
Amakhasimende anokufinyelela ekubukeni okune kwezinga lebhizinisi nge-Snowflake. Standard, Enterprise, Business Critical, kanye Virtual Private Snowflake yizinguqulo ezine ezitholakalayo. Lonke ulwazi lwentengo luyatholakala lapha.
Ngakolunye uhlangothi, ama-tier amanani amathathu okuhweba ahlinzekwa yi-Databricks ayisisekelo, i-premium, kanye nebhizinisi. Ungabuka lonke uhlu lwamanani kwesokudla lapha.
Isiphetho
Amathuluzi amahle kakhulu wokuhlaziya idatha afaka i-Snowflake ne-Databricks.
Kunezinzuzo kanye nezithiyo ngayinye. Amaphethini okusetshenziswa, umthamo wedatha, umthwalo wokusebenza, namasu edatha konke kuyasiza lapho unquma ukuthi iyiphi inkundla elungele ibhizinisi lakho.
I-snowflake ifaneleka kangcono kulabo abanolwazi nge-SQL kanye nokuguqulwa kwedatha okujwayelekile nokuhlaziya.
Ukusakaza, i-ML, i-AI, kanye nomsebenzi wesayensi yedatha kuzifanele kangcono i-Databricks ngenxa yenjini yayo ye-Spark, esekela ukusetshenziswa kwezilimi eziningi.
Ukuze uhambisane nezinye izilimi, i-Snowflake yethule ukusekelwa kwePython, Java, ne-Scala.
Abanye bathi i-Snowflake inciphisa isitoreji ngesikhathi sokuthatha, ngakho-ke ingcono kakhulu emibuzweni esebenzisanayo.
Ukwengeza, kuhle kakhulu ekukhiqizeni imibiko namadeshibhodi nokuphatha imithwalo yemisebenzi ye-BI. Mayelana ne-warehouse yedatha, isebenza kahle.
Kodwa-ke, abanye abasebenzisi baqaphele ukuthi ihlushwa ngamanani amakhulu wedatha, njengalawo abonwa ezinhlelweni zokusakaza bukhoma. I-snowflake iyanqoba emqhudelwaneni oqondile osuselwe kumakhono okugcina idatha.
Nokho, i-Databricks empeleni akuyona indawo yokugcina idatha. Inkundla yayo yedatha ibanzi futhi ine-ELT, isayensi yedatha, namandla okufunda ngomshini ku-Snowflake.
Abasebenzisi abalawuli izindleko zesitoreji sento ephethwe lapho bagcina khona idatha yabo. Ichibi ledatha kanye nokucubungula idatha yizihloko eziyinhloko.
Nokho, iqondiswe ngokukhethekile kososayensi bedatha nakubahlaziyi abanekhono ngokwedlulele.
Sengiphetha, i-Databricks inqoba izethameli zobuchwepheshe. Bobabili abasebenzisi abanolwazi lobuchwepheshe nabangenalwazi ngobuchwepheshe bangasebenzisa kalula i-Snowflake.
Cishe zonke izici zokuphatha idatha ezinikezwa yi-Snowflake zitholakala nge-Databricks nokunye okuningi. Kodwa kunzima kakhulu ukusebenza, kuhlanganisa ijika lokufunda eliphezulu, futhi kudinga ukunakekelwa okwengeziwe.
Kodwa-ke, ingakwazi ukuphatha ibanga elikhulu kakhulu ledatha yokusebenza nezilimi. Futhi labo abajwayele i-Apache Spark bazoncika kumaDatabricks.
I-Snowflake ifaneleka kangcono kumakhasimende afuna ukufaka ngokushesha inqolobane yedatha enhle nenkundla yezibalo ngaphandle kokugxilwa ekusetheni, imininingwane yesayensi yedatha, noma ukusetha okwenziwa ngesandla.
Lokhu futhi akusho ukuthi i-Snowflake iyithuluzi elilula noma labasebenzisi abasha. Lutho neze.
Akukona ukuphela okuphezulu njengeDathabricks; leyo nkundla ifaneleka kakhulu ubunjiniyela bedatha obuyinkimbinkimbi, i-ETL, isayensi yedatha, nezinhlelo zokusebenza zokusakaza-bukhoma.
I-Snowflake iyinqolobane yedatha yezibalo egcina idatha yokukhiqiza. Ukwengeza, kunenzuzo kubantu abafisa ukuqala kancane futhi bakhuphuke kancane kancane kanye nabaqalayo.
shiya impendulo