Businesses are swamped with a flood of real-time data coming from sources such as websites, social media, digital activity records, various sensors, cloud technology, and numerous machines and gadgets. The list keeps getting longer. The need for immediate analysis and customer insights has pushed the growth of this kind of data sky-high. And with that comes an urgent need to swiftly gather valuable business insights from it.

But, it’s not always a smooth process to collect and use this continuous flow of data. We will explore some of the hurdles in dealing with this data and suggest a tool that can make the job easier. In this article, we will discuss all the challenges that need to be considered when dealing with data streaming.

Top 10 Challenges of Streaming Data That Enterprises Face in 2023

1. Handling Unbounded Data Streams

The main challenge when dealing with data streams is the immense amount and speed of the data that must be processed instantly. Systems and structures used for stream processing must manage an ongoing flow of data, which can be extensive and originate from multiple sources.

Moreover, these data processing systems also face the issue of limitless memory needs. This is due to the fact that data streams are ongoing and don’t have a set endpoint, meaning the system must have the capability to store data for an unlimited period of time, or at least as long as needed.

2. Navigating Stream Processing Complexity

One of the tough parts about processing data streams is the intricate system setup that’s needed. Systems for stream data processing are often spread out and have to manage a great number of simultaneous connections and data sources. This can be tricky to oversee and watch for any problems that might pop up, especially when the system is large.

Creating your own system for data streams can be quite a challenge due to the specific and complex structure it requires. First, you need a stream processor to take in the streaming data from the source. Next, you need a tool to examine or search the data, transform it, and deliver the results so that the user can react based on the information. Finally, you must find a place to store all this streamed data.

3. Adapting to Dynamic Streaming

Since data streams are constantly changing, the systems that process them must also be flexible to handle the shifting nature of the information, called concept drift. This situation can make some data processing methods ineffective, especially when there’s a time and memory constraint.

Additionally, large-scale data streaming inherently has changing characteristics, which makes it tough to determine upfront the optimal or desired number of groupings or clusters. This makes methods that need this information beforehand unsuitable for analyzing real-time data streams. In such situations, the streaming data needs to be examined on the fly, with a processing system that can scale up, allowing for decision-making in a tight timeframe and space.

4. Stream Query Processing

A processor for stream queries must be capable of managing multiple ongoing queries over a set of incoming data streams. This capability is crucial for serving a wide variety of users and applications. Two main factors that influence the efficiency of processing a bunch of queries over incoming data streams are the memory resources that the stream processing algorithm can use and the time the query processor needs to process each data item.

The first factor is a notable challenge when designing any system for data stream processing. This is because, in a typical streaming scenario, each ongoing query only gets limited memory resources. Consequently, the stream processing algorithm must be efficient in memory usage and speedy enough to process data at the same pace new data items are coming in.

5. Processing and Debugging Data Streams

Data streams that can be searched for information can come from a single source or many different ones. However, sorting and delivering this data can be tough because the data travels through a distributed system and typically needs to be processed in the right sequence.

In this situation, the data stream management system (DSMS) needs to choose between maintaining data consistency – meaning it will report an error if all the received data isn’t the most recent, or ensuring high availability of data – where all the data is included in all reads but might not be the latest.

When troubleshooting a data stream processing system, the first step is to replicate the system environment and test data. After that, a variety of debugging tools can be employed to keep track of the system’s performance and spot any slowdowns or errors.

It’s also crucial to have a way to compare the processed streaming data results with the expected outcomes to confirm the system is working correctly. This can be achieved by using a known dataset and running it through the system or by creating synthetic data that are known to meet certain criteria.

6. Ensuring DSMS Resilience

A crucial trait for any distributed system, including data stream management systems (DSMSs), is the ability to keep running properly even when individual components fail. There are two primary strategies to make them resistant to faults: duplication and logging, which can be merged to achieve high availability.

