Think of a massive river. Where does it originate, how does it move, and where does it end? Imagine the volume and velocity of the water that the river moves and you get an idea regarding the flow of elements. Data that grows exponentially has the same properties, movement, and flow as a fast-moving river. Data streaming is the tool that connects the data source to the end user by moving data in a fast, safe, and secure manner.
What We Mean by Data Streaming
Data streaming refers to the processing and movement of data in a continuous and never-ending stream. The audio or video data is endlessly moved in an unbroken stream without actually having to download the information to one’s hard drive. As explained by experts at Bedrock IT, Dynamic Data Management Services deploy cutting-edge technology to ensure that the viewer/listener can play video and audio files immediately as they arrive without having to wait for the entire file to be downloaded.
Streaming basically preserves the ownership of the content, the owner retaining a copy of the log file without it being downloaded to the viewer’s hard drive.
The Myriad Sources of Data That We Handle
The databases we are referring to have multiple sources. The recordings and readings emanating from connected devices, the transactional files generated by customers using web applications, the whole gamut of dealings that define e-commerce, the information generated through social media interaction, and the geospatial information that web mapping services use to generate and analyze locational data are significant examples.
Batch Processing vs. Live Data Streaming
There are two ways we can process large data. The conventional method is to process the data in batches, but this takes time and has to be repeated at frequent intervals – for example once every 24 hours. This process can become inefficient because if you’re examining data in batches at timed intervals, you could end up processing data that has gone stale and doesn’t truly reflect what sensors are conveying.
If we make data gathering and analysis a continuous never-ending process we are better equipped to handle real-time changes and respond accordingly. That’s the power of live data streaming.
The All-Round Significance of Data Streaming
Now that we have a basic idea about data streaming, we begin to realize how vital streaming is for gathering and processing data generated by devices connected to each other in the Internet of Things (IoT). The same applies to the wealth of data that we generate through traffic sensors, health monitoring devices, and transactional files that web applications create.
The significance of data streaming is that it enables you to aggregate data in real-time; data that can be filtered, sampled and correlated for more effective analysis. Data streaming gives you more profound insights into telemetering, monitoring of server activity, geolocation finding, and even the tracking of customer clicks and conversions that occur frequently.
5 categories of data streaming services
Live Streaming of Events
You film an event in real-time and stream images from the venue to the server so viewers can access the film on demand. It could be a wedding, a conference, a company PR event, or live news broadcasting.
On-Demand Streaming
Files such as movies, audio clips, business presentations, product demos, or news clips are pre-recorded and stored in a server to be streamed to viewers on demand.
Pay Per View (PPV)
This is a paid service where files are streamed to viewers and listeners on prepayment of a fee or subscription.
Internet TV Broadcasting and Radio Channels
Playlists are prepared in advance, files are meticulously classified and archived, live events are recorded, and then packaged and streamed as a TV or Radio service. Netflix and Radio Free Brooklyn, New York are popular examples.
Mobile Streaming
This is where media is streamed directly to handheld devices without the smartphone user having to download the entire file to the hard drive. This technology is opening the gateway to the globe’s largest captive audience and is the main reason for the success of media giants such as Netflix.
8 Real-Time Information Management Scenarios That Data Streaming Enables
The Stock Market
Investment portfolio managers track the stock market indices and use the information to identify and calculate the risk profile of stocks. High-risk volatile stocks can be off-loaded, and risk-averse commodities can be bought to restore balance to investment portfolios. This activity can be customized to the risk profiles of single and institutional investors.
Real Estate
A website handling real estate dealings can analyze the preferences indicated by customers to identify and recommend properties that are best suited to the customer, locating properties meeting the consumer’s needs.
Smart Power Grids
A traditional power grid can become a “smart grid” by identifying areas that are deficient or excess in power generating capacity. Excess power can be redistributed to power-deficient households thereby eliminating wastage and improving efficiency in power distribution.
Supply Chain Management
Devices with smart sensors connected via IoT and deployed in industries, farms, businesses, logistics shipping lines, and retail supply chains can pinpoint deficiencies and machine errors that can be rectified in real-time.
