{"id":22121,"date":"2018-08-08T07:45:24","date_gmt":"2018-08-08T07:45:24","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=22121"},"modified":"2018-08-08T07:45:24","modified_gmt":"2018-08-08T07:45:24","slug":"apache-avro-features","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/apache-avro-features\/","title":{"rendered":"12 Best Apache Avro Features | Reasons Why Avro is Popular?"},"content":{"rendered":"

In this Apache Avro tutorial<\/strong>, we will discuss Avro Features. These Avro features are the basic reasons which make\u00a0it so popular. So, in this article\u00a0of Features of Avro, we will see the reasons for Avro Popularity in detail.<\/p>\n

So, let’s start exploring Avro Features.<\/p>\n

\"Avro<\/a>

12 Best Apache Avro Features | Reasons Why Avro is Popular?<\/p><\/div>\n

Features of Avro<\/span><\/h2>\n

So, here is the list of the reasons why Avro is a good choice. Let’s discuss these Avro Features one by one:<\/span><\/p>\n

a. Schema Evolution<\/strong><\/h4>\n

While data is written or read, Avro does requires schemas. However, the best part is we can use different schemas for serialization and deserialization purpose. Though Avro handles the missing\/extra\/modified fields.<\/span><\/p>\n

b. Code Generation <\/strong><\/h4>\n

Avro has the ability to generate code although it is not mandatory make Avro works.<\/span><\/p>\n

c. Versioning<\/strong><\/h4>\n

Due to serialization schemas in Avro, it is easy to achieve versioning by defining correctly read schema.<\/span><\/p>\n

d. Untagged Data<\/strong><\/h4>\n

As Avro offers a schema with binary data it permits that it is possible to write each datum without the overhead. So, that results in more compact data encoding, as well as faster data processing.<\/span><\/p>\n

e. Language Support<\/strong><\/h4>\n

While it comes to language support, there are so many implementations, such as C, C++, C#, Java<\/strong>, PHP, Python<\/strong> as well as Ruby.<\/span><\/p>\n

f. Transparent Compression<\/strong><\/h4>\n

We don\u2019t need to compress Avro files manually because the framework itself does some size optimizations.<\/span><\/p>\n

g. Dynamic Typing<\/strong><\/h4>\n

Basically, serialization and deserialization occur without even code generation. So, Dynamic typing complements the code generation, which is present in Avro especially for statically typed languages as an optional optimization.<\/span><\/p>\n

h. Splitability<\/strong><\/h4>\n

Avro files are Splittable in nature, it is\u00a0possible\u00a0because of synchronization markers which are used between blocks. Basically, these blocks allow efficient splitting of files for MapReduce processing.<\/span><\/p>\n

i. Native Support in MapReduce<\/strong><\/h4>\n

As input, output or intermediate files, we can use Avro files in each step of MapReduce jobs<\/strong>. Moreover, all useful classes, like readers or reducers, are in the package: org.apache.avro.mapred<\/strong>.<\/span><\/p>\n

j. Avro Schema Provides Future-Proof Robustness<\/strong><\/h4>\n

Avro Schemas offers future proof of our data and also make it more robust by supporting all past (older Consumers), future (Big Data<\/strong>) as well as current use cases. Basically, for making the data robust for streaming architectures, the schema with its support for evolution is essential. <\/span><\/p>\n

k. JSON Libraries<\/strong><\/h4>\n

In all the languages those already have JSON libraries, Avro has the facility for implementation, due to its JSON defined Avro schemas<\/strong>.<\/span><\/p>\n

l. Rich Data Structures<\/strong><\/h4>\n

Apache Avro provides rich data structures along with compact, fast, binary data format. Because of its rich data structures, it can create a record which may consist of an array, an enumerated type, and a sub-record.<\/span><\/p>\n

So, this was all in Avro features. Hope you like our explanation.<\/p>\n

Conclusion: Avro Features<\/span><\/h2>\n

Hence, we have seen 12 best Avro features, those make it stand out from the crowd and also makes it Popular. However, to learn Avro in more detail, stay tuned at Data Flair. Learn more Learn well!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

In this Apache Avro tutorial, we will discuss Avro Features. These Avro features are the basic reasons which make\u00a0it so popular. So, in this article\u00a0of Features of Avro, we will see the reasons for...<\/p>\n","protected":false},"author":7,"featured_media":22157,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[737,739,1286,1321,2624,4098,4579,7821,8072,11604,12627,13202,14902,15167,15366,16129,16130],"class_list":["post-22121","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-avro","tag-apache-avro","tag-apache-avro-features","tag-avro-features","tag-avro-tutorial","tag-code-generation","tag-dynamic-typing","tag-features-of-avro","tag-json-libraries","tag-language-support","tag-rich-data-structures","tag-schema-evolution","tag-splitability","tag-transparent-compression","tag-untagged-data","tag-versioning","tag-why-avro-is-a-good-choice","tag-why-avro-is-so-popular"],"yoast_head":"\n12 Best Apache Avro Features | Reasons Why Avro is Popular? - DataFlair<\/title>\n<meta name=\"description\" content=\"Avro Features,Features of Avro,why Avro is So popular,reasons for Apache Avro is a good Choice,Avro & JSON,Avro Schema,JSON Libraries,Untagged Data\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/apache-avro-features\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"12 Best Apache Avro Features | Reasons Why Avro is Popular? 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