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As data scientists, we are used to working with data that is either in batches or stored in a database. However, with the advent of big data and streaming data, we now need to be able to work with data as it arrives, often in real-time. This blog post will discuss some tips for working with streaming data successfully. Streaming data is becoming increasingly popular due to the ever-growing volume of data and the need for real-time analysis. You can look more into data streaming applications and uses here: https://www.rudderstack.com/product/event-stream/ 

Understand The Different Data Streaming Models 

There are several different models for streaming data, and it is essential to understand the differences between them to choose the right one for your use case. The most common models are the push model and the pull model. 

In the push model, data is pushed from a source to a destination as it becomes available. This model is often used when data needs to be processed in real-time, such as for monitoring or analytics applications. 

In the pull model, data is pulled from a source by a destination when needed. This model is typically used when there is less need for real-time processing and more flexibility when the data is accessed. 

How To Choose The Right Streaming Model For Your Use Case 

The push model is generally used for real-time processing applications, such as monitoring or analytics. The pull model is typically used when there is less need for real-time processing and more flexibility when the data is accessed. 

It would help to consider the volume of data, the frequency of updates, the latency requirements, and the resources required when choosing a streaming model. For example, if you are working with a large volume of updated data, you will likely need to use the push model so that data can be processed as soon as it becomes available.  

On the other hand, if you are working with a smaller volume of updated data less frequently, you may be able to use the pull model and access the data on a convenient schedule. 

Latency is also an essential factor to consider. The push model generally has lower latency since data is processed as soon as it becomes available. The pull model typically has higher latency since data is only processed when it is pulled from the source.  

Finally, you should also consider the resources required for each model. The push model often requires more resources since data needs to be processed in real-time. The pull model typically requires fewer resources since data can be accessed on a convenient schedule for you. 

Use The Appropriate Data Processing Techniques For Streaming Data 

There are a variety of data processing techniques that can be used for streaming data. The most common methods are batch processing, stream processing, and micro-batching. 

  1. Batch processing is the process of collecting data over some time and then processing it all at once. This technique is often used when there is no need for real-time results.  
  1. Stream processing is continuously collecting and processing data as it arrives. This technique is often used when there is a need for real-time results or near-real-time results. 
  1. Micro-batching is collecting data in small batches and then processing them all at once. You can use this one when there is a need for real-time results or near-real-time results, but the volume of data is too large to process in a single batch. 

It would help if you chose the data processing technique that best fits your use case. If you need real-time results, you should use stream processing or micro-batching. If you do not need real-time results, you can use batch processing.  

It is also essential to consider the resources required for each technique. Batch processing often requires more resources since all of the data must be processed at once. Stream processing and micro-batching typically require fewer resources since data can be processed in small batches. 

Finally, you should also consider the latency requirements for your application. Batch processing has higher latency since all of the data must be collected before processing. Stream processing and micro-batching have lower latency since data is processed as soon as it arrives.  

Handling Errors And Unexpected Events In Streaming Data Applications 

You can do a few things to handle errors and unexpected events in streaming data applications.  

The first thing you can do is to use an error handling operator. This operator will catch any errors and route the data around the error.  

The second thing you can do is to use an event log. This log will keep track of all of the events that have occurred in your application. You can use this log to debug your application and to determine what went wrong when an error occurred.  

Finally, you can use a recovery mechanism. This mechanism will try to recover from any errors that occur. For example, if there is a network error, the recovery mechanism may try to reconnect to the network or may try to use a different network. 

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