Wahi a Statista, e 2025 ka nui o ka mākeke data nui e ulu i 175 zettabytes i hoʻohālikelike ʻia me 41 i 2019 (). No ka loaʻa ʻana o kahi hana ma kēia kahua, pono ʻoe e hoʻomaopopo pehea e hana ai me nā ʻikepili nui i mālama ʻia i ke ao. Ua hōʻuluʻulu ʻo Cloud4Y i kahi papa inoa o nā papa hana ʻenekinia 12 uku a manuahi e hoʻonui ai i kou ʻike ma ke kahua a hiki ke lilo i wahi hoʻomaka maikaʻi ma kāu ala i nā palapala hōʻoia ao.
Kauwehe
He aha ka mea ʻenekinia ʻikepili? ʻO kēia ke kanaka nona ke kuleana no ka hana ʻana a me ka mālama ʻana i ka hoʻolālā ʻikepili i loko o kahi papahana ʻepekema Data. Hiki i nā kuleana ke komo i ka hōʻoia ʻana i ka holo ʻana o ka ʻikepili ma waena o ke kikowaena a me ka noi, ka hoʻohui ʻana i nā polokalamu hoʻokele data hou, ka hoʻomaikaʻi ʻana i nā kaʻina ʻikepili kumu, a me ka hana ʻana i nā pipeline data.
Nui ka nui o nā ʻenehana a me nā mea hana e pono ai i ka ʻenekinia ʻikepili ke haku i mea e hana ai me ka computing cloud, data warehouses, ETL (extraction, transformation, loading), etc. Eia kekahi, ke ulu nei ka nui o nā mākau koi i nā manawa āpau no laila, pono e hoʻopiha pinepine ka ʻenekinia ʻikepili i kona ʻike ʻike. Loaʻa i kā mākou papa inoa nā papa no ka poʻe hoʻomaka a me nā ʻoihana ʻike. E koho i ka mea kūpono iā ʻoe.
1. ʻIkepili ʻEnekinia Nanodegree Certification ()
E aʻo ʻoe pehea e hoʻolālā ai i nā hiʻohiʻona ʻikepili, hana i nā hale kūʻai ʻikepili a me nā loko ʻikepili, hoʻokaʻawale i nā pipeline ʻikepili a hana pū me nā pūʻulu o nā waihona. I ka pau ʻana o ka papahana, e hoʻāʻo ʻoe i kāu mau mākau hou ma ka hoʻopau ʻana i kahi papahana Capstone.
Duration: 5 mahina, 5 hola o ka pule
'Ōlelo: Pelekania
kuai: $ 1695
ilikai: mua
2. E lilo i palapala hōʻoia ʻikepili ()
Ke aʻo nei lākou mai nā kumu. Hiki iā ʻoe ke holomua i kēlā me kēia ʻanuʻu, me ka hoʻohana ʻana i nā haʻiʻōlelo a me nā papahana lima e hana ai i kāu mau akamai. Ma ka hopena o ke aʻo ʻana, e mākaukau ʻoe e hana me ML a me ka ʻikepili nui. Manaʻo ʻia e ʻike iā Python ma ka liʻiliʻi loa.
Duration: 8 mahina, 10 hola o ka pule
'Ōlelo: Pelekania
kuai😕
ilikai: mua
3. E lilo i mea ʻenekinia ʻikepili: ʻike i nā manaʻo ()
E hoʻomohala ʻoe i ka ʻenehana ʻikepili a me nā mākaukau DevOps, e aʻo pehea e hana ai i nā noi Big Data, e hana i nā pipeline data, e hana i nā noi i ka manawa maoli me ka hoʻohana ʻana iā Hazelcast a me kahi waihona..
Duration: Pili ia oe
'Ōlelo: Pelekania
kuai: mahina mua - manuahi
ilikai: mua
4. Nā Papa Hana ʻIkepili ()
Eia kahi papahana o nā papahana e hoʻolauna iā ʻoe i ka ʻenekinia data a aʻo iā ʻoe pehea e hoʻomohala ai i nā hopena analytical. Hoʻokaʻawale ʻia nā papa i nā ʻāpana e pili ana i ka pae paʻakikī, no laila hiki iā ʻoe ke koho i kekahi e like me kāu pae ʻike. I ke aʻo ʻana e aʻo ʻoe e hoʻohana iā Spark, Hadoop, Azure a hoʻokele i ka ʻikepili hui.
Duration: Pili ia oe
'Ōlelo: Pelekania
kuai: pili i ka papa koho
ilikai: hoʻomaka, waena, holomua
5. ʻEnekinia ʻIkepili ()
Pono kēia papa inā loaʻa iā ʻoe ka ʻike me Python a makemake ʻoe e hoʻonui i kou ʻike a kūkulu i kahi ʻoihana ma ke ʻano he ʻepekema data. E aʻo ʻoe pehea e kūkulu ai i nā pipeline data me ka hoʻohana ʻana i ka Python a me nā pandas, e hoʻouka ana i nā pūʻulu ʻikepili nui i kahi waihona Postgres ma hope o ka hoʻomaʻemaʻe, hoʻololi a hōʻoia.
Duration: Pili ia oe
'Ōlelo: Pelekania
kuai: pili i ka palapala kau inoa
ilikai: hoʻomaka, waena
6. ʻEnekinia ʻIkepili me Google Cloud ()
E kōkua kēia papa iā ʻoe e loaʻa nā mākau e pono ai ʻoe e kūkulu i kahi ʻoihana ma ka ʻikepili nui. No ka laʻana, hana pū me BigQuery, Spark. E loaʻa iā ʻoe ka ʻike e pono ai ʻoe e hoʻomākaukau no ka hōʻoia ʻana i ka ʻoihana Google Cloud Professional Data Engineer.
