{"id":48092,"date":"2018-01-29T04:34:12","date_gmt":"2018-01-28T23:04:12","guid":{"rendered":"http:\/\/blog.odango.com\/?p=48092"},"modified":"2023-02-04T09:30:36","modified_gmt":"2023-02-04T04:00:36","slug":"guide-on-data-streaming-in-big-data","status":"publish","type":"post","link":"https:\/\/asha24.net\/blog\/guide-on-data-streaming-in-big-data\/","title":{"rendered":"Guide on Data Streaming in Big Data"},"content":{"rendered":"\n[et_pb_section bb_built=”1″ next_background_color=”#000000″][et_pb_row][et_pb_column type=”4_4″][et_pb_post_title _builder_version=”3.0.106″ title=”on” meta=”off” author=”on” date=”on” categories=”on” comments=”on” featured_image=”off” featured_placement=”below” text_color=”dark” text_background=”off” title_font=”Titillium Web||||||||” title_font_size=”47″ title_font_size_tablet=”40″ title_font_size_phone=”35″ title_font_size_last_edited=”on|desktop” title_text_color=”rgba(0,0,0,0.8)” title_text_align=”left” text_shadow_horizontal_length=”0.08em” text_shadow_vertical_length=”0.08em” text_shadow_blur_strength=”0.08em” title_text_shadow_horizontal_length=”0.08em” title_text_shadow_vertical_length=”0.08em” custom_margin=”|||10%” \/][\/et_pb_column][\/et_pb_row][et_pb_row][et_pb_column type=”4_4″][et_pb_text _builder_version=”3.12.2″ text_font=”Titillium Web|300|||||||” text_font_size=”20″ text_font_size_last_edited=”on|desktop” text_text_color=”rgba(0,0,0,0.8)” text_line_height=”1.6em” max_width=”800px” custom_margin=”|||10%” text_line_height_last_edited=”off|phone” max_width_last_edited=”off|phone” custom_margin_last_edited=”off|desktop”]\r\n\r\n

\r\nWhat is Streaming in Big Data?<\/strong><\/p>\r\n

Streaming is a process in which big data<\/a> is instantly processed so as to extract real-time insights from that. The processing is done while the data is in motion.<\/p>\r\n

It is a speed-focused approach wherein a stream of data is processed. Processing streams can be done by processing \u201ctime windows\u201d of data in memory across the servers.<\/p>\r\n

This data demands to be processed sequentially on any record-by-record basis or sliding time windows basis and is used for a type of analytics like collecting, filtering, and then sampling.<\/p>\r\n

Streaming data<\/a> is an analytic computing platform which is focused mainly on speed. This is because applications require a continuous stream of unstructured data to be processed.<\/p>\r\n

Thus, data is continuously analyzed in memory before it is stored on a disk. Processing streams of data work by processing \u201ctime windows\u201d of data in memory across a cluster<\/a> of servers.<\/p>\r\n\r\n[\/et_pb_text][et_pb_image _builder_version=”3.17.6″ force_fullwidth=”on” src=”https:\/\/asha24.net\/blog\/\/wp-content\/uploads\/2018\/03\/download-121.png” \/][et_pb_text _builder_version=”3.12.2″ text_font=”Titillium Web|300|||||||” text_font_size=”20″ text_font_size_last_edited=”on|desktop” text_text_color=”rgba(0,0,0,0.8)” text_line_height=”1.6em” max_width=”800px” custom_margin=”|||10%” text_line_height_last_edited=”off|phone” max_width_last_edited=”off|phone” custom_margin_last_edited=”off|desktop”]\r\n\r\n

\r\nWhat are the benefits?<\/strong><\/p>\r\n

Streaming data processing is profitable in utmost scenarios where dynamic data is produced on a continual basis. It refers to the maximum of the industry segments and big data use cases.<\/p>\r\n

Companies usually start with simplistic applications of collecting system logs and rotating min-max computations.<\/p>\r\n

