Spark streaming is the hottest topic today. Apache Spark, a very popular and useful big data tool is creating quite a wave. Among all other industries that Spark serves, one industry that is catching everyone’s eye is the telecom sector. In these few months, Ksolves being a core Spark development company has seen immense growth in the projects leveraging Apache Spark and it has now become a trend among the telecom operators.
Today in this blog post, we will discuss the challenges faced by Artificial Intelligence in streaming data and how stream processing frameworks like Apache Spark and Flink can help in yielding better performance for telecom operators and bring solutions to these problems.
Spark Streaming Framework
Here we will talk about three phases as Input, Process and Output and also about various machine learning and data analytics techniques that are used at stream processing frameworks to enable control and optimization. Let’s discuss in detail-
As we know there are various input sources like file and database but things have changed now. Development in the current setup is all about how efficiently we can use Kafka with Spark Streaming platform. In addition, there has been a direct technique which resolves performance and duplication issues. We have to make sure to use a simple approach to maintain the distribution techniques. The telecom industry also requires great accuracy. This direct technique reduces the complexity to handle failures.
This phase involves three main pillars. Extraction, Transformation and Loading (ETL)
Remember the days when we used to practice stream processing. We used to discuss Bolts and our main task was to determine deployment topology. Then we started talking about micro-batches and how they are so fault-tolerant. We also used to talk about Lambda architecture. But, these days due to its immense popularity, many industries are shifting towards Spark streaming framework where even flat tables are treated as streaming data and processed.
In the telecom industry, we have several transformations like number mapping, cleaning replacing full values , etc. to perform these operations we use Apache Flink as there is no micro-batch processing. For operations like replacing missing values, means of last N values, and anything that requires historic data, we use Spark streaming with structural querying as our first choice.
For the telecom industry, we need to create both trained models and test data. Here we have found that hierarchical models are much easier to perform the incremental model updates. These data models can be easily deployed using Spark streaming frameworks. We know that Apache Flink is flexible and has a pure streaming nature, the implementation of reinforcement learning can be easily realized.
After the data processing layer, we can store data into various options such as permanent data store or distributed memory. We have tried to store data in Cassandra and found it to be very useful for fine-tuning to achieve consistency and availability. There is internal optimization which is done at the sink phase to ensure data locality.
In this blog we have addressed some of the concerns in Stream processing frameworks and the best practices of working. This discussion on the data pipeline on streaming systems can help in tuning your systems to yield better performance. Spark streaming is becoming popular day by day and is being utilized by the telecom sector to get the desired results. If you are looking for Spark services, you have landed on the correct place. Ksolves is one of the best Spark services providers in India and The USA with experienced developers that offers you the best solution with round the clock support.
If you wish to apply Spark streaming for your telecom industry, write to us in the comment section or give us a call right away!
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