Uhlolojikelele
I-Databricks iyinkundla yedatha ne-AI ehlanganisa ubunjiniyela bedatha, izibalo, nokufunda komshini kusisekelo esisodwa 'se-lakehouse'. Kubalulekile ngoba ivumela izinkampani ukuthi zilawule amasethi edatha amakhulu futhi zakhe i-AI ngqo lapho idatha yazo isivele ihlala khona.
I-Databricks iqondwa kangcono kumongo wesu, ukufinyelela kwemodeli, izinqumo zeplathifomu, nobambiswano lwe-ecosystem.
I-Deep Dive
I-Databricks yasungulwa ngo-2013 ngabadali bokuqala be-Apache Spark, okuhlanganisa u-Ali Ghodsi no-Matei Zaharia, abaphuma ku-AMPLab ye-UC Berkeley. Umbono wayo wesiginesha 'i-lakehouse'—okuhlanganisa indawo yokugcina eshibhile, evumelana nezimo yechibi ledatha nokuthembeka nokusebenza kwendawo yokugcina idatha, enikwe amandla ifomethi yethebula le-Delta Lake evulekile. Phezulu kuhlezi i-Unity Catalogue yokuphatha, i-MLflow yokulandelela ukuhlolwa, kanye ne-Databricks Runtime eyakhelwe ku-Spark. Ngo-2023 i-Databricks yathola i-MosaicML futhi kamuva yakhipha i-DBRX, imodeli yolimi enkulu evulekile, ebonisa i-pivot eqinile ebheke ekukhiqizeni i-AI. Inkundla manje imaketha 'I-Data Intelligence Platform' yokwakha nokuphakela ama-agent e-AI kudatha yebhizinisi.
I-Technical Insight
Emgogodleni wayo, i-Databricks isebenzisa ikhompuyutha esabalalisiwe ku-Apache Spark, ihlukanisa imisebenzi emikhulu phakathi kwamaqoqo emishini. I-Delta Lake yengeza okwenziwayo kwe-ACID kanye nelogi yokwenziwayo phezu kwendawo yokugcina izinto eshibhile, ukuze amachibi edatha aziphathe ngendlela ethembekile njengemininingwane yolwazi. I-MLflow ilinganisa umjikelezo wempilo we-ML—imigijimo yokulandelela, amamodeli okupakisha, nokuphatha ukusetshenziswa. Ku-AI ekhiqizayo, amathuluzi e-Mosaic AI aphatha ukulungisa kahle, ukusesha ivekhtha, nokunikeza amamodeli, okuvumela izinkampani ukuthi zakhe abasizi ababuyiswayo abathuthukisiwe ngokuqondile ngokumelene nedatha ebusayo.
I-Mastering Databricks
I-Databricks iyinkundla yedatha ne-AI ehlanganisa ubunjiniyela bedatha, izibalo, nokufunda komshini kusisekelo esisodwa 'se-lakehouse'. Kubalulekile ngoba ivumela izinkampani ukuthi zilawule amasethi edatha amakhulu futhi zakhe i-AI ngqo lapho idatha yazo isivele ihlala khona. I-Databricks iqondwa kangcono kumongo wesu, ukufinyelela kwemodeli, izinqumo zeplathifomu, nobambiswano lwe-ecosystem. Ukuze wakhe ukuqonda okujulile, phatha i-Databricks njengemodeli yokusebenza, hhayi isici esisodwa: chaza imiphumela oyifunayo, ucacise ukucabanga, futhi uhlukanise lokho isistimu engakwenza ngokwethembeka kulokho okusadinga ukwahlulela kochwepheshe.
Empeleni, amaqembu aqinile asebenzisa i-Databricks ahlola isu lomthengisi, ukwethembeka kwemephu yomgwaqo, kanye nobungozi bokukhiya ngaphambi kokwenza. Babhala imibandela yempumelelo ecacile, ukuhlola okuqhathaniswa nedatha engokoqobo nokugeleza komsebenzi, futhi baphindaphinde ngokusekelwe kumaphethini okuhluleka aqashiwe esikhundleni sokuwina kwebhentshimakhi yesikhathi esisodwa. Yilapho ukuqonda kwethiyori kuguquka kube amandla ahlala njalo kuwo wonke umkhiqizo, inqubomgomo, kanye nokusebenza.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ngesikhathi esifanayo, izimemezelo Zokuqalisa zingase zidlule ukuzinza ekusebenzeni kwangempela kokukhiqiza. Indlela eqine kakhulu iwukuhlanganisa isivinini sokuhlola nesiyalo sokuphatha: qhuba abashayeli bezindiza, bamba ubufakazi, ushicilele amalogi ezinqumo, futhi ubuyekeze izivikelo ngokuqhubekayo njengoba imodeli yokuziphatha, okulindelwe ngabasebenzisi, kanye nezimfuneko zokulawula zishintsha.
I-Strategic Impact
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo.
