Challenges of Data Stream Processing: Big Data Streams 1:1

Data streaming is a relatively new technology that is gaining in popularity due to the ever-growing demand for big data analytics solutions. However, streaming data comes with its own set of unique challenges that must be considered by data engineers when implementing and scaling stream processing solutions.

In this blog post, we will list and explain all the different challenges that need to be considered when dealing with streaming data. Let’s get started.

The Shift to Stream Processing Systems

The overwhelming amount of information being collected by data scientists nowadays in response to a growing demand for faster analytics and customer insights makes streaming data access an important case for close cooperation between IT and business. Large volumes of data are ingested from a variety of sources, including social networks, orders, clickstreams, sensor data, cloud computing solutions (ML or data analytics systems), and Internet of Things (IoT) devices.

Real-time data analysis is therefore required to extract valuable information that can help businesses make decisions. That’s where the stream model comes in as an approach to processing data either as it is ingested or very soon after when it is required in near-real time for applications such as log analysis, financial fraud detection, real-time customer behavior analytics, network monitoring, and security, and IoT devices monitoring.

Steam processing has largely replaced batch processing in most applications for which real-time data analysis is required, now that computing power is sufficient to handle large amounts of data. The latter method waits to group data and handles them together in batches, but the greater availability of resources has allowed for the relatively recent shift to the former.

The advantages of stream processing compared to batch processing are its low latency (the time delay between an event occurring and the stream processor reacting to it), scalability (reacting to changing requirements, e.g., data access and volume), flexibility (easy processing of each different data format, accommodate changes to the data sources), lower costs, and consistent output to a data lake (data can be reprocessed if necessary to maintain accuracy).

Data Stream Model

Data stream management systems (DSMSs) are a type of stream processing system that captures, stores, analyzes, and delivers data from continuous, fast-moving data sources called data streams. A DSMS processes input streams to generate modified output streams.

Data streams have a few key characteristics that distinguish them from other types of data, including that they are:

• continuous – data streams are generated continuously, and there is no defined end
• unbounded – there is no limit to the amount of generated data streaming
• time-sensitive – data streams are processed as they are generated in near-real time to support instant analytics
• high-volume – often generated at a very high rate that makes them challenging to process
• heterogeneous – data streams can come from a variety of sources and be of different types

The defining characteristic of a DSMS is its ability to handle unbounded streams of data by continuously receiving inputs and generating outputs. DSMSs are designed to address the challenge of real-time big data stream analysis by providing a scalable, fault-tolerant platform that can handle a high volume of data with low latency.

There are two main models for DSMS: the shared-state model and the message-passing model.

The shared-state model is the most popular and widely used. In this model, all stream processors share a common state that is updated as new data comes in. The shared state can be either global or local. A global shared state is one that all stream processors have access to, while a local shared state is specific to a particular stream processor.

The message-passing model is less commonly used but has some advantages over the shared-state model. In this model, each stream processor has its own private state that it does not share with other stream processors. Stream processors communicate with each other by passing messages. This model is more difficult to implement but has the advantage of being more scalable and easier to manage when there are a large number of stream processors. On the other hand, it has greater complexity and can be more difficult to program.

Challenges of Processing Streaming Data

Data Volume and Unbounded Memory Requirements

The biggest difficulty when processing data streams is the sheer volume and velocity of data that needs to be processed in real-time. Stream processing frameworks and architectures need to handle a continuous data stream that can be very large in size and come from various sources.

Data processing infrastructures also have to deal with unbounded memory requirements. This is because data streams are continuous, and there is no defined end, so the system needs to be able to store data indefinitely or at least for as long as necessary.

Architecture Complexity and Infrastructure Monitoring

Another challenge of data stream processing is the complexity of the architecture and infrastructure required. Stream data processing systems are often distributed and need to be able to handle a large number of concurrent connections and data sources, which can be difficult to manage and monitor for any issues that may arise – especially at scale.

Data streams rely on a specific type of complex architecture, so building your own can be a daunting task. First, a stream processor is required to ingest the streaming data from the input source; then, you need a tool for analyzing or querying them, performing data transformation, and outputting the results so that the user can respond accordingly to the information. Finally, the streamed data must be stored somewhere.

Keeping Up With the Dynamic Nature of Streaming Data

Because data streams are dynamic in nature, stream processing systems have to be adaptive to handle concept drift – which renders some data processing methods unsuitable – and operate with limited time and memory.

Furthermore, big data streaming has inherent dynamic characteristics, which means that it’s hard to know in advance what the necessary or desirable quantity of clusters would be, making approaches that require prior knowledge of this unsuitable for analyzing real-time data streams. In this case, streaming data must be analyzed dynamically with scalable processing to allow for decision-making in a limited window of time and space.

Query Processing over Data Streams

A stream-query processor must be able to handle multiple standing queries over a group of input data streams so that it can support a wide range of users and applications. There are two key parameters that determine the effectiveness of processing a collection of queries over incoming data streams: the amount of memory available to the stream processing algorithm and the per-item processing time required by the query processor.

The first of these is a particular challenge of designing any data stream processing system, because each standing query will only have a limited amount of memory resources made available to it in a typical streaming environment. As a result, the stream processing algorithm must be memory-efficient and able to process data quickly enough to keep up with the rate at which new data items are arriving.

Testing and Debugging Streaming Data Processing

Continuous data streams that may be queried for information can arrive from either a single source or many different ones. Still, ordering and delivering the data is a challenge because they move through a distributed system and will generally have to be processed in the correct order.

As such, the DSMS needs to decide between having consistent data – meaning that an error is returned if all the received data are not the most recent write, or data being highly available – in which case data is contained in all of the reads but not necessarily the most recent.

To debug a data stream processing system, it is first necessary to reproduce the system environment and test data. Then, various debugging tools can be used to monitor the system’s performance and identify any bottlenecks or errors.

It is also important to have a method for comparing the process streaming data results with what’s expected in order to verify the correctness of the system. This can be done by using a known dataset and running it through the system or by generating synthetic data that is known to conform to certain properties.

Fault Tolerance

The ability of a system to continue operating correctly in the face of failures of individual components is a necessary property of any distributed system, and DSMSs are no exception. There are two main approaches to them being fault tolerant: replication and logging, which can be combined in order also to achieve high availability.

The former involves having multiple copies of data streams and processing the streams in parallel. The latter requires keeping a log of all the data stream items that have been processed, which can then be used to replay the stream and reprocess any items that were lost due to a failure.

Data Integrity

Having some form of data validation is necessary to maintain the integrity of the data being processed by a DSMS. This can be done with a checksum or hash function to compare the data that is being processed with a known good value and thus detect any modifications that may have occurred.

It is also possible to use digital signatures to verify data integrity by having the stream producer sign each item before it is sent. This approach requires more overhead, but it can be used to provide a higher level of assurance that the data has not been tampered with.

Finally, the confidentiality and integrity of data streams can be protected through encryption, which is most often used in applications such as finance, where security is a critical concern.

Managing Delays

Delays can occur in data stream processing for a variety of reasons, including network congestion, slow processors, or backpressure from downstream operators (more on that specifically below). There are a few different ways to deal with delays, depending on the specific requirements of the application:

1. Use a watermark – a timestamp that indicates the maximum amount of delay that is tolerable beyond which data items are dropped, which is suitable for applications where the data stream can be processed out-of-order.
2. Buffer the delayed data items and process them when they eventually arrive – necessary for applications where the data must be processed in the order in which it was received, but can lead to increased memory usage. It’s possible for the buffers to get full and start losing data if delays are too long.
3. Use a sliding window – allows for a certain amount of delay while still processing the data in order and can be used to trade off accuracy for speed (especially in conjunction with a watermark) by only considering the most recent data items within the window.

Handling Backpressure

Backpressure is a condition that can occur in data stream processing when an operator is processing data faster than its downstream operators can consume it, which can lead to an increase in latency and can eventually result in data loss if the buffers of the operator start to fill up. Backpressure can be dealt with by:

1. Buffer the flow – by increasing the size of the buffers used to accumulate incoming data spikes temporarily.
2. Use an adaptive operator – an operator that can automatically adjust its processing rate based on the rate of the downstream operator to avoid having to adjust the flow control manually.
3. Partition the data – divide the data stream into multiple streams and process them in parallel to increase the overall throughput of the system.
4. Dropping data items – if an operator is unable to keep up with its incoming data stream, it may be necessary to drop some of the data in order to avoid losing everything (e.g. by sampling % of data over time frames). This should only be done as a last resort, however, as it will result in a loss of accuracy.

Computational Efficiency and Cost Efficiency

DSMSs need to be able to process large data streams in a timely and affordable manner. One way to meet the first requirement is with operator pipelining, which means chaining together multiple operators so that each operator can start processing its input as soon as it becomes available without having to wait for the previous operator to finish.

Achieving cost efficiency relies on using the right mix of on-premises and cloud-based resources. For example, it might make sense to use the former for the initial data collection and processing and then the latter for storage and analysis. Also, consider the costs of data stream processing when designing a DSMS, as many of the same techniques that are used to achieve computational efficiency can also be costly in terms of resources.

For example, data skipping might require more expensive storage or computing resources to keep track of which data has been processed and which hasn’t, whereas a system that needs to process real-time streaming data may be more expensive than one that can tolerate some delays in processing.

How to Overcome the Challenges of Processing Streaming Data?

Although data stream processing presents some key challenges, there are a few ways that they can be overcome, including using the right mix of on-premises and cloud-based resources and services, choosing the right tools, setting up a reliable infrastructure for monitoring data integration and processing, improving efficiency with operator pipelining and data skipping. partitioning data streams to improve overall throughput, adjusting processing rates automatically with an adaptive operator, and implementing proper flow control to avoid backpressure. Read more about stream processing use cases here.

With the right mix of resources, data architecture, and approaches, it is possible to overcome the challenges of processing very complex streaming data and reap the benefits of real-time data analytics. If you need more help, then consider consulting with nexocode data engineers. Our IT teams are here to support your cloud engineering efforts.