Real Time – Days are gone when decisions were taken based on runtime occurrences. Now the business dynamics have changed. This change is very well exhibited in the form of a digital business regime. Businesses are using data analytics techniques to study market conditions as well as the strengths and weaknesses of their competitors. As of now and foreseeing the future, it won’t be wrong to say that data is as important, for companies, as their capital standings.
This much dependency on the data has forced them to take stern measures to ensure around the clock data availability. Rigorous data mapping and recordings are done continuously, and, in such conditions, if data access is lost, the grand purpose of data analytics dies off. In a nutshell, continuous data access is now the key to business growth.
The need for continuity becomes even more concerning when it comes to real-time data access and sharing. Basically, real-time data is defined as the type of data that is accessed or delivered right after being collected. There should not be any tolerance with regard to the information delay. Real-time data access and share models are widely in practice. Most of the smart systems, such as smart energy solutions, smart homes, smartwatches, and many others, use real-time data access and share models to perform their functions. Furthermore, weather forecast systems and navigation systems also use real-time data modelling to serve their due functions.
Tools for Real Time Data Sharing
Before going into the depth of tools, there is a need to not consider data streaming separate from data sharing. Data streaming is the phenomenon of transferring continuous data in real time at a steady and high speed. With the influx of cloud technology, IoT, and high-speed internet, there has been a great increase in data integration and analysis. Following are some of the popular real time data sharing tools:
Amazon Kinesis Firehose
Amazon Kinesis Firehose is a landmark AWS (Amazon Web Service) platform for the processing of big data in real time. This tool is capable of processing hundreds of terabytes of data in the fraction of hours from multiple streaming sources such as social media feeds, financial transactions, and operational logs.
Apache Flink
Apache Flink is the distributed data processing platform for accessing and analysing data residing in the Hadoop clusters. This tool is capable of handling both streaming as well as batch assignments. Here data streaming is being used as the default implementation, whereas batch related jobs are being run as a special case version for various data streaming applications.
Kafka
Kafka is capable of handling terabytes of data without any hassle. It has proven to be a great tool for real-time data analytics, streaming, and IoT monetisation. Kafka is highly reliable as it can support multiple subscribers at one time. It is so reliable that in case of failure, it automatically enters into the balanced state. Since it is a distributed system, therefore, it can be scaled easily and quickly.
Apache NIFI
Apache NIFI is a real-time data streaming tool, offering the rich features of integrated data logistics. This feature enables Apache NIFI to serve as a feasible platform for the automation of data sharing between the destination and the source. In addition to this, it also supports distributed sources, such as videos, social media feeds, files, log files, and many others. It can move data from any source to the desired destination. Furthermore, it is also equipped to trace the real-time data, just as UPS and FedEx run their delivery services.
Flume
Flume data streaming tool has established good connectivity, supported by the Hadoop. Flume requires the predefined targets known as sink and works in the way of one to one messaging. However, one thing must be kept in mind that it isn’t redundant. This is what makes up for one serious drawback of Flume which states that if a client is using this data streaming tool, then in case of failure, all data will be lost and there won’t be any replication events.
Cloud Technology
While the above-mentioned tools are quite sophisticated and are capable of complex jobs, it has been noted that these tools require a high level of expertise. Since the primary goal is to share and access data in real time, this purpose can also be served using cloud technology. The foremost benefit will be the easiness, security, and diversification of cloud technology. The admin or host can begin data sharing of the concerned device on the cloud storage network. Admin has the right to restrict its access to none, some people, or some groups. Then, anyone with the authorised credentials, log in to this cloud storage network and can access real-time data.
Since it is easy and more accessible, anyone with little knowledge can use it. There are various cloud storage providers offering different cloud storage pricing plans to assist users in terms of cloud storage and real time data accessing and sharing. The important thing to consider is that in order to look for best cloud-based real time data sharing tools, focus should not only be on the price spectrum, but network security should also be taken into account.
Conclusion
On a conclusive note, based on the usability and future scope of the above tools, it can clearly be seen that cloud technology presents as a major evolving and effective medium for real time data sharing. Since cloud-based services are easily configurable, accessible, and requires minimal resources, they are an ideal choice for real time data accessing. Furthermore, affordable storage plans and the flexibility to scale up or down as per requirement also makes cloud technology a rather feasible choice over other data streaming tools and techno
