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Yekupedzisira Nongedzo kune Big Dhata Yekushandisa Yekuongorora

Boka remadhigidhi makuru e data asingakwanise kugadziriswa uchishandisa echinyakare komputa matekiniki inozivikanwa a Big Data. In the processing of Big Data various tools, techniques and frameworks are involved. Data creation, storage, retrieval, and analysis are related to big data which is outstanding in terms of volume, diversity, and rate. 

Rather than testing the individual features of the Software chigadzirwa kuyedzwa Big Data kunyorera zvimwe zvekusimbiswa kwaro kugadzirisa data. Performance and functional testing are the keys to Big Data kuedzwa.

Kuongorora kwekubudirira kwekugadziriswa kweterabytes yedata uchishandisa sumbu yezvinhu uye zvimwe zvinotsigira zvinoitwa neinjiniya mu Big Data kushandisa michina testing. As the processing is quite fast, high level of testing skills are required. Adding to this, in big data testing, data quality also plays an important role. Before you test the application, it is crucial to check the data quality as it is a part of the Database testing. Various traits such as conformity, accuracy, duplication, consistency, validity, data completeness, etc are also involved.

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Big Data Testing can be categorized into three stages:

Nhanho 1: Kugadziriswa Kwe data

The first stage of big data testing is also known as a Pre-Hadoop stage which comprises of process validation.

  1. Kugadziriswa kwedata kwakakosha kwazvo kuitira kuti iyo data iunganidzwe kubva kwakasiyana sosi seRBMS, weblogs nezvimwe zvichiongororwa zvobva zvawedzerwa kuhurongwa.
  2. Kuti uve nechokwadi chekuenderana kwedata iwe unofanirwa kuenzanisa sosi dhata neiyo data yakawedzerwa kune iyo Hadoop maitiro.
  3. Ita shuwa kuti iro rakakodzera data rinotorwa kunze uye rinotakurwa munzvimbo chaiyo yeHDFS

Nhanho 2: "Mepu Deredza" Kusimbiswa

Validation of “Map Reduce” is the second stage. Business logic validation on every node is performed by the tester. Post that authentication is done by running them against multiple nodes, to make sure that the:

  • Maitiro eMepu Kuderedza anoshanda zvakakwana.
  • Pane iyo data, iyo data kuunganidzwa kana yekuparadzanisa mitemo inoiswa.
  • Kugadzirwa kwemakiyi-kukosha mapara aripo.
  • Mushure meMepu-Kuderedza maitiro, Dhata yekusimbisa yaitwa.

Nhanho 3: Kuburitsa Kwekusimbisa Chikamu

The output validation process is the final or third stage involved in big data testing. The output data files are created and they are ready to be moved to an EDW (Enterprise Dhata Warehouse) kana chero imwe sisitimu yakadai sezvavanoda. Chikamu chechitatu chaive ne: 

  • Kuongorora iyo shanduko mitemo inonyatso shandiswa.
  • Muchirongwa chakanangwa, inoda kuona kuti data rakatakurwa zvinobudirira uye kuvimbika kwedata kunochengetwa.
  • Nekufananidza iyo yakanangwa data neiyo HDFS faira system data, inotariswa kuti hapana huori hwe data.

Uyewo verenga: Iyo Big Data Automation Inokanganisa Sei Dhata Sayenzi

Big Data Automation kuyedza: Iyo Yakadzika Mhando

Kuvakwa Kwekuvaka:

NaHadoop, makuru kwazvo mavhoriyamu e data anogadziriswa uye anonyanya zviwanikwa zviwanikwa. Saka kuyedzwa kwekuvaka kwakakosha kuti uve nechokwadi chekuti kubudirira kweiyo Big Data chirongwa. Kana iyo system isiriyo kana isina kugadzirwa nenzira kwayo inogona kukonzeresa kudzikisira kwekuita, uye magumo zvinodiwa hazvizadzikiswe. Saka iyo Performance uye Yakundikana-Pamusoro pekuyedza masevhisi anofanirwa kuitiswa munzvimbo yeHadoop.

Kuedzwa kwebasa rekupedzisa basa, ndangariro kushandiswa, dhata kubudikidza uye yakafanana system metric chikamu chekuyedza kuita. Chinangwa chikuru cheiyo failover bvunzo basa kuwana kuti kugadzirisa data inoitika isina chairo mamiriro ezvinhu ekundikana kwemashoko edhata

Performance Kuedza:

For Big Dhata, kuyedza kuita kunosanganisira zvinotevera:

  • Dhata Kuisa uye Kwese:  The tester verifies at this stage how the fast system can get through data from various data source. Identifying different message that the queue can process in a given time frame is involved in testing. It also comprises of how swiftly data can be inserted into a fundamental data store for example Rate of insertion into Mongo and Cassandra Database.
  • Data Processing:  Mune izvi, iyo inomhanyisa inosimbiswa nayo iyo mibvunzo kana mepu inoderedza mabasa inoitwa. Kuedza iyo kugadzirisa data mune yakasarudzika kana iyo yepasi pechitoro chedata ichigara mukati meiyo data maseti akaverengerwa mune izvi. Semuenzaniso, kumhanya kweMepu Kuderedza mabasa pane iri pasi peiyo HDFS.
  • Sub-Chikamu Chekuita: Multiple zvinhu zvinoshandiswa pakugadzira masisitimu aya uye zvakakosha kuyedza chimwe nechimwe chezvinhu izvi mukuparadzaniswa. Semuenzaniso, kuti inokurumidza meseji yakanyorwa sei uye kudyiwa, mepu inoderedza mabasa, kubvunza mashandiro, kutsvaga uye zvichingodaro.

Uyewo verenga: Iyo Big Data Automation Inokanganisa Sei Dhata Sayenzi

Big Data Kuedza: Kukosha Chaiko

Big Data kushandisa michina Testing helps one to find out that the data in hand is qualitative, precise and healthy. The data collected from a number of sources and channels is confirmed which helps in further decision making. Big Data Testing is quite important as there are a number of reasons for the same. Given below is the list of them.

1. Zviri nani Kuita Sarudzo 

Kana data rikaenda mumaoko echokwadi vanhu, inova yakanaka chimiro. Saka kana iwe ukawana iyo chaiyo mhando yedata newe pachako, chichava rubatsiro rwakakura kuita sarudzo dzakanaka. Izvo zvinobatsira kuongorora mhando dzese dzenjodzi uye chete iro data rinopa mukuita sarudzo kuita kunoitwa mukushandisa.

2. Data Akarurama 

Iyo data inofanirwa kuongororwa inofanira kuwanikwa uyezve unofanirwa kushandura iyo data kuita fomati yakarongedzwa isati yambocherwa. Kuve nerudzi rwakakodzera rwedata chikomborero kumabhizinesi sezvo zvichibatsira mukutora kwenzvimbo dzisina kusimba uye kugadzirira vanhu kurova makwikwi.

3. Zvirongwa Zvirinani uye Enhanced Market Zvinangwa

With the kushandiswa kweBig Data you can have better decision making a strategy or automate the decision making process. All the validated data should be collected, analyzed, understand the user behavior and make sure that all of them is present in the Software testing process so you can find out something when required. By looking at the information, you can optimize bhizimisi strategies by using big data muedzo.

4. Kuwedzera purofiti uye Kuderedza Kurasikirwa 

If the data is precisely analyzed then the loss in bhizimisi will be minimal. If the collected data is of poor quality, the bhizimisi will go through huge losses. Valuable data from structured and semi-structured information should be isolated so that no mistakes take place when there is customer dealing.

Une a Big Data project in head? Then reach out to us for a consultation.

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