The duplication method entails creating several copies of data streams and processing them concurrently. On the other hand, the logging approach involves recording all processed data stream items. This log can then be utilized to rerun the stream and reprocess any items that went missing due to a malfunction.

7. Securing DSMS Data Integrity

Ensuring the accuracy of data processed by a data stream management system (DSMS) is crucial. One way to verify the data is by using a checksum or hash function, which compares the processed data with a known reliable value, thereby detecting any changes that might have occurred.

Digital signatures are another method where the stream producer signs each data item before sending it. Although this method requires more resources, it offers a higher degree of certainty that the data hasn’t been tampered with.

It’s also important to consider what someone can do with your IP when considering data security. For instance, if someone gets unauthorized access to your IP, they could potentially intercept the data streams, leading to loss of data integrity or even data theft. Therefore, encryption is often used to safeguard the confidentiality and integrity of data streams, particularly in sectors like finance, where security is of utmost importance.

8. Managing Processing Delays

Delays can pop up in data stream processing due to a range of reasons, such as network traffic jams, slow processing speeds, or pressure from subsequent operators. There are several methods to manage these delays based on the particular needs of the application:

• Implementing a watermark: This is a timestamp that signifies the maximum delay that’s acceptable. If this delay is exceeded, data items are discarded. This method is apt for applications where the data stream can be processed out of sequence.
• Storing the delayed data items and processing them when they finally arrive: This is crucial for applications where the data has to be processed in the order it was received. However, it could result in higher memory usage. If delays are too long, the buffers could become full and start losing data.
• Using a sliding window: This allows a certain amount of delay while still processing the data in sequence. It can be used to balance speed and accuracy (especially when used along with a watermark) by only considering the most recent data items within the window.

9. Handling Backpressure Issues

Backpressure is a situation that can occur in data stream processing when a data handler is processing data faster than its following operators can handle it. This situation can cause an uptick in delay and could eventually lead to data loss if the handler’s buffers start getting too full. Backpressure can be managed in several ways:

• Buffer the flow: This can be done by increasing the size of the buffers used to temporarily accommodate incoming data surges.
• Use a flexible operator: An operator that can automatically alter its processing speed based on the speed of the downstream operator. This can help avoid having to manually adjust the flow control.
• Partition of the data: Split the data stream into multiple streams and process them concurrently to boost the overall data handling capacity of the system.
• Discarding data items: If an operator can’t keep up with its incoming data stream, it might be necessary to discard some of the data to prevent losing everything (for instance, by sampling a percentage of data over specific timeframes). However, this should only be done as a last resort as it will result in a loss of accuracy.

10. Efficiency in DSMSs

Data stream management systems (DSMSs) need to be able to handle large amounts of data swiftly and cost-effectively. One method to ensure timely processing is operator pipelining, which involves linking multiple operators together. This allows each operator to start processing its input as soon as it’s ready without having to wait for the previous operator to complete.

Cost efficiency can be achieved by using the right blend of local and cloud-based resources. For instance, it might be wise to use local resources for initial data collection and processing and then cloud resources for storage and analysis. It’s also important to consider the costs of data stream processing when designing a DSMS, as many techniques used for computational efficiency can be resource-intensive.

For example, data skipping might require pricier storage or computing resources to monitor which data has been processed and which hasn’t. Meanwhile, a system that needs to process real-time streaming data might be more expensive than one that can handle some processing delays.

How to Overcome These Data Streaming Challenges?

While data stream processing comes with certain challenges, there are several strategies to tackle them effectively:

• Using a balanced combination of local and cloud-based resources and services
• Selecting appropriate tools for the task
• Establishing a sturdy infrastructure for overseeing data integration and processing
• Boosting efficiency with techniques like operator pipelining and data skipping
• Dividing data streams to enhance overall data handling capacity
• Automatically adjusting processing speeds with an adaptive operator
• Implementing effective flow control measures to prevent backpressure issues