Imephu yemigwaqo yabathengisi ithonya izici ithimba lakho elingazakha ngokulandelayo. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi.
Imigomo yezohwebo nezinketho zokuthunyelwa zithinta izindleko zesikhathi eside nobungozi. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka.
Izinxephezelo zenkampani zibumba okuzenzakalelayo kwemikhiqizo, ukuma kokuphepha, nokuvuleleka. Ekusetshenzisweni kwekhwalithi ephezulu, lokhu kuhunyushwa emithethweni yokusebenza elinganisekayo, imingcele yobunikazi, nemikhuba yokubuyekeza ephindelelayo ukuze amaqembu akwazi ukukala ukuzethemba esikhundleni sokukala ukungaqondakali.
Ukuqaliswa Komhlaba Wangempela
Umthengisi wenza imisebenzi yasebusuku ye-Spark ku-Databricks ukuze acubungule izigidigidi zamarekhodi okuthengisa abe amathebula ahlanzekile ukuze abikezele.
Ithimba lesayensi yedatha lisebenzisa i-MLflow ku-Databricks ukuze lilandelele izivivinyo futhi likhiphe imodeli yokubikezela kwe-churn.
Ibhange lakha i-chatbot elawulwayo ngosesho lwevekhtha ye-Mosaic AI ephendula imibuzo ngemibhalo yenqubomgomo yangaphakathi.
Iqembu lezibalo lisebenzisa i-Delta Lake ukuze linikeze idatha engcolile amathebula athembekile, ohwebo kumadeshibhodi e-BI.
Amaphethini Okusebenzisa
Databricks in practice
Umthengisi wenza imisebenzi yasebusuku ye-Spark ku-Databricks ukuze acubungule izigidigidi zamarekhodi okuthengisa abe amathebula ahlanzekile ukuze abikezele.
Umthengisi wenza imisebenzi yasebusuku ye-Spark ku-Databricks ukuze acubungule izigidigidi zamarekhodi okuthengisa abe amathebula ahlanzekile okubikezela Amathimba ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka komuntu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Databricks in practice
Ithimba lesayensi yedatha lisebenzisa i-MLflow ku-Databricks ukuze lilandelele izivivinyo futhi likhiphe imodeli yokubikezela kwe-churn.
Ithimba lesayensi yedatha lisebenzisa i-MLflow ku-Databricks ukuze lilandelele izivivinyo futhi likhiphe imodeli yokubikezela kwe-churn Amaqembu ngokuvamile athola imiphumela engcono uma echaza izilinganiso zekhwalithi ngaphambili, egcina indlela yokukhuphuka yabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Databricks in practice
Ibhange lakha i-chatbot elawulwayo ngosesho lwevekhtha ye-Mosaic AI ephendula imibuzo ngemibhalo yenqubomgomo yangaphakathi.
Ibhange lakha i-chatbot elawulwayo ngosesho lwevekhtha ye-Mosaic AI ephendula imibuzo ngemibhalo yenqubomgomo yangaphakathi Amathimba ngokuvamile athola imiphumela engcono uma echaza izinga eliphezulu ngaphambili, egcina indlela yokukhuphuka kwabantu yamacala abucayi, futhi alandelela kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Databricks in practice
Iqembu lezibalo lisebenzisa i-Delta Lake ukuze linikeze idatha engcolile amathebula athembekile, ohwebo kumadeshibhodi e-BI.
Iqembu lezibalo lisebenzisa i-Delta Lake ukuze linikeze ichibi ledatha elingcolile elithembekile, amathebula ohwebo amadeshibhodi e-BI Amaqembu ngokuvamile athola imiphumela engcono lapho echaza imingcele yekhwalithi ngaphambili, egcina indlela yokukhuphuka kwabantu yamakesi asemaphethelweni, futhi alandelele kokubili izinzuzo zokukhiqiza nezindleko zamaphutha ngokuhamba kwesikhathi.
Izingozi & Guardrails
Izimemezelo zokwethula zingase zeqe ukuzinza ekugelezeni komsebenzi wangempela wokukhiqiza.
Izintengo ze-API noma izinguquko zenqubomgomo zingaphula ukucabanga ngobusuku obubodwa.
Ukuncika komthengisi oyedwa kukhulisa izindleko zokukhiya nokufuduka.
Ukuqalisa Umhlahlandlela
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha.
Linganisa abahlinzeki usebenzisa eyakho imisebenzi namasethi edatha. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa.
Buyekeza ubumfihlo, ukuphepha, nemibandela yomthetho ngaphambi kokuhlanganiswa. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi.
Gcina uhlelo lokubuyela emuva kuwo wonke amamodeli noma abathengisi. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu.
Gada amanothi okukhululwa ukuze izinguquko zemephu yomgwaqo zingamangazi amaqembu. Phatha isinyathelo ngasinye njengesango lobufakazi: uma imibandela ingafinyelelwa, misa ukukhishwa, vala igebe, bese unweba ukusetshenziswa.