Duration: 4 mahina
'Ōlelo: Pelekania
kuai: manuahi i kēia manawa
ilikai: hoʻomaka, waena
7. ʻIkepili ʻIkepili, ʻIkepili Nui ma Google Cloud Platform ()
He papa hoihoi e hāʻawi ana i ka ʻike kūpono o nā ʻōnaehana hoʻoili ʻikepili ma GCP. Ma ka papa, e aʻo ʻoe pehea e hoʻolālā ai i nā ʻōnaehana ma mua o ka hoʻomaka ʻana i ke kaʻina hana. Eia hou, e kālailai ʻoe i ka ʻikepili i kūkulu ʻia a i hoʻonohonoho ʻole ʻia, e hoʻopili i ka scaling auto, a e hoʻopili i nā ʻenehana ML e unuhi i ka ʻike.
Duration: 3 mahina
'Ōlelo: Pelekania
kuai: manuahi i kēia manawa
ilikai: hoʻomaka, waena
8. UC San Diego: ʻIkepili Nui ()
Hoʻokumu ʻia ka papa ma ka hoʻohana ʻana i ka Hadoop a me Spark framework a me ka hoʻohana ʻana i kēia mau ʻenehana data nui i ke kaʻina ML. E aʻo ʻoe i ke kumu o ka hoʻohana ʻana iā Hadoop me MapReduce, Spark, Pig, a me Hive. E aʻo pehea e kūkulu ai i nā hiʻohiʻona wānana a hoʻohana i ka ʻikepili kiʻi e hoʻohālike i nā pilikia. E ʻoluʻolu, ʻaʻole pono kēia papa i kahi ʻike polokalamu.
Duration: 8 mahina 10 hola o ka pule
'Ōlelo: Pelekania
kuai: manuahi i kēia manawa
ilikai: mua
9. Hoʻopili i ka ʻikepili nui me Apache Spark a me Python ()
E aʻo ʻoe pehea e hoʻohana ai i ke kahawai kahawai a me nā papa ʻikepili ma Spark3, a loaʻa ka ʻike pehea e hoʻohana ai i ka lawelawe ʻo Amazon's Elastic MapReduce e hana pū me kāu hui Hadoop. E aʻo e ʻike i nā pilikia i ka nānā ʻana i ka ʻikepili nui a hoʻomaopopo i ka hana ʻana o nā hale waihona puke GraphX me ka ʻikepili pūnaewele a pehea ʻoe e hoʻohana ai iā MLlib.
Duration: Pili ia oe
'Ōlelo: Pelekania
kuai: mai 800 rubles a i $149,99 (ma muli o kou laki)
ilikai: hoʻomaka, waena
10. Polokalamu PG ma Big Data Engineering ()
Hāʻawi kēia haʻawina iā ʻoe i ka ʻike pehea e hana ai ʻo Aadhaar, pehea e hoʻopilikino ai ʻo Facebook i ka nūhou, a pehea e hoʻohana ʻia ai ʻo Data Engineering ma ka laulā. ʻO nā kumuhana koʻikoʻi ka hana ʻikepili (me ka hoʻoili ʻana i ka manawa maoli), MapReduce, nā ʻikepili nui.
Duration: 11 mahina
'Ōlelo: Pelekania
kuai: ma kahi o $3000
ilikai: mua
11. ʻEpekema ʻIkepili ʻOihana ()
E aʻo ʻoe i ka papahana ma Python, e aʻo i nā ʻōnaehana no ka hoʻomaʻamaʻa ʻana i nā neural network Tensorflow a me Keras. E aʻo i ka ʻikepili MongoDB, PostgreSQL, SQLite3, e aʻo e hana pū me nā hale waihona puke Pandas, NumPy a me Matpotlib.
Duration: 300 mau hola hoʻomaʻamaʻa
'Ōlelo: Lukia
kuai: mua ʻeono mahina manuahi, a laila 3900 rubles i kēlā me kēia mahina
ilikai: mua
12. ʻEnekinia ʻIkepili 7.0 ()
E loaʻa iā ʻoe kahi noiʻi hohonu o Kafka, HDFS, ClickHouse, Spark, Airflow, lambda architecture a me kappa architecture. E aʻo ʻoe pehea e hoʻopili ai i nā mea hana i kekahi, e hana ana i nā pipeline, e loaʻa ai kahi hopena baseline. No ke aʻo ʻana, pono ka ʻike haʻahaʻa o Python 3.
Duration: 21 mau haʻawina, 7 pule
'Ōlelo: Lukia
kuai: mai 60 a 000 rubles
ilikai: mua
Inā makemake ʻoe e hoʻohui i kahi papa maikaʻi ʻē aʻe i ka papa inoa, hiki iā ʻoe ke kāpae i nā manaʻo a i ʻole ma kahi PM. E hōʻano hou mākou i ka leka.
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Kau inoa i kā mākou -channel i ʻole e poina i ka ʻatikala aʻe. ʻAʻole mākou e kākau ʻoi aku ma mua o ʻelua manawa i ka pule a ma ka ʻoihana wale nō. Hoʻomanaʻo pū mākou iā ʻoe ma Mei 21 ma 15:00 (Moscow manawa) e paʻa mākou ma ke kumuhana "Ka palekana ʻike ʻoihana ke hana mamao." Inā makemake ʻoe e hoʻomaopopo pehea e pale ai i ka ʻike koʻikoʻi a me ka ʻoihana ke hana nā limahana mai ka home, e hoʻopaʻa inoa!
Source: www.habr.com