Solar Energy Distribution
The solar panels deployed in urban and rural households can be remotely monitored. The power generated beyond the household’s needs can be rerouted through a smart power grid to power-deficient areas. Automated payments will reimburse power supplying household in real-time.
Clickstream Recording
A website sourcing clickstream records form various social media platforms can enrich the same data with the demographic profiles of its key audience. This generates valuable information regarding the relevance of the website’s products, and that can be tailored for creating a better user experience.
Online Gaming Industry
The online gaming industry receives a boost if it can gather and analyze data generated by gamers interacting with online games. A real-time data streaming analysis can be programmed to offer automatic incentives to high-end gamers and engage such gamers more effectively.
Web Security Services
A web security service can analyze clickstream records and identify abnormal behavior, and the system can be programmed to beep an alert so that erratic behavior can be tracked, isolated, and cordoned for a security check.
4 Firefighters That Make Data Streaming Child’s Play
For a growing business, data acquired in real-time must be acted upon with minimum delay. Otherwise, the business loses its capacity to respond faster to ever-changing customer preferences. This is where data streaming helps by scanning sensor-sourced data, and logging files and price changes at a faster pace. This enables businesses to strategize customer conversions effectively. To keep pace with the breakneck speed of data aggregation and management, growing businesses turn to data streaming.
Amazon Kinesis Data Firehose
Firehose is an essential component of the Kinesis streaming data platform which automatically delivers data to the intended destination, and the service can be customized to address only the data that is suited to business operations. The entire system is cloud-based and can be scaled to stream any amount of information in real-time.
Apache Software Foundation’s Apache Kafka
Apache Kafka refers to an open-source data streaming platform that uses Scala and Java programming languages. The platform is capable of streaming large amounts of data in a scalable message queue which benefits enterprises that are growing fast.
Apache Flink Developed by Apache Software Foundation
If Apache Kafka provides significantly larger storage capacity enabling movement and processing of large data streams, Apache Flink makes you more efficient when you handle data streams in performance-intensive computing clusters. Both Kafka and Flink can be deployed together but act independently. Flink tackles data streaming more efficiently using resource managers such as Yet Another Resource Negotiator (YARN), Mesos, and Kubernetes.
Apache Storm
Created by Back Type and open sourced by Twitter, Apache Storm is a computation framework that uses Clojure programming language which has acquired a reputation for extensive use in machine learning. With Apache Storm real-time, analytics receives a boost riding on high-velocity data streaming.
Data Integration Through the Alooma Cloud
Streaming data may not be your sole data provider. Data can emerge from structured and unstructured sources, so you need resources that handle and integrate data arising from differing backgrounds. Alooma, like different rivulets merging into one mighty river, gathers data from all sources into one power-packed platform, allowing you to select and capture data streams of your choice.
The High Performing Integrator
Whether you’re tracking a handful of sensors or scaling to multiple layered devices within the IoT, Alooma integrates data and delivers results displaying a higher level of performance.
The Growth Engine
It’s easy to underestimate your data management requirements. Alooma solves the issue by rapidly scaling operations as your business needs grow.
Data Accuracy Guarantee
Alooma tackles data integrity issues, so you’re not weighed down by schema discrepancies, data duplication, and formatting issues.
Stronger Data Encryption
Whether data is in storage mode or in motion, businesses need stronger security protocols to safeguard sensitive information. Through Amazon Web Services’ protected data centers, Alooma provides a higher level of safety using a virtual private cloud where hosts are strongly firewalled.
Conclusion
Whether you’re a solo entrepreneur making a tentative landing in an emerging market, a startup creating a mark in a new niche, or a business that is exponentially scaling operations to a higher plane, data streaming displays the potential to energize intelligent strategies to maximize customer satisfaction and user experience.
For dealing, processing, and analyzing growing data pools in real-time, you need a data streaming environment that supports cluster computing and parallel processing in a massive way. Who can tell? Your cutting-edge research could lead to new medical discoveries. If you’re an insurance company wouldn’t you be interested in correlating traffic fatalities to geographical locales and weather conditions? Just as the applications of data streaming are endless, how you leverage the technology depends entirely on you.
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