These applications are more advanced for real-time processing. Initially, applications may process data streams to generate simple reports and perform simple actions in reply, such as emitting alarms when key measures exceed specific thresholds.<\/p>\r\n

Eventually, these applications make more complicated forms of data analysis, like applying machine learning algorithms, and extract insights from the data.<\/p>\r\n

Some key principles applicable while streaming:<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 It is necessary to determine a buying opportunity at the point of engagement, either by social media or by messaging.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 Getting information about the movement around a reliable position.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 To be able to react to an event that needs an immediate acknowledgment, such as a service interruption or a change in a patient\u2019s medical status.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 Real-time consideration of values that depend on variables such as usage and available sources.<\/p>\r\n

Some real-time data streaming tools and technologies which are available:<\/p>\r\n

Flink:<\/strong><\/p>\r\n

Flink<\/a> is a streaming data flow engine which aims to offer facilities for distributed computation across streams of data.<\/p>\r\n

Treating batch processes as a specific case of data streaming, Flink is effective both as a batch and real-time processing framework.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 Flink is integrated with many other open-source data processing ecosystems.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 As compared to others, Flink is more stream-oriented.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 Highly Flexible Streaming Windows for Continuous Streaming Model.<\/p>\r\n

Storm:<\/strong><\/p>\r\n

The storm<\/a> is a distributed real-time computation system. Its applications are designed as directed acyclic graphs. Storm can be utilized with a programming language. It is known for processing highly scalable and provides processing job guarantees.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 The storm is discovered for processing very large bytes.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 It is scalable which works on equal considerations that run across a bunch of computers.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 The storm is reliable. It ensures that each segment of data will be processed at least once. Messages are only replayed when there are collisions.<\/p>\r\n

Kafka:<\/strong><\/p>\r\n

Kafka<\/a> is a distributed published subscribe messaging system which integrates data streams. It automatically balances consumers at the time of failure which is very much reliable in comparison to messaging services.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 It is Highly Reliable<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 This is capable to scale quickly and smoothly without obtaining any downtime.<\/p>\r\n

\u2022\u00a0\u00a0\u00a0 This gives High Performance for both publishing and subscribing.<\/p>\r\n

Difficulties in working with Streaming Data:<\/strong><\/p>\r\n

Streaming data processing requires two layers: a storage layer and a processing layer.<\/p>\r\n

The storage layer needs support to ordering and strong density to read and write large streams of data with fast speed, inexpensive, and repayable.<\/p>\r\n

The processing layer which is capable to use data from storage layer and guides to delete data which is unnecessary by running estimates on that data to the storage layer.<\/p>\r\n

We also ought to design for scalability and data durability in both the storage and processing layers. As a result, many platforms have developed the infrastructure needed to build streaming data.<\/p>\r\n

If you appreciate and want to know more about Big Data and\u00a0 Hadoop Distributed File System, click here<\/a> and read up<\/p>\r\n\r\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section bb_built=”1″ prev_background_color=”#000000″][et_pb_row][et_pb_column type=”4_4″][et_pb_team_member _builder_version=”3.0.106″ name=”Nitesh” position=”Author” facebook_url=”” twitter_url=”” google_url=”” linkedin_url=”” background_layout=”light” body_font=”Titillium Web||||||||” body_font_size=”16″ body_font_size_last_edited=”on|desktop” header_font=”Titillium Web|700|||||||” image_url=”https:\/\/asha24.net\/blog\/\/wp-content\/uploads\/2018\/03\/Nitesh.gif”]\r\n\r\n

\r\nBonjour. A curious dreamer enchanted by various languages, I write towards making technology seem fun here at Asha24.<\/p>\r\n\r\n[\/et_pb_team_member][\/et_pb_column][\/et_pb_row][\/et_pb_section]\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":4,"featured_media":48096,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[33],"tags":[],"class_list":["post-48092","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data"],"yoast_head":"\nGuide on Data Streaming in Big Data - Asha24 Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/asha24.net\/blog\/guide-on-data-streaming-in-big-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Guide on Data Streaming in Big Data - 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