Top 8 Real-Time Data Streaming Tools and Technologies – Brief Survey

Top 8 Real-Time Data Streaming Tools and Technologies – Brief Survey

Did you know that the big data analytics is all set to reach by $103 billion by 2023? There are so many Real-Time Data Streaming Tools that are now being introduced that more than 90% of the data has been created in just 2017 and 2018.

We are producing an immense amount of data and then as there is this change in technology over the years, there are so many Real-Time Data Streaming Technologies now.

Entrepreneurs are now adopting these real-time data streaming tools to make their business marketing campaigns easier. It is also easy for financial trading or marketing messages. Can you believe Netflix almost saved $1 billion by using these data streaming platforms?

Well, now they do seem interesting, don’t they?

There are several Real-Time Data Streaming Tools which can help your enterprise only if you know how and what to do about it. You need to know how you are selecting it and then some experts can give advice as to which are the top 8 real-time data streaming tools and technologies.

These Real-Time Data Analysis tools can help you with the saving of resources.

What is Real-time Data Streaming?

Well, Real-Time Data Streaming is the process which is used for analyzing a large amount of data as it is produced. You can extract all the valuable information for the enterprise when it is stored or made.

It can also be explained that these help in analyzing the data produced in a real-time and live environment. They can use real-time analytics for reporting the current data and the historical one. They can also use to receive all the alerts on the basis of certain parameters.

Here are the few top real-time data streaming tools that could interest you

Real-Time Data Ingestion Tools

When you are streaming through a data lake, it is considering the streaming in data and can be used in various contexts. Thus, when you are executing the data, it follows the Real-Time Data Ingestion rules.

For example, the data streaming tools like Kafka and Flume permit the connections directly into Hive and HBase and Spark.

This can help to data ingest and process the whole thing without even writing to the disk. Hence, the robust functionality is followed here which is the principle of data lake architecture.

Flume

  • It is known to be sable and has well-established connectivity that is supported by Hadoop. It requires a predefined target called sink and is one to one messaging. Apart from this, it is not redundant.
  • It has been the most of the supported in all of the commercial Hadoop distributions. This is a very attractive and essential feature. Also, Kafka and Flume can have connections to each other.
  • One of the drawbacks of the Flume data streaming tool is that if it fails, data will be lost and hence there won’t be any events replication.

Kafka

  • It is highly redundant and available everywhere. It is quite scalable and has this feature of one to many messaging.
  • Though it is redundant, it is a new technology than others which makes it a bit hard to operate. Apart from that, it lacks the commercial support the other data streaming tools have garnered. It also lacks the built-in connectors which are important.
  • Kafka is more of the broadcast where it is quite scalable than Flume. Kafka also has a certain mechanism for features like fault tolerance and the data redundancy. It is like when one Kafka agent goes down, then someone else re-broadcasts the topics. In general, you cannot expect the same commercial connectivity lie Flume.

Kafka and Flume are not mutually exclusive and they are like sink and source for Kafka. You can link both of them even in the large scale production systems. For the small scale systems, it is best if you choose one system based on your current needs and expected needs.

Apache NIFI

  • Apache NIFI is another Real-Time Data Streaming It has integrated data logistics features which make it the platform for automating the data movement between different sources and destinations.
  • NIFI also supports the distributed sources which can be like files, social feeds, log files, and videos, etc. It can move the data from any source to any destination. It can also trace the data in real-time and is just like how FedEx, UPS delivery services work.

Real-time Data Processing and Streaming Tool

This is important when you have a stream of data that is headed for your data lake. There are so many options for data processing and with Flume, write directly to the HDFS, with built in the sinks.

Storm

  • A storm is another Real-Time processing framework. The storm is known to have a few drawbacks such is not latent enough and also that it is only suited to that kind of data which is ingested as one entity.
  • The storm has been used in a lot of industries at the production stage and has got great Hadoop support. Storm, however, does have a lack of direct YARN support. It can be run on Mesos or a slider process on the YARN. It cannot guarantee that the data shall be processed only once.

Spark

  • Spark is another Real-Time Data Analytics. It is also known for its in-memory processing capabilities and the Spark streaming component has the working on the same basis. It is not actually a real-time system but its processes in the micro-batches at a defined interval.
  • When it has some latency, it makes sure that the data is processed in a trustworthy manner. There is this traditional Spark processing which can be integrated with the newer version to make development easier and better.

Flink

  • Flink is like a hybrid between the Spark and Storm. Spark is the batch framework and it doesn’t have any real streaming support. Flink has frameworks for both streaming and batch processing. This allows Flink to be low latent yet have the data fault tolerance of Spark.
  • It can also have several user-configurable windowing and redundant settings. But with Flink, there is a problem with the lack of having enough existing production deployment. It does not have the native commercial support that a lot of other Hadoop distributions have.

Kinesis

  • It is quite similar to Kafka. It is streaming data tool and it has the enterprise-class solution. Kinesis was by Amazon and it composes of shards.
  • In Kafka, you call it as partitions. Kinesis is great if your company want to take the full advantage of real-time data analytics.
  • In fact, it is scalable, cloud-based services which have the capability of allowing you to do real-time data streaming and processing. This is all about real-time data and it follows the Real-Time processing data ingestion. It helps to analyze the real-time data.

Samza

  • Apache Samza is one of the best real-time stream processing frameworks which can be worked out on similar lines as the Kafka messaging tool. It is designed to match with the unique architecture of Kafka and it guarantees any kind of fault tolerance. Apart from just fault tolerance, it can also work against buffering and state storage.
  • Just like a few other real-time data streaming tools, Samza uses YARN for its resource negotiation too. It can by default rely on the rich features that are built into YARN. There is a definite requirement of a Hadoop cluster in this streaming technology. Samza can offer you to give at least one delivery guarantee.
  • But the downside of having Samza is that it does not offer any reliability and recovery accuracy. It also has high-level abstractions which can be easier to work with. Samza can work much faster than Storm that has been getting commercial support from Hadoop for a long time.
  • Another downside of this framework is that it supports JVM language which may not have much flexibility.
  • Samza is loaded with simple API and it can provide a simple call back based message API when you compare it to other frameworks.
  • Apart from that, it manages things like snapshotting and restoration of the stream processor’s rate. It has high fault tolerance and it works with YARN when a machine in the cluster fails.
  • Samza also has great scalability and is distributed on all levels.

Advantages of having Real-Time Data Streaming technologies around

With so many Real-Time data analytics tools above, we know for a fact that they are quite essential for business development.

They help us in data visualization and give great business insights and security. You can take an example of fraud detection. When you have these real-time data streaming tools, they can immediately detect the fraud.

What is Big Data Techniques?

When you use a common tool to work on Real-Time Data Streaming and data analytics from data sets, then they are done by using different techniques. These big data analytics techniques add a lot of business value to the firm.

Hence, when customer data is mined, it is used to determine the segments which are most likely to react to the offer. If you are a Web Development Company, you could foray into the big data analytics field. You have techniques like regression analysis, segregation analysis, etc.

Conclusion

Hence, this is all about real-time data streaming tools. We can now conclude that a real-time data analytics platform has steps like real-time stream sources, real-time ingestion, real-time stream storage, and real-time stream processing.

If you are an App Development company, you can get to make an app which has information about all the services so that it is easy for the people to know and make use. Figure it out what works best for you and then choose the real-time data streaming technology you are comfortable with.

What is Real-Time Streaming Data?

Real-time streaming data applications processes by which big volumes of data are neatly processed. They are done quickly so that when an organization is trying to extract the data, it can be able to react to the changing condition in real-time.

Which Tool is used for capturing Streaming Data?

There are different capturing tools like Apache Storm, Apache NIFI, Data Torrent, etc. These are all real-time data streaming tools. You have others too like Flume, Sqoop, Samza, White Elephant that are real-time streaming processing tools.

What is real-time processing with Examples?

When you talk about real-time data processing, it is the execution of data in a short period. It will provide a very fast output. Now, some of the good real-time processing examples are the bank ATMs, traffic control systems, mobile devices. The real-time data processing is known as stream